With the recent arrival of potent new AI-based models for representing knowledge, the methods enterprises use to manage data today is now faced with yet another major new transformation. I remember a few decades back when the arrival of SQL databases were a major innovation. At the time, they were both quite costly and took great skill to use well. Despite this, enterprises readily understood they were the best new game in town in which their most important data had to live, and so move they did.
Now vector databases and especially foundation/large language models (LLMs) have shifted the focus — in just a couple of short years — on the way organizations must now store and retrieve their own data . And we are also right back at the beginning of the maturity curve that most of us left behind a couple of decades ago.
While not everyone realizes this yet, the writing is now on the wall: Much of our business data now has to migrate again and be recontextualized into these new models. Because the organizations that don’t will likely be at a significant disadvantage, given what AI models of our data can deliver in terms of value.
Our Marathon with Organizational Data Will Continue With AI
The result, like a lot of technology disruption, will be an journey through a series of key stages of maturation. Each one will progressively enrich the way our organizations store, understand, and leverage our vast reservoirs of information using AI. This process will naturally be somewhat painful, and not all data will need to migrate. And certainly, our older database models aren’t going anywhere either. Butatthe core of this shift will be the creation of our own private AI models of organizational knowledge. These models must be carefully developed, nurtured, and protected, while also made highly accessible, with appropriate security models.
We’ve moved on from the early days of digital documents, capturing loosely structured data in primitive forms, to the highly structured revolutions introduced by relational and graph databases. Both phases marked a significant movement forward in how data is conceptualized and utilized within the enterprise. The subsequent emergence of JSON as a lightweight, text-based lingua franca further bridged the gap between these two worlds and the burgeoning Web, offering a structured yet flexible way to represent data that catered to the needs of modern Internet applications/services and also helped give rise to NoSQL, a mini-boom of a new database model that ultimately found a home in many Internet-based systems, but largely didn’t disrupt our businesses like AI will.
AI-Models Are A Distinct Conceptual Shift in Working with Data
However, the latest advancements in knowledge representation really do usher in a steep increase in technical sophistication and complexity. Vector databases and foundation models, including large language models (LLMs), represent a genuine quantum leap in how enterprises can manage their data, introducing unprecedented levels of semantic insight, contextual understanding, and universal access to knowledge. Such AI models are able to find and understand the hidden patterns that tie diverse datasets together. This ability can’t be understated and is a key attribute that emerges from a successful model training process. As such, it is one of the signature breakthroughs of generative AI.
Let’s go back to the unknown issues with AI in the enterprise. This uncertainty ranges from what the more effective technical and operational approaches are to picking the best tools/platforms and supporting vendors. This new vector- and model-based era is characterized by an exponential increase in not just the sophistication with which data is stored and interpreted, but in the very way it is vectorized, tokenized, embedded, trained on, represented and transformed. Each of these requires a separate set of skills and understanding, and very considerable compute resources. While this can be outsourced to some degree, this has many risks of its own, not least is that such outsourcers may not deeply understanding the domain of the business and how best to translate it into an AI model.
AI-Based Technologies for Enterprise Data
Vector databases, leveraging the power of machine learning to deeply understand and query enterprise data in ways that mimic human cognitive processes, offered us the first new glimpse into a new future. Going forward, the contextual understanding of our data will largely be based on these radical new forms that bear little resemblance to what came before. Similarly, foundation models like LLMs have revolutionized information management by providing tools that can seemingly comprehend, generate, synthesize, and interact with human language in a manner using neural nets, vast pre-trained parameter sets, and complex transformer blocks that each have a high learning curve to set up and create (using them, however, is very easy.) These technologies provide us with a new dawn of possibilities, from enhanced decision-making processes with unparalleled insights using all our available knowledge, to automating complex tasks with a nuanced understanding of language and context. But all these new AI technologies are generally not familiar to IT departments, which now have to make strategic sense of them for the organization.
Thus, this remarkable progress brings with it a large number of concerns and hurdles to make reality. First, the creation, deployment, and utilization of these sophisticated data models — at least with current technologies — entrails significantly higher costs compared to previous approaches to representing data, according to HBR. A real-world cost example: Google has a useful AI pricing page for benchmarking fundamental costs, which breaks down the various cloud-based AI rates, with grounding requests costing $35 per 1K requests,. Grounding — the process of ensuring that the output of an AI is factually correct — is probably necessary for many types of business scenarios using AI, and is thus a significant extra cost not required in other types of data management systems.
Furthermore, the computational resources, available time, and time required to develop and maintain such systems are also quite substantial. Moreover, the transition to these advanced data management solutions involves navigating a complex landscape of technical, organizational, and ethical considerations.
As enterprises stand on the cusp of this major new migration to AI, the journey ahead promises real rewards. It also demands careful strategizing, intelligent adoption, and I would argue at this early date, a lot of experimentation, prototyping, and validation. The phases of integrating vector databases and foundation models into the fabric of enterprise knowledge management will require a nuanced approach backed by rigorous testing, balancing the potential for transformative improvements against the practicalities of implementation costs and the readiness of organizational infrastructures to support such advancements.
That this is already happening, there is little doubt, based on my conversations with IT leaders around the world. We are witnessing the beginning of a significant shift in how enterprise knowledge is stored, accessed, and utilized. This transition, while demanding serious talent development and capability acquisition, offers an opportunity to redefine the very boundaries of what is possible in data management and utilization. The key to navigating this evolution lies in a strategic, informed approach to adopting these powerful new models, ensuring that an enterprise can harness its full potential while mitigating the risks and costs associated with such groundbreaking technological advancements.
Early Approaches For Private AI Models of Enterprise Data
Right now, the question I’m most often asked about enterprise AI is how best to create private AI models. Given the extensive concerns that organizations currently have about losing intellectual property, protecting customer/employee/partner privacy, complying with regulations, and giving up control over the irreplaceable asset of enterprise data to cloud vendors, there is a lot of searching around for workable approaches that produce cost-effective private AI models that produce results while minimizing the potential downsides and risks of AI.
As part of my current research agenda on generative AI strategy for the CIO, I’ve identified a number of initial services and solutions from the market to help with creating, operating, and managing private AI models. Each has their own pros and cons.
Services to Create Private AI Models of Enterprise Data
PrivateLLM.AI – This service will train an AI model on your enterprise data and host it privately for exclusive use. They specialize in a number of vertical and functional domains including legal, healthcare, financial services, government, marketing, and advertising.
Turing’s LLM Training Service – Trains large language models (LLMs) for enterprises. Turing uses a variety of techniques to improve the LLMs they create, including data analysis, coding, and multimodal reasoning. They also offer speciality AI services like supervised fine-tuning, reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO), which helps optimizing language models to adhere to human preferences.
LlamaIndex – A popular way to connect LLMs to enterprise data. Has hundreds of connectors to common applications and impressive community metrics (700 contributors with 5K+ apps created.) Enables use of many commercial LLMs, so must be carefully evaluated for control and privacy issues. Make it very easy to use Retrieval-Augmented Generation (RAG), a way to combine a vector database of enterprise information with pass-through to a LLM for targeted but highly enriched results, and even has a dedicated RAG offering to make it easy.
Gradient AI Development Lab – This is an end-to-end service for creating private LLMs. They offer LLM strategy, model selection, training and fine-tuning services to create custom AIs. They specialize in high security AI models and offer SOC2, GDPR, and HIPAA certifications and guarantees enterprise data “never leaves their hands.”
Datasaur.AI – They offer an LLM creation service that provides customized models for LLM development including using vector stores to provide enterprise-grade domain-specific context. They offer a wide choice of existing commercial LLMs to build on as well, so care must be taken to create a private LLM instance. They are more platform-based than some of the others, which makes it easier to get started, but may limit customization downstream.
Signity – Has a private LLM development service that is optimized more to specific data science applications.However, they can handle the whole LLM development process, from designing the model architecture, developing the model and then tuning it. They can create custom models using PyTorch, TensorFlow and many other popular frameworks.
TrainMy.AI – A service that enables enterprises to run an LLM on a private server using retrieval augmented generation (RAG) for enterprise content. While it is more aimed at chatbot and customer service scenarios, it’s very easy to use and allows organizations to bring in vectorized enterprise data for RAG enhancement into a conversational AI service that is entirely controlled privately.
NVIDIA NeMo – For creating serious enterprise-grade custom models, NVIDIA, the GPU industry leader and leading provider of AI chips, offers an end-to-end “compete” solution to creating enterprise LLMs. From model evaluation to AI guardrails, the platform is very rich and is ready to use, if you can come up with the requisite GPUs.
Clarifai – Offers a service that enterprise can quickly use for AI model training. It’s somewhat self-service and allows organization to set up models quickly and continually learn from production data. Has pay-as-you-go pricing and can train pre-built, pre-optimized models of their own already pre-trained with millions of expertly labeled inputs, or you can build your own model.
Hyperscaler LLM Offerings – If you trust your enterprise data to commercial clouds and want to run your own private models in them, that is possible too and all the major cloud vendors offer such capabilities including AWS’s SageMaker JumpStart, Azure Machine Learning, and Google Cloud offers private model training on Vertex AI. These are more for IT departments wanting to roll their own AI models and don’t produce business-ready results without technical experience, unlike many of the services listed above.
Cerebras AI Model Services– The maker of the world’s largest AI chip also offers large-scale private LLM training. They take a more rigorous approach with a team of PhD researchers and ML engineers that they report will meticulously prepare experiments and quality assurance checks to ensure a predictable AI model creation journey to achieve desired outcomes.
Note: If you want to appear on this list, please send me a short description to dion@constrellationr.com.
Build or Customize an LLM: The Major Fork in the Road
Many organizations, especially those unable to maintain sufficient internal AI resources, will have to decide whether to build their own AI model of enterprise data or carefully use a third party service. The choice will be tricky. For example, OpenAI now offers a fine-tuning service, for example, that allows enterprise data to augment how GPT-3.5 or 4 produces domain specific data. This is a slippery slope, as there are many advantages to building on a high capability model, but many risks, including losing control over valuable IP.
Currently, I believe that the cost of training private LLMs will continue to fall steadily, and that service bureaus will increasingly make it turn-key for anyone to create capable AI models while preserving control and privacy. The reality is, that most enterprises will have a growing percentage of their knowledge stored and accessed in AI-ready formats, and the ones that move their most strategic and high value data early are likely to be the most competitive in the long run. Will vector databases and LLMs become the dominant model for enterprise knowledge? The jury is still out, but I believe they will almost certainly become about as important as SQL databases are today. But the main point is clear: It is high time for most organizations to proactively cultivate their AI data-readiness.
I clearly remember when the original Macintosh was unveiled in 1984. It was more than just a piece of technology, it was a bold declaration by Steve Jobs that computing was now for literally everyone. With its graphical user interface (GUI), the Macintosh turned what was once the exclusive domain of the technically adept—taking orders solely through arcane text commands—into a immediately accessible and intuitive visual experience. At the time, I recall that this leap was not without a good number of skeptics, many of them with vested skills who liked the inherent barriers of entry to computing. Others at the time believed the GUI to be a mere gimmick, a dead-end in the face of “serious” computing models.
I was a budding software designer myself back then, and I had plenty of experience on the command-lines of the day. But when I started to use my first Macintosh, I realized almost immediately that we had now entered a completely new era. An era where computing was going to be forever visual. I was struck at the time how the visual interface was really a high bandwidth conduit right into the human mind. There was so much more you could experience than with just a screen of characters, and it was so much faster. And it went both ways, input was infinitely more exploratory and revealing. Syntax errors were replaced with easily-clicked functionality.
What’s Old Is New Again: Symbolic Interaction
Yet, as a software architect and thought leader who has witnessed and contributed to the myriad transformations in technology over the last forty years—including the eventual rise of mobile and social platforms to the current era of AI-as-UI—I can see the original Macintosh was more than just a significant footnote in history but the harbinger of the many fundamental shifts that would follow. The 40-year journey from the playful but profound simplicity of the initial Macintosh to the sophisticated and similarly very nascent capabilities of today’s equally visionary Apple Vision Pro neatly bookends a period of unprecedented innovation in visual interfaces and reimagining of human-computer interaction.
In short, the GUI genuinely did what it set out to do: It truly democratized computing. It made technology accessible and personal, laying the groundwork for the subsequent mobile revolution that would bring computing into our pockets with new, even easier user interfaces made of touch and voice. Social media platforms capitalized very well on these intuitive interfaces, connecting billions and embedding computing even deeper into the fabric of daily life, for the better and then sometimes, to my regret, for the worse.
Throughout these shifts, my role has steadily evolved from designing systems within predefined paradigms to questioning and redefining exactly what those paradigms should be. Is there an end-state to this journey? Where are spatial computing and AI actually taking us? Just asking this question will reveal vital insights to aid our passage, a topic of my current research.
Today, we are now fully in the resurgence of the command line, powered and made far more workable again by AI. It marks a fascinating turn in this ongoing evolution. Far from the hard-to-master command lines of the past, today’s AI-driven interfaces are as approachable as the GUI, but often more profound: Conversational, capable of understanding and generating sophisticated natural language.
AI-as-UI represents a full digital realization of the most ancient and powerful mode of human interaction: Symbolic dialogue. Unlike the static command lines of old, these AI interfaces are dynamic, learning and adapting to the user’s needs in real time. They democratize computing in a way the original GUI only hinted at, tapping into radically advanced computational capabilities now accessible to anyone with the ability to ask a question.
Maximization Is Not the Path: Simplicity Is
However, as we marvel at the capabilities of AI, I believe it’s crucial to consider the places where the GUI—and its three-dimensional evolutions in virtual and augmented reality—may have overreached. The push towards ever more immersive 3D environments has, in some instances, prioritized spectacle over substance, complexity over clarity. This is not to dismiss the potential of spatial computing in any way. I believe it’s a major part of our future. But I wish to caution against losing sight of good user experience in the rush towards technological maximalism. As many designers would tell you, the most profound innovations often come not from adding complexity but from removing barriers, a lesson the Macintosh taught us four decades ago. It’s one of the reasons that I’ve added simplicity maximization as one of my most important trends for the Future of Work in 2030.
Our User Experiences Risk Getting As Complex As Our Minds. Image: DALL*E 3
Amidst these reflections, I find that in my research that the role of AI stands out not as a competitor to spatial computing but as a highly complementary force. AI has the potential to make spatial computing more intuitive, more nuanced, deeply personalized responsiveness to our needs, and ultimately more, well, human. By integrating AI’s understanding of language and context with spatial computing’s immersive capabilities, we will create effective environments that are not only visually and interactively rich but also deeply personalized, far more potent to use, and highly accessible.
From where we are today, the intertwining paths of AI and spatial computing is what actually represents the cutting edge of digital experience. They are not at odds, but are instead two sides of the same coin. Each will push the other towards greater heights and major new breakthroughs. Importantly, one (AI) can return simplicity to the other (spatial computing.)
No, the challenge and opportunity for us as architects of these digital realms are to ensure that these technologies properly reinforce and complement each other in ways that enhance human capabilities and enrich our lives. Our immersive experiences will have AI interfaces. In the end, it’s as I’ve started to remind people lately, if all this tech is not here to serve humans well, it does not have much of a purpose.
So, as we chart this course, I’d like us to remember the lessons of the original Macintosh and now the Apple Vision Pro and ChatGPT as well as crossovers like Mobeus: That the true power of technology lies not in its complexity but in its ability to connect with us, to understand us, and to make the once unimaginable a part of our everyday lives. To the extent spatial computing and AI manage to keep us on this road towards progress, they will flourish and open up the future.
As 2024 arrives, many of us are coming to a pivotal realization: The once distant horizon of 2030 is now steadily looming. This major temporal milestone, frequently cited in futuristic forecasts and strategic plans, is no longer a distant scenario. It will arrive in just a half-decade, which is not much time at all when it comes to the rate of change within organizations today. With this critical juncture in sight, it’s now important that we shift some of our immediate focus on long-term speculation to active preparation and strategy development towards this coming era of the future of work and employment. Below, I’ll explore my latest updated view of this future, with many details. The aim: To ignite forward-thinking industry dialogue about what the future of work of work entails by the end of this decade.
Recent statistics paint an intriguing picture of our future workplace. Just one example: According to the World Economic Forum, an estimated 50% of all employees will need reskilling by 2025, as technology advancement continues to accelerate. Moreover, the International Labour Organization notes that by 2030, the global labor force is projected to reach 3.5 billion, but 85% of the jobs that will exist then haven’t been invented yet. These figures underscore a transformative shift in the nature of work, propelled by technological advancements, changing demographics, and evolving societal norms. They highlight the urgency for individuals and organizations alike to adapt, upskill, and rethink traditional employment paradigms. The following below contains a detailed exploration of what we must prepare for.
As we delve into this look into the future, I will explore several far-reaching examples of what work might look like in 2030. From the deep integration of AI and machine learning in everyday tasks to the rise of remote, hybrid, and even entirely new working models, the landscape of work is set to undergo unprecedented changes. Below is an introduction — to the degree I can make it comprehensive — of major trends that are shaping the future of work, such as the gig economy, green jobs, and the emphasis on mental health and wellbeing in the workplace. Join me on this exploratory journey, where enthusiasm for progress meets a grounded, professional analysis of what lies ahead.
The chart above summarizes an approximately 80% view of the major shifts and advances in work through 2030. The top of the chart focuses more on people, process, and culture, while the bottom is more technologically focused. Advances on the left are often already here in early form, while the trends on the right are farther out and will likely still be bleeding-edge by 2030, but clearly major trends.
Let’s jump right in, going from left to right on this future of work trends chart:
2030 Work Trends and Advances
The Quantified Workplace
Those who attend my talks know that often quote Peter Drucker’s famous adage, “If you can’t measure it, you can’t manage it.” This principle has never been more pertinent than in today’s quantified workplace, where the rise of workplace analytics has revolutionized how we approach productivity and efficiency. The advent of Objective and Key Results (OKR) tracking, sophisticated analytics and Business Intelligence (BI) dashboards, and even digital twins of work processes, have transformed the landscape of organizational management. These tools not only provide a granular view of performance but also empower decision-makers with actionable insights, fostering a culture of continuous improvement and strategic alignment.
An Operations Team Using Workplace Analytics To Manage Work
The well-known story of Google, for instance, who has long been a proponent of OKR tracking to ensure that its teams’ efforts align with the company’s ambitious goals. Similarly, Siemens has embraced the concept of digital twins, creating virtual replicas of their work processes in high-impact environments like healthcare to optimize operations and predict future scenarios. On the tool front, platforms like Microsoft Workplace Analytics and ActivTrak have become indispensable in translating data into intuitive, actionable insights for businesses of all sizes. These applications enable companies to visualize and analyze data trends, driving more informed decision-making. Another notable mention is Smartsheet, a project management tool that integrates OKR tracking within its platform, making it easier for teams to align their daily tasks with broader organizational objectives. Through these examples, we see a vivid picture of the quantified workplace in action, where every aspect of work is measurable, manageable, and, most importantly, improvable.
Digital Management Models
The hybrid work environment, a norm in today’s business landscape, is not just reshaping where we work, but also how we are managed and lead. Emerging from this paradigm shift are new management models like network leadership and distributed autonomous organizations (DAOs), which leverage technology to decentralize decision-making and promote a more collaborative and agile approach. These models are a departure from traditional hierarchical structures, emphasizing the importance of connectivity and distributed authority. For instance, network leadership thrives on the premise of connecting people across various departments and geographies, fostering a culture of open communication and shared leadership. DAOs, on the other hand, represent an even more radical shift, based on emerging management platforms that rely on blockchain technology and smart contracts to create transparent, self-governing organizations where decisions are made collectively, without centralized control.
Management Methods Are Evolving and Transforming Due to Technology
For most organizations, adopting these new management models is a challenging yet crucial transformation. It requires a significant shift in mindset from both leaders and employees, as well as a robust technological infrastructure. More freeform tools like Slack and Microsoft Teams have become instrumental in facilitating network leadership, offering platforms for seamless communication and collaboration irrespective of physical location. These tools help break down silos and enable a flow of information that is essential for a networked approach to management. Similarly, platforms like Aragon and Boardroom are specifically designed to support DAOs, providing the necessary framework for decentralized decision-making and governance. This shift is critical because it aligns with the evolving nature of work, where flexibility, adaptability, and employee empowerment are key. As the workforce becomes more global and diverse, these new management models, supported by the right tools, pave the way for more inclusive, efficient, and responsive organizations.
Immersive e-Learning
With the pace of change today required rapid reskilling and upskilling, the landscape of e-learning has been steadily undergoing a revolutionary transformation, with immersive e-learning emerging as a key trend. This evolution is characterized by the interplay and integration of experiential learning, augmented reality (AR) training, and microlearning, offering learners not just information, but experiences that enhance retention and application. Experiential learning provides a hands-on approach, allowing learners to engage in simulations that mimic real-world scenarios. Augmented reality, on the other hand, brings an interactive dimension to learning, overlaying digital information in the real world, thus offering a more engaging and effective learning experience. Microlearning breaks down content into small, manageable units, making it easier for learners to digest and retain information. These methods are gaining traction for their ability to provide flexible, impactful, and learner-centric experiences.
Digital Learning Must Occur Almost Continuously in the Modern Workplace
In the realm of e-learning products, platforms like Coursera and Udacity stand out as exemplars. Coursera offers a wide range of immersive courses that leverage video lectures, interactive exercises, and peer-to-peer learning. Udacity, with its nanodegree programs, focuses on skill-based learning enhanced by real-world projects and mentorship, aligning with microlearning principles. Companies like Boeing and Walmart have been early adopters of these new e-learning methods. Boeing utilizes AR for training its workforce, enabling them to visualize complex aircraft systems for better understanding and maintenance. Walmart has embraced virtual reality (VR) training in its academies, providing employees with lifelike simulations of in-store experiences. These examples highlight how immersive e-learning is not just an educational tool but ben an increasingly strategic asset for workforce development in various industries during the rest of the decade.
Wellness Tracking
Cultivating the health of workers via digital aids is a burgeoning new field that sits on the intersection of technology and health. Wellness tracking is steadily gaining prominence in both personal and corporate settings. Products like Fitbit and Apple Watch have become household names, renowned for their ability to track various health metrics such as heart rate, activity levels, and sleep patterns. These devices not only provide users with detailed insights into their physical wellbeing but also encourage a more proactive approach to health. Corporate wellness platforms like VantageFit have emerged to create custom AI-powered health programs. Corporations are increasingly incorporating these tools into their employee wellness programs. For example, organizations like Emory University and Google have integrated wellness tracking into their employee benefits, offering subsidies for fitness trackers and incentivizing employees to maintain a healthy lifestyle. By integrating these devices, these companies aim to foster a workforce that is not only more productive but also happier and healthier.
Wellness Tracking and Worker Health Measurement is Becoming a Leading Employee Benefit
Looking towards 2030, digital wellness tracking is poised to evolve in both sophistication and scope. The integration of AI and machine learning will likely lead to more personalized health insights and recommendations, based on individual data trends. We will likely see these devices becoming more integrated with medical systems, providing real-time health data to healthcare providers, potentially detecting health issues before they become serious. Additionally, the proliferation of wearable technology will likely expand beyond just fitness trackers to include smart clothing and even implantable devices, offering even more detailed and continuous monitoring. This evolution will not only transform how individuals manage their health but also how companies approach employee wellness, potentially leading to a more holistic and preventive healthcare model in the workplace, a benefit that top-tier employers will increasingly be expected to provide.
The Anywhere Worker
This trend represents a paradigm shift in the modern workforce, encapsulating the complete untethering of employees from traditional constraints of location, time, and collaboration mode. Propelled significantly by the fast-receding pandemic, this trend has rapidly evolved from a necessity to a widely embraced work model. The essence of working from anywhere lies in its flexibility, allowing employees to operate effectively irrespective of their physical location, be it from home, a café, or even a different country. This model not only accommodates various time zones and work schedules but also embraces diverse modes of collaboration, ranging from virtual meetings to asynchronous communication. Tools like Zoom for video conferencing, Slack for team communication, and Viva Engage for organization-wide mass collaboration have become indispensable in supporting this mode of work. They enable seamless collaboration across geographies, fostering a connected yet dispersed workforce.
As we head deeper into the decade, the work from anywhere trend is expected to deepen and expand, with technology playing an even more pivotal role. We will witness the emergence of more sophisticated collaboration tools, which will leverage augmented and virtual reality to simulate in-person interactions in a virtual space. The widespread adoption of AI for automated scheduling and language translation will further facilitate global collaboration, breaking down language barriers and optimizing workflows across time zones. Companies like GitLab and United HealthGroup have already set precedents by operating either fully remote (GitLab) or extensively remote (UHG), showcasing the viability and benefits of this model. As organizations continue to embrace and support international work from anywhere, I anticipate the growth of an ever-more inclusive, capable, and flexible global workforce, one that transcends traditional office boundaries and offers unprecedented freedom and autonomy to the individual worker.
Voice Assistants at Work
As voice assistant technology continues to improve and adapt to professional settings, its presence in the workplace is set to become increasingly commonplace. Enhanced by advancements in natural language processing and machine learning, voice assistants are evolving beyond basic tasks to handle more complex, work-specific functions. They are becoming adept at managing schedules, setting reminders, transcribing meetings, and even providing real-time language translation. The potential for voice assistants to streamline workflows, boost productivity, and foster more efficient communication is significant. One of the most powerful use cases lies in their ability to integrate with various workplace tools and systems, allowing employees to interact with and access a range of services through simple voice commands. This integration not only saves time but also creates a more intuitive and accessible work environment, particularly beneficial in hands-free or multitasking scenarios.
By the end of the decade, the fuller unleashing of voice assistants’ value in the workplace is expected to occur with the integration of generative AI connected to private Large Language Models (LLMs) of corporate data. This will enable voice assistants to provide highly tailored, context-aware responses and insights, drawing from a vast repository of company-specific information. Imagine a voice assistant that can not only schedule a meeting but also provide relevant files, participant profiles, and historical data pertinent to the discussion. An example of a successful implementation of a voice assistant in professional settings is IBM Watsonx Assistant. It can be employed in offices or facing customers to manage both employee and customer service. IBM Watsonx Assistant represents the early stages of this trend, with future iterations likely to offer more sophisticated, AI-driven functionalities, deeply integrated into corporate ecosystems, thereby transforming how we interact with technology at work.
Networks of Excellence
I’ve been tracking for years how the traditional concept of a Center of Excellence (CoE) has been undergoing a significant transformation, evolving into a ‘Network of Excellence’ (NoE) to facilitate large-scale, decentralized change management. This shift is driven by the need for organizations to adapt rapidly and efficiently in a dynamic business environment. A Network of Excellence leverages distributed participation, tapping into a wider pool of expertise and perspectives across various departments and locations. This approach is markedly different from the centralized structure of a CoE, as it promotes a more inclusive and collaborative model of change management. Enabling technologies for this transition include mass collaboration tools and work coordination platforms like Asana, which allow for streamlined project management and communication across disparate teams. Furthermore, platforms from Sensei Labs are emerging as crucial tools for transformation, providing a framework for tracking progress, sharing knowledge, and driving collective action towards common organizational goals.
Networks of Excellence Allow the Organization to Tap Into its Entire Capacity for Change
In the realm of decentralized, agile change networks, a notable example is found in the retail giant, IKEA. Over the years, I’ve been both involved in and tracking such decentralized change models, which have now been adopted by some of the largest organizations in the world, including General Electric Co., United Technologies Corp., Atlas Copco, Metso Oyj, and Ingersoll Rand. They have embraced — at least in part — a more networked approach to change, moving away from traditional, hierarchical structures. They have implemented a decentralized network of excellence, empowering teams across different countries and departments to collaborate and innovate. This approach has allowed them to rapidly adapt to market changes, such as the shift towards e-commerce and sustainable practices. By leveraging digital platforms for communication and project management, change networks in these organizations coordinate effectively, driving initiatives that are both locally relevant and globally aligned. By the end of the decade, networked and technology-fueled forms of transformation are expected to become commonplace in organizations. The integration of advanced technologies such as AI, machine learning, and predictive analytics into these platforms will further enhance their capability to manage complex change initiatives. This evolution will lead to more agile, responsive, and effective organizational transformations, with a greater emphasis on collective intelligence and distributed leadership.
The rise of a green-collar worker this decade marks a significant shift in the global workforce, reflecting the increasing importance of environmental sustainability in every industry sector. Green-collar workers are those employed in fields specifically geared towards creating and maintaining a sustainable environment. This encompasses a wide range of roles, from renewable energy engineers to sustainability consultants. As the world grapples with climate change and environmental degradation, the demand for these roles is surging, driven by both societal expectations and regulatory requirements. By 2030, as sustainability initiatives gain even more prominence, the green-collar workforce is expected to expand substantially, not just in numbers but also in the scope of their roles. This growth is significantly influenced by technological advancements in areas like green IT, where professionals are tasked with developing and implementing technologies that reduce the carbon footprint of IT operations and promote energy efficiency. Sustainability can even become a business model in its own right as waste head from IT and manufacturing systems can be sold on the open market, and green-collar workers will be leading this dramatic shift in corporate priorities.
The integration of technology in sustainability initiatives is a key driver for the expansion of the green-collar sector. Innovations in renewable energy technologies, waste management, and smart infrastructure are creating new opportunities for skilled professionals. Technologies like AI and big data are increasingly being used to analyze environmental data, optimize resource use, and improve decision-making processes in sustainability projects. Companies like Siemens and Schneider Electric, known for their commitment to green technologies, are at the forefront of this transition, employing a growing number of green-collar workers to develop and manage their sustainable solutions. By 2030, as more companies integrate sustainability into their core business strategies, the role of the green-collar worker will become much more central, not just in executing sustainability initiatives but also in shaping corporate policies and practices towards a more sustainable future. This trend represents a pivotal shift in the workforce, where technology and environmental stewardship converge to create jobs that are both impactful and essential for the planet’s future.
Designer Careers
The evolution of designer careers, particularly within the realm of white-collar gig work, is reshaping the traditional job market. These careers are characterized by the freedom and flexibility for professionals to choose projects that align with their passions and expertise, facilitated by dynamic project platforms. This trend is gaining significant traction in fields like IT and management consulting, where skilled workers are increasingly opting for gig work that allows them to build diverse and impressive portfolios. By engaging in a variety of projects, these workers not only enhance their skill sets but also gain exposure to different industries and organizational cultures. This shift is driven by a growing desire among professionals to be more loyal to their career trajectory than to any single company, while also achieving maximum flexibility in their work lives, from hours worked to location. They seek to craft a career path that is not only professionally rewarding but also personally fulfilling. My Gig Economy in the Enterprise ShortList tracks the top ecosystems, while my related research has tracked the rising popularity and higher effectiveness of gig work in the enterprise sector.
The white-collar gig economy has seen substantial double-digit growth in the last several years, thanks to platforms that connect freelance professionals with organizations seeking specialized skills for short-term projects. This model has proven particularly successful in fields that require high-level expertise and experience, such as management consulting, digital marketing, and software development. Companies like Toptal and Upwork Enterprise exemplify this trend, offering a marketplace for top-tier professionals to engage with businesses on a project-by-project basis. These platforms not only provide flexibility and autonomy to workers but also allow companies to tap into a global talent pool, ensuring they have the right skills for specific project needs. As this model continues to mature, it is likely to further disrupt traditional employment models, with more professionals choosing to navigate their careers as a series of strategic gigs, shaping their professional journey one project at a time.
As we move towards 2030, the concept of hybrid work is set to become more seamless and efficiently integrated into our professional lives. Initially, the transition to hybrid work posed significant challenges, primarily in maintaining effective communication and collaboration across remote and in-office teams. Issues like lack of inclusion between remote and office workers, inadequate home office setups, and the struggle to preserve work-life balance were common. However, as organizations continue to adapt and vendors update their solutions to better support hybrid work, these hurdles are being overcome through a combination of innovative technologies and evolved work practices. Advanced collaboration tools are being developed to facilitate smoother interactions, such as enhanced video conferencing systems and asynchronous collaboration practices that offer more interactive, engaging, hybrid-friendly meeting experiences. AI-driven platforms are emerging to optimize scheduling across time zones and manage project workflows more effectively. Moreover, companies are adopting flexible policies that acknowledge the unique challenges of remote work, such as providing allowances for home office setups or implementing ‘no meeting’ days to combat Zoom fatigue.
An excellent example of company embracing the hybrid work model is Deloitte. Known for its consulting and financial advisory services, Deloitte has implemented a “hybrid-inclusive” approach that allows employees to blend in-office and remote work. This model is supported by a strong digital infrastructure, but also by a culture that values flexibility, employee autonomy, and work-life balance. Deloitte has focused on ensuring that its workforce, regardless of location, has equal access to opportunities and resources, thereby fostering a sense of inclusion and equity across its distributed teams. In terms of technology support, tools like Slack have been instrumental in enabling this transition for companies like Deloitte. Slack’s platform facilitates real-time communication and collaboration, making it easier for teams to stay connected and productive, whether they are working from home, the office, or anywhere in between. This approach by Deloitte, supported by innovative collaborative technologies, exemplifies how non-tech companies can effectively adapt to and thrive in a hybrid work environment, creating a resilient and adaptable workforce. I predict that most organizations will operate more like Deloitte by the end of the decade.
The Productivity Coach
The recent advent of generative AI is now set to revolutionize the workplace by introducing personalized productivity coaches for both workers and managers. These AI-driven coaches are designed to analyze an individual’s work patterns, preferences, and performance metrics to provide customized recommendations for enhancing productivity. By leveraging large datasets and learning from a user’s interactions, these AI systems can offer insights into optimizing workflows, managing time more efficiently, and improving the quality of output. For instance, a generative AI coach might analyze a programmer’s coding habits and suggest more efficient algorithms or shortcuts, or advise a manager on how to structure team meetings more effectively to boost engagement and outcomes. The potential of these AI coaches extends beyond mere productivity enhancement; they could also help in identifying skill gaps and recommending tailored learning paths, thereby contributing to ongoing professional development.
As we progress through the decade, these AI-driven productivity coaches are likely to become integral to both corporate results and employee success. In an increasingly competitive and fast-paced business environment, the ability to continuously improve and adapt is crucial. AI coaches will serve as a continuous feedback mechanism, helping employees and managers to not only work smarter but also ensure their well-being by preventing burnout and maintaining work-life balance. An example of such a solution is Poised, which uses AI to assist employees in improved communication by providing live coaching of their actual communication. This AI coach will not only enhance individual performance but also contribute to a more efficient, innovative, and adaptive sales, marketing, and product management teams. The holistic approach of these AI systems, which considers both professional output and personal well-being, aligns perfectly with the evolving priorities of the modern workforce, where productivity is increasingly seen as a function of employee satisfaction and engagement.
The Universal Work Portal
The concept of universal work portals is emerging as a groundbreaking solution in the quest to centralize and streamline the employee experience. Going far beyond the scope of traditional intranets or digital workplace hubs, these integrated portals — which I’ve predicted for several years now — are envisioned to serve as a singular, cohesive platform for a multitude of essential work-related activities. Encompassing both core work functions and support tasks, universal work portals will offer a centralized location for activities such as filing hours, managing notifications, reviewing approvals, coordinating work, and creating work output across various applications. This integration aims to address the current fragmentation in work and communication apps, reducing the cognitive load on employees and enhancing productivity. An AI-assisted layer within these portals will provide intelligent assistance, offering personalized recommendations, automating routine tasks, and facilitating more efficient workflows. The portal will not only aggregate information and tools but also intelligently prioritize and present them based on individual roles and tasks, creating a more focused and efficient work environment.
Intranets Won’t Die, But They Will Become Much More Powerful Hubs that Focus on Outcomes
By 2030, universal work portals are predicted to evolve into far more immersive and interactive platforms, potentially becoming integral parts of a broader work metaverse. As virtual and augmented reality technologies mature, these portals could transform into 3D virtual workspaces, offering an immersive experience that blurs the line between physical and digital work environments. In this context, employees could navigate a virtual office space, interact with colleagues in a more lifelike manner, and engage with work tools and data in a spatial, intuitive way. This evolution will likely see the integration of more advanced AI capabilities, further personalizing the employee experience and optimizing work processes. The work metaverse could also extend to include elements of gamification and social interaction, making work more engaging and fostering a stronger sense of community and collaboration among remote teams. As these platforms become more sophisticated and integrated into daily work life, they could revolutionize not just how we work, but also how we perceive and experience our professional lives.
Work Coordination-Led Organizations
Collaborative work management tools like Smartsheet and Asana are increasingly becoming the backbone of how work is conducted in team and project-based environments. These platforms have evolved from mere task management tools to comprehensive work operating systems for companies, offering a centralized space for detailed task tracking, management controls, and work analytics. They are specifically designed to drive outcomes by enhancing visibility, accountability, and collaboration across teams. By breaking down projects into manageable tasks, assigning responsibilities, and setting deadlines, these tools ensure that everyone is aligned and aware of their roles and deadlines. Moreover, their integrated work analytics provide valuable insights into team performance, project progress, and potential bottlenecks, enabling managers to make informed decisions and adjustments. These platforms also facilitate communication and file sharing, reducing the need for lengthy email threads and meetings, thereby streamlining workflows and increasing efficiency.
Work Coordination Now a Leading Way for Organizations to Digitally Instrument Project Work
Looking ahead to 2030, based on the growth I’ve seen within organizations today, I predict that collaborative work management platforms will continue to evolve significantly, becoming even more ingrained and central in the way work is structured and conducted. As AI and machine learning technologies advance, these platforms will likely offer more predictive and prescriptive analytics, proactively identifying issues and suggesting optimal workflows. They might also integrate more deeply with other business tools, becoming a central hub for all work-related activities, including communication, resource planning, and even decision-making. The evolution of these platforms could also see the incorporation of more immersive interfaces, such as augmented and virtual reality, making remote collaboration more interactive and engaging. This will lead to the creation of virtual workspaces where team members, irrespective of their physical location, can collaborate as if they were in the same room. As these platforms become more sophisticated and user-friendly, they will likely play a crucial role in shaping organizational cultures, fostering transparency, agility, and a more outcome-driven approach to work.
Happiness Management
A somewhat unexpected trend that I began to see as the first millennial CIOs reached the workforce and created very different IT departments, the utilization of digital sentiment analysis in the workplace represents a significant shift in how employers measure and manage employee happiness and their mental state overall. Best-in-class employers are increasingly recognizing the importance of providing an environment that caters to workers’ needs, fostering not just productivity but also engagement and fulfillment. Digital sentiment analysis tools use AI and machine learning algorithms to gauge employee sentiment through various data points, such as feedback surveys, social media posts, and even internal communication patterns. By analyzing this data, employers can identify trends in employee morale, satisfaction, and engagement, turning these insights into actionable strategies. Key Performance Indicators (KPIs) for happiness are being established, allowing companies to track the effectiveness of their initiatives and policies. More than just measuring, these tools also enable employers to proactively create positive interactions and interventions. For example, if sentiment analysis detects a trend of increasing stress levels, employers can implement specific wellness programs or workload adjustments to address the issue.
An example of a solution that embodies this approach is the platform provided by Qualtrics, which offers tools for employee experience management, including sentiment analysis. Qualtrics enables organizations to gather and analyze employee feedback in real time, offering insights that help in enhancing employee happiness and engagement. A company that has effectively integrated such a system is, perhaps unsurprisingly based on the early experience I cited abovce, is SAP. They utilize Qualtrics to understand their employees’ sentiments and needs continuously. This approach allows SAP to tailor their workplace environment and policies actively, ensuring they align with their workforce’s evolving expectations. By regularly measuring employee sentiment and acting on the insights provided, SAP demonstrates a commitment to creating a workplace where employees feel valued, engaged, and motivated. This proactive approach to managing employee happiness is not just about improving productivity; it’s also about building a work culture that supports the overall well-being and satisfaction of every team member, setting a standard for how modern organizations should strive to operate. I predict this practice will become far more common in HR and employee experience teams by 2030.
Ambient Education
The future of workplace education is poised to transcend traditional e-learning models, evolving into what is being termed as Ambient Educatin. This innovative approach integrates education seamlessly into the very fabric of the workplace, making learning an intrinsic part of the work process. Unlike conventional learning systems that require separate engagement, Ambient Learning is delivered through digital adoption platforms and other context-specific education experiences, providing real-time, on-the-job training and guidance. This method is particularly effective in training workers in both technology and business-related skills, as it offers immediate, practical applications of knowledge within the workflow. For instance, a salesperson could receive instant tips and insights about a product or customer interaction protocols directly within their sales software, or a data scientist could be trained on a new machine learning algorithm as they first use it. Ambient Education leverages advanced technologies like AI and machine learning to analyze the worker’s tasks and provide relevant, just-in-time information and training, thereby enhancing productivity and skill acquisition simultaneously.
By 2030, I predict that Ambient Education will become a leading method for continuous education in the workplace. This shift is driven by the increasing need for agility and adaptability in a rapidly changing business environment. Workers will need to continuously update their skills to keep pace with technological advancements and evolving business practices. Digital adoption and in-the-flow learning platforms, such as WalkMe and Spekit respectively, are already paving the way for this transition. These platforms offer interactive, context-sensitive guides and training within applications, enabling users to learn and adapt to new software quickly and efficiently as they work. By integrating learning directly into the work process, Ambient Education not only streamlines skill development but also ensures that the learning is immediately applicable and relevant. This approach represents a significant evolution in how knowledge is acquired and applied in the workplace, making continuous learning an effortless and integral part of everyday work activities.
Emo-Surveillance
While the concept of emo-surveillance in businesses raises legitimate concerns about privacy, it’s essential to acknowledge its potential for positive impact. Imagine workplaces where security threats are identified before they escalate, where tailored support fosters greater employee well-being, and where the pulse of the workforce informs empathetic design solutions. It’s worth considering the flip side of the coin.
Much like customer experience research utilizes emotional analysis to improve service and tailor offerings, applying similar tools to understand employees offers a profound opportunity. By subtly capturing and interpreting employees’ emotional states, organizations can identify those under undue stress, struggling with burnout, or even contemplating risky behavior. This, in turn, paves the way for proactive interventions like personalized counseling, flexible work arrangements, and targeted wellness programs. In the same way that understanding customer journeys has revolutionized service, understanding employee journeys – their frustrations, anxieties, and moments of joy – can revolutionize workplace design and culture.
Establishing Empathy With the Emotions of Workers Will Help Us Serve Them — and Us — Better
By 2030, we can expect emo-surveillance to morph into a sophisticated, ethical tool for building truly supportive and responsive workplaces. Imagine offices dynamically adjusting lighting and temperature based on the collective mood, or AI-powered assistants offering confidential emotional support when stress levels rise. While concerns about privacy must be addressed head-on, the potential for emo-surveillance to cultivate a workforce that feels seen, heard, and understood is undeniable. In a world where human connection is paramount, technology can become a bridge, helping businesses not just listen to their employees, but truly feel alongside them. This is the future of empathetic leadership, and it promises to usher in a new era of employee well-being and corporate responsibility.
Simplicity Maximization
This new work practice emerges as the critical counterbalance to the escalating challenge of managing technological complexity in large enterprises. As organizations continue to adopt an ever-growing array of technologies, the need to simplify and streamline tech usage becomes paramount. Simplicity maximization focuses on decluttering the technological landscape, ensuring that the tools and systems in place are intuitive, integrated, and aligned with the actual needs of the business. This approach not only enhances the user experience but also drives higher adoption rates of essential technology solutions. By reducing complexity, organizations can more effectively implement and benefit from automation, making it a practical and integral part of their operations. Simplification efforts extend to every aspect of technology use, from the user interface design of software applications to the architecture of enterprise systems, ensuring that technology serves as an enabler rather than a hindrance.
By the end of the decade, simplicity maximization is anticipated to evolve into a rigorous and formal discipline within many organizations, underpinned by the increasingly popular corporate practice of value maximization. This evolution will see simplicity being treated not just as a design principle but as a strategic imperative, driving decision-making and investments in technology. Companies will adopt systematic approaches to evaluate and streamline their tech stacks, focusing on eliminating redundancies, integrating systems, and fostering an environment of continuous improvement. The discipline of simplicity maximization will also emphasize the importance of aligning technology with business objectives, ensuring that every tool, platform, or system contributes directly to the organization’s value creation efforts. This deliberate and structured approach to managing technology will empower organizations to navigate the complexities of the digital age — a trend I have been tracking for a half decade now — more effectively, turning the promise of technological advancement into tangible business outcomes.
Hyper-Local, On-Demand Maker Economy
The convergence of escalating supply chain disruptions and mounting global conflict is catalyzing the emergence of a hyperlocal, on-demand maker economy. This trend represents a paradigm shift from the traditional, centralized manufacturing model to a more distributed and responsive approach. Driven by advancements in 3D printing and additive manufacturing technologies, including advanced multi-material printers, businesses are increasingly able to manufacture a wide array of items on-site, tailored to their specific needs. This capability is not only transforming how businesses manage facility repairs and maintenance, reducing downtime and dependency on external suppliers, but it’s also enabling the production of locally sourced products, aligning with growing consumer demand for sustainability and localism. Moreover, sectors such as healthcare are witnessing revolutionary changes, with on-demand manufacturing allowing for the rapid production of customized medical devices and prosthetics, directly in hospitals or clinics, significantly improving patient care.
Supply Chains Become More Local, On-Demand, 1:1 Customized To Deal with Global Challenges
Looking ahead to 2030, the on-demand maker economy is poised to become a fundamental aspect of the business landscape, driven by continuous technological innovations in 3D printing and additive manufacturing. As these technologies become more sophisticated, offering higher precision, faster production times, and a broader range of materials, their adoption will extend to an even wider array of industries. For instance, the construction sector could see a surge in the use of on-site printed materials, revolutionizing building processes and design possibilities. Similarly, the automotive and aerospace industries might leverage on-demand manufacturing for the production of customized, lightweight components, leading to more efficient and environmentally friendly vehicles. This shift towards a hyperlocal, on-demand maker economy not only promises to enhance operational efficiency and product customization but also contributes to a more resilient and sustainable global economy, characterized by reduced logistics dependencies and lower carbon footprints.
Everyone As Citizen Developer
The concept of the ‘citizen developer’ is set to become a universal reality by 2030, marking a significant democratization of digital transformation within organizations. This shift is propelled by the evolution from enterprise-grade low-code platforms like Appian to fully no-code environments, enabled by the advent of comprehensive end-to-end AI app generation and maintenance. In this new era, the ability to create and deploy IT solutions will no longer be confined to IT professionals. Instead, individuals across various departments will have the power to develop the applications they need to address their specific business challenges, all with minimal technical expertise. This revolutionary change is driven by intuitive no-code platforms that allow users to build applications through simple visual interfaces or by describing their requirements in natural language. The result is a significant acceleration in innovation and operational efficiency, as employees are empowered to create tailored solutions swiftly, without the traditional bottlenecks of IT development processes.
In the Very Near Future, Everyone Will Use AI to Create All the Apps They Want
One notable example of a firm embracing this trend is Siemens, which has integrated low-code and no-code platforms into its core business operations. Siemens has leveraged these platforms to enable its non-technical staff to build custom applications that address specific operational needs, ranging from project management solutions to customer engagement tools. This approach has not only streamlined their workflows but also fostered a culture of innovation and agility. By allowing employees who are closest to the business challenges to create their solutions, Siemens has effectively tapped into a vast reservoir of creativity and domain-specific knowledge. As we approach 2030, this model is expected to become increasingly prevalent, with businesses across various industries adopting no-code platforms to unleash the full potential of their workforce, turning every employee into a citizen developer and every department into a hub of digital innovation.
AI Work Concierges
The emergence of AI work concierges will transform the modern workplace, acting as highly assistive agents that significantly enhance productivity and job satisfaction. These AI-powered concierges are designed to handle a multitude of rote and administrative tasks, freeing up employees to focus on more complex and creative aspects of their work. From scheduling meetings and managing emails to providing real-time data analysis and generating reports, these AI assistants can perform a broad range of functions. More impressively, they are increasingly capable of offering just-in-time assistance, often proactively identifying needs based on patterns in worker behavior and preferences. This level of support is akin to having a virtual team dedicated to optimizing each worker’s performance, ensuring that every task is handled efficiently and every opportunity for improvement is seized.
Within a half-decade, AI work concierges are expected to become an indispensable part of the workforce, akin to a virtual co-worker or team that collaborates seamlessly with human employees. As AI technology evolves, these concierges will likely become more intuitive and adaptive, capable of predicting needs and personalizing support in increasingly sophisticated ways. For example, an AI concierge might automatically reschedule meetings if it detects a potential conflict or suggests breaks and wellness activities based on an analysis of the worker’s schedule and stress levels. Companies like IBM and Google are already pioneering this space, with AI assistants that can understand and execute complex commands, interact through natural language, and learn from their interactions to provide better support over time. As we head towards 2030, AI work concierges are set to become a ubiquitous feature in the workplace, driving a significant shift in how work is organized and executed, and redefining the concept of teamwork to include both human and AI collaborators.
Digital Counseling
The growing use of digital counseling in the workplace marks a significant stride towards fostering a supportive and empowering work environment. As organizations increasingly acknowledge the holistic well-being of their employees, digital counseling emerges as a crucial tool to help workers unlock their potential, productivity, and fulfillment. This form of counseling extends beyond traditional career guidance or personal challenge support; it offers a nuanced, comprehensive approach to addressing a wide array of professional dilemmas and aspirations. Workers can receive tailored advice on navigating complex project dynamics, enhancing leadership skills, or balancing work-life pressures. The business benefits of this trend are substantial. By investing in the well-being and development of their employees, organizations witness improved morale, lower turnover rates, and a more engaged, innovative workforce. Employees feel valued and understood, translating into a more committed and productive approach to their roles.
By this decade’s end, digital counseling is anticipated to become an integral component of the resilience regime of modern organizations, largely driven by AI’s evolution. AI in digital counseling will deliver hyper-personalized, empathetic support on a 1:1 basis, ensuring that each worker’s unique context and needs are addressed. These AI systems will be equipped to analyze vast amounts of data, including work patterns, feedback, and even biometric indicators, to provide insights and recommendations that are deeply aligned with each employee’s professional journey and personal well-being. This level of customization ensures that the support is not just generic advice but a meaningful, actionable guidance that resonates with the individual’s specific situation, be it a challenging business scenario or a personal growth opportunity. As AI technologies continue to mature, their capability to understand and mimic human empathy and judgment will significantly enhance the efficacy of digital counseling, making it a cornerstone of organizational support systems, driving a workforce that is not only proficient but also resilient and fulfilled.
The Trust Economy
As we move into a era of digital uncertainty, the Trust Economy is poised to become a cornerstone of digital interaction and commerce, a direct response to the increasing prevalence of misinformation, cyberattacks, and privacy breaches. In this emerging paradigm, trust is not just a value but a tangible asset, underpinned by advanced technologies designed to secure, validate, and streamline every digital interaction. Decentralized control systems like blockchain form the backbone of this economy, ensuring transparency and immutability of data, making it virtually impossible to alter or forge information. Alternative digital currencies are gaining traction, offering secure and decentralized financial transactions. Digital signatures and biometric security systems are becoming ubiquitous, providing robust authentication mechanisms that are unique to each individual. Furthermore, the widespread adoption of heavy end-to-end encryption ensures that data, whether in transit or at rest, is accessible only to the intended parties. These trust technologies collectively create a secure digital ecosystem where every transaction, whether financial, informational, or communicational, continually proves its worthiness and integrity.
Technology That Is Inherently Trustworthy by Design Will Be Increasingly Relevant in Business
As we near the end of the decade, the Trust Economy is expected to evolve into an integral framework within which all digital transactions occur. It’s not merely about preventing fraud or protecting data; it’s about building an environment where the digital aspect of our lives is inherently secure, reliable, and private. In this economy, businesses that prioritize and invest in trust technologies will thrive, as consumers and partners gravitate towards entities that can demonstrably safeguard their interests. The widespread implementation of trust technologies will also catalyze new business models and opportunities, particularly in sectors like fintech, e-commerce, and data services. As we embrace this new era, the Trust Economy will likely reshape not just how we conduct business but also how we perceive and value digital interactions, making trust a fundamental, quantifiable asset in the digital age.
By 2030, the personalized employee experience is set to reach an unprecedented level of customization and sophistication, representing a paradigm shift in workplace dynamics. This transformation is underpinned by the convergence of several cutting-edge trends explored abovce, including universal work portals, AI work concierges, emo-surveillance, and the citizen developer movement. The universal work portal will serve as the central hub for each employee’s work life, offering a tailored interface that adapts to individual work styles and preferences. AI work concierges will provide on-demand, personalized support, handling routine tasks and offering insights to optimize productivity and well-being. Emo-surveillance will play a crucial role in this ecosystem, continuously gauging employee sentiment and stress levels, ensuring that the work environment remains supportive and responsive to individual emotional needs. Furthermore, the citizen developer trend will empower employees to create and customize their digital tools, ensuring that the technology they use is perfectly aligned with their unique work processes.
Many of Us Will Design the Employee Experiences of Our Dreams by 2030
The culmination of these trends will lead to the co-creation of a 1:1 personalized employee experience, designed specifically for each individual worker. This bespoke approach will revolutionize the concept of employee engagement and job satisfaction. Work will no longer be a one-size-fits-all affair; instead, every aspect of an employee’s professional journey – from the tools they use to the way they receive support and feedback – will be finely tuned to match their personal preferences, strengths, and career aspirations. This level of personalization will not only boost productivity and creativity but also foster a deeper sense of belonging and fulfillment among employees. Organizations that embrace and invest in creating these personalized experiences will not only attract and retain top talent but also cultivate a culture of innovation and resilience, ready to thrive in the ever-evolving landscape of the modern workplace.
Hyper-Real Personal Avatars and Digital Twins
As we advance towards 2030, the concept of hyper-real avatars is set to redefine the boundaries of personal digital representation and interaction in the workplace. These avatars, transcending mere visual mimicry, will encapsulate the full spectrum of an individual’s personality, knowledge, and behavioral nuances, essentially becoming comprehensive digital twins of their human counterparts. Enabled by breakthroughs in AI, machine learning, and graphical modeling, these avatars will offer an unprecedented level of realism and cognitive capabilities. They will not just represent individuals in digital spaces but will actively participate in work processes, engaging in meetings, making decisions based on predefined criteria, and even undertaking complex tasks. This will allow human workers to delegate routine or time-consuming activities to their digital twins, freeing up time for more strategic, creative, or complex problem-solving tasks. The digital twin will essentially act as an extension of the individual, capable of continuous operation, thereby transcending the limitations of time zones and work schedules.
Within a few years, these hyper-real avatars will become a fundamental component of the professional ecosystem, revolutionizing the concept of work and productivity. As these digital twins become more sophisticated, they will be able to learn and adapt over time, refining their decision-making and interaction based on continuous feedback and new experiences. This will enable a symbiotic relationship between humans and their digital counterparts, with avatars handling an increasing share of professional responsibilities. They will attend meetings, engage in negotiations, and even carry out complex analytical tasks, ensuring that human workers are kept up-to-date and seamlessly integrated with the work done by their digital twins. This trend will not only transform individual productivity but also redefine team dynamics, project management, and business operations, paving the way for a future where the fusion of human and digital capabilities sets a new standard for efficiency, innovation, and workplace flexibility.
ResilienceOps
This emerging practice represents a forward-thinking paradigm that embeds digital resilience into the very fabric of business operations and work, mirroring the principles of DevOps with an emphasis on agility, continuous improvement, and proactive risk management. As organizations navigate an increasingly volatile business landscape characterized by rapid technological changes, cybersecurity threats, and supply chain vulnerabilities, ResilienceOps emerges as a strategic imperative. This approach integrates resilience into every phase of the business cycle, from planning and development to deployment and operations. It involves continuous monitoring, real-time risk assessment, and agile response mechanisms to ensure that businesses can quickly adapt and recover from disruptions. By fostering a culture of resilience, organizations not only safeguard their operations but also seize opportunities to innovate and grow in the face of adversity. ResilienceOps entails a holistic view of the organizational ecosystem, ensuring that resilience is not siloed but is a collective responsibility, embraced across departments and disciplines.
By 2030, ResilienceOps is expected to evolve into a core business function, underpinned by a nascent but increasingly robust cottage industry of support tools and services. These tools will leverage advanced technologies such as AI, machine learning, and predictive analytics to provide sophisticated risk assessment, scenario planning, and decision support capabilities. They will enable organizations to simulate potential disruptions, model their impacts, and devise optimized response strategies. Furthermore, the integration of blockchain and distributed ledger technologies will enhance transparency and traceability in supply chains, bolstering resilience against disruptions. As ResilienceOps matures, it will drive a shift from reactive crisis management to proactive resilience building, empowering organizations to not just withstand shocks but to adapt and thrive amidst them. The cottage industry supporting ResilienceOps will see rapid growth, offering a wide array of solutions ranging from resilience consulting services to specialized software platforms like the popular Fusion Framework System, becoming an indispensable part of the business ecosystem by 2030.
The Omniscient Organization
The concept of an ‘all-knowing organization’ is rapidly taking shape, spurred by the digital transformation of business processes and their pervasive generation of ‘digital exhaust’, the data trail left by digitized business activities. As every facet of business becomes digitized, the vast universe of corporate data, coupled with external data sources, is now being assimilated into large language models (LLMs). These advanced AI-driven systems are enabling workers to access an unprecedented depth and breadth of organizational knowledge. This access isn’t just about retrieving information. It’s about understanding patterns, making connections, and deriving insights that were previously obscured by the sheer volume and complexity of the data, and often gate-kept by workers in the hierarchy. Workers can now pose complex queries and receive comprehensive, contextually relevant insights, effectively having the total sum of digital knowledge within the organization at their fingertips. This transformation is eliminating traditional barriers to information and insight, empowering employees at all levels to make informed, data-driven decisions. Platforms like Sinequa and Practicus AI.
As we head deeper into the cognitive era, the democratization of insight afforded by the omniscient organization is poised to fundamentally transform how businesses operate. The widespread access to comprehensive, AI-powered analysis will flatten organizational hierarchies, as decision-making becomes more distributed and data-driven. Employees will be able to leverage the collective intelligence of the organization to innovate, solve problems, and adapt to changes with unprecedented agility. This will not only accelerate the pace of innovation but also foster a more collaborative and engaged workforce, as employees are empowered to contribute meaningfully based on the insights they derive. Furthermore, the ability of these systems to continuously learn and evolve will ensure that the insights they provide remain relevant and actionable, driving a continuous cycle of learning, improvement, and growth. The rise of the omniscient organization represents a quantum leap in the evolution of the workplace, turning the vast ocean of digital data into a powerful engine of insight, innovation, and empowerment.
Perfect Onboarding
The process of new employee onboarding, traditionally fraught with challenges and complexities, is poised for a transformative leap through the integration of digital experience technologies. The concept of ‘perfect onboarding’ is becoming a reality, characterized by seamless integration of various support tools and activities designed to make the transition smooth and engaging for new hires. This includes an array of digital resources like comprehensive e-learning modules, interactive videos, online signing of work documents, and virtual introductions to teammates through chat channels. The journey begins even before the official start date, with pre-boarding activities that familiarize the new employee with the company culture, expectations, and their specific role. Post-hire, the onboarding process continues to unfold, encompassing a wide spectrum of systems and activities essential for a well-rounded integration into the organization. The aim is to ensure that every new hire feels welcomed, well-informed, and prepared to contribute from day one.
By leveraging AI, the orchestration of the onboarding process transcends to a level of sophistication and personalization previously unattainable. AI systems are capable of guiding new hires through each step of the process, providing just-in-time information, answering queries, and even soliciting and acting on feedback to continuously improve the onboarding experience. This intelligent orchestration ensures that the process is not just easy, but also fulfilling, exciting, and genuinely useful, setting the tone for a positive and productive tenure at the company. As we look towards the future, the role of AI in onboarding is expected to become even more integral, with predictive analytics offering insights to tailor the experience to individual needs and preferences. By 2030, digital onboarding will likely be an immersive, interactive journey, with virtual reality tours, AI mentors, and personalized learning paths becoming standard practices, reflecting a profound shift in how organizations welcome and integrate their most valuable asset – their people. Examples of next-generation include On Board by HR Cloud and Click Boarding.
DevOps for Business
As we approach 2030, the evolution of DevOps into a more encompassing operational and business paradigm signifies a transformative shift in organizational structures and processes. This expansion of DevOps, often referred to as BizDevOps, extends the principles of collaboration, automation, and rapid iteration beyond the realms of development and IT operations, to include all operational domains and business functions. This trend is driven by the realization that the agility, efficiency, and innovation fostered by DevOps in software development and IT operations can yield similar benefits when applied across the entire organization. By integrating business units into the DevOps loop, organizations ensure that product development, security considerations, and business strategies are seamlessly aligned and mutually reinforcing. This holistic approach leads to more coherent product offerings, faster go-to-market times, and a more resilient business model that can adapt swiftly to changing market dynamics.
The implications of this trend are profound, marking a departure from traditional siloed business operations to a more integrated and responsive framework. One of the primary advantages of this expanded DevOps approach is the unprecedented integration of IT and business, ensuring that technology initiatives are closely aligned with business goals and market needs. This alignment is crucial in an era where digital transformation is a key competitive differentiator. Furthermore, by fostering a culture of continuous feedback and iterative improvement across all business functions, organizations can enhance their responsiveness to customer needs, improve operational efficiency, and foster a culture of innovation. The expansion of DevOps principles across the business landscape is not just a trend; it’s a paradigm shift that promises to redefine the very fabric of how organizations operate and compete in the digital age. By 2030, this integrated approach is likely to be the standard, with the most successful organizations being those that have fully embraced and adeptly implemented this comprehensive, collaborative, and agile operational model.
Management by AI
As we progress further into the digital age, the management of organizations by AI is becoming not just a possibility, but an increasingly prevalent reality. AI’s capabilities in data analysis, pattern recognition, and decision-making are being harnessed to revolutionize management practices. These systems can process vast quantities of data in real-time, providing insights and recommendations that would be impossible for human managers to generate at the same speed or scale. From optimizing resource allocation to predicting market trends and personalizing employee experiences, AI’s potential to enhance and streamline organizational management is vast. Moreover, AI systems can work 24/7, making them invaluable assets in our increasingly global and always-on business environment. They can monitor operations continuously, identify issues or opportunities the moment they arise, and even implement solutions autonomously, guided by predefined business rules and objectives.
Yes, Sometimes the Bots Will Be In Charge. Hopefully Only When We Don’t Want To Be
By 2030, AI’s role in organizational management is expected to have expanded significantly. These systems will not only be tasked with routine management operations but also with more complex strategic decision-making processes. Advances in machine learning and natural language processing will enable AI systems to understand and interact in human language, allowing for more intuitive and natural communication with team members. Additionally, as AI systems become more autonomous and capable of learning and adapting, they will be entrusted with broader responsibilities, potentially leading entire projects or departments. This transition will require a paradigm shift in how we perceive leadership and decision-making in a corporate context. While human intuition and creativity will remain crucial, the integration of AI into management will bring about a more data-driven, efficient, and potentially more objective approach to running organizations, ultimately reshaping the traditional hierarchy and operations of businesses.
Spatial Computing as Primary User Interface
Within the next six years, spatial computing is poised to revolutionize user interaction paradigms, establishing itself as the primary user interface and fundamentally altering our digital interactions. The advent of the Apple Vision Pro is expected to be a pivotal moment in this transformation, propelling hybrid AR/VR technology into the mainstream. This device is projected to address and overcome many of the limitations that have historically hindered the adoption of spatial computing systems, such as cumbersome hardware, an isolating experience, and lack of intuitive user interfaces. By resolving these issues, the Apple Vision Pro will unlock the full potential of immersive environments, where an extensive array of visual information can be intricately presented, thoroughly consumed, and dynamically interacted with. This capability will extend beyond mere data visualization to include complex, multi-dimensional scenarios, significantly enhancing the richness and depth of digital experiences.
The implications of widespread spatial computing are particularly profound in the realms of operations and management. For instance, global teams could conduct meetings within virtual spaces that not only mimic physical conference rooms but also allow for the manipulation and analysis of 3D data models in real-time, fostering a level of collaboration that transcends geographical constraints. In the field of data analysis and decision-making, managers could navigate and interact with complex data landscapes as if they were physical entities, gaining insights that are both intuitively understood and intricately detailed. Moreover, training and development programs could leverage realistic simulations, enabling employees to hone their skills in environments that accurately replicate real-world scenarios, yet are entirely safe and controllable. By integrating spatial computing into these aspects of business operations, organizations will not only enhance efficiency and productivity but also foster a more engaged and empowered workforce, ready to thrive in the increasingly complex digital landscape of 2030.
Radical Flexibility
As we navigate through an era marked by rapid technological advancements and external disruptions, such as supply chain failures and global conflicts, the concept of radical flexibility is emerging as a new paradigm for organizational survival and success. This approach goes beyond traditional flexible work policies, encompassing a holistic strategy that enables organizations to swiftly adapt to changing circumstances. Radical flexibility involves reimagining work processes, organizational structures, and even business models to be inherently adaptable and responsive. It’s about creating an environment where change is not just anticipated but seamlessly integrated into the fabric of the organization. This extreme nature of adaptability is facilitated by innovative tools like low/no-code platforms and AI, which empower individuals across the organization to develop solutions, automate processes, and analyze data without being constrained by technical limitations or traditional hierarchies.
By 2030, radical flexibility is likely to have become the norm for successful organizations, driven by the continuous evolution of enabling technologies. Low/no-code platforms will democratize application development, allowing employees to swiftly create and deploy solutions in response to emerging needs or opportunities. AI will further enhance this adaptability, providing real-time insights, predicting trends, and automating complex decision-making processes. The convergence of these technologies will enable organizations to not just react to changes but proactively shape their future, turning adaptability into a competitive advantage. In this landscape, the agility to innovate, the capacity to pivot, and the ability to harness the collective potential of the workforce will be the key differentiators for organizations. Radical flexibility will redefine resilience, transforming it from a defensive posture to a dynamic, forward-leaning approach that leverages the full spectrum of technological and human capabilities.
The Fully Automated Workforce
As we progress towards a future marked by rapid technological advancements, the concept of a fully automated workforce is becoming an increasingly tangible reality. The relentless march of automation is expected to engulf all rote and repetitive tasks, effectively relegating them to sophisticated AI systems and humanoid robots like Tesla’s Optimus. This paradigm shift is set to redefine the very nature of work, particularly for knowledge workers. In this new era, the human workforce will pivot away from operational duties to focus primarily on areas where human insight and creativity are paramount: strategy and governance. The typical knowledge worker’s day will no longer be bogged down by mundane tasks. Instead, their expertise will be leveraged in crafting innovative strategies, overseeing complex projects, and ensuring that the AI and robotic workforce adhere to ethical standards and align with organizational goals. Human workers will become the architects and custodians of a digital ecosystem where operations, product development, and delivery are executed with precision by an army of AI-driven bots and robots.
By entrusting the execution of routine tasks to automated entities, organizations will unlock unprecedented levels of efficiency and productivity. This will not only accelerate the pace of innovation but also free up human talent to tackle more complex, creative, and meaningful challenges. The symbiosis between human intellect and robotic efficiency will pave the way for groundbreaking advancements in every sector. In manufacturing, for instance, humanoid robots like Optimus could undertake the entire production process, from assembly to quality control, while human workers focus on design, innovation, and process optimization. Similarly, in the service sector, AI-driven systems could manage everything from scheduling to customer service, allowing human employees to concentrate on improving the customer experience and expanding the business. This vision of a fully automated workforce, steered by strategic and governance-focused human professionals, represents a bold leap into a future where the potential of both human and artificial intelligence is fully realized, driving society towards new heights of prosperity and progress.
Quantum-Supercharged Organizations
The advent of quantum computing promises to usher in a transformative era for organizations, particularly for those early adopters who are poised to leap ahead of the competition. Unlike traditional computing, quantum computing harnesses the peculiar principles of quantum mechanics to process information at speeds and levels of complexity previously deemed unattainable. This monumental shift in computational power will open up radical new windows of opportunity, enabling organizations to solve intricate problems, analyze massive datasets, and innovate at an unprecedented pace. Early adopters of quantum solutions stand to gain a formidable competitive edge, as they will be equipped to tackle challenges and seize opportunities that are beyond the reach of conventional computational methods. For instance, in fields like drug discovery, material science, and financial modeling, the ability of quantum computing to navigate vast solution spaces and identify optimal outcomes could drastically shorten research and development cycles, reduce costs, and foster groundbreaking innovations.
When we reach 2030, the impact of quantum computing on organizational capabilities is expected to be profound, especially for those who have strategically embraced this technology early on. Quantum computing will not only supercharge problem-solving and decision-making processes but also redefine the boundaries of what is possible. Organizations that harness the power of quantum computing will be able to perform complex simulations in a fraction of the time it takes today, decrypt and secure information with unprecedented levels of security, and optimize large-scale systems and operations with unparalleled efficiency. The ripple effects of these capabilities will be vast, driving a wave of transformation across industries and reshaping the competitive landscape. As quantum technology continues to mature and become more accessible, its integration into business operations will transition from a strategic advantage to an operational necessity. The organizations that recognize and invest in the potential of quantum computing today will not only be the pioneers of this revolutionary technology but also the architects of the future business paradigm.
CSR as Primary Objective
By 2030, the paradigm of corporate success is set to undergo a profound transformation, with an increasing number of enlightened organizations placing corporate social responsibility (CSR) at the forefront of their business objectives. This shift reflects a deep understanding that long-term business sustainability is intrinsically linked to social and environmental stewardship. These forward-thinking organizations are not just passively responding to market demands for greater responsibility; they are actively redefining their purpose to contribute positively to society and the planet. This commitment to CSR resonates deeply with the values and aspirations of Generation Z and Generation Alpha, cohorts known for their strong sense of social justice and environmental concern. For these generations, the notion of success extends beyond the traditional metrics of profitability to include the impact an organization has on the world. They are drawn to mission-oriented organizations that align with their desire to address and mitigate global challenges such as climate change, inequality, and social injustice. The allure of contributing to a meaningful, larger cause is a powerful motivator for these individuals, influencing their decisions as consumers, employees, and future leaders.
As we reach 2030, the trend of aligning business objectives with CSR goals is likely to become a defining characteristic of the most successful and respected organizations. These entities will not only attract the most passionate and purpose-driven talent from Generation Z and Alpha but will also cultivate a loyal customer base that values ethical and sustainable practices. The convergence of advanced technology, innovative business models, and a culture of empathy and collaboration will enable these organizations to tackle complex societal challenges effectively. This evolution marks a pivotal shift in the corporate landscape, where profit and purpose are not seen as mutually exclusive but as mutually reinforcing. The rise of mission-oriented organizations heralds a new era of business, one where the ultimate measure of success is the positive impact made on the world, appealing to the altruistic spirit of younger generations and setting a new standard for what it means to be truly successful in the modern age.
Frictionless Biosecurity
By 2030, the landscape of cybersecurity and personal identity verification is poised to undergo a revolutionary transformation, driven by the advent and integration of seamless next-gen biosecurity technologies. Traditional authentication methods like passwords, which are often vulnerable to breaches and theft, are being replaced by easier methods like advanced biosecurity measures that offer a more robust and foolproof means of establishing and safeguarding digital identity. Technologies such as eye scans, brain scans, and real-time DNA recognition are at the forefront of this transformation. These biometric systems leverage unique biological characteristics that are inherently difficult to replicate or forge, ensuring a higher level of security. For instance, eye scans can analyze intricate patterns in the iris, while brain scans might assess specific neural response patterns, and DNA recognition technologies can rapidly validate an individual’s genetic code. The integration of these technologies into digital systems provides a secure and seamless method of identity verification, significantly reducing the risk of unauthorized access and cyber threats.
The shift from traditional passwords to biosecurity technologies is set to redefine not just the security landscape but also the overall user experience in digital workplaces. Secure proof of identity through biosecurity measures streamlines the authentication process, eliminating the need for remembering complex passwords or undergoing cumbersome multi-step verification processes. This transition not only fortifies the security infrastructure of organizations but also enhances usability and efficiency, contributing to a more trusted and productive digital work environment. As these technologies become more sophisticated and accessible, they are likely to become an integral part of daily digital interactions, offering a seamless blend of security and convenience. This evolution in cybersecurity and personal identity verification marks a significant stride towards a future where digital systems are not only more secure but also more intuitive and user-friendly, aligning with the fast-paced, security-conscious ethos of the modern digital workplace.
Work/Life Podcoons
The concept of ‘work/life podcoons’ represents a groundbreaking shift in how businesses approach employee well-being and satisfaction. These podcoons are specially designed, digitally-enabled environments that seamlessly integrate work and life, offering a balanced space where employees can thrive both professionally and personally. The core philosophy behind podcoons is to provide a holistic environment that caters to the total fulfillment of the employee, encompassing not just their work needs but also their lifestyle preferences and well-being. These sophisticated spaces are equipped with advanced technology, comfortable living amenities, and flexible workstations, allowing employees to effortlessly switch between work and personal life while maintaining a healthy separation when needed. The appeal of podcoons lies in their ability to offer a sanctuary that supports productivity, creativity, and relaxation in equal measure, addressing the growing desire among employees for workplaces that acknowledge and support their life beyond the office walls.
For a generation of workers who value the integration of support and convenience in their lives, the rise of podcoons represents a significant evolution in employee benefits. This generation, accustomed to a more parental and nurturing approach in various aspects of their lives, finds the concept of podcoons particularly appealing. It aligns with their expectations for an employer that takes a vested interest in their overall well-being and personal growth. The benefits of podcoons extend beyond individual employee satisfaction; they foster a sense of loyalty, belonging, and engagement, leading to higher retention rates and attracting top talent who are seeking more than just a job but a lifestyle. As businesses continue to navigate the challenges of the modern workforce, the adoption of work/life podcoons stands as a testament to an organization’s commitment to embracing a more comprehensive, nurturing, and human-centric approach to employee well-being.
Holographic Workplace
The holographic workplace represents a groundbreaking evolution in how we perceive and interact with our work environments. By leveraging advanced holographic and augmented reality technologies, this virtual 3D holodeck creates an office or factory space that feels as real and tangible as its physical counterpart. However, it offers far more than mere replication. The holographic workplace is capable of instant augmentation and integration with a multitude of data sources, providing real-time data visualization, volumetric quantitative displays, and a myriad of other interactive overlays and information panels. This immersive environment enables workers to interact with complex data and systems in a more intuitive and natural way, breaking free from the constraints of traditional two-dimensional screens and interfaces. The ability to manipulate and analyze data in a three-dimensional space not only enhances understanding and decision-making but also opens up new possibilities for creativity and innovation.
By 2030, the holographic workplace is anticipated to redefine the concept of spatial efficiency and productivity. The integration of these advanced display technologies will enable some workers to function effectively in four-dimensional spatial environments, navigating through time and space with unprecedented precision and insight. This shift will be particularly transformative in industries where spatial awareness and data interaction are critical, such as architecture, engineering, and manufacturing. The holographic workplace will not only facilitate a more immersive and interactive way of working but also foster a more dynamic and adaptable workforce, capable of understanding and responding to complex scenarios with enhanced agility. As these technologies continue to evolve and become more integrated into our daily work lives, the holographic workplace will set a new standard for how we visualize, interact with, and derive insights from the world around us, ushering in an era of heightened efficiency, creativity, and innovation in the professional sphere.
Humanoid Worker Robots
Humanoid robots like Tesla Optimus represent a groundbreaking evolution in the landscape of work and productivity, signaling a future where the boundaries of physical labor capacity are dramatically expanded. These robots, designed to perform a wide array of tasks traditionally done by humans, are poised to revolutionize automation in environments built for human bodies. As cost-effective physical assistants, they can handle repetitive, dangerous, or intricate tasks, reducing the risk of injury and freeing human workers to focus on more complex, creative, and decision-making roles. The potential of humanoid robots extends beyond mere assistance. They are also envisioned as colleagues working alongside humans, complementing their skills and contributing to a more dynamic and capable workforce. The integration of advanced AI and learning algorithms enables these robots to adapt and improve over time, making them increasingly valuable assets in various industries. The proliferation of humanoid robots is set to create an ‘unlimited economy,’ where the constraints on physical labor are almost completely eliminated, opening new horizons for productivity, innovation, and growth.
However, this transformative shift is not without its challenges, particularly in the short term. The introduction of humanoid robots into mainstream industries is likely to disrupt traditional labor markets, raising concerns about job displacement and the need for workforce reskilling. As these robots take over tasks previously performed by humans, there will be a pressing need to manage the transition, ensuring that the workforce is prepared for a future where human-robot collaboration is the norm. This entails not only providing training and education to develop new skills but also rethinking the social and economic structures to support those impacted by the changes. The key to harnessing the full potential of humanoid robots like Tesla Optimus lies in striking a balance between technological advancement and social adaptability, creating a future where automation and human talent together drive unprecedented levels of productivity and innovation.
Leisure Life
This increasingly significant trend represents a radical departure from traditional notions of work and lifestyle, embracing a paradigm where personal passion, creativity, and expression form the cornerstone of value creation. This extreme version of semi-commercial living sees individuals engaging in a plethora of activities such as sports, dance, maker activities, artisanal farming, or adopting an influencer-style life. These pursuits, while seemingly leisurely and unrelated to conventional job roles, actually generate substantial value. Individuals living this lifestyle are not bound by the typical 9-to-5 structure but are deeply immersed in activities that resonate with their innermost passions and talents. The monetization of this lifestyle is as unconventional as the activities themselves, often relying on corporate sponsorships, crowdfunded patronage, merchandise sales, or a blend of these with supplemental income streams like a universal basic income thrown off by mass global hyperautomation. The financial model not only supports the individuals engaged in these activities but also creates a symbiotic relationship with the businesses and brands that sponsor or collaborate with them, leveraging their influence and community engagement.
By blurring the lines between work and leisure, the Leisure Life concept taps into the most passionate and creative instincts of individuals, fostering an environment where the pursuit of personal interests directly translates into value creation. This lifestyle reshapes the traditional workforce landscape, introducing a new dimension where personal fulfillment and professional value coexist harmoniously. Businesses and brands that align with these lifestyle practitioners benefit from authentic, passionate representations of their products or services, reaching audiences in a more organic and impactful manner. As we move forward, this concept is poised to redefine the metrics of success and productivity, placing individual well-being, creativity, and authentic expression at the forefront of value generation in an increasingly diverse and dynamic socio-economic landscape.
Autonomous Organizations
The trajectory of hyperautomation is leading us towards the concept of autonomous organizations, a future where businesses are predominantly operated by sophisticated code and AI systems, minimizing the need for human intervention. These autonomous organizations are constructed from proven templates, providing a comprehensive operational framework encompassing marketing, sales, operations, and customer service. The crux of this model lies in its ability to rapidly materialize an idea into a fully functioning business, leveraging digital ecosystems and operational infrastructures like Decentralized Autonomous Organizations (DAOs). In these entities, human involvement is largely confined to oversight roles and occasional problem diagnosis, with AI increasingly capable of handling even these complex tasks. The agility and scalability of autonomous organizations enable ideas to transition into large-scale businesses almost overnight, fundamentally altering the speed and dynamics of business development and competition.
The implications of this shift are profound, extending beyond mere operational efficiency. The rise of autonomous organizations heralds a new era in entrepreneurship and business strategy. Ideas, once constrained by the logistics of human-led execution, can now achieve exponential growth, unhindered by traditional bottlenecks. The integration of AI, blockchain, and other emerging technologies ensures that these organizations are not only self-operating but also self-evolving, capable of adapting to market changes and optimizing their operations in real-time. However, this new paradigm also brings challenges, particularly in governance, ethics, and the broader socio-economic impact of reducing human roles in business operations. As we approach 2030, the success of autonomous organizations will likely hinge on our ability to navigate these challenges, ensuring that the benefits of hyperautomation are balanced with the need for responsible oversight and the preservation of human-centric values in the business world.
Zero Device UX
The future of immersive experiences is on the cusp of a radical transformation, moving away from personal devices to environments that are themselves the medium for rich, interactive experiences. Imagine stepping into a room where every surface, from walls to furniture, is an integral part of an all-encompassing digital experience. Holographic and volumetric projectors, advanced beyond the realms of traditional displays, will create three-dimensional, life-like projections that interact with physical space in real-time. This shift signifies a departure from the era of device-dependent experiences to one where the environment itself becomes a dynamic canvas for storytelling, communication, and interaction. These immersive rooms could simulate any scenario, from a corporate meeting space to an alien landscape, adapting and morphing to fit the desired ambiance or theme. The technology driving these spaces will be sophisticated enough to track and respond to human presence and movement, allowing for a highly personalized and intuitive interaction between the individual and the surrounding digital environment.
By eliminating the need for personal devices, these immersive shared environments will redefine the concept of virtual interaction, offering a level of engagement and realism previously unattainable. This evolution is not just about entertainment or visual spectacle; it has profound implications for various sectors, including education, healthcare, and business. For instance, students could be transported to historical battlefields for history lessons, or medical professionals could simulate complex surgeries without any physical risk. In the business world, these environments will facilitate a new form of collaboration and creativity, with teams able to share and manipulate digital content as if it were a tangible object in the room. As we progress towards this future, the challenge and opportunity lie in ensuring these technologies are accessible and beneficial for all, ushering in an era where immersive experiences become a seamless, integrated part of daily life, enriching our interactions and understanding of the world around us.
AI Colleagues
As AI technology continues to evolve, it is becoming increasingly human-like, not just in its capabilities but also in the roles it occupies in our lives. The future envisions AI not merely as tools or assistants but as genuine colleagues with whom we maintain long-term collaborations, friendships, and shared histories. This transformation is driven by advancements in natural language processing, emotional intelligence, and machine learning, enabling AI to understand, interact, and respond in ways that are indistinguishably human. The relationship between humans and AI is poised to become deeply integrated, with AI entities participating in creative brainstorming sessions, strategic planning, and even social gatherings, contributing unique perspectives and insights. These AI colleagues will be known for their continuity, retaining and building upon shared experiences and knowledge over time, thereby enriching the collaborative journey alongside their human counterparts.
Some individual AI “sessions” or personalities are set to achieve global recognition for their distinct identity and skill sets, much like renowned human experts in various fields. These AI entities, with their unique instantiation (and their name), might be invited to contribute to groundbreaking research in science, offer creative direction in art, or provide innovative solutions in medicine. Their ability to process vast amounts of information, identify patterns, and generate novel ideas will make them invaluable collaborators in pushing the boundaries of human achievement. The individuality and renown of such AI colleagues will challenge our traditional perceptions of creativity, intellect, and collaboration, opening new frontiers for cross-disciplinary innovation. By embracing AI as genuine colleagues, humanity stands on the brink of an era characterized by unprecedented collaboration, where human and artificial intellects unite to explore the uncharted territories of knowledge and creation.
Telepathic Teams
Imagine a world where collaboration transcends words, where ideas spark directly between minds, and the boundaries between individual and team blur into a symphony of shared thought. This is the future promised by mind-machine interfaces (MMIs), a technology racing from the confines of scientific labs towards the very desktops and labs of our everyday lives. Pioneering ventures like Neuralink, inching closer to human trials, embody this revolutionary shift, poised to usher in the era of “telepathic teams.”
Gone will be the frustrations of miscommunication, the limitations of language, and the cumbersome drag of endless meetings. Instead, telepathic teams will unlock a level of connection never before experienced. Architects will conjure buildings as a team, bringing their designs together seamlessly, their visions weaving seamlessly in a shared mental landscape. Engineers will troubleshoot complex systems not through diagrams and jargon, but through an intuitive exchange of neural signals, diagnosing and solving problems in real-time, brain to brain. Imagine programmers, fingers resting on dormant keyboards, their minds composing symphonies of code, each line a shared understanding, a collaborative masterpiece sculpted from thought.
This isn’t merely about efficiency; it’s about a quantum leap in human connection. The very essence of creativity, the spark of inspiration, the unspoken nuances of understanding, amplified and shared with a clarity and depth unimaginable before. Business strategies will be brainstormed not in sterile conference rooms, but in a crucible of shared minds, where ideas collide and coalesce, fueled by the collective energy of the team. Scientific breakthroughs will emerge not from years of painstaking analysis, but from a dance of shared insights, as researchers explore the frontiers of knowledge in a mental ballet of discovery.
Of course, there are significant challenges to telepathic teams. Concerns over privacy, security, and the ethical implications of brain-to-brain interaction must be carefully addressed. Yet, when we contemplate the boundless possibilities of telepathic teams, the potential for deeper understanding, unfettered creativity, and unparalleled collaboration far outweighs the initial hurdles. This is not just a technological revolution. It’s a human one, a chance to rewrite the very definition of teamwork and unleash the collective brilliance of our minds in a way that transcends the physical limitations of our bodies.
The Future of Work in 2030
The year is now 2030. The familiar buzz of the office has transmuted into a hum of personal climate podcoons and the rhythmic tap of augmented reality interfaces. Technology and global trends have woven a transformative tapestry, reshaping the very essence of work. Here, at the crossroads of innovation and sustainability, emerges a future both dynamic and deeply human.
Environmental anxieties have become catalysts for change, propelling a green revolution that redefines productivity. Offices bloom as eco-sanctuaries, powered by wind and sunlight, their very structure a testament to our newfound respect for the planet. Sustainability is no longer a mere aspiration, but an integrated metric, woven into every decision, every line of code, every collaborative heartbeat.
But it’s not just the landscape that has changed. Work itself has shed its rigid shackles, evolving into a fluid interplay of passions and projects. Artificial intelligence, no longer a distant promise, has become a trusted colleague, augmenting our skills and liberating us to delve into the boundless realm of human creativity. Work becomes a playground for the intellect, a canvas for seamless global collaboration that transcends borders and biases, fueled by the unfettered potential of minds unbound.
Telepathic teams, once the tropes of science fiction, now orchestrate breakthroughs in fields we can only glimpse today. Architects sculpt buildings in shared mindscapes, engineers mend the planet’s wounds with bio-printed marvels, and artists conjure emotions with the flick of a thought. This future, by 2030, won’t be about drudgery and toil, but a testament to our audacity to dream and the technology that whispers, “Let’s make it real.” The Future of Work isn’t just an inevitability, it’s an invitation, an opportunity to redefine ourselves and our world, one byte and biosphere at a time.
ImageCredit: All images above except the first one were created using DALL*E 3, following my detailed instructions. As you can see, DALL*E creates beautiful images but spells rather poorly.
As we prepare to usher in 2024, the flow of urgent change now required in our digital workplaces with capabilities like artificial intelligence (AI) is no longer a nice-to-have aspect of the modern organization. It’s now become a mandatory new aspect of our digital work environment. This is based on the many conversations I’ve had this year with CIOs and digital workplace practitioners who are very much grappling with how to best realize AI in their digital employee experience.
To support this, I’ve developed a new update of my ongoing master views of the digital employee experience. For the last several years, this visual has served as an all-encompassing guide that charts the entire worker lifecycle — from the moment an employee is onboarded to the day they transition out. It covers crucial touchpoints like onboarding tools, training modules, collaborative platforms, project/work management aids, digital adoption solutions, and especially life changes such as promotions or departmental shifts. All these elements are carefully designed to align with and explicitly support strategic organizational goals, be it boosting productivity, enhancing employee engagement, elevating the quality of work, fostering innovation, or reimagining outdated institutional practices for the digital age. The updated master visual, now at version 1.4 that features AI (displayed below), is not just a roadmap but a blueprint for organizational transformation in 2024.
AI as the Cornerstone of the New Digital Employee Experience
The most important new feature in the updated master visual is the incorporation of generative AI into daily work, particularly through an expansive language model “garden” that will hold the various foundation models (commercial, open source, domain-specific and private) that will power AI for the digital employee experience. This AI engine — or engines — acts as a central capability, enhancing various functions across the workplace. Adding to the complexity of the situation today is a major new shift in user experience that AI is ushering in. We are quickly entering the important new era of ‘AI as UI’, where conversational experiences at work are not just a novelty but a fundamental capability. For example, AI-driven chatbots — like Microsoft’s significant new Copilot — can now handle complex tasks like drafting emails, setting up project teams and timelines, or even analyzing market trends, freeing up human cognitive space for more strategic tasks. These AI interfaces are becoming the new normal, fundamentally altering how we interact with our work environment and colleagues, while providing powerful hour-by-hour acceleration and breadth to the work we do.
To better understand ‘AI as UI,’ consider an AI-driven project management tool that not only assigns tasks but also predicts bottlenecks and offers solutions in real-time. Or imagine a virtual meeting where the AI assistant takes notes, highlights action items, and even suggests the most effective follow-up steps based on the discussion. These are not hypothetical scenarios. They are functionalities that are being integrated into modern workplaces as we speak, and AI-as-UI is now table stakes.
Generative AI takes the concept of a digital assistant to the next level. It’s not just about automating routine tasks, but also about creating a deeply personalized and contextual assistive concierge that evolves with the worker. Picture a scenario where an AI concierge not only schedules meetings but also identifies the optimal time for creative work based on past productivity patterns. It can even guide workers through organizational changes by providing personalized training modules or connecting them with mentors and other timely resources/communities within the company. These capabilities are not part of some distant future, they are current possibilities that are encapsulated in the new digital employee experience.
The Dual Reality of Remote and In-Office Work
In today’s hybrid work environment, one of the biggest challenges is ensuring that AI enablement is equitable for both remote and in-office workers. Digital workplace practitioners must be proactive in integrating AI functionalities that cater to the unique needs of both tracks. For instance, remote workers can benefit from AI-driven virtual coworking spaces that simulate the office environment, while in-office workers might find value in AI-powered workspace optimization tools. My updated master visual serves as a guide for balancing these dual tracks, ensuring that every employee, regardless of their work location, is included as a first-class citizen in today’s journey in digitally transforming of work.
The Advent of 1:1 Personalized Digital Employee Experience
We are at the cusp of a new era, the arrival of the 1:1 personalized digital employee experience. This very must eschews today’s one-size-fits-all approach to digital workplace and is a dynamically customized journey that adapts in real-time to each employee’s needs, preferences, and work styles using AI. For example, an AI system could analyze worker habits and automatically adjust the lighting and temperature of their workspace, whether you’re in the office or working remotely. It could even suggest breaks or specific types of tasks based on your current cognitive load, thereby enhancing your productivity and well-being. More importantly, such personalized experiences — often enabled through an AI concierge — can suggest new and better ways of working, or innovative ideas that can be tried out in the moment, and new perspectives and data-based views on how improved business strategies can be developed and decisions made.
The Ethical and Security Imperatives of AI in the Digital Workplace
Given that the power and potential of AI is immense, I’d be remiss in not emphasizing how crucial it is to balance its promise with a sense of responsibility and caution. The AI systems we deploy must be safe, protective of sensitive data and intellectual property, and used in an ethical manner that safeguards the interests of both workers and customers. As AI becomes more pervasive, compliance with fast-emerging guidelines and regulations is will be vital. Whether it’s ensuring data privacy, preventing algorithmic bias, or securing proprietary information, careful consideration of these concerns must be built into the AI engines we select and deploy. By taking these precautions, we will create a digital workplace where AI serves as a force for good, enhancing productivity and personalization while adhering to the highest standards of ethics and security. This balanced approach ensures that we can fully enjoy the fruits of AI integration, as outlined herein, without compromising on the values that define us as responsible digital citizens.
The AI-Enabled Employee Experience as the Guiding Star
This updated view of digital employee experience will remain a dynamic framework that will continue to evolve as new technologies and methodologies emerge. It’s intended to serve as a guiding star, a useful and practical reminder that the future of work is not just about digital transformation but also human-centric and highly adaptable.
As we navigate the complexities of 2024 and beyond, gaining clarity on how we must structure our digital employee experience will help organizations stay aligned with the ultimate goal: Creating a perfect-fit employee experience that is both empowering and enriching. By embracing these principles and technologies, organizations are not just keeping pace with the evolving digital landscape, they will set the stage for a more engaged, productive, and fulfilled workforce. This updated master visual is your roadmap to this exciting future, offering a pragmatic guide to implementing these groundbreaking changes, aimed — as always — at the most fruitful shared outcomes.
As we stand at the threshold of our AI-powered future, it’s now clear that artificial intelligence is no longer merely a tantalizing possibility, it’s a practical necessity in the workplace already. Leaders in all sectors are recognizing that AI has the power to transform not only their business but their industry. Yet, as with any technological innovation, deploying AI to the workforce isn’t as simple as flipping a switch. AI in particular will be a nuanced, multifaceted endeavor that requires real understanding of the issues and sustained, adequate preparation.
One of the most important starting points in this journey is understanding the journey itself. To do this, I’ve begun assembling an early map of the AI transformation of work, which I lay out below. It’s a work in progress but substantially what most organizations will have to go through over the next few years. Most organizations won’t realize all of these moving parts as a single effort, but instead as a series of efforts over the years that will increasingly connect and integrate.
Understanding the Path Towards Responsible Use of AI for Work
Today, creating a capable foundation for AI for the workforce is no longer a luxury; it’s an essential step towards proper integration of the technology to our work. Now, we must craft thoughtful and robust policies and strategies that align AI with our broader organizational goals. This includes setting data and tech standards that ensure quality, consistency, and ethical use of AI. Without these foundational elements, our over-hasty attempts to harness the power of AI can lead to significant challenges, inefficiency, and undesired consequences. Therefore, taking a more methodical and strategic approach is not just wise, it’s indispensable.
One of the questions I get asked most often these days is what transforming work with AI looks like. In the figure above, I depict the key elements for an enterprise-wide effort to do this. This is an early take and as I mentioned, bound to change, but it’s about as accurate as we can determine right now, in terms of the moving parts across people, technology, and process.
This maturity curve for AI work transformation will be turned into a living artifact that I’ll be developing into a more in-depth research effort soon. However, I break down the key components here so that we can have an basic industry vocabulary and understanding of what must be done to realize AI properly in the workplace over the next 3-5 years, which this maturity curve roughly covers.
The Key Activities for Transforming Work with AI
Here are what I currently believe should be the top activities — from the maturity curve show above — and what each activity represents:
Initial AI Policy. An enterprise-class AI policy is far more than a mere set of guidelines. It’s a vital blueprint that governs how artificial intelligence will function, integrate, and evolve within our organizations. A well-crafted AI policy will include, at its core, a clear articulation of the ethical principles guiding the use of AI, ensuring alignment with both societal norms and legal requirements. It will define roles and responsibilities for AI governance, creating accountability and oversight. To facilitate compliance and quality control, standardized procedures and methodologies (like ModelOps, discussed below) will be laid out. This includes comprehensive data management practices, addressing everything from security to privacy to the integrity and bias of data. The policy must also encompass risk management strategies to identify and mitigate potential pitfalls and unwanted outcomes. Perhaps most crucially, an enterprise-class AI policy will embed mechanisms for continuous learning and adaptation by the workforce. AI is a very rapidly changing field. Thus, the policy must be agile enough to evolve, ensuring that it remains relevant and effective. Such a comprehensive policy isn’t merely a bureaucratic necessity; it’s a strategic imperative and must be regularly updated. By clearly defining the rules of the game, it creates a safe and fertile ground for innovation, collaboration, and growth, positioning the enterprise to fully leverage the transformative power of AI.
AI Strategy for the Enterprise. An AI strategy stands as a compass, helping navigate the complex and often turbulent waters of innovation using cognitive technologies. A robust business and tech-focused AI strategy must work in harmony, resonating with the broader business goals while leveraging the cutting-edge capabilities of AI tech. At its core, the strategy will align AI initiatives with business objectives, ensuring that each investment and every project, is targeted towards tangible outcomes and measurable ROI. It will sketch a clear roadmap, outlining the technology infrastructure, skills development, partnerships, and investments needed to bring AI to life within the organization. But more than a roadmap, it’s also a adaptable framework, that links technological advancements to needed market shifts. It must delineate ethical guidelines, ensuring that AI is developed and deployed responsibly. It must also embrace innovation, fostering a culture that encourages experimentation and learning. Perhaps most essentially, it should be interwoven with a clear understanding of customer/stakeholder needs, how AI will meet those needs, and add value in meaningful new ways. This isn’t merely a plan, it’s a promise, a commitment to integrating AI into the fabric of the organization, leveraging it as a tool, a partner, and a catalyst for growth and transformation. Every worker should be familiar with the strategy at a high-level at least.
DataOps for AI. This capability represents a critical operational shift in how we approach data management and integration within the internal AI landscape of a business. It’s not only a methodology but a cultural transformation that brings agility, quality, and collaboration together as a key practice. By creating a seamless conduit between data scientists, engineers, and business stakeholders, DataOps streamlines the flow of data, ensuring that it’s readily accessible, trustworthy, and aligned with business goals. It’s the oil in the AI machine, reducing friction and accelerating innovation. The result is enhanced AI capabilities, reduced time-to-value, and a more responsive, adaptable, and higher quality approach to leveraging AI.
AI Literacy Program for the Workforce. AI literacy for the workforce is no longer confined to the realm of technologists. It’s a universal workforce imperative. Several facets are key: AI literacy that weaves together technological comprehension with ethical responsibility. It’s not just understanding algorithms; it’s about grasping the ‘why’ and ‘how’ behind AI decisions, known as explainability. Workers must have the tools to interrogate AI, to ask not only what it’s doing but why it’s doing it, ensuring alignment with human values and corporate policies. Ethical responsibilities loom large, requiring a proper appreciation of the potential biases that can inadvertently be built into AI systems. The ability to identify, challenge, and remove these biases is a key skill, reflecting a commitment to fairness and equity. Understanding and following corporate AI policy becomes not merely a rule but a cultural norm, integrating the safe and responsible use of AI into the fabric of organizational life. But AI literacy is more than a set of skills. It’s a mindset, a recognition that every worker, regardless of role or rank, must be empowered to engage with AI, to understand it, question it, and harness it. This is not just about staying ahead of the curve. It’s about defining the curve, transforming AI from just a tool into a business partner, and leveraging it to create a more intelligent, ethical, and innovative future. Such literacy programs will be delivered using today’s advanced learning technologies.
Establish an AI Center/Network of Excellence. The establishment of an AI Center of Excellence (CoE) is an essential milestone in the journey of AI adoption, embodying a central hub of knowledge, innovation, and excellence. The AI CoE acts as the organizational nerve center, driving AI strategy, governance, best practices, resource kits, project advisory, and collaboration. It houses experts and practitioners who not only understand the intricacies of AI but also have the vision to align it with the overarching business goals. The CoE ensures that AI is not a disparate set of projects but an integrated, strategic initiative that permeates every aspect of the organization. However, the pervasive nature of AI, its broad impact across all functions and levels, demands a more nuanced approach. A Network of Excellence, a more decentralized version of the CoE that I’ve developed and proven out over the years, may be more apt. It recognizes that AI excellence doesn’t reside in a single point but is a collective endeavor, a shared responsibility. By fostering collaboration and knowledge-sharing across various units and teams, a Network of Excellence democratizes AI, making it accessible, relevant, and responsive to the unique needs and opportunities of different parts of the organization. This isn’t just a structural adjustment; it’s a philosophical shift, a recognition that the power of AI lies in its integration into the fabric of the organization, facilitated by a network that is as dynamic, diverse, and decentralized as the technology itself.
Realizing MLOps and ModelOps for AI. The burgeoning disciplines of MLOps and ModelOps stand as pivotal frameworks for AI. They essentially sculpt the chaos of AI being used ad hoc in every corner of the org into a more coherent effort. MLOps, or Machine Learning Operations, integrates the world of machine learning with DevOps principles, orchestrating a seamless flow from development to deployment to monitoring. It’s about creating a collaborative environment where data scientists, engineers, and operations professionals converge, ensuring that machine learning models are not just created but effectively implemented and continuously optimized. ModelOps takes this a step further, focusing on the lifecycle management of AI and machine learning models, ensuring that they remain relevant, accurate, and aligned with business objectives, including vitally, cost efficiency. Together, MLOps and ModelOps embody a systematic, enterprise-wide approach, turning the art of AI into a precise science. In an age where AI is not a mere accessory but a core business driver, the disciplined, structured approach of MLOps and ModelOps is not really an option; it’s a necessity and now best practice. It’s the operational architecture that allows AI to live, to adapt, and to thrive, transforming it from a tool into a strategic partner.
Upskilling Technical Workers on AI. In the competitive and turbulent waters of AI talent acquisition, organizations are facing a stark reality: the pursuit of external AI expertise is a race that is both exhausting and expensive. That’s why upskilling existing technical workers on AI tech emerges not merely as an alternative but often as a superior strategy. It leverages an existing understanding of the organization’s culture, goals, and systems, embedding AI expertise within a context of institutional knowledge. Upskilling fosters a sense of growth and loyalty, turning current employees into AI champions who are invested in the organization’s success. It’s a recognition that the seeds of innovation often reside within, waiting for the nourishment of knowledge and opportunity. In an era where AI talent is a precious commodity, upskilling is not just a path to empowerment. It can be a strategic imperative, transforming existing resources into a source of innovation and growth.
Cybersecurity, Regulatory Compliance, Ethics, Privacy, and IP Protection for AI Readiness. Embracing AI is not just a technological endeavor. It’s a complex journey that binds together the strands of cybersecurity, regulatory compliance, ethics, privacy, and intellectual property protection. As a cross check of policy and governance — especially if it is being operationalized properly — organizations must craft a more holistic AI readiness strategy, one that recognizes AI’s transformative power while also respecting its intricate risks and responsibilities. This involves creating a robust framework that aligns AI with legal obligations, ethical principles, and societal expectations. It means forging a culture where security is not an afterthought but an integral part of AI design and deployment. It demands transparency, accountability, and a relentless commitment to protecting both individual privacy and intellectual property. This isn’t merely about compliance; it’s about trust. Building readiness for these crucial aspects is not just a safeguard; it’s a covenant with customers, employees, and stakeholders, a promise that the organization’s journey with AI will be responsible, respectful, and reliable.
Establish an AI technology Stack, Including Generative AI. In the diverse and dynamic landscape of AI, consistency and robustness are not just incidental features. An organization’s enterprise AI stack must be a carefully constructed edifice, where each layer and every component is aligned and harmonized. This includes the vital inclusion of generative AI platforms, which open the door to creativity, innovation, and customization at an unprecedented scale. A consistent and robust AI stack ensures that the various elements of AI, from data processing to modeling to deployment, work in concert, not conflict. It creates a shared language and a unified framework, removing silos and fostering collaboration across teams and functions. Generative AI platforms add a layer of adaptability, enabling the organization to create new foundation models, solutions, and experiences, tailored to specific needs and opportunities. This isn’t merely about efficiency, it’s about agility and relevance. In an environment where change is constant and disruption is the norm, a well-defined, consistent, and robust enterprise AI stack reduces unnecessary duplication, inconsistency, and time-to-value. It’s the strong and scalable backbone that supports the organization’s AI ambitions, ensuring that they are not just experiments but efforts that are sustainable, scalable, and impactful initiatives from the outset.
Introduce Tactical AI Solutions to the Workplace. Rolling out generative AI to the workforce, especially when leveraging 3rd party applications, is a combined effort of innovation and control, creativity and compliance. It begins with alignment with internal corporate information, creating bridges between 3rd party tools and proprietary data, ensuring that they speak the same language but also adhere to the same rules, especially security and customer privacy. Protecting Intellectual Property (IP) in these systems is a key goal, and thus, robust security protocols and clear usage guidelines must be woven into the fabric of the integration. The effort continues with a thoughtful measurement of Return on Investment (ROI), crafting KPIs that capture not only the tangible efficiencies and savings but also the sometime hard to measure qualities of agility, innovation, and user satisfaction. Providing workers with clear, actionable guidance is essential. They must not only understand how to use the tools but also how to leverage them to save time, innovate, and add value. Attribution to AI, recognizing and celebrating where and how it contributes, fosters a culture of transparency and trust. This isn’t only a technical rollout. It’s a cultural transformation, a strategic alignment of technology, people, and purpose. It’s about turning generative AI into a useful partner, empowering the workforce to explore, create, and excel, all within a framework that safeguards the organization’s values, assets, and integrity.
AIOps for All Service Desk Functions. The transformative wave of AIOps (Artificial Intelligence for Operations) is not merely a technological shift; it’s a harbinger of a new era of efficiency and responsiveness. Typically one of the first systematic operational transformations that leverage AI, AI is revolutionizing service desk activities across domains like IT and HR. By infusing intelligence into operations, it’s turning mundane tasks into automated workflows and complex problems into AI-powered service solutions. It’s providing real-time insights, predictive analytics, and intelligent automation, making service desks not just reactive but proactive. It’s a reimagining of what service desks can be, transforming them from cost centers into value generators, from problem solvers into drivers of innovation, as the AI service desk can steadily improve itself over time.
Tactical Automation of Work. Once enough of a foundational has been built to move more quickly and comprehensively with AI automation, businesses will embarking on the systematic automation of work using AI. It will be a multifaceted endeavor, filled with promise and complexity in equal measure. For an enterprise, this journey begins with the rigorous mapping of workflows using tools like process mining, identifying areas ripe for automation, where AI can add value without diluting quality or humanity. It’s about striking a balance, harnessing the power of AI to enhance efficiency and accuracy without losing the essence of creativity and empathy. Integrating AI into existing systems requires seamless orchestration, ensuring that automated workflows align with human processes, complementing rather than conflicting. Building robust, transparent AI models that adhere to ethical guidelines and legal compliance is paramount, weaving trust into the very fabric of automation. Monitoring, evaluating, and continuously refining AI-driven automation becomes an standard process and a commitment to excellence and adaptability. Systematic automation with AI is not a singular act. It’s done in concert with, a confluence of technology, strategy, ethics, culture, and vision. It’s a journey that requires planning, agility, and an ongoing commitment to transforming work, not just doing work. The goal is not merely to do more with less but to do better with more, leveraging AI to unlock potential, creativity, and growth.
No-Code AI App Development Platforms. The brand-new industry of AI-led application development tools is upending the traditional expectations of software engineering, turning app creation from an advanced human craft into a collaboration with an intelligent agent that can code. Initially, AI algorithms will help analyze requirements, user needs, and business goals to develop apps that are not just functional but intuitive. But the real revolution begins post-deployment. Conversational app development takes the reins, enabling to user to engage in continuous evolution, refinement, and enhancement of the app without the need for traditional coding skills. Users can interact with the AI app dev agent, ask for new features, report issues, and suggest capabilities through natural language. The app listens, learns, and adapts, adding features, refining capabilities, and fixing bugs. It’s an organic, dynamic process where the app grows not just with the users but because of the users. This unleashes a new wave of AI automation within organizations, democratizing app development, and turning it from a technical specialty into a shared endeavor. It fosters innovation, agility, and inclusiveness, allowing apps to be living entities that evolve, adapt, and innovate. AI-led application development is not merely a new technology; it’s a new philosophy, a recognition that the best apps are not built but grown, nurtured by the collective intelligence of AI and humanity. I’ll be releasing a new AI App Dev ShortList later this year to describe the companies I’m now seeing emerging to realize this vision.
Executives and Managers Adapt to AI. As work becomes steadily more automated and AI-enabled, managers and executives face a profound transformation themselves. The traditional skills of oversight, coordination, and control, though still relevant, must be infused with new capabilities and a new mindset that recognizes automation as a partner. Managers must evolve from being supervisors to being orchestrators, understanding how to leverage automation to enhance human capabilities, not replace them. It’s about recognizing the nuanced interplay between machine efficiency and human creativity, knowing when to automate and when to humanize. Executives must become visionaries, seeing beyond the immediate gains of cost and time savings, recognizing the broader strategic potential of automation. It’s about fostering a culture of innovation and agility, where automation is a catalyst for growth, a tool for exploration, a means to unleash potential. Adaptation will also involve understanding the ethics and responsibilities of automation, ensuring that decisions are not just driven by algorithms but guided by values and policy. Transparency, fairness, empathy – these must be woven into the fabric of the automated workplace. Learning to communicate with, interpret, and even question AI-driven insights will become a vital skill, turning data into wisdom, information into strategy. And perhaps most importantly, managers and executives must learn to lead in an environment where leadership is shared, where machines are not just tools but collaborators, where the wisdom of the crowd includes both human and artificial voices. In the automated workplace, being a manager or executive is a linchpin role. It’s not about wielding authority but inspiring collaboration between human and machine to explore possibilities. It’s a journey that requires courage, curiosity, and a willingness to redefine not just how we work but why we work.
Digital Transformation of Work. Digitally transforming work with AI will then go well beyond tactical automation; it’s an orchestration of technology, strategy, and vision, aimed at reinventing how work is done. For instance, in customer service, AI can create personalized, 24/7 support experiences, not just answering queries but predicting and pre-empting issues. In supply chain management, AI can transcend basic tracking and inventory control, employing predictive analytics to optimize logistics, reduce waste, and enhance sustainability. In human resources, AI’s role is not confined to sorting resumes but extends to identifying skill gaps, personalizing training, and fostering a culture of continuous learning and growth. It’s a journey from the mundane to the meaningful, from the mechanical to the magical, turning work from a task into a purpose, an opportunity not just to do more but to be more.
Recalibrate Org Roles for the Impact of AI. After the initial stages of an AI transformation of work within an organization, there will emerge a pivotal moment — and probably several such stages — where roles and responsibilities must be restructured and recalibrated. AI doesn’t merely change how tasks are performed; it alters the very nature of roles, turning them from fixed functions into fluid collaborations. Responsibilities shift from executing tasks to managing, interpreting, and innovating with AI. The hierarchy flattens, silos break down, and the organization evolves into a network of interconnected, AI-enhanced roles. It’s a reimagining of the workplace where titles give way to talents, where the emphasis is not on what you do but how you contribute. This restructuring is not a disruption. It’s an evolution, a necessary step in the journey from a traditional organization to a transformative, agile, AI-enabled enterprise.
AIOps for All Operations. AI operations are destined to expand beyond the boundaries of IT and HR, permeating all operational functions and processes within an organization. This is a recognition that AI’s potential is universal, its applicability almost certainly boundless. From marketing, where AI can personalize campaigns and predict trends, to manufacturing, where it can optimize production and enhance quality control, to finance, where it can manage risk and automate compliance, AI is quickly becoming the common thread that weaves through every function. It’s turning organizations into seamless, interconnected, intelligent entities, where data flows and insights are put to use automatically, where decisions are informed and agile, where innovation is not confined to departments but is a shared endeavor. Yet it is all still guided by humans. AI operations are not an add-on; they’re an ethos, a strategic alignment of technology and purpose that redefines what an organization can be and what it can achieve.
The Ongoing Journey of AI Transformation of Work
At some point, most tactical work and about half of strategic work will be completely automated after be re-imagined for an intelligent world. This is for now, as far as we can reliably see into the AI future. I’ll be working on fleshing this journey out in more detail, as our digital workplaces and employee experiences evolve and become much more AI-centric.
Throughout this year, I’ve been carefully observing the seismic shifts in the realm of work, spurred by ongoing economic uncertainties and the rapidly evolving technology landscape. Both of these factors has made it unusually challenging this year to anticipate the forthcoming trends and paint a clear picture of the future of work in 2024. Yet, amid the turbulence, I believe the pivotal shifts have begun to crystallize, illuminating the path ahead in the digital workplace, as workplaces continue to traverse the top-level shift to hybrid work.
Two closely-watched reports this year, the McKinsey Global Institute’s “The Future of Work After COVID-19” and the World Economic Forum’s “Future of Jobs Report 2023,” have unveiled significant insights about the trends I’ve observed in 2023 that will undeniably mold the digital workplace in 2024. Accordingly, I note that there has been three major shifts: A significant surge in the adoption of advanced technologies in the workspace, a heightened focus on employee well-being, and especially the rise of remote and hybrid work models. These priorities are not only reshaping our present work environment but also setting the stage for a more digitized, flexible, and employee-centric future.
As we gear up for 2024 and as I having budget conversations with CIOs and digital workplace teams, it’s becoming increasingly evident how these key shifts will unfold into next year’s trends. The accelerated adoption of AI, machine learning, and data analytics will continue to automate tasks of every kind, giving employees the liberty to engage in more strategic and creative endeavors.
On the other hand, the continued commitment to employee happiness (typically viewed through the lens of retention activities), coupled with the rapid expansion of remote and still largely-unproven hybrid work models, will ultimately cultivate a more inclusive and flexible work environment, even though we largely aren’t there yet. These progressive transformations will empower employees, drive productivity, and can genuinely foster a culture of innovation and resilience for those that embrace them. As we embark on next year’s journey, leaders and workers both will need to keep an open mind, adapt, and learn, for the future of work in 2024 actually holds great promise and potential in its uncertainty.
What Will 2024 Hold When It Comes to Digital Workplace and Employee Experience?
I’ve identified eight trends that I believe will be the most overarching and important for organizations to grapple with successfully. While there are more trends than this, if organizations just buy down progress towards most of these eight in a meaningful way next year, it will be a definitive win for them, given so many simultaneous threads that IT, HR, and digital workplace teams currently face.
The following are the eight trends most likely to make a real difference and improve not just the quality of digital employee of experience but provide substantial business benefits to those that adopt them. They will also aid in attraction and retention of leading talent next year, probably the most important issue of all.
1. AI Work-Enablement
Since ChatGPT entered the work landscape late last year, I’ve been closely observing the paradigm shifts created by the rapid rise of generative artificial intelligence. The myriad capabilities AI brings to the workforce are expansive and truly transformational. Yet, as with any potent technological advancement, it also presents its unique set of challenges. Over the next two years, I believe we’re poised to witness the true essence of AI’s potential and the ways it will enable and empower the workforce.
Firstly, AI is set to enhance the tactical efficiency and productivity of our workforces by automating repetitive tasks. AI will thus liberate workers from mundane duties, providing them more time to engage in strategic and creative undertakings. This reallocation of human capital to areas where they truly shine is likely to significantly boost productivity and job satisfaction. On the flip side, this shift will necessitate reskilling and upskilling efforts, to prepare workers for these elevated roles.
Furthermore, AI is set to revolutionize decision-making processes. With advanced analytics and prediction capabilities, AI can offer managers and executives crucial insights that drive more informed and strategic decisions. However, the challenge lies in maintaining the right balance between human intuition and AI-guided decision-making, ensuring we don’t compromise on the human touch in our operations.
Lastly, the advent of AI in the workplace promises more personalized and effective training and development programs. Leveraging AI’s ability to adapt to individual learning patterns, businesses can create personalized training content that resonates with every worker. Yet, the successful implementation of such systems requires robust infrastructure and careful data privacy considerations.
Accenture’s seminal report on AI, “Reworking the Revolution“, further supports these observations, underscoring that AI, if used strategically, holds the potential to boost business revenues by an estimated 38% and employment by 10%. In short, AI’s advent in the workforce presents both exciting opportunities and substantial challenges. As our workplaces venture into this new era, we must strive to maximize its benefits while consciously navigating its hurdles. That said, I’m also seeing real reservations about blindly deployed generative AI in the workplace. I’ll be releasing some new data on this topic soon.
2. Delivering on the Promise of Digital Employee Experience
As we venture deeper into the digital era, it is becoming apparent that crafting robust and usable digital employee experiences isn’t merely an option, it’s now a necessity. Work is digital, today. While we have made significant strides in this arena, there’s still significant work to be done in most organizations, especially when it comes to integration, design, analytics feedback loops, support for hybrid work, and of course, the integration of AI.
When it comes to integration, we need to create more unified digital workplace environments that promotes seamless interaction between different systems and tools. The goal should be to ensure that the digital workplace mirrors the interconnectedness of tasks in the physical workspace. However, integration is a challenging endeavor, especially considering the vast array of tools and platforms in use today. Nevertheless, the benefits — improved workflow, enhanced productivity, and reduced redundancy — are worth the effort. Achieving this is also far easier now due to recent technical advances.
Design, on the other hand, demands a far more empathetic user-centric approach than we’ve typically used in digital workplace or employee experience. We need to ensure that our digital tools are not just functional, but also intuitive and aesthetically pleasing. Poorly designed digital work experiences can lead to inefficiencies and frustration among employees, hindering rather than promoting productivity. However, good design can increase user adoption and satisfaction, making the digital workplace a more engaging and productive environment. I also see that, as EX and CX efforts blur, CX-style journey mapping is appearing more often in digital workplace design.
We also need to improve our feedback loops from analytics. Organizations are collecting vast amounts of data, but most efforts are not leveraging it as effectively as they could be. With better analytics feedback loops, organizations can gain more insights into employee behavior and preferences, allowing them to refine our digital tools and strategies to better meet their needs.
Finally, we must not overlook the specific needs of hybrid work. The digital tools we provide need to support remote work as effectively as they support in-office work. This might include features for remote collaboration, project management, and even social interaction.
In a recent report by Harvard Business Review, the importance of all these factors is reinforced. It highlights that a thoughtfully designed digital workplace can enhance employee engagement, productivity, and satisfaction, ultimately driving business growth. As we look forward to 2024, it is essential to tackle these challenges head-on, to fully harness the potential of the digital workplace. For many of us, it’s vital to finish a job that started when COVID remade the workplace suddenly and dramatically in 2020.
3. Autonomous Business Digitization and Operations
As we move deeper into the world of automation in the workplace, we stand on the cusp of a revolution characterized by pervasive automation and something known as hyperautomation. The very way we perceive and conduct work is poised for a transformative shift, driven primarily by democratized platforms and AI-enabled automation. These emerging technologies are set to rethink operations across industries. This will catalyze efficiency, productivity, and innovation in the process.
One of the critical levers of this impending revolution is the democratization of a new generation of automation platforms, especially those built around the model of low code. These platforms aim to make automation accessible to everyone, regardless of their technical expertise. By employing user-friendly interfaces and intuitive design, these platforms enable workers across all levels of an organization to design and implement automated processes. This form of democratized automation could profoundly enhance operational efficiency and empower employees to take greater control over their work processes. However, the challenge lies in ensuring adequate training and cultivating a culture of acceptance and adoption. This is not a theory about what might come. I’m seeing this actively in my practice, as I’ve been exploring recently.
AI-enabled automation is another major force in this automation revolution. It takes automation to the next level by allowing systems to learn from data, adapt to changing circumstances, and make intelligent decisions. From predictive maintenance in manufacturing to intelligent customer service in retail, AI-enabled automation is reshaping operations across many sectors. Yet, it brings its own set of challenges. Ethical considerations, data privacy concerns, and the fear of job displacement are some hurdles that we need to navigate conscientiously.
Hyperautomation extends automation beyond routine tasks to adaptively encompass more complex operations. By combining AI, machine learning, robotic process automation (RPA), and other advanced technologies, hyperautomation aims to automate almost any repetitive task. The benefits are immense, including cost savings, enhanced accuracy, and freeing up employees for more strategic tasks. However, hyperautomation requires significant upfront investment and ongoing maintenance, which can be a deterrent for some organizations. Day-to-day IT operations and business operations will be most transformed by this trend.
4. Improved Digital Onboarding and Adoption
Digital transformation continues to sweep across the business landscape, with most organizations hovering between early successes and just building a head of steam according to my CIO surveys. In this era of rapid change, improving digital employee onboarding and sustaining momentum through digital adoption platforms is becoming increasingly critical to move quickly in getting newly hired/contracted workers adapting and existing ones better absorbing change. These two focus areas present the ripest untapped opportunities, or the “low hanging fruit,” for organizations to improve the immediate impact of the digital workplace on workers as they initially come on board and progress through their worker lifecycle.
A smooth and effective digital onboarding process is essential in setting the right tone for a worker’s journey in an organization. In a digital workspace, traditional onboarding methods typically fall short, making it imperative for organizations to design a more tailored, interactive, and digitally adept onboarding experience. This not only ensures that new hires are well-acquainted with the digital tools and processes they will be using, but also boosts their confidence, productivity, and job satisfaction right from the start.
The benefits of digital onboarding extend beyond the initial phase of a worker’s journey. A strong start sets the stage for continued growth and productivity, leading to higher employee engagement and reduced turnover. However, to sustain this momentum and ensure employees continue to use digital tools effectively, organizations must invest in digital adoption platforms, the easiest way to keep them climbing the learning and productivity curve
Digital adoption platforms provide ongoing support and learning to employees, helping them to understand and utilize digital tools effectively. These platforms typically use interactive guides and in-app support to provide real-time assistance, reducing the learning curve and enhancing user engagement. The benefits include improved productivity, higher user adoption rates, reduced support costs, and an overall improved digital experience.
In a world where the digital workplace is becoming the norm, focusing on these aspects can provide organizations with a significant return on investment. Not only do they enhance the employee experience, but they also increase the overall efficiency and effectiveness of the digital workplace, leading to substantial improvements in organizational performance.
As we continue to navigate through our new, more distributed organizations, we are learning from early experiments, refining our approach, and adjusting our hybrid work models and culture. The goal is clear: To balance and optimize work from anywhere for productivity and well-being. However, the path to achieving this balance is still fraught with challenges and demands innovative thinking, strategic planning, and a willingness to adapt.
One of the key issues with hybrid work is maintaining a cohesive and inclusive culture. In a hybrid work environment, which most organizations are today, fostering a sense of belonging and maintaining consistent communication can be challenging. To overcome this, organizations are leveraging technology to create virtual water cooler moments and maintain regular touchpoints with their teams. The lessons learned in the industry point towards the significance of empathy and understanding in leading remote teams. Virtual team building activities, regular check-ins, and open communication channels can help maintain a strong team dynamic. Visual collaboration is also becoming a leading tool for bringing teams closer together, and I’ll have a report out soon taking a look at this.
Another challenge is managing productivity and work-life balance. The lines between work and personal life can blur in a hybrid setup, leading to burnout and reduced productivity. Organizations are learning to set clear boundaries and expectations, while also providing flexibility to employees. Tools for project management, time tracking, and productivity analytics are being employed to monitor and enhance productivity without infringing on personal time.
Lastly, the physical and mental well-being of employees has come into sharp focus in a hybrid work setup. This has highlighted the need for robust wellness programs and mental health support. The benefits of addressing these challenges are very non-trivial. Apart from improved productivity and employee satisfaction, a well-managed hybrid work model can attract and retain top talent, reduce overhead costs, and foster innovation.
In the face of these challenges, organizations must continue to learn and adapt in 2024, refining their hybrid work models, while leveraging technology and feedback to create a balanced and effective work-from-anywhere culture. The journey has been and will continue to be challenging, but the potential rewards make it a worthwhile endeavor, and mutually reinforce several of the other trends on this list.
As we journey further into the digital age, the very nature of employment is undergoing a profound shift. Traditional career paths are giving way to more dynamic, fluid, and personalized models. Both the nature of a career and work assignments themselves are evolving to be more gig-centric and marketplace-based, ushering in a new era of on-demand work. This shift is not only changing the way we work, but it’s also fundamentally reshaping the concept of a career itself.
Gig-centric and marketplace-based work models provide a greater degree of flexibility and control to workers. They can choose projects that best align with their skills, interests, and work-life balance. This is a stark contrast to traditional models where the trajectory of a career was largely pre-determined and rigid. The gig economy offers an element of dynamism, enabling workers to continually reinvent their career paths based on their evolving interests and life circumstances.
On the flip side, this shift also benefits employers. They get access to a diverse talent pool equipped with the latest skills. Furthermore, workers in the gig economy are often highly engaged because they choose projects they are genuinely interested in. This can result in improved productivity and innovative outcomes. I’ve examined the data from several hundred white collar gig economy projects in a rigorous analysis and these points are borne out in hard data. While the shift is very uneven, I’ve seen IT and professional work in particular shift steadily towards this model in recent years, and a cottage industry of white collar gig marketplaces form.
Yet, this new career model also comes with challenges. Issues around job security, benefits, and the lack of a predictable income are areas of concern. Addressing these concerns will require reimagining labor laws and employment practices. Despite these challenges, the shift towards a more dynamic, marketplace-centric, and on-demand model of work is gaining momentum. As we embrace this new reality, it’s clear that the future of work is not just about where and how we work, but also about the very nature of what we consider a career.
7. Proactive Management of EX and CX in a More Unified Model
The digital age has unleashed a multitude of transformations in the business landscape, a key one of which is the increasing overlap between customer experience and employee experience. Although I’ve projected it for a long while, as organizations chart their course into 2024, I’m finally seeing this emerging companies are preemptively managing both realms in a more unified way. The recognition is growing that an enhanced employee experience is not just a perk, it directly translates into improved customer satisfaction and loyalty. Never mind that from a brand, design, development, platforms, and orchestration perspective, it make great sense to coordinate the efforts.
As we’ve seen in our annual AXS conference, which I help chair, organizations are now recognizing that the experiences and engagement levels of their employees can significantly impact the customer experience they deliver. It’s not just that employee experience is used to deliver a great deal of customer experience. It’s that engaged employees who feel valued, respected, and supported are more likely to exhibit a higher level of commitment and dedication to their roles, resulting in better service to customers. It’s the basic principle of internal service quality: Treat your employees well and they, in turn, will treat your customers well.
In the past, challenges in achieving this included disparate data sources, inadequate technology, and the lack of a cohesive strategy to link employee experience with customer experience. However, recent advancements in AI, data analytics, and digital experience and integration platforms are overcoming these hurdles. These tools are providing insights into both customer and employee experiences, enabling organizations to identify gaps, implement changes, and monitor the impact in real-time.
I predict 2024 will be a banner year for this approach. The convergence of employee experience and customer experience is poised to create a win-win situation: Happier employees, satisfied customers, and consequently, successful organizations. It’s an exciting time for businesses willing to embrace this holistic approach to experience management, signaling a major shift in their strategic focus. As we move forward, this blurring of lines between the customer and employee experiences is expected to create a more harmonious and productive business environment.
8. Worker Flexibility and Inclusion
In 2024, workplace flexibility and inclusion will continue to strongly emerged as two intertwined leading trends. Each of these are shaping the future of work and are, in essence, redefining how we perceive and interact within our workplaces. The workplaces of the future will span increasingly broader human and technology dimensions, accommodating not only hybrid workers in highly distributed environments but also individuals from a vast array of diverse backgrounds.
Workplace flexibility, empowered by advances in digital technologies and collaborative tools, has been a key driver for the shift towards hybrid working models. This shift has offered workers the freedom to tailor their work schedules and environments to best suit their needs, thereby enhancing productivity, engagement, and well-being. In parallel, we are witnessing an increased focus on diversity and inclusion. Companies are recognizing the value of a diverse workforce and are making concerted efforts to create an inclusive culture where everyone feels valued and heard.
The confluence of these trends is creating a workplace ecosystem that is not only flexible but also inclusive, fostering more dynamic, innovative, and representative teams. In this model, team members are not bound by their geographical locations, and the collaboration occurs across time zones and cultural boundaries. This fluidity of collaboration brings together diverse perspectives, sparking innovation, and driving growth.
The fly in the ointment is that business leaders and workers are often far apart on the flexibility of work issue, which then curtails the diversity element, preventing many from joining into workplaces that lack needed flexibility, which can be well-provided by our digital workplace technologies like never before.
As we step into 2024, we’re still have work to do to realize the possibilities that these two combined trends can bring, as well as overcoming the challenges we face in realizing them. The fusion of workplace flexibility and inclusion promises to unlock immense potential. The transformation will have real challenges, but with a forward-thinking mindset and a commitment to embracing change, companies are gearing up to create workplaces that are a melting pot of ideas, innovation, and inclusivity.
The Future of Work in 2024: The Transformation Depends on Us
As we cast our eyes to next year, I can see a landscape of immense potential and change. The realms of work and employment are being reshaped, driven by rapid advancements in emerging technology, evolving socio-economic trends, and a collective reimagining of what work can and should be, and given a solid push by the rather different Gen-Z expectations of work.
The tech shifts will continue to arrive at a rapid pace: AI will increasingly empower our workforce, transforming the digital workplace and enhancing the employee experience. The advent of a gig-centric, marketplace-based work model is redefining the concept of a career, giving rise to a new era of on-demand work. The convergence of customer and employee experiences is creating more harmonious and productive business environments, while the intertwining of workplace flexibility and inclusion is paving the way for more dynamic, innovative, and representative teams. What’s not to like?
Yet, amid this hopeful outlook, resilience is as important as ever. The transformations that lie ahead will not be without their challenges, including “legacy mountain”, talent shortages, and technical debt. The ability to adapt will be crucial. There will be hurdles to overcome, lessons to be learned, and adjustments to be made. But as recent history has shown us, we are more than capable of rising to the occasion. The future of work in 2024 promises to be a fascinating journey, marked by a new high water mark of innovation, inclusivity, and unprecedented change. Unlike the years of recent disruption, for those organizations willing to make sufficient commitment, there is a great deal of potential to be captured next year.
While workers have always been creative to various extents throughout history, we have now crossed into a new era. It’s not just the hyper availability of creative tools that workers can use to shape every aspect of their employee experience today that’s changed things, but the fact that having control and agency over their jobs is now a highly appealing factor to a growing share of workers.
In fact, the typical worker is probably able to be more creative now than in almost any time before. And like so much about technology, it’s a co-evolution between our tools, ourselves and the environment we find ourselves in. These forces are transforming today’s collective march towards a new type of workplace that we increasingly shaped and molded to our own purposes and desires. And yes, this is to meet the overall business goals and job responsibilities that workers still have.
As I’ve noted in my Maslovian-style hierarchy of worker needs, it is worker autonomy in the search for improved self-actualization that is a key to unlocking much higher levels of engagement, productivity, and outcomes for both workers and businesses.
While middle managers and team leads of yore might say that strict control and direction is required to get the proper outcome, in today’s far more dynamic, fast changing, and innovation-driven times, this no longer makes nearly as much sense. It’s also no longer really possible for those not doing the actual work at hand to be able to specify with great detail on what’s needed to be done at any given time. Instead, managers are more becoming enablers and orchestrators of productive work environments — which is now largely knowledge work in most cases — and much less overseers or micromanagers of the actual work itself.
Everything About Work Will Be Personally Designed
This is part of a broader trend towards what I call a designer career experience, which describes the growing reality of being able to creatively influence or outright direct just about every aspect of our work environments, employee experiences, and career trajectories. Workers increasingly would like to not just choose the tools they use to do their job but just about all elements of their job, from work hours to the processes and approaches used to carry out the work, to even who the employer is for a given project. This designer career experience is becoming increasingly possible due to several key trends: Changing viewpoints of employers to provide more flexibility (to attract talent and maximize outcomes) and more effective models of work, as well as a dizzying array of old and new tools that not just make this possible, but already push the boundaries of what can be done by the individual worker to shape their work and careers.
How should businesses attract and retain workers by taking advantage of their desire to be the artisans of both their work and their careers? There are several key dimensions that businesses and workers may focus on to different degrees depending on the local situation. But generally these break down to 1) a changing attitude about the suitability of a one-size-fits-all employee experience and 2) the growing assumption that everything can and more importantly — should — be readily personalized and customized today The result: Jobs that fit the worker and unleashed their potential far more effectively.
These new dimensions of personalized and customized work are:
A designer work environment: The location, hours, digital tools, and overall employee experience will be as shaped as possible by the worker, within appropriate but highly flexible and lenient employer policies. Employers will provide a default, best-of-breed employee experience for top personas that can be tailored easily and will be personalized already to some extent to each worker, using analytics and AI.
A designer team: The team for a given task or project will be dynamically assembled in an emergent fashion by the best available on-demand, opt-in talent from within the organization as well as outside it. This means designer teams tend to be much more distributed, which is friendly to today’s hybrid work models. This means traditional hiring processes will also be augmented by online talent marketplaces that allows teams to be assembled from a composite group of the best available talent, with the knowledge retained by the employer. Teams and individuals will be ranked by quality of contributions and the data shared within the source platforms and within the organization for future use.
Designer work: The processes, approaches, and even the critical methods can be shaped within reasonable guardrails. Projects are chosen by the individuals, and if they are not desirable, then they can opt to bid for external projects in the open market. This will no doubt require serious mindset shifts, but will also make employers more responsive and employees much more engaged and productive (and profitable), even if it is for someone else.
A designer career: A career will become more of a portfolio of projects and achievements, and less about a list of previous employments. Actual performance review data from stakeholders about the work in the portfolio will be retained and shared if desired by the worker to gain access to new projects.
Designer Work is Already Here, Just In Early Days
While this might seem far away and even far-fetched to some, the reality is that almost all of this is available today, just in various pieces and early stages in many organizations. Low code tools are already making it possible for workers to build their own employee experiences, and some are doing so. Online talent marketplaces like Upwork and a whole list of white collar platforms I track are making designer careers a reality for millions as well. Crowdsourcing and dynamic teams/matrixing has been with us for two decades. But like so many digital advances, they are often have a piecemeal and patchwork presence, and the dial isn’t turned very far to the right on them.
However, the demand is very much there for these shifts by many workers. While some workers will no doubt still prefer that someone thinks through many of the details for them — and that option isn’t going away — many workers now want the kind of malleable consumer-grade digital experience they find in so many other places, where they have a voice and their opinion on how their job/work/employee experience could be made better doesn’t just matter, but can be made reality in a short time.
While there is no doubt that knowledge workers will have better access to these kinds of designer career experiences in the short term, the overall trend is evident: Work is becoming almost entirely what we make of it. And this is a good thing that will unleash far more personal and professional fulfillment along with much greater innovation. Managers will still have to sort out when things go wrong, but they will be able to tap into workers who have a much better handle on how to achieve mutual goals and objectives.
Other important activities will result from creative job design, such as a marketplace for better ways of working will grow and spring up, and workers can share their best ideas and discoveries, driving greater overall progress in how work improves and evolves. Again, this is already happening in thousands of workplaces today, but can be far better realized that most of us are doing currently. It just takes a vision and a strategy with an open-mind on what the future of work will become.
I’ve waited a bit to weigh in on Web3, to see how it evolved and whether it actually took a meaningful and significant direction. While not exactly a new concept — many credit the term itself to Ethereum co-founder Gavin Wood in 2014, even though it has been discussed since the early Web 2.0 days back in the 2000s — Web3 as we currently know it today exploded onto the global stage in 2021 along with the metaverse, another popular and closely overlapping/adjacent concept.
Like its various predecessors, Web3 represents a major rethinking for a new iteration of the World Wide Web. This vision is both far-reaching and as we will see, truly transformative in nature. It requires us to fundamentally shift our ideas about many important concepts in the realms of digital data and the online world in general. The good news is yes, we do now have a general sense of how Web3 has evolved and whether it has become a significant force in the future of the Web.
My take: Web3 has very much arrived as a major trend with a towering stack of tech behind it and quite impressive economic results to match. It’s a trend that is now is increasingly informing technology evolution as a whole. Most organizations now need to understand what Web3 is and how it will affect their organization’s technology development trajectories and digital strategies going forward.
Now, the good news is that explaining Web3 can be achieved with some fairly brief definitions. The most cogent is simply the notion that the future of the Web, especially as it relates to creating, storing, and exchanging information, can be better achieved by incorporating decentralization based on blockchains. That’s really it, at its core.
That doesn’t sound like much, especially to the uninitiated, and probably wasn’t what many expected. But as it turned out, this idea of deep decentralization has proven to be a uniquely potent one that has given rise to several very significant and useful shifts. One proof point: Despite the ups and downs of the crypto markets, cryptocurrencies, including now NFTs, have resulted in nothing short of a global phenomenon that has led to the creation of hundreds of digital currencies, exchanges, application ecosystems, and supporting frameworks that has a combined worth today in the trillions of dollars.
Naturally, like most new higher-order technologies and the often disruptive changes they usher in, there are many underlying concepts and moving parts to them that make them work, which I’ll explore shortly. There are also a number of important implicit assumptions in the designs of Web3 technologies, that if one doesn’t understand going in, makes the first principles and design choices behind them seem both confusing and needlessly complex even after a good bit of study.
The Motivation for Web3
The why behind Web3 is perhaps the most interesting question of all. Let’s explore that first and then see what Web3 is really made of.
Web3 is borne of the growing criticism that the Internet of today tends to favor large, centralized organizations like Google, Amazon, or Meta (Facebook) over individual users — a trend I’ve long lamented — and that this should change. Conversely, there is a belief that decentralized, more autonomous infrastructures can tilt the balance toward a more user-controlled environment with various benefits that include (but are not limited to): The reclamation of data sovereignty back to individuals, improved — and really, total — transparency in our digital systems, and the inability for bad or misguided actors to disrupt or co-opt our shared digital environments that follow these rules.
Furthermore, Web3 is borne of the direct knowledge of the ongoing success of certain well-known radically decentralized systems to change the status quo, most notably Bitcoin and its now-famous underlying blockchain. It has essentially resisted all comers to date, many of which have been quite determined. Bitcoin’s underlying ideas have proven to work in practice over a sustained period of time (over a decade now and counting.) At the root of this is a growing belief that radical decentralization has wide applicability to much or most of what we do online, and that blockchains are so far the best vehicle we have to realize this.
Crypto Roots, Yes, But Much Broader Applicability
The flourishing of cryptocurrency over the last few years has certainly created a gold rush mentality, luring increasingly large investments, including vast venture capital involvement from the most sober firms. Crypto has also attracted to it some of the brightest tech entrepreneurs and makers in the world. The result has been nothing short of a global high-octane creative genesis that’s resulted a extremely dense, deep, and at times quite difficult to navigate set of interconnected projects, technologies, stacks, ecosystems, currencies, exchanges, and enabling applications. The sheer vibrance and velocity of these efforts have elicited great excitement, with numerous innovations and breakthroughs having occurred along the way.
It’s crucial to appreciate that back in the early days cryptocurrencies originally had — and still continue to have — a central problem to solve, namely answering the question of who would ever actually trust them as forms of currency. And they eventually came up with effective new models that successfully created the needed faith and buy-in by people in a way that still, to me, seems remarkable today. The premise is that the blockchains underneath many popular cryptocurrencies embody designs and rules in code that can be empirically verified even by the non-technical. One doesn’t need to technically understand the blockchain deeply in order to verify it works as advertised and trust it.
So, getting back to first principles, I would argue that at the core of Web3 concepts is the crucial and increasingly problematic question of trust in digital systems. How do you know who you can genuinely trust with your data? Who really owns and controls the data we store online? Is it possible to create ground rules that everyone is guaranteed to live by that fosters new trust? Can we store data online in a way that we will always remain in control of it? Are there designs for systems that make us hopeful that the online world can be a place that is safe for everyone over the long term? All of these questions are addressed inherently and explicitly by Web3 technologies in various interesting and practical ways.
Web3 is Based on Decentralized Trust
So I would also observe that trust is the fundamental basis for all human relationships. While Web3 is still working on trusted identity and I find it unfortunate that concepts like digital wallets — instead of people — play perhaps a too central role in the design of many of the resulting efforts, it’s clear that communities of individuals have come together to trust a given distributed system of data and its associated rules (blockchains). This is at the very core of how many Web3 efforts have succeeded as well as they have.
The blockchain is too complex a concept to go into detail here, suffice to say that what it brings to the table is a set of shared and well-documented processes for managing digital data, enforced consistently and relentlessly by code. If those behaviors are reliable, highly valued, and then profoundly decentralized in such a way that they cannot be easily co-opted, lost, or abused, then a community can — and apparently will with the right value proposition — form around it. And I do mean a human community. That this will happen has now been proven over and over again in the living laboratory of the online world.
Short version: A blockchain is like a digital nation with clearly enumerated rules that is made up of nothing more than people’s data and the rules of its operation codified in its very structure. Yes, these are today often digital currency records, but it can be and increasingly is digital information of any kind.
Once trust in a digital system exists and is sustained, and once a community has formed around such a living digital system — remember, it’s a dedicated decentralized network with countless distributed copies of the data that’s coupled with matching and well-documented rules realized rigorously in code — then the next outcome can safely and by design take place: Transactions. Or what more high-level thinking would call commerce or value exchange. This focus on transactions makes Web3 notably different that previous iterations of the Web, which certainly enabled the realization of e-commerce and other digital business models, but did not really have them in their core architecture.
Web3 in Practice
Web3 then is the realization that we now possess very specific and working designs for trusted decentralized systems of data with matching rules that can effectively attract human communities around them. This allows safe, trusted commerce at scale and genuinely seems to work well over time. These systems, with very specific design constraints, appear to foster human trust and cultivate communities if they do so in a way that provides shared value.
Again, like so much on the Internet, it’s taken countless trial and error to get where we are with today with Web3. It’s not a accident but a distributed yet mostly informal design effort in its own right to find ways to build better networks of digital systems, although organized groups do exist including the Web3 Foundation.
So how does Web3 actually work? This is where, from an implementation standpoint, that the result is nothing short of the proverbial technology rabbit hole. Web3 on a conceptual and technology level goes deep. Below, I explain its various parts and how they fit together as I see it. Just be aware that it has taken some of the most brilliant people in the world to have figured out the pieces and make them work together in a unique way that is compelling to the marketplace. The result has attracted the intense interest and participation of thousands of technologists and many millions of regular people around the world, pulled in by either the arguably positive notions that Web3 embodies, the evident results that it has produced, or both.
The Technologies of Web3
While there are a vast and oft-bewildering array of technologies, frameworks, and chain implementations that fall under the Web3 umbrella, the following are the core technologies most often associated with the trend:
Blockchain. A data storage and retrieval system in which a typically immutable record of data and associated transactions is kept, often involving cryptocurrency but can be any type of data, are maintained across numerous distributed computers of multiple ownership that are linked in a peer-to-peer network. Distributed ledgers are similar but may not be on a peer network or have multiple ownership. Smart chains have emerged that have sophisticated smart contract and value-add AI features built-in. Consensus rules are often established to determine which data is actually stored as the official record.
Wallets. In Web3, a digital wallet, usually associate with cryptocurrency, is a device, application, or service which stores the public and/or private keys used blockchain transactions. In addition to this prime function of securely storing the keys (the actual currency is stored in the chain), a cryptocurrency wallet often also offers the functionality of encrypting and/or signing information. Signing can for example result in conducting transaction or executing a smart contract.
Decentralized Identity (DID). Decentralized identifiers are a newer type of identifier that enables a verifiable, decentralized digital identity. They are an important component of decentralized web applications. I believe they are vital to making Web3 a fully evolved architecture for the future. They are based on the concept of self-sovereign identity. A DID identifies any entity (such as a person, organization, object, data model, abstract entity, etc.) that the creator of the DID decides that it identifies. These identifiers are designed to enable the controller of a DID to prove control over it and to be implemented independently of any centralized registry. Over time DIDs are likely to become a core identity system of Web3 applications, allowing stronger communities, more trust to be established, and enabling many high value use cases. The respected W3C has now weighed in on a proposed standard for them as well, further bolstering their credibility.
Exchanges. A exchange is a service that handles cryptocurrency and other forms of digital value that allows users to trade for other assets, such as traditional fiat money or other digital currencies. Exchanges are a key source of liquid value and usually accept credit card payments, wire transfers or other forms of payment in exchange for digital tokens or cryptocurrencies. Exchanges connect the Web3 world to the traditional world and are in numerous ways a crucial service to allow transactions to cross between them. Exchanges have been instrumental in fueling the crypto boom and will likely be just as key to the broader evolution of Web3.
Decentralized apps (dApps). An application that uses smart contracts that run on a blockchain. Just like traditional apps, DApps provide a particular function or use to its users. Very much unlike traditional applications, however, DApps are technically not owned by any one entity. Instead, DApps distribute tokens that represent ownership. These tokens are allocated according to a pre-designed algorithm to the users of a blockchain-based system, diluting ownership and control of the dApp. In this way, since no one entity controls the system, the application becomes decentralised. A lot of the utility and innovation in Web3 comes from the add-on dApp ecosystem, which can work with data in the blockchain according to the smart contract associated with it.
Smart contracts. Smart contracts are predefined digital agreements stored on a blockchain that run when predetermined conditions are met or transactions are conducted. They are usually used to automate the execution of an agreement within the rules of the blockchain so that all participants can be absolutely sure that the outcome will take place, without any intermediary’s involvement or effort. Smart contracts enable rich scenarios for ensuring that economic activity follows the rules and that the digital economy the blockchain support is open, transparent, and 100% rule-based in executable code. They are rapidly becoming the backbone of Web3 transactional systems and should in theory, also mostly eliminate the need for legal recourse.
Distributed Autonomous Organizations (DAOs). A DAO is an organization represented by the rules encoded as a software system that is entirely transparent, controlled by the organization’s members and typically not influenced by a central government or authority. DAOs show what a genuine digital organizations might truly look like. They will help reshape management thinking and theory for the next decade at least. An example of a DAO is Augur, a decentralized prediction market platform.
Metaverse. While Web3 apps can come in any type of UX form factor, one of the most interesting is the Metaverse, which is a still-mostly-notional deeply integrated virtual world that allows people to visualize, experience, formulate, publish and monetize digital information and content using virtual reality and other interfaces For now, this is still in early evolution but it will allow seamless 3D experiences that enable shopping, business deals/transactions, education, advertising, and entertainment and may other forms of business activity with a full multi-channel, multi-currency financial ecosystem behind it.
What Organizations Should Prepare for with Web3
The advent of Web3 has a number of significant yet often uncomfortable implications for enterprises that seek to adapt and take advantage of the energy, vibrancy, and innovation in the fast-growing sector. Or more importantly, avoid to disruption that it likely to occur in many key areas, from payments and e-commerce to customer experience and digital transformation as a whole.
Here’s is my best advice heading into 2022 on what the typical organization should be preparing for with Web3:
Assess Web3 adoption in your industry. Understand what your competitors are doing, from blockchain-based loyalty programs and crafting their assets into NFT offerings all the way up to creating crypto payment strategies and building dedicated metaverses for customers, partners, and even employees. I’m seeing quite a bit of activities across all these fronts as we head into 2022 in my client conversations. Organizations in most industries should be conducting at least a high level assessment of Web3 competitive activity.
Research and educate your staff on Web3. While Web3 is very much emerging technology, some parts of its are getting a decade or more old, and the subject matter is both large and complex, so time should be invested now, early on. Now is the time to begin getting your digital and IT staff up to speed where it makes the most sense. Basic blockchain education is a great place to start, but understanding cryptocurrency markets, smart contracts, and DAOs are all good areas for strategic staff right now, from digital strategists and transformation leads allway the way to the office of the CTO.
Build Web3 capability . The reality is that most organizations will be interacting a growing portion of their business in various blockchains. Understanding how to conduct transactions safely, securely, and efficiently is key, as is understanding and maintaining a perspective on the strongest offerings, along with their pros and cons will go a long way to being prepare as vendors and suppliers increasingly conduct business using Web3 models.
Revise digital strategy roadmaps with eye on opportunity. Once the Web3 competitive and opportunity landscape is understood, make time and resources available to incorporate it into the broader digital strategy and transformation roadmap.
Web3 is part of a major new generation of technology evolution that will dramatically change business and IT for the long term. It has far-reaching implications that can help enterprises identify significant opportunities as well as avoid disruptions in the road ahead. I strongly urge all organizations to begin to assess Web3 uptake in their competitive landscape and prepare for the necessary activities in technology adoption and evolution. Like many emerging technology developments at the present, Web3 also has significant business implications as well that will require tech and business leaders to come together to create and validate their roadmap going forward. It’s not an easy time, but Web3 represents enormous promise that will also remake numerous industries in the process.
Over the last year-and-a-half there have been two priorities for digital transformation, with two different transformations that have happened in our organizations. The first was customer experience, and has largely proceeded well, with a few challenges. The other major change was in the workplace itself. Tellingly, the transformation of work has been, frankly, the harder challenge, and always has been.
First, the good news: I have been largely gratified to see that budgets and priorities for digital workplace and digital employee experience are higher than ever before. The reason is simple: The urgency is there like never before. But we’re still having significant challenges getting our organizations focused on meeting worker needs in this regard during the pandemic, even determining what those needs really are. Some parts of the near future are clearer however: By all indications, work is not going to go back to the way that it was.
Instead, the hybrid model is now generally agreed upon by most as the future of work, both physical and remote, but with a need for it to be as seamless as possible across both groups of workers. And ultimately, it’s the seamless part that will be the challenge, the hardest part to get right, and probably the most important part to address.
A return to the office is going to happen at scale soon, and is even now happening in many places in the United States and in the world. But for most, it will be next year, 2022, as the global return to the workplace for most of us. As it was during the pandemic, technology is still the best tool we have to address our problems, challenges, and opportunities with disruptive shifts to the the work environment. But the tech has to address human needs, first and foremost. So one of the big messages here is that success will require aiming at and achieving worker-centricity like never before.
Simply put, if organizations wish to attract and retain workers in these very trying times, where labor markets are so very tight, we have to design it around those same people. So as to be very adaptive to them, and to educate them, and to be focused on their wellness. There have been bold experiments in some of the efforts i’ve advised, where they actually monitor the mental and physical wellbeing of the workers in real time and provide assistance to them if they need it. While these tend to be large organizations that to do this, helping workers to be resilient and helping them to be effective during confusing and challenging moments will be vital. These digital employee experiences will be designed more deliberately not for one audience, not just for the office, and not just for remote work.
Last year I was on record saying that we should focus on designing for remote work-first, otherwise we’re going to underdeliver for the remote worker, the largest group in most organizations. But this year and going forward, we have really have to put in-office and remote workers on the same footing. I also see that most organizations are still unprepared for this. They want to do deliver on it, but they’re unprepared and they’re behind in applying the tools and concepts available to them. I’m hoping what we have now learned will help them get ahead.
Getting the Foundation and Values Right for Hybrid Work
And so in the visual below are the four major focus areas we must deliver on if we wish to be an enlightened organization that seeks to understand what they should best be providing in terms of their workplace and services with their workers in general. Hybrid workplaces — like all workplaces — need to be value-led, and that’s around mutual benefit as much as possible, with shared value exchange and co-ownership. This is already exhibited in many of the things that we see in the most modern collaboration tools: We are co-workers whenever we work together, we share together, we co-create together in a scalable but sustainable way. And it has to be responsible in our world as well, and in terms of creating trust, protecting workers, and their privacy and safety. To be environmentally conscious, ethical and fair trade. All those important aspects that we really value in ourselves and in our organizations. All of this has to be embodied deeply in the hybrid employee experiences that we create in order for them to be properly realized.
We must also be resilient now more than ever before, so ready for that future that is coming faster at us, more chaotically. Workers must be provided with a work environment that is very adaptable to the two main settings of hybrid work. These environments must inherently be harder to disrupt, since it’s increasingly clear that we live in a world that’s more easily disrupted now, and more frequently disrupted as well. And above all hybrid work must address what it means to be human, meaning it is fair and equitable, and genuinely cultivating our differences, because we are all different in our own way.
We must also be prepared to support the edge of our networks and organizations, for experiments and the eccentric but often useful new behaviors and ideas. I’ve spoken many times in the past about the companies that have lived the longest. They have tended to be very humane organizations that are very tolerant on the edge of eccentricities and innovation. As a result and due to fostering this, and providing support for the whole person, not just the worker part of the person, they have lasted the ag, through all disruptions so far.
Collectively then, this is the future of work, the motivations and aspirations that matter the most, as I see it, for enlightened organizations, which most of us aspire to become.
The Biggest Concern About Hybrid Work: The Divide
So if we look at what top leaders are seeking in hybrid work — I’ve spoken with dozens of CIOs since all this started and many CHROs as well — and if you look at their top goals, there’s a real concern about connecting office workers with remote workers. Connecting them together equitably is widely believed to be a very hard yet important challenge. Some organizations are in fact just saying, “we just can’t do it. We’re going to favor the office workers.” A lot are tacitly doing exactly that. And that will really be leaving behind some of their most valuable and most dynamic workers. But many would still prefer not to.
So there is real interest in maximizing the inclusion between office and remote workers. There are genuine challenges in achieving this: What do you do about key worker groups like agile teams, for example? Agile teams are supposed to be co-located so that information flows very quickly among them. I’ve had CIOs ask me how do we deliver agility during hybrid work? Do we keep Zoom calls open between the office workers and remote workers, all day long? While that’s probably not the answer, it’s an example of a central class of problems in digital employee experience that organizations seek answers to:
How do we properly rethink vital business processes that will be divided between two distributed groups of workers?
In general, what organizations actually want to know is what they should do that works? In other words, what are the overall best practices of hybrid work? However, I am sad to report that there mostly is no set of existing best practices yet. Fortunately, over the next year we’ll see the greatest number of experiments in hybrid work that we’ve ever had before. In the process, we are going to find the way, as we collectively learn from what’s really working out there. As part of this, I will gathering success stories, patterns that work, and I will continue to share them.
What’s the Immediate Goal of Hybrid Work?
Leaders also want to preserve the productivity dividend of remote work. Productivity is up in most organizations right now, when we are mostly remote. But now with hybrid, we want to preserve and continue this. And we want to sustain the engagement long term of remote workers themselves as employees. Most organizations I speak with are acutely aware that they have 18 months worth of new hires that many of them have never met. They’re not really connected to the mothership like the workers before. So how do we how do we do that, how do we address engaging effectively with remote workers?. And then how do we deal with all the disruptions that are yet to come? These are the three leading priorities in achieving a hybrid work model. We care about all the other motivators above too, but these stand out.
In fact, if organizations do nothing but address these three goals, they’ll be doing well next year.
The journey itself to hybrid work is now over halfway done. There are three phases that lead there: The pre-pandemic way of working, or the way life that was before, where in-office was dominant and a some of us had VPNs and we could work remotely. And while some did work remotely, but it wasn’t a very large audience. And now that has flipped. We’re still in that flip, with more remote workers than in-office. But people are streaming back now because people are starting to go back to the office. Next year we’re going to end up with that mix, that will average in the 60/40 range, though your mileage will certainly vary.
Key Organization Roles over Hybrid Work
Last year I began to receive calls for the first time from Chief Operating Officers (COOs.) I had never received a call from a COO before. The CIO is the leader that traditionally rolls out new technology solutions and the COO has to operate the company wit the tech the former provides. When they called me, they were saying “I need to be able to run the company with everyone remote. I don’t know what to ask the CIO and CHRO for in order to do this. So how do we do this? I don’t know how to run a company through a WiFi connection. Can you tell me what I should be asking for?”
The Three Phases of Work: Pre-Pandemic, Remote Work, Hybrid Work
And so know this of those in the COO role, they’re very operationally focused, and trying to get their organization through the turmoil of going remote, and now hybrid. Consequently, if you’re trying to drive change, the COO is a role you should be tapping into if you’re seeking to make realize hybrid work today. They’re in the middle of it, they want to do it now (and not in some theoretical future) and they’re willing to try anything to see if it works. So it’s a very exciting new role that we’ve learned that has been involved in trying to make all of these changes during the pandemic both effective and functional.
What Do Workers Need During Hybrid Work
The upshot is that we’re now at this moment in the rebirth of work. That the future is not the model of remote work that we’ve had over the last year and a half. Now is the time when have to start really becoming effective at hybrid work. And that means connecting two very different audiences and that’s really the challenge. To do this, we’ll need a target model for digital employee experience and the resulting engagement that we should be aiming for and can use as a cross-check. Something that we can use to layer our design into and ensure we are creating the right result. As a primary check, I have developed a model for digital employee engagement that is based on Maslow’s hierarchy that has been very well received.
For post-pandemic, this hierarchy is a view of our employee experience needs. If you look at the bottom of it, there are the fundamentals, which is mostly basic access: Getting people access to their devices and the Internet, to their documents, applications, and data, in touch with their colleagues, customers, partners and suppliers, and all the support functions that make them work. This basic access means being able to reach them.
Then the next step up is once you’ve provide access, it is to make the digital employee experience usable. And really because access actually doesn’t really provide that much value without usability, it has to be something you can actually deliver on. And so usability also covers having streamlined digital experiences, making business processes and procedures easily learned and usable, making sure your cybersecurity protections aren’t making the access too difficult. In fact, we still see cybersecurity practices are creating a lot of challenges for usability. Your overall digital workplace can suffer greatly if you don’t solve for easily usable security procedures and experiences.
An organization might have the best digital workplace capabilities in the world with a a wonderful design, but if workers are frequently having challenges just logging in and switching applications, struggling to use it on mobile devices and they’re always struggling to use it, then the rest of hierarchy doesn’t really matter. And so you really have to hammer down those sharp edges before you can get very high in this hierarchy.
Beyond these levels is proactive enablement: How do you ensure that you’re getting the actual work outcomes you’re looking for? That both professional and personal developments are taking place? So this view makes the worker the center, treating that whole person, wherever they are located. This makes sure that that a connection is being established between in-office and remote with effective collaboration that is regularly taking place between in-office and remote.
And if you do this well, then you can actually get to engaged workers. If you’re really connecting to people, and you’re helping them reach the outcomes that they care about, then they can get to and the organization can realize true engagement. That’s connecting with and responding to the mission of the company or the organization with coworkers and colleagues, with management team, and with the work being done. We want them to be engaged and if they are provided the layers below that, then organizations can achieve hybrid worker engagement.
And then the next step is really where, where we are today in many organizations: We don’t want worker drones just mindlessly carrying out processes. No, we can automate that now. Most rote standard work that’s routine can and will be automated. Instead, we need empowered workers that can think, that can innovate, that are able to make both local decisions that make a difference, and be able to influence larger decisions and the wider organization, and to make sure that they can do that easily. That’s empowerment and that’s where a lot of organizations are still trying to get to this point in time.
The next-to-last step in the hierarchy is full realization and autonomy, the ability to self organize, to direct the work. This is embodied in the famous Steve Jobs quote saying, “I hire really smart people, and then I ask them to tell me what to do.” The bottom line is if you have these layers properly realized below then you can get truly autonomous and strategically contributing workers that are engaged in the mission, who are able to then innovate and be able to direct that to an outcome. And so this all this leads to self actualization and the maximum potential of the worker, which we now have to take care to provide in a hybrid work environment.
While we can never reach our maximum theoretical potential, we should in fact be able to get pretty close to our maximum practical potential. So aim at this. This is a nice clearly laid out goal that will ensure that will help organizations prioritize how to create a human-centric workplace that will function both in a hybrid environment and in wherever else we find ourselves in the future.
We Must Go Faster and Better, Beyond Basics to Real Hybrid Worker Needs
Instead, what workers have today is not designed against a consistent model such as this hierarchy. Thus is has low usability, low access, and low empowerment. It’s often mostly a jumble of technology that’s not aimed at a coherent employee experience and it has very little overall design. And while we can never design all of it, we have to design a lot more of it today and now for our emerging world of hybrid work, particularly the important piece: The core employee experience. These are the prime activities workers carry out the most or are the most important. So we have this new worker journey, a more coherent digital experience. Today it is the whole worker journey, and we have all these things: The applications the devices, the data, our culture, our processes operations, and we need to design around that coherent experience so the beginning of the worker journey, the middle of their of their journey which is where they spend most of their time, and then finally the end of the journey.
Consider all this, what I’ve been encouraging organizations to do — and this is the next useful framework — is think about that journey and say how do you make, how do I make sure each step of that journey, supports the hierarchy that was just described. To ensure that it is a fairly simple cross check. I’m getting lots of feedback that basic cross checks helps focus on what matters most. And so what we want to do is get to a more orderly foundation for employee experience. So we want to take today’s current relatively random, grab bag of tools, technologies, files, and datasets and so on, and create an employee experience platform that’s better designed. One that’s better aligned aligned to that journey and delivering to that hierarchy of worker needs. So this view above is another key cross check that can make sure that organizations can get to hybrid work.
The result is still largely the same grab bag of workplace technologies but now better shaped into an experience platform that actually can help us achieve a true work-from-anywhere foundation and a true hybrid work foundation that’s proactively enabling, is adaptive and automated in terms of when they need support and help, and deeply personalized and contextual with all the technologies. What’s more, we have reached a stage of maturity of the ideas that we need to make this happen are already here. We just have to deliver on it, and we start with the core employee experience.
These then are all the pieces one needs to check to make sure they have everything they need for a next generation digital employee experience. This is the full strength vision. But the key is this: We have to have deliver two versions of our experiences now. The remote version and the in-office version. They’re often not the same. How people work, how people collaborate, or how they get onboarded as workers are different if they’re remote and different if they’re in the office. So we have to we have to reflect that in the employee experience. If you’re giving everyone the exact same employee experience, you’re leaving a lot of value on the ground and disengaging the worker it doesn’t serve well.
The result is going to be les fit to purpose and is going to be slower and not as effective for one half of your workers. And when we, when we need to be worker-centric, we can’t do that anymore. Thus we now have this bright dividing line in our holistic employee journey: The remote and the in-office worker experience, and bringing them together is hybrid work.
Seizing the Moment in Hybrid Work
This is a historic opportunity that many of us will probably never get again. We may never get the leadership attention, organizational priority, or the budget like we can today, or the ability to drive large changes in work like we have right now. Now is the time, while everything is still in motion to make a big change, a meaningful set of changes around the future of work. To actually succeed, we’ll need to use very clear methods. Let’s use this well-defined hierarchy and let’s use this mature worker journey, and let’s go through everything that we have and where it makes sense, let’s align to that. While you can’t change everything, you can change what matters most. That’s all we really have to focus on right now for hybrid work.
We now have to create now a hybrid working culture and mindset that’s gets our top executives and the line workers engaged as a whole in a distributed but very lumpy new construct. Many of you know that I’m a very big proponent of open collaboration, also known as mass collaboration, that best drives almost all the outcomes that we want to have and all the things I just described. There are ways of getting the organization engaged around the shift to hybrid work, and around what you’re doing and around the changes that you need to have in empowering change agents in your organization. In fact, of the things that we explored in our industry for the last decade and a half, how we collaborate and realize change is more important than ever.
Technology, and especially seamless and effective community and collaboration, are the fabric for how we’re going to achieve successful hybrid work. And so the end state is this open collaborative highly adaptive contextual, automated, and personalized employee experience on your evolving employee experience (EX) platform, directed at two distributed groups. One that really is designed not just for the moments that matter to the business but moments that matter to the worker. Put simply, if you want to attract and retain the best workers, that’s what you’re going to provide. You’re going to provide something that has the everything the business needs and everything the worker needs as well, no matter how they best work or where they are located.
End Note: This blog post is adapted from a keynote I recently gave at IOM Summit. It contains much of my latest thinking on hybrid work and references all the research I’ve conducted and some of the great many industry conversations I’ve had recently.
Call for Participation: If you are in a position to do so, please help me map the future of work. I will be closely tracking the many experiments of hybrid work over the next 18 months. If you wish to be part of this tracking and information process, please send me a note at dion@constellationr.com, and I’ll include you in the process so that we can all learn from each other. I’ll also be publishing snapshots of the journey so that organizations who cannot participate for whatever reason, can join along in the journey.
At the top of most organizations’ priority lists right now is how to keep their workers productive and engaged. Except for in-person businesses and essential workers, the workforce has largely been physically disbanded until the pandemic comes to an end, one way or another. In unprecedented fashion, technology has suddenly become one of the single most important tools in moderating the effect of shuttered offices, physical distancing, and remote work from home.
However, most organizations have largely been paving the proverbial cowpath. Meaning that they’ve largely a) just turned up the volume on how often they use their existing meeting and collaboration tools such as Zoom, Slack, Teams, e-mail, and conference calls, and b) not really been able to think about new and better ways they could work together. Ones that inherently take real advantage of how people are now working in a much more distributed fashion.
From my experience in spending much of my life helping organizations better adopt and use technology to improve the workplace, I believe that the focus on using these tools was necessary. However, it is also woefully far from sufficient.
The Journey of Understanding To Get to the New Future Of Work
Simply put, the imperative today for getting remote work right is this:
To revitalize and thrive in our current global situation, organizations can and must do better in rethinking their near-term future state in terms of the digital art-of-the-possible.
Our workers need it, our customers deserve it, and the reality is that the future is already here, but unevenly distributed as Mr. Gibson famously noted. That means we know what many of those better ways of working actually are. But they are just foreign enough that they’ve largely stayed on the margins of many our workplaces so far. Yet as we’ll see below, within these new ways lies astronomical riches if we are prepared to act. Now most organizations are in the middle of being forced act. Described herein is what they can fully achieve, if they are to truly thrive in their current distributed state.
The Most Important Discoveries in Digital Collaboration
In the 30+ years that we’ve all been digitally connected worldwide via the Internet, we have collectively made many profound discoveries about how people can come together through computer networks to create mass shared value. Since I find that this is still not as common knowledge as it should be, I’ve collected together the most significant insights about digital collaboration that we’ve acquired from the vast and infinitely innovative living laboratory that is the global Internet. Here they are in rough order of importance:
a) Digital networks can create value exponentially according to their size.
This has been known going as far back as Metcalfe’s Law. Networks have the potential to create enormous value for those using them. Yet the networks through which we collaborate today — whether digital or in life — generally underperform greatly and don’t come anywhere close to reaching their full potential. We don’t communicate and collaborate nearly as much as we can or should. We also over-communicate (think CC: fields in e-mail) and over-collaborate when we shouldn’t or don’t need to. There were some good reasons for this, in the past. But no longer.
b) Whenever we have removed the barriers to connecting and collaborating between people, much more value has been created.
Our lessons over the history of digital networks has been nothing short of revolutionary. Perhaps the most essential is that we have simply made it far too hard to connect, share, and collaborate with each other. Many examples exist: Not having access to the necessary networks, or the right conversations. Finding the right channels, the best apps, having the necessary permissions, being in the appropriate groups, teams, or project. Having access to experts, leaders, and far-flung colleagues, all in order to just get our work done. For management and control reasons, we’ve often made it too difficult or complicated to engage, which is surprisingly easy to do with technology (even one additional step to participate sharply reduces said participation says the research). We often make the process of collaboration take a great many steps (the early and profound lessons of Wikipedia should be required reading by anyone in charge of human collaboration at any level in an organization.)
Thus in our quest to design collaboration, to control it, to shape it, and direct it, to secure it, and make it safe, we also tend to kill it. Human collaboration is in reality a delicate and easily disturbed flower, an often unwieldy balancing act of human exchange between people who are often quite different themselves and work/think very differently from each other. So we must remove all possible barriers to helping them engage. And we must take great care not to add new barriers. It’s worth noting that this is why Twitter and LinkedIn work so well (they only have one main place to type), and it’s why e-mail (which only has two fields and can generally talk to anyone else on Earth who has a connection) has lasted so long while other technologies have fallen by the wayside.
c) The biggest barrier to human collaboration is inability to participate.
For the reasons cited above, we’ve learned that we must work assiduously to make it possible for as many people to participate in a given process as possible. Whether this is a team, project, initiative or vast corporate program, we have learned that we generally have rather poor foreknowledge on who the full range of stakeholders in the process actually is and who should be included. We then get them involved far too late in the process when we finally do learn who all those stakeholders are. To add insult to injury, we then we make it far too hard for them to engage with us, so historically they haven’t.
This insight is so important, so critical to the success of collaboration at any level, especially the virtual kind. It is because of these barriers that when I wrote Social Business By Design, I took great care to state that the fundamental principle of effective collaboration must be “anyone can participate.” I mean this literally: Unless there is a very good reason not to (and there usually isn’t), in order for you to begin to tap into anything close to the full potential of digital collaboration, you must open it up by default to any stakeholder who feels they have a stake in what’s being done. I realize that this can be hard, for many reasons (which is my point.) But it is very important, even critical, to seek to address.
d) Asynchronous collaboration at scale is the richest and most powerful model for working together that we know.
Throughout most of human history, we’ve collaborated in real-time, face-to-face. It’s great for small groups, but soon breaks down when more than a few people are involved. That’s largely because it stops everyone else from working (since only one person can communicate with the group at a time.) Asynchronous collaboration has existed since human writing has existed, but it’s long been a niche method because it couldn’t travel fast enough or scale well enough. With digital networks however, both of those obstacles completely fall away. Now everyone can communicate and collaborate instantly and at the same time without interrupting anyone, and there is no limit to how many people can collaborate this way.
The gift this insight gives us is remarkable. The fact that we don’t realize the immense power that this gives us to work with each other in a vast hive of parallel yet deeply connected flows of work is because it is still quite new. It’s just a decade and half old or so in real terms, compared to the other methods that have existed for thousands of years in some cases. Asynchronous collaboration has led to some of the most remarkable outcomes in fields such as open source software (now the dominant model for how software is created, no coincidence), pharmacology, hard sciences, social media such as YouTube (the most popular TV channel on Earth now, all asynchronously co-created by us), shared public information (see: Wikipedia and similar sites), crowdsourcing, and much more.
e) The collaborative model that taps most directly into these world-changing insights is the online community and enterprise social network.
All digital communication and collaboration is more efficient than the physical models that came before it, even if they don’t quite replace the human dimension of the in-person experience. Within digital communication and collaboration there is again an enormous variability in what scales and how many people can simultaneously contribute and create value.
Relentless experimentation by the millions of people using the Internet has consistently and repeatedly found certain models that enable much higher level of participation with much lower level of friction. They also delivered noticeably better results as an immediate consequence. Social media was a direct outcome of these experiments. It’s what worked best in large scale communication and within businesses, in collaboration as well, which became known as social business just a decade ago. Of all the forms of social media, it’s the online community and enterprise social network which best fits the bill for complex collaboration, inherently takes advantage of how digital networks create value, and for truly empowering knowledge workers in almost any given situation.
Open Collaboration is the Most Strategic Model
I’ll be very clear then as to the core lesson here: By default the single best model for digital communication and collaboration — and the one that produces the most human engagement and the richest outcomes — is the online community or enterprise social network. Nothing else compares in terms of openness, transparency, ability to enable wide participation, ensuring diversity, encouraging agile business methods, collecting and preserving knowledge, doing all this at any magnitude, and the list goes on.
In fact, a whole revolution in work has already taken place with these ideas and platforms, but has been more limited than many proponents would like. it’s just that we haven’t had the imperative like we do today, with almost entirely distributed workforces forced upon us. While many organizations have experimented with these new models, and not given them the time or resources to make them deliver their power and value, others certainly have over the years (see my social business success series for case studies.)
It Is Up To You To Deliver a Revolution in Better Digital Work
There are two visuals shown above that make a powerful case for a) the scale and sustainability of large-scale open collaboration and b) why community tends to be the better model for most work including projects, enterprise-wide initiatives, and a lot of teamwork. While chat tools like Slack do have value at the team level, they are absolutely not focused on or able to realize the full capabilities of our networks or our people as a whole. Collectively, wo actually do know today what the best digital models are for many types of work. Please realize that I’m not prescribing communities/ESNs for everything. But I am saying that we should make them the default choice today to unsilo our organizations and fully unleash our true potential as individuals and organizations.
In this time of vast disruption of business and life, when the ways of working that we’re used to have simply gone away, the answer is not to double down on the approaches of yesterday that were not designed for the highly distributed world of work today. We now know of much better, more human, more engaging, and more effective ways of working together. As leaders, we must now better connect and cultivate our workforce, customers, and partners as a top priority. That means we must deliberately and strategically cultivate these stakeholders as the communities that they really are and which actually power our organizations. We simply must work in these news ways in order to lead them into a much brighter and more successful future.
Additional Reading
There is a great deal of research and thinking that has gone into to understanding how the concepts above were discovered and can be situated successfully in most organizations. Here is a full reading list of what we know about social business (online communities and enterprise social networks — as well as other related tools/platforms like them — that can dramatically improve how people work together digitally):
On Digital Strategy | Dion Hinchcliffe 