Enterprises Must Now Rework Their Knowledge into AI-Ready Forms: Vector Databases and LLMs

April 10, 2024 Leave a comment

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.

Enterprise Information Evolution: Documents, Key-Values (JSON), relational SQL database, graph databases, vector databases, and LLMs

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. But at the 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.

Related: How to Embark on the Transformation of Work with Artificial Intelligence

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.

My Related Research

AI is Changing Cloud Workloads, Here’s How CIOs Can Prepare

A Roadmap to Generative AI at Work

Spatial Computing and AI: Competing Inflection Points

Salesforce AI Features: Implications for IT and AI Adopters

Video: I explore Enterprise AI and Model Governance

Analysis: Microsoft’s AI and Copilot Announcements for the Digital Workplace

How Generative AI Has Supercharged the Future of Work

How Chatbots and Artificial Intelligence Are Evolving the Digital/Social Experience

The Rise of the 4th Platform: Pervasive Community, Data, Devices, and AI

Filed under AI, Analysis, Digital Transformation, The Enterprise Tagged with , , , ,

How to Embark on the Transformation of Work with Artificial Intelligence

August 4, 2023 Leave a comment

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.

Transforming Work with Artificial Intelligence: A Maturity Curve

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.

Video: How ModelOps is Essential to Enterprise-Wide AI Governance

ModelOps: Making AI work as an Enterprise-Wide Construct

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.

Related Research: How Generative AI Work Apps are Supercharging the Future of Work

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.

Please provide feedback in comments below or drop me an e-mail at dion@constellationr.com.

My Additional Related Research

The Future of Work in 2024: Navigating Through Uncertainties

An Analysis of Microsoft’s AI and Copilot Capabilities for the Digital Workplace

Every Worker is a Digital Artisan of Their Career Now

How to Think About and Prepare for Hybrid Work

Why Community Belongs at the Center of Today’s Remote Work Strategies

Reimagining the Post-Pandemic Employee Experience

It’s Time to Think About the Post-2020 Employee Experience

Research Report: Building a Next-Generation Employee Experience: 2021 and Beyond

The Crisis-Accelerated Digital Revolution of Work

Revisiting How to Cultivate Connected Organizations in an Age of Coronavirus

How Work Will Evolve in a Digital Post-Pandemic Society

A Checklist for a Modern Core Digital Workplace and/or Intranet

Creating the Modern Digital Workplace and Employee Experience

The Challenging State of Employee Experience and Digital Workplace Today

The Most Vital Hybrid Work Management Skill: Network Leadership

Filed under Analysis, Digital Transformation, Digital Workplace, Strategy, The Enterprise, The Future of Work

How Work Will Evolve in a Digital Post-Pandemic Society

April 15, 2020 1 Comment

The current outbreak of COVID-19 is stress testing our institutions, infrastructure, governments, and societies more than any event in most of our lifetimes. We have to go all the way back to the two World Wars to find similar precedents. Yet, as our businesses and personal lives are profoundly impacted, some of us can also perceive great forces of change in motion that offer us hope for positive and important new outcomes that we might influence.

The realization has also set in that we won’t likely be able to roll things back to how they recently were — at least any time soon — so we must now look at what is likely to be the next new normal, as it was famously known as during the 2007-2008 financial downturn (and which now sadly looks increasingly minor by comparison.)

In the last month and a half, I’ve been exploring how organizations must rapidly adapt themselves to the pandemic as most of our organizations now consist purely of digital workers connected over our global networks. As many of us in the digital workplace and employee experience community have noted of late, there are now major opportunities to follow-up on significant yet often slow or stalled transformations of human-centered work.

But first we must face our current situation and likely trajectory.

Profound Disruption of Work is Here

There’s just no avoiding it: The disruption we are facing today is as profound as it is pervasive. Yet I deeply believe it also offers an increasingly fertile and robust landscape into which we can drive meaningful and sustained change for good. Our timing must be careful and the thinking behind it — combined with effective action at scale — both crisp and clear, albeit real challenges in our fast-changing times.

There’s also no denying that how we’ve worked before is simply gone. Something much better than what we currently have must replace our current unwieldy situation for many of us: Weeks long slogs through endless video calls, tiring teleconferences at all hours, with our team chat windows scrolling mindlessly past our gaze. We can and must now create a much better design for our current working realities. Whether you will focus on remote work, more quarantine-friendly physical facilities, or a comprehensive rethink of the modern enterprise for being near 100% digital, we will have to go as deep as the core ideas that underpin work itself.

The Post Pandemic Organization for the Future of Work

We must also — to make it much easier to evolve going forward — start designing our workplaces and our work itself much more as a contemporary digital product in an ongoing and continuous exercise of collaboration and co-creation. I once asked in Designing the New Enterprise, “how do we adapt sustainably to constant change?” Now the question is also, “how do we adapt sustainably to large disruptive change?”

Answering these big questions will require profound and outside-the-box thinking. Our very foundations are in the midst straining. We now live in an era where even the traditional nation-state as well as the new global order both seem threatened. Answers to how we will thrive in a post-modern pandemic-stricken world seem stubbornly hard to find. Neither model seems sufficiently effective at providing adequately coordinated leadership or proactive response.

If we move down from the macro level of the global stage down to the size of our organizations (corporations, state/local governments, associations, non-profits, etc.) and other related but long-standing business structures like unions, partnerships, alliances, consortiums, and so on, we see that these too are now struggling to help their constituents in many cases.

The Ways Forward are Unfamiliar and Unknown, But Not For Long

Many better connected and easier to operate digital alternatives — at least in our currently locked down global state — do now exist, but seem either rather immature and/or unproven in comparison. These include global digital communities (yes, Facebook, and others), the larger and older open source groups/projects, and digital communities like LinkedIn and Github do seem to show that massively scaled communities can share information, powerful ideas, and help each other in compelling new ways, as many of us have long hoped. While there are plenty of downsides to these too, because the pandemic resistance of digital networks is outstanding, no other workable new modes exist.

We’re now entering a phase where we must begin to plan for post-pandemic. This does not mean going back to where we were. It cannot, because we now know the reality of the impact of a return of a new pandemic or a newly mutated coronavirus:

It’s simply irresponsible and unacceptable to go back to the entirely too fragile and so easily-disrupted operating models of the pre-COVID-19 world.

What does this suddenly urgent near-future of work look like you ask? No one has all the answers, but the good news is that we’re about to discover very quickly what is working and what isn’t in the vast global living laboratory of #suddenlyremote.

From my conversations the last few weeks with CIOs, my fellow futurists and thought leaders in the Future of Work, digital workplace leads, and employee experience groups (mostly in IT, but some in HR), there’s a recent but increasingly broad swing from the tactical, as in just getting everyone onboard with the basics of working remotely, to the strategic, where we look at where we must now go, both in-pandemic and post-pandemic, and quite possibly the next pandemic.

How Work Will Evolve

From this vantage point, which I am very fortunate to have in the industry, I can see a number of likely outcomes that will allow us to take a precarious economic reopening and flailing early growth and turn it into a stronger story of resilient resurgence, no matter what happens:

  • Designing for loss of control. By taking advantage of the tendency of systems and external agents to use an organization’s people, ideas, resources, systems, and data to do new and interesting things, organizations can deliberately create thousands of emergent outcomes at scale, many of which they have a stake in (see: platforms, ecosystems, etc.) The raw components are well known and understood for making this happen. Now it is an imperative to drive rapid recovery and growth.
  • A strong preference for tools with exponential potential and leverage. The pandemic catches us at a time of exponential change, and is further driving it. We simply can’t fight exponential change with yesterday’s linear tools. Organizations now need access to near-instant response to large events at scale. This is only possible with capabilities that can respond in kind. This means everything from mass decentralized automation and AI enablement to using digital communities and social networks as our primary organizational structures.
  • The rise of fully open and agile new operating models. The biggest question is whether our traditional institutions lead the world out of the pandemic, or will citizens around the globe come together and opt instead for something different using our global networks? We’ve seen the inexorable shift in agile methods in recent years, which came from key insights and experiences in the technology world, and which I’ve long noted has begun to infiltrate the broader world of business itself. The envelope of agile has expanded to something we now call DevOps, and that envelope will continue to expand and merge with mass digital collaboration models that now existing within the realms online forums, enterprise social networks, and team chat channels: Communities of practice, communities of interest, and now, communities of business, a notion I’ll expand on soon as I am currently collecting growing evidence for them.
  • Self-organization, self-service, and pull-based models for reorganization, restructuring, rebuilding, reviving, and thriving. The single most powerful model for work is humans collaborating together in open, transparent, and self-organizing processes. As I’ve often strongly encouraged businesses and people: Let the network do the work. There is no time in modern history where this concept is more important. It’s how we’ll each have enough access to resources, skills, ideas, and capabilities to do almost anything that needs to ne done. We’re already seeing things like this happen such as the formation of the Open COVID Pledge to mobilize invaluable IP quickly to respond to the pandemic in any and all ways necessary. The list of now free, but previously commercial, services available to help individuals and businesses is impressive as is the list of initiatives to help businesses most impacted. Again, all these resources have digital communities or capabilities at their core.

I also predict that our digital communities of citizens, workers, and organizations will be the single most influential and important resources that we have in surmounting the challenges of the current pandemic. It’s an easy prediction, because that’s largely all that government, society, organizations, and our institutions are at this moment. While there are badly needed and greatly appreciated people out there in the real world still growing our food, staffing our hospitals, and keeping the peace, these still represent only a tiny fraction of the total sum of our global cognitive power, operating capacity, and economic capability. The rest, for better or worse, has just gone almost completely digital.

We absolutely require the best ways of operating in this new reality. My point is that we largely have them, but the hard work remains to adopt, adapt, and succeed with them. It will be one of the most profoundly positive changes in human history, unleashing untold autonomy, human diversity, bold new ideas, dramatically transformative action, as well as human freedom and potential. Or not. The choice is ours to make, right now.

Additional Reading

When Our Organizations Became Networks

The Challenging State of Employee Experience and Digital Workplace Today

Revisiting How to Cultivate Connected Organizations in an Age of Coronavirus

My 2020 predictions for the Future of Work

My recent video interview with Bjoern Negelmann about these topics for Digital Work Disruption

Filed under Analysis, Digital Transformation, Digital Workplace, The Enterprise, The Future of Work Tagged with , , , , , , , , , ,

Four Strategic Frameworks for Digital Transformation

October 18, 2018 2 Comments

Collectively, the world of business and IT just isn’t learning about effective ways to digitally transform nearly as quickly as it could be or should be. However, as we reflect on previous efforts, we can begin to see why this is: Lack of good storytelling, inadequate structuring for speed and agility, poor sharing of effective best practices harvested from hard-won industry experiences, or having these lessons collected together into understandable and applicable frameworks that reflect the realities of how hard large scale digital change really is.

We almost universally know now we must adapt to the digital future, to change and grow. But how best to do it remains the top question.

We’ve also learned along the way there are numerous submerged obstacles to digital transformation that won’t be denied and must be overcome before we can really even get started. Sometimes, as they say, we must first go slow to go fast later.

Stubborn and long-standing issues related to technology like technical debt or poor master data posture, to name just two, threaten to derail efforts before they even start. Issues related to the nature of people take up the rest, and can sometimes seem intractable.

Four Frameworks to Describe and Drive Digital Transformation

Consequently, in my work advising and/or leading digital transformation efforts, I’ve developed and refined four key frameworks built out of years of repeated use and validation in organizations around the world. These reflect many of the central issues that I believe we’ve learned that we must address and then codified them into a plan that most organizations can execute against. The motivation: I’m asked for what frameworks to use for digital transformation more and more frequently these days. So I thought it would be useful to share them along with some key insights in how they were captured.

An Adaptable Framework for Digital Transformation by Dion Hinchcliffe

The Adaptable Digital Transformation Framework. This originally came from my exploration of the organizational culture issues and long-term journey with digital transformation. It’s also one of my oldest and most seasoned frameworks.

This framework reflects at its core an ongoing cycle of (hopefully, self) disruption, refinement, growth, and renewal, backed by key pillars including culture change, leadership, goals/roadmap, business redesign, communications, education, and skill building. It also makes the key point that emergent innovation is perhaps one of the biggest outcomes, enabled by the key digital era technique of designing for loss of control, such as critical strategy of turning your business into an open platform that others can build on at scale.

A Digital Transformation Initiation Framework. I used to get asked more often than now about how to get started with digital transformation than I do today (as the majority of organizations have already begun in some way.) This framework focuses mostly on the first 100 days of an organization-wide effort and reflects the key activities that must occur.

If there is something I’d tweak about this now it’s the “honesty assess” task in the first column. I’d underscore it far more. That’s because most organizations aren’t going far enough in the deeply reflective examination and soul-searching they must conduct early-on at every level to understand what they’re really facing when it comes to digital change and adaptation. This step must be particularly emphasized in the framework or organizations will struggle to even start the journey. Technical debt and master data barriers are just the start on the technology side. Culture, inclination, skill, and talent are bigger issues and are softer human ones that are very challenging to resolve. For many organizations, these obstacles will take far more than 100 days to overcome.

Other than that emphasis, I’m pleased with the current state of this framework, even if too many organizations don’t take the cultivating and full-scale activation of change agents nearly to the level they should.

Modern Digital Leadership Unleashed by Network Effects: Digital Transformation

A Leadership Framework for Digital Transformation. More of a process flow view than a prescriptive view on how leadership should go about digital transformation, this framework is useful for showing how critical it is for executives and digital change leaders are responsible for defining a new business future state, rich in new products and services in the realm of customer experience and digital platform. The major change I’d make today is that recent data now shows that the CEO is now the leader most often involved in driving forward enterprise-wide digital transformation, and I’d position it so in this picture.

The Digital Transformation Target Model: Customer Experience, Employee Experience, and Supplier Experience

The Digital Transformation Target Model. Less of a framework and more of a description of the transformation journey from silos of function (marketing, sales, delivery, operations, customer service, R&D/innovation) to the three main experiences that must result from a successful digital transformation. Right now, customer experience is the focus, with employee experience a distant second, but supplier experience is finally bringing up the rear and becoming a genuine conversation. I’d not make many changes to this view based on recent lessons learned, and organizations should take this view deeply to heart in their efforts in digitization.

Frameworks: A Living Artifact of Digital Transformation Knowledge

One unfortunate fact is that organizations often developed or adapt their frameworks from the material they encounter, such as the ones above. But they fail to make it a living artifact that captures lessons learned and teach those that must join in and continue the journey.

Thus, if there is a lesson learned above all, is that as digital transformation becomes a long-term journey that organizations will remain on as long as they exist, they must do a much better job in capturing, codifying, and spreading the learning of what works and what doesn’t, as it changes and evolves through time. In fact, learning is ultimately the primary activity of digital transformation, so any successful effort will tend to emphasize it and capture it in their own frameworks.

Additional Reading

The Digital Power Values for The New C-Suite: The Modern Mindset of the CEO, CIO, CMO, CDO, CCO

Why IT Leaders Struggle with Digital Transformation

The Leadership Challenges of Digital Transformation | The Conference Board

Filed under Analysis, Digital Transformation, Strategy, The Enterprise

Why Microservices Will Become a Core Business Strategy for Most Organizations

September 7, 2018 1 Comment

As an industry, we have collectively returned to that eternal debate about what constitutes a largely technical evolution versus when an important digital idea becomes a full-blown business trend. This has happened before with Web sites, e-commerce, mobile applications, social media, and other well-known advances. It can be hard to remember that at first these were looked at as mostly technology sideshows. Yet they all went on to become serious must-have capabilities on the business side.

Microservices is now a current topic of this debate, as the overall approach is perhaps the most strategic technology trend that’s come along in quite some time. First, a brief definition: Microservices provide a well organized digital structuring of our business capabilities that are exposed to stakeholders who need what our organizations can do, and are usually accessed via open APIs. The concept is now poised to — sooner or later — become the primary digital collaboration fabric with all our enterprise data, IT systems, 3rd party developers, business parters, suppliers, and other stakeholders.

So, you read it here first: Microservices are how most organizations will eventually conduct the majority of their business, internally and externally.

Yet there is still considerable debate and confusion about whether microservices are merely just slightly more elegant network plumbing of our digital systems, of if they actually represent the primary conduit for operating our organizations. I fall in the latter camp, as this platform way of thinking in general has steadily emerged as the leading model for composing and integrating networks of systems and organizations. Don’t get me wrong: We had SOA, Web services, and APIs before — where I once posited that this would turn into a global service phenomenon, which it has — but these each had key details missing or not quite right. At this time, microservices does appear to be the best model we have, honed and culled from over a decade of thousands of organizations experimenting with various approaches.

I am now also clearly seeing from many of my CIO and IT contacts that developing a microservices strategy is rapidly becoming a key priority this year. Not sure that this is broadly the case? Just take a look at the recent JAX Enterprise IT priorities survey, which shows that microservices are currently the 3rd leading IT priority, nearly eclipsing the big trend on the block, cloud computing, one of the other hottest IT topics of recent years.

Yet microservices are often conflated with concepts like APIs, for which there is indeed a considerably close relationship, and so can often be relegated to the ‘we’ve been here already’ bin.

Why the sudden popularity and interest in what appears to simply be a more refined technique to easily integrate and communicate between digital systems? For almost all the same reasons that the Business of APIs and the API Economy had their days in the sun: Microservices take so many of the lessons learned in creating more composable, reusable, and platform-centric version of our digital organizations, strips them down to their very basics in terms of design and consumption, and then places them at the very center of how our organizations operate. (Note: Not everyone would agree at the strategic level that microservices should be designed and offered at the business domain or architecture level but many, including myself, do.)

Naturally, the question is why would we do this, and why would it be just about the most important thing we could do to enable a host of vital business activities and outcomes? Put simply, microservices hold the promise of truly unleashing the greatly underutilized assets of our organizations, both strategic and tactical. These assets include everything from data to talent to innovation, and up until now, we’ve been doing it piecemeal and without a real enterprise-wide design (though I’m cautious about overly top-down efforts here as well.)

Microservices: Building Blocks of the Modern Digital Value Chain

Microservices, by virtue of offering a well-structured way to engage and integrate with the world at large in scalable, digital terms, now appear to hold the answer to enabling faster digital transformation, lowering our levels of of tight coupling and technical debt, and substantially increasing much needed levels of IT integration. More centrally to business impact and growth, they also make it possible for us to build and cultivate bigger and more robust digital ecosystems with our stakeholders. This includes 3rd party developers and business partners to our very own workers and customers.

For me, I first saw the writing on the wall several years ago when I was helping develop the API strategy for the CIO of one of the largest organizations in the world. We had just completed an all-day workshop studying the benefits of opening up systems and data more simply and easily to make them as consumable as possible. I stressed these key points: 1) Open APIs make it far easier to create and innovate on top of existing IT and data, 2) they make it easy to create additional value many times over through nearly effortless integration between systems, 3) they achieve this asynchronously and highly cost effectively by systematically designing a high leverage and productized point of global interaction upfront, instead of hundreds of expensive point-to-point integrations over time. Upon reviewing this, the CIO suddenly sat back, the light clearly having come on, and said, “I get it now. The logical conclusion of all of this is that we need to provide a URI for every piece of data in our organization.” He was exactly right.

Put simply, this means that every element of enterprise data would have a unique link to it through a well-defined interface, which anyone can easily find and use to (yes, securely) access it and update it if appropriate. As I’m fond of saying, civilization advances when formerly difficult things become easier. This is exactly the vision behind microservices: Build and provide an incredibly simple and straightforward way of exposing our businesses in a highly useful and constructive manner so that the effort to connect systems into value chains becomes essentially near zero in practical terms.

Mindset: What Would Happen If Anyone Could Build Anything On Your Business?

The question I then put to those still trying to understand all this is the following: If we could access all our enterprise data simply and easily and could then integrate systems together with just a few lines of code, what could we do this with power? Virtually anything we can dream of, with almost no economic, technical, organization, or political barriers to achieving whatever we — or, and this is the big key, others — could dream of doing with our systems and data.

Because once strategic microservices that enable this are operational, then anything is possible. That’s because virtually all of our enterprise data can be reached, it can be harnessed, analyzed, and it can flow through to wherever it needs to be to extend and empower the stakeholder/customer experience. In fact, it’s the most potent way we know of yet to create and capture shared value and to do this so efficiently that literally orders of magnitude more high value integrations, connections, and innovations will take place (see: How Amazon Web Services makes most of Amazon’s profit.)

So why hasn’t this happened except in organizations at the very leading edge of the digital maturity curve? Because it takes 1) an understanding of the vital — even existential — importance of doing so in order to rapidly gather around a vibrant ecosystems of app creators, integrators, partners, suppliers, customers, and stakeholders and 2) the pre-emptive removal of the aforementioned economical, technical, organizational, and political barriers to doing so. In short, creating microservices, though they themselves are profoundly elemental network-accessible business capabilities to our organizations, takes real work, much of it consisting of softer, non-technical obstacles in the realm of culture, mindset, inclination, and leadership.

We already see examples of this happening at the enterprise vendor level. A particularly compelling example of a global set of microservices that expose much of what an organization does is Microsoft Graph, along with their microservices-friendly Service Fabric. While some will quibble with whether MS Graph is a set of microservices in the pure sense, the point is this: Much of what Microsoft offers its customers via its products is accessible within a well-organized enterprise-class set of data services. This is strategic to the point that Sayta Nadella has even called Microsoft Graph their “most important bet”, for all the previously cited reasons.

Microservices are also well established at some of the leading organizations in the world, including Amazon, Netflix, Uber, and a good many others. Less clear is traditional enterprise adoption at the strategic level, though my personal anecdotal evidence is that this is now very much underway in a growing number of organizations. Another proof point of expected growth is that business consulting firms like Deloitte are seriously talking about microservices as enablers for open banking and other industry transformations.

Microservices and Business: The Future

However, in today’s extremely fast-moving world, coming to the conclusion through a largely accidental and piecemeal route that microservices are the future will simply take too long from a competitive standpoint. This will result in a very much less than optimal set of services for your stakeholders. Thus, my advice on microservices in the enterprise is currently this:

  1. Most organizations should now begin a concerted effort to create an enterprise-wide set of microservices. And do it as a part of an overarching business strategy.
  2. This effort should be decentralized but a centrally coordinated effort. To be used to identify and design needed microservices.
  3. A commitment must be made to be in the business of integration and dynamic digital value chain building. Half measures have long-doomed efforts at SOA, APIs, developer networks, etc.
  4. Use design thinking to understand the needs of microservices consumers, then meet them. Understanding what the needs are, and being deeply empathetic to key issues like ease-of-use, performance, and the right to build a 3rd party business on them is key.
  5. Operate your microservices like your core business. Because they soon will be. Invest in them, advertise them, evangelize them, encourage usage, support them, and generate revenue with them.

A growing number of organizations I work with, including most recently one of the largest federal government agencies in the U.S., are now fully cognizant that most of their business will soon be conducted through digital channels. That aforementioned agency is already doing over a quarter of its business through APIs, and expects it will be over half in the next few years. They believe moving from data-based APIs to business-oriented microservices is their next task to go to the next level. So should it be for most organizations.

For the enterprise, achieving success with microservices is certainly possible through a patchwork of department APIs that are designed and operated without an overall business strategy, design, or structure. Or we can adopt a holistic microservices approach to create a more uniform, rational, consistent, and contextual set of open digital capabilities that also forms the basis of business strategy and architecture for the organization. The story is unfolding rapidly, and as I mentioned, I’m seeing an all-time high interest in microservices at the most strategic IT levels. Now that story must be told, understood, and realized on the business leadership side as well.

Update on September 20th: A few commenters have noted that they don’t think that most organizations believe microservices and APIs are actually viewed as business strategy, much less core to it. However, supporting many of the assertions I make above, I recently encountered a recent study from Cloud Elements. Their 2018 integration survey (which included 400+ companies, 27 industries with 26 outside of tech including finance, communications, engineering, and transportation on 6 continents) reported that 61% found APIs to be critical to their business strategy, and 85% fundamental:

APIs (open access to microservices) is Essential to Business Strategy

Additional Reading

My current Astrochart for the New C-Suite: Microservices figures prominently as a key C-level technology and business strategy

A Discussion of the Past and Future of Web APIs with Dion Hinchcliffe | InfoQ

How can businesses keep up with tech change today? | ZDNet

Filed under Analysis, Digital Transformation, Enterprise Architecture, Strategy, The Enterprise, Trends and Statistics

Designing the Digital Workplace for the End-to-End Employee Experience

May 10, 2018 1 Comment

As digital becomes instrumental to virtually every aspect of how we do our work in organizations today, two parallel and closely related concerns have joined the industry discussion. These two concerns, workforce engagement (which technology can very much help with) and the employee journey, have risen as urgent topics and joined the overall conversation about the needed capabilities of our work environments. This is because the designs of our future digital workplaces will so deeply inform and define these issues.

Over the last few years, I’ve noticed that most enterprises are still not adequately addressing how to effectively develop and maintain a straightforward and effective approach to technology enablement of the most important activities in the workplace. The proximate cause is sheer complexity as well as experiential noise, mostly of too much information with too little filter. Yet ironically, our businesses actually need to incorporate more technology and data into work procsses, not less, to do our jobs better and evolve the organization.

Thus, the way workplace technology is selected, provided, situated, and supported as a whole has proven generally insufficient to the task of addressing the trio of concerns I’d outlined above. We also have some significant new headwinds that aren’t helping and must be addressed constructively: Pronounced channel proliferation and fragmentation as well as an explosion of apps that run or better enable the business, especially in the mobile space. We generally need these applications, but not when their isolation (most don’t connect well to other systems) and fragmented data creates cognitive overload or involves too much effort for us to effectively use.

The Digital Transformation of the Workplace for End-to-End Employee Experience

Thus I still see many too many workers that in their day-to-day jobs still have to focus on spending much of their time feeding their work systems manually, via import/export and numerous other means, cobbling together an ad hoc experience across dozens of apps, just to prepare to begin their jobs for the day, instead of focusing on the more strategic higher-order knowledge work at hand.

The bottom line: Most practitioners I speak with believe there is plenty of room to improve this situation considerably, but aren’t generally sure how yet. Because of this unclear path forward, most of workplaces are still not expending any real effort in developing a more workable and usable overall employee digital experience. This is a major lost opportunity and it ultimately fails to serve our workers, our organizations, and our customers in vital ways. What’s more, it’s only going to become more of a challenge in the near future as IT continues to proliferate in every part of our enterprises.

Yet I do find that some of the solution(s) to this situation — and which will take real vision, commitment, and sustained change to realize — do exist in early form and are increasingly at hand.

Reconciling digital workplace with employee experience

To address all this, a while back I suggested that we were going to have to develop multi-layered strategies based on one or two experience hubs to cope with the increasingly dense and rich landscape of digital workplace tech. Sooner, rather than later, that we’re going to have to make the user experience, data experience, and community experiences more connected, holistic, and integrated, into some form of better integrated whole that probably looks like a) an enterprise social network, b) an intranet platform, or c) other experience platform where the employee digital experience can be better designed, orchestrated, simplified, aggregated, and connected to the apps and data needed to get work done.

I still believe this, but I also now realize that even with this we’re still neglecting the overall picture of employee experience, something that human resources (HR) has long focused on but that IT generally has not, even though our workplaces have inexorably become more and more digitized.

The opportunity is clear: By apply coherent purpose and design to the full end-to-end employee experience (pre-hire, employment, and post employment) — yet also proactively allowing ‘eccentric activity’ all around the margins that will drive needed the digital competition for new ways of working (and therefore rapid forward progress) — we can simplify, streamline, and direct the design of our workplaces (digital and physical) as it relates to technology to realize a far better employee experience.

To be clear, we won’t — and can’t — design or control the entire employee experience. That’s simply not possible, nor desirable, in today’s highly complex, fast changing, and sophisticated operating environments. Instead, we’ll use a design for loss of control mindset to transform the employee experience while focusing on the major use cases and employee journeys that matter most, while letting local change agents pioneer new ideas around the edge.

Using Design Thinking and Digital Workplace Strategy to Design and Develop a Better Employee Experience

To realize this change we’ll need to make digital workplace a higher order design journey with close partnership between HR and IT (really, in my projects, it’s mostly had to be the CIO and CHRO, who almost exclusively have the purview to mandate bringing together employee experience of every kind under a single umbrella.) Organizations that go from an accidental digital workplace to a more designed one will have much better results with their overall employee experience as well as targeted use cases (typically sales, project management, operations, product development) that have both high impact and strategic significance to the organization.

I’ll be exploring this confluence of the three main organizational experiences (worker, customers, and supplier) increasingly as part of my work in understanding the digital leadership issues in the enterprise. I believe these must be the primary focus of our organizations going forward, and addressing one helps address the others.

Catch me in person: You also can join me in Rotterdam, the Netherlands on May 21st, 2018 to further this discussion as I explore how to apply design thinking and digital workplace strategy to end-to-end employee experience from my latest digital workplace project efforts.
Engage Workshop, Rotterdam, Netherlands with Dion Hinchcliffe and Ellen Feaheny on Digital Workplace and End-to-End Employee Experience

Filed under Digital Experience, Digital Transformation, Digital Workplace, The Enterprise, The Future of Work

Digital Transformation in 2018: Sustainably Delivering on the Promise at Scale

January 5, 2018 2 Comments

In 2017, we witnessed organizations take up the mantle of digital transformation with more conviction and effort than any time before. Funding, commitment, and leadership support was at its highest level ever and only showed signs of increased dedication. Ongoing success stories from many leading organizations showed that large scale technological and business transition was also possible for the typical company, not just industry leaders. Perhaps most vitally, the imperative itself became even clearer to leaders as disruption began to penetrate even into long resistant industries like healthcare, finance, and even insurance.

Yet it was also evident that last year was another major learning year, because through our efforts many of us gained an even fuller appreciation of the sheer size and scope of the required journey ahead of us. Combined this with the steady proliferation of new and important technologies last year and we gained both fresh urgency and a better understanding of the true challenges facing us. In 2017, the Internet of Things and artificial intelligence were felt particularly profoundly on the transformation agenda in the industry, along with data science, analytics, and other forms of capitalizing on the vast and invaluable streams of new information that better digitized businesses generate. 2018 will see the same, but with much more focus on reaching the market effectively and seizing network effect, and less on experimentation.

Of all the many lessons learned on digital transformation last year, perhaps the most important was that the complexity and pervasiveness of the necessary changes — organizational, cultural, and especially mindset — as well as the new technologies themselves require powerful new tools and techniques that simply didn’t exist a couple of years ago.

The Two Dimensions of Digital Transformation in 2018: Upside and Oversight for Opportunity, Governance, and Risk Management

The Twin Digital Transformation Lessons of 2017

Two of these new tools and techniques — culled from the hard won experience of the early movers in digital transformation — are particularly worthy of calling out.

The first was a result of the realization that a single, overly centralized change entity like the IT department, the digital line of business (usually led by the chief digital officer), or tech incubator was not sufficient in realizing the profound rethinking and realization of the entire organization in more digital terms. In fact, these entities might not even be that helpful in that they are overly focused on technology and may not have the requisite experience in applying to the redesign and transformation of the business itself. Instead, more decentralized yet highly engaged entities like empowered groups of change agents or networks of transformation teams seem to be more effective are driving long-term change both deeply and widely across the organization. This evidence is backed up by careful research last year by Professor Gerald Kane and his colleagues that digitally mature companies are more likely to have impactful enterprise-wide transformation efforts.

The second insight was that the raw building blocks for digital transformation that existed were simply too primitive, not situated for business use, too little informed by the vital patterns and practices now known to be necessary, and not designed to rapidly incorporate new technology and additional lessons learned as they emerged. In the past, we would have said we needed frameworks for digital transformation, and while those emerged as well, what we really needed was much more operational constructs that had these vital ingredients: A relatively complete cloud tech stack, workable blueprints for specific industries, architectures designed for high leverage that support rapid change, and business solutions crafted to a 40-60% level of completeness and waiting for the details of your business to fill in the rest. While Amazon Web Services, Microsoft Azure, and Google Cloud provided some of these building blocks, they simply weren’t complete on their own. Organizations such as SAP (with its Leonardo offering), Accenture, and others have thus created what I’ve called digital transformation target platforms, which are more mature, complete, and business-focused transformation vehicles and operational capabilities. Note: For more details, you can find a fuller explanation and list of such enabling target platforms on my recent shortlist.

Combined, these two lessons learned — which are equally balanced between the people equation and the technology challenges — are vital in my analysis to successfully tackle the digital change obstacles and opportunities at sufficient speed and scale. That’s because there are very significant competitive implications — that would be irrelevance and/or outright disruption — to moving too slowly or tackling digital change too narrowly and in silos.

The good news in my experience over the last year: More and more organizations are now indeed staring to find these onramps to the superhighway of much more rapid and effective digital transformation. Enough now so that it’s led to a second major — and steadily growing — issue that must itself now be managed as a top priority new purview. This quickly accumulating new tech and business portfolio which comes from achieving a higher change velocity must be well-managed and governed. We simply must keep our new digital businesses secure in an age of Meltdown and Spectre as well as complying with GDPR and all the other rapidly emerging digital regulations that threaten to impede our efforts.

The Two Dimensions of Digital Transformation in 2018

As we’ve emerged from the very early days of digitization, there is now a clearer sense of how to tie emerging technologies to specific outcomes. A generic example of such a map is shown above, depicting how technologies can combine and reinforce key desired outcomes ranging from data-driven management of the business and better employee engagement to satisfied customers and higher growth and revenue, while also optimizing the results, governing it all, and keeping everything running safely and securely. These outcomes can be broken down today into two different key dimensions.

The first dimension of digital transformation outcomes, what I call the upside objectives, is what most organizations have been mostly focused on until now, as they try to get out of the gate to create initial wins. You can see from the accompanying visual above, that technology does indeed define the art-of-the-possible when it comes to disruptive new products and services (blue circles, center.) While lightweight IT integration, cloud, analytics, architectures of participation, and smart mobility have been technology approaches we’ve had for a while, the modern focus on digital transformation tends to be today on building and wielding customer-facing experiences infused with digital business models, interconnected ecosystems, services built on top of the Internet of Things, and with many flavors of artificial intelligence to make it personal and differentiated. Even the digital workplace is seeing fairly comprehensive overhauls in many organizations precisely to provide the tools and environment for workers en masse to be more effective at transforming their part of the organization. As a result, low code tools, citizen developer, personalized digital workplaces, hackathons, and other ways of spreading out the hands-on transformation process to the edges of the organization to move more quickly are a focus here.

The second dimension of digital transformation outcomes, let’s call it oversight objectives, is a newer one that hasn’t had nearly as much focus so far but is about to become very important as organizations digitally innovate faster and create far more complex ecosystems and stakeholder-facing experiences. Otherwise known as operations, governance, performance optimization, risk management, and cybersecurity, these oversight capabilities must get better and scale just as much as the upside portion of the portfolio. To ensure these capabilities are funded and resourced just as well as the other side of the digital transformation coin is going to be one of the next big challenges.

The reality is that most legacy organizations are not structured or funded for delivering on continuous change as the norm, to do it sustainably, or at the scale required today. While we’re seeing next-generation organization models that will help, we’re all still learning a great deal about how to design the contemporary digital organization. That we simply have to figure it out is the reality for most of us, but the good news going into 2018 is that we have some promising avenues to explore for more successful results.

Additional Reading

In Digital Transformation, The Art-of-the-Possible and Average Practice Are Diverging

Digital Transformation and the Leadership Quandary

What’s really holding back today’s CIO from digital transformation?

Filed under Analysis, Digital Transformation, Strategy, The Enterprise Tagged with , ,

In Digital Transformation, The Art-of-the-Possible and Average Practice Are Diverging

October 6, 2017 Leave a comment

I’ve long noticed an interesting phenomena when it comes to more fully digitizing our organizations. Namely, that it mostly looks like what other organizations have already been doing. Because we are all almost entirely still early pioneers in a brave new technologically-infused world, this shouldn’t really come as a surprise. Since there are an almost infinite number of directions we could go, copying that which we see that works well just makes good sense.

This herd mentality of digital actually has numerous causes: Proven best practices for digital are too few and far between, successful experiments are often hoarded for competitive motivations, digital innovators by definition take on often untenable risks we’d prefer to avoid, and perhaps most of all, we are still trying to get used to the rapid pace of learning that digital requires to stay abreast.

A big reason for this state of affairs is because digital is inherently complex in its realization, intangible by nature (thus it can be hard to study and assess), and difficult to actually understand in context since it’s now so deeply connected to everything else today. This makes it hard to identify the root cause of any desired effects. Combined with the slow rate of change in people when it comes the requisite shifts in culture, skill, and inclination for new digital ways of working, and the result has been a clustering of most organizations around a similar level of digital maturity: Relatively low.

Digital Maturity: Technology Is Driving the Leaders and Laggards Apart

Digital Maturity is a Team Sport

This was made evident a little while back when McKinsey published their in-depth analysis of 150 representative organizations around the world and their digital maturity in 18 dimensions (see graph above.) It uncovered a wide range of digital maturity, but most notably revealed a sort of inverse Lake Wobegon effect, where most organizations were in fact performing well below average.

In other words, average practice is steadily and inexorably diverging from the art-of-the-possible in an exponentially changing era of technology evolution. This is leaving a great deal of space for leaders to find the leaps forward that are dramatically better and thereby own the market opportunities.

Yet, we also know that when applied for its unique strengths — for faster growth, better engagement, reducing friction in commerce, improved efficiency, and so on — technology can be a tremendous force multiplier (something noted about a decade ago by Andrew McAfee and Erik Brynjolfsson), propelling the leaders that focus carefully on these strengths far head of the laggards. This gap is real, which we can see from the data above, and it’s growing quickly in my experience.

Nevertheless, whether I look at the digital workplace, customer experience, or digital transformation efforts that I’ve been involved in over the years, I tend to see the same thing: The application of average practice that, while proven, will assuredly put most organizations into the also-ran list and fail to propel them forward digitally in a meaningful way.

Over time, this has led me to ask what the digital leaders are actually doing that has gotten them much farther out ahead. In short, my ultimate analysis is that they appear to be learning better and faster about digital in key ways — and from a larger variety of sources — than most other organizations. They also then apply these lessons effectively to their business. Digital leaders tend to eagerly gather lessons and evidence broadly and early, especially outside their organizations. Without this, they are limited to what they’re able to learn linearly on their own, through solely their own efforts. There is also good evidence that this is what most organizations do that have survived a long time, from the work of Shell’s Arie de Geus (and which I frequently cite in my keynotes and talks):

These companies were particularly tolerant of activities in the margin: outliers, experiments and eccentricities within the boundaries of the cohesive firm, which kept stretching their understanding of possibilities.

This same line of reasoning has led industry colleagues like John Hagel to conclude that scalable learning, especially across organizations as Don Tapscott has noted in his research on Global Solution Networks, is essentially the only sustainable competitive advantage. But as I mentioned above, competitors usually don’t like to share lessons learned, and it’s often hard to transfer lessons from one style of organization to another, say across industries or geographies.

The key existential question now is this: How can we use today’s capabilities to learn much better as organizations?

Overcoming Digital Transformation Maturity Barrier with Community Learning, Outsourcing, and Copying for Fast Follower

Three Roads Over the Digital Maturity Barrier

How then are digital leaders overcoming the digital maturity better? In my experience, they are doing one of several things that allows them to pool their digital experiences and investments, then tap much more widely and sustainably into shared lessons learned that they can each use and quickly build upon:

  • Community learning. Non-competitors can come together across organizations to share their digital knowledge and lessons learned, and especially, tackle digital challenges too big even for large enterprises. This kind of cross-entity learning primary comes in three forms, though there are numerous ways to do it: Industry consortiums, which we’ve long had, as well as more digital versions of consortiums such as collaborative multi-organizational Networks of Excellence and of course, the aforementioned Global Solution Networks. These require the highest level of effort but are also the most sustainable, effective, and most likely to reduce the risk of disruption by truly capturing and wielding collective intelligence.
  • Outsourcing. Pull in expertise gleaned from hundreds or thousands of other companies by building on someone else’s mature and evolving ecosystem or digital blueprints. Amazon’s cloud stack and Apple’s iOS platform are great examples of this that countless companies are using today (Netflix using Amazon, for an industry leading example), while increasingly we’re seeing industry blueprints emerging for digital transformation of their entire organization. See the overview of my Digital Transformation Target Platforms ShortList for some details on blueprints.
  • Copying the Leaders. This has long been a corporate strategy of so-called fast followers and it works well for some. This approach basically uses 3rd party investments, discoveries, and exposure to risk in an arbitrage fashion, for their own benefit, picking and choosing what works and avoiding the downsides almost entirely, though some have certainly criticized the fast follower approach, others have cited organizations like Samsung as becoming market leaders by using it. Although technically another form of outsourcing, this model also works in a group of competitors. Downside: You won’t have any “moon shots” or big digital breakthroughs on your own and so you’re still at high risk of disruption.

Clearly, this list is in rough order of preference, though all are workable strategies and will likely be used in combination. That said, the vast majority of organizations are taking the easier routes of the second and third items on the list. This means letting Amazon, Google, Microsoft, IBM, and SAP pathfind their future and build on their capabilities/ecosystems, or being content to cherry pick from the successful digital pioneers and hopefully to attain success in that way.

Digital Maturity Requires Harnessing Collective Intelligence

The third way (first on the list), which I see more advanced and mature organizations engaging in, is to work far smarter by combining knowledge, investment, and experience as whole together, creating a network that can learn many times faster than a single entity. The competitive issues can and are usually worked out.

Are there good examples of multi-stakeholder learning? Yes. Some of the most strategic can be found in the list of known Global Solution Networks, but others that I’ve had personal experience with are the famed Fraunhofer Society, open source software projects (many people/organizations coming together to collaborate on common goals via shared technology innovation and development, and the American Society of Association Executives (and indeed, the entire professional association space, which is becoming increasingly digitized and community-centric.)

There is also a fourth route, which many will observe seems to be the case with certain top digital firms: Hire the smartest people on the planet and turn them loose. This is certainly possible, but it’s also an unsustainable zero sum game that the vast majority of organizations simply don’t have as an option to employ (the smartest people always work for someone else, it has been observed.) Instead, we need additional options for reaching digital maturity that are generally attainable by most of us.

Thus, in the flat and hyper-competitive world of the Internet, average practice is just not sufficient to thrive, nor to survive. Organizations must find ways to learn and evolve faster, more widely, and with much more scale than in the past. Cultivating change agents has emerged as one such way to actually achieve this, but these actors need a steady stream of knowledge on emerging new practices in order to drive the organization forward. This is through scalable learning.

As Scott Brinker’s now-famous law (Martec’s Law) tells us, technology changes exponentially but organizations only change logarithmically. The good news is that it’s very much not clear if this is an inherent limitation of organizations, or that’s just that way because of how we have traditionally learned and changed in the past. From my experience in the field of mass collaboration, my view is that it’s almost certainly the latter. There we now have new and better ways to change if we choose to use them.

The reality is that if we don’t find ways to change more rapidly and effectively, the results are potentially calamitous for us as enterprises and institutions. Fortunately, we now have powerful new tools to apply when it comes to digital learning and change. I believe these approaches may be enough for most organizations for now. If it’s not however, I remain confident that we will find even more and better ways to evolve and grow. The digital future is bright, if we’re ready to learn.

Additional Reading

Using Online Community for Digital Transformation | Slideshare Storytelling Version

How Should Organizations Actually Go About Digital Transformation?

The Eight Essential Digital Strategies

Digital Transformation and the Leadership Quandary

Let the Network Do the Work

The Hardest Lesson of Digital Transformation: Designing for Loss of Control

The emerging case for open business methods | ZDNet

The Top Business Trends for the New C-Suite in 2017
(See: Digital Transformation Programs, Change Agent Initiatives, etc.)

Filed under Analysis, Digital Transformation, Online Community, Strategy, The Enterprise

Internet of Things Strategy: It Will Determine Your Organization’s Future

September 18, 2017 Leave a comment

Few technology developments will ultimately have the global cultural, business, and economic impact of the Internet of Things (IoT.) While today IoT still looks like an industry largely concerning itself with factory automation, connected light bulbs, air conditioning controls and so on, the eventual objective is clear even to a casual observer: Nearly everything in the world is about to become connected and data-driven, from the most trivial object to virtually every significant item in our personal and work lives.

The implications of this shift are profound: We’re about to be able to measure and quantify just about everything that exists. While there will be the requisite debates about whether this is always a good thing, the implacable march of technology development will ensure it’s going to happen anyway. When a new enabling technology arrives and is useful, it finds its way into just about everything.

The implications alone for IoT and the healthcare, insurance, financial services, logistics, manufacturing, and energy industries — to name just the most affected — are profound: For the first time in human history, most aspects of our business will be measurable, and therefore to paraphrase the famous Peter Drucker saying, they can actually be managed in a more direct and effective way than ever before possible.

Internet of Things: The Next-Generation Customer Experience

IoT will also be critical to the next generation of customer experience, allowing us create more personalized and far more useful experiences while maintaining direct and continuous connection — and most importantly, value exchange — with our customers like never before. Customer experience is the product we must all produce in the future and IoT is how we’ll realize it. As Stuart Lombard, CEO of Ecobee, noted last week during his appearance on DisrupTV.

As the current wave of Internet of Things emerged on a scene a few years ago, I wrote an analysis on whether IoT is truly strategic to the enterprise (short version: it is.) Though the exact growth projections continue to be debated, given the inevitably vast numbers of devices and the staggering data volumes they’ll create (large enough that it’s even driven the need to push cloud capabilities back to the edge of the network), it’s now evident that IoT will be the largest new technology industry to date, far eclipsing even mobile computing.

In other words, tens of billions of connected IoT devices, many streaming rich media and other high volume data types around the clock, are already in the process of arriving today and steadily over the next few years. In the process, they will remake the digital and business landscape as they do, as they represent enormous opportunity for new disruptive new products, services, and business models. At the same time, IoT will also pose very significant infrastructure, operational, management, governance, and security challenges for most enterprises due to scale, skill shortages, build-out, and related issues. Organizations must prepare at the highest level for this and put IoT in the middle of their digital value chain as they digitally transform. The resulting IoT strategies will determine their future as a business in profound way.

It’s evident that IoT is a core part of the future of digital and is the next customer experience mandate. We will simply have to be connected to our stakeholders in this way, holding them close across myriad digital channels, providing value in a way that only real, sustained, and live connectedness and engagement can.

Deep Digital Connectedness Requires a New Mindset

To help frame up this story, I was pleased to contribute recently to a significant new IoT strategy ebook produced by SAP, along with many of my industries colleagues. The book does an excellent job teeing up the mindset and thinking required to capitalize on the historic opportunity of the Internet of Things. Thanks to Amisha Gandhi, Jim Dever, and the SAP team for inviting me to contribute. Note: My contributions start on page 7.

The Future of IoT ebook itself is free, has a nice digital customer experience of its own, and covers the following topics:

Future of IoT ebook: Insights on the Future of the Internet of Things (IoT)

1. Focus Forward on the IoT and Business
2. The “Intelligence of Things”
3. The evolution of smart devices and how business will leverage the IoT
4. The Customer Journey
5. How will the IoT affect the daily lives of consumers?
6. The Internet of Truth
7. Concrete data leads to better decisions
8. The Forward Focus of Business
9. Strategic advice on the IoT for business leaders

SAP eBook: The Future of the Internet of Things (IoT), with Dion Hinchcliffe

Other contributors were an all-star cast and include Ronald van Loon, Yves Mulkers, Maribel Lopez, Bob Egan, Christina “CK” Kerley, Bill McCabe, Ahmed Banafa, Joan Carbonell, Jim Harris, Daniel Newman, Evan Kristel, Chuck Martin, Dez Blanchfield, Isaac Sacolick, and Giulio Coraggio. Brian Solis also shared his thoughts about the ebook as well.

Additional Reading

The Essential Digital Strategies

SAP Leonardo, IoT, and Digital Transformation: The Strategic Implications

Visual: The Top Digital Shifts the Enterprise Must Take On Today

The enterprise technologies to watch in 2017 | ZDNet

Tech Trends AstroChart for The New C-Suite, Q3 2017 | Constellation Research

Filed under Analysis, Digital Experience, Digital Transformation, Strategy, The Enterprise

The Essential Digital Strategies

August 15, 2017 2 Comments

The reality today is that despite seemingly endless advances and a steady river of emerging technologies, many of the key insights, strategies, and lessons in the digital age have still yet to be discovered. Looking back, we are frankly still early in the pioneering phase of digital, despite significant early ground being claimed and several generations of impressive success stories emerging.

Therein lies the opportunity for most of us.

Thus, despite all the esoteric talk over the years of network effects, the red queen treadmill, strategic platform plays, and winner-take-all, it’s now clear that the digital market is so fluid, self-creating, and essentially infinite, that most of the value by far still remains to be created and captured.

When I say digital age, I do mean since the advent of the Internet, which as we look back on it now was a truly epochal event whose impact will be felt profoundly for the remainder of this century, both inside and outside our organizations. This isn’t an understatement: The vast, easy, simple enabling of global digital networks of people and organizations has been a genuinely a revolutionary one. Today’s networks can be of any size, any form, and can effortlessly enable us to come together en masse and collaborate for purposes of creating incalculable human value — many of which were hitherto simply impossible, and all at a cost that relentlessly falls towards zero.

Far too many people I talk to today, including many digital strategists I find, still do not fully appreciate this time in our history. Most of us are coming to terms with and beginning to understand what digital can do, both for positive outcomes and otherwise (as all technology is a two-edge sword.)

Digital Setting the Global Growth Pace

Yet while digital in all its many forms is now well down the path of transforming our economies, enterprise, and society, we do have a growing sense of what the art of the possible is. It’s clear that digital is now of the primary aspects of how we live and work, and so we must shape it into what we want it to become. We have only to look at tech firm exemplars like Amazon, Google, Facebook, Airbnb, and the 183 companies currently in the so-called “unicorn club”, namely digital startups worth over $1 billion, to find the companies that are creating the most new large-scale business potential. In fact, over the last 10 years, digital companies have surpassed the traditional corporation dramatically, now making up five of the six most valuable companies in the world by market capitalization:

The World's Most Valuable Companies: 2006-2016 - Apple, Alphabet, Microsoft, Amazon, Facebook, Exxon

Put simply, it’s far easier — and more valuable economically — to grow a company in a digital world than it takes to do it the traditional way with physical offices, departments, divisions, stores, factories, and vast workforces to run them all so that you can build products and deliver them to customers individually. The cost of doing it the old way is by comparison simply enormous and increasingly prohibitive, even if we’re not talking about eliminating the old world entirely. An amalgam of the two is happening, as we’ll see.

Note: In a digital world, you still need people of course, and some infrastructure, just orders of magnitude less typically. A canonical example of this is WhatsApp, which only needed 50 direct employees to deliver services to 900 million users at the time they were acquired for $19 billion by Facebook.

What Are the Top Digital Strategies Today?

There have been a good number of attempts lately to quantify what the top-level “known quantity” digital strategies are, since for all the reasons above these should be the top targets for the digital transformation process within most organizations. One of the most recent examinations of this is an exploration by Jacques Bughin and Nicholas Van Zeebroeck’s what they believe today’s 6 core digital strategies are. It’s a good overview, especially the insight that for over 2,000 organizations the value of such digitalization has in general been only a little above the cost of the capital to get there. In other words, most efforts don’t generate unicorn outcomes. (Though to be fair, they shouldn’t be expected to. Digital is a numbers game and it’s why VCs invest in pools of startups than in one or two efforts, but that challenge is another story.)

However, I’d argue that Bughin and Zeeborekc’s digital strategies tend to be ones that traditional organizations would be more able to carry out by their existing inclinations and nature. It’s far easier to move into e-commerce, for example, that it is to become a platform company, as the former seems familiar to traditional organizations, while the latter has entirely different rules.

Being a truly digital organization means thinking quite differently than an industrial age organization. I find the above mentioned strategies to be less transformative and meaningfully sustainable than what is possible and evidently required to get the gains that the more green field unicorns are seeing.

The Essential Digital Strategies for Business and Transformation Today

Instead, other research has come up with a slightly different list of strategies, notably recent research by Libert, Wind, and Beck, which shows a breakdown that focuses on price-to-revenue impact. They identify asset builders, service providers, technology creators, and network orchestrators, in that order, with the latter coming out far head. As we’ll see, this identifies more strategic value creators for digital than the previous set of strategies, yet I find that it’s also incomplete in terms of describing digital strategy by not taking into account some of the more tactical approaches.

In my own work with clients, I’ve used a more comprehensive and strategic list of digital strategies — along with applied integration with some of the many proven and/or emerging digital business models that now exist — to identify where organizations should be focusing their valuable leadership time, execution resource, and organizational capacity.

The 8 Essential Digital Strategies Today

With the disclaimer that we’re learning more all the time about which are the most significant and impactful digital strategies, here’s the leading models that exist today, in increasing order of strategic value:

1. Automation

This was the first generation of applying digital to business and didn’t even require networks, though they certainly added an inflection point when they arrived. ERP, CRM, and business process management (BPA/BPM) are all examples of IT automation of the business. The growth of corporate productivity as a direct result of technology automation is a well known story. There is plenty of value here worth investing in, but primarily of the cost-cutting and efficiency variety. Automation will not even prepare organizations for their digital future, so its score is the lowest of all the digital strategies, but is nevertheless the most common form of digitalization. IT vendors such as IBM, Microsoft, SAP, and Oracle have long played a key enabling role in this strategy, but most of them have since moved their new products and services to other digital models below.

2. Legacy product/service digitization

This strategy involves taking existing products and services and putting a digital face on them. This was done by the entire airline and hotel industry in the 1990s and was finally taken up by the retail, media, and financial services industries in the 2000s, in the form of transactional Web sites. Telecom and other industries most affected by digital disruption have often done a very poor job of legacy digitization. While most organizations must look to digitize legacy products to sustain their organizations during digital business model transition, the rise of the unicorns shows us that the largest growth and value is in new markets and technologies. Unfortunately, the majority of traditional enterprise have done a relatively poor job creating effective customer experiences for digital, though the lessons are getting clearer now. Bottom line: Like automation, legacy digitization is a responsible and required investment, but not necessarily highly strategic nor likely to enable survival for the long term by itself.

3. Digital channel distributor

Getting digital products and services to market requires far more than a digital experience at a handful of touchpoints. Instead, it requires marshaling digital channels of all kinds, both self-realized as well as enabling 3rd parties, to flow value from source to customer. Digital affiliate programs (Walmart pays 4% or more gross commission to enable this, for example), marketplaces, arbitrage services, business app stores, open APIs, and other channel reach models such as Amazon’s Alexa Skills are all examples. Even the stodgy insurance industry has gotten into the digital channel game, with insurance giant Chubb partnering with Suning to distribute insurance products to the online Chinese retailer’s ecommerce network, with 230 million users.

4. Marginal market making

Once you have a digital audience, it allows you to expose them to new offerings and digital experiences. This enables incremental new gains that would have been cost-prohibitive without pre-existing investment in that digital market or channel. For instance, Amazon, a good example in so much of digital, allows any of its customers to become individual sellers, tapping into an existing marginal segment that would not have been worth the investment otherwise. While not a big business by itself, this strategy can further tip the competitive scales by generating additional revenue while becoming even more valuable to customers.

5. Technology creation

While having formidable barriers to entry due to capital expenditure, though certainly much less so on the software than the hardware side, there’s no denying that creating a must-have technology remains one of the top digital strategies on the market. Technology creation has created trillions in economic value over the years for companies that solve important problems for their customers. From hardware like smart devices to must-have apps for social networking and messaging to search and media consumption, technology creation can lead directly to value creation like few others digital strategies.

6. Digital platform ownership

Most of us are now familiar with the digital platform discussion, made famous back in the PC days with Microsoft vs. IBM, and now much more familiar to us as iOS vs. Android or Amazon Web Services vs. Microsoft Azure. If you build a platform that can be extended, instead of a just a single point technology, it can be enriched many orders of magnitude further by others, creating an unbeatable network effect over time. Apple and Google have attracted millions of apps collectively to their mobile platforms, while hundreds of thousands of businesses and software companies have built on top of AWS and Azure, making them indispensable foundations that will be vibrant and growing largely through the effort and investment of their platform partners.

7. Network orchestration

What if you could take the assets and technologies that already exist on the network, connect them, and turn them into business models? That’s the premise of this digital strategy, which the likes of Uber and Airbnb have shown pay off in spades. The essence of this strategy is the following: Use existing infrastructure and resources (connecting people who have cars with people who needs rides, for example) and make it the most appealing process. Organizations can create fast-growth new lines of business in very short amounts of time than using the traditional, slow, and out-dated approach of trying building everything yourself, a prohibitive and unnecessary cost today.

8. Ecosystem cultivation

Orchestrating your own platform and enabling it become an ecosystem is the most valuable digital strategy of all. Amazon does this with Amazon Web Services by extending it with marketplace built on top of it, as does SAP with its new but already large and growing SAP App Center. The key here is not just in owning a platform but in making it a recombinant living ecosystem that is directly enabled and extended by each and every new partner, through their own respective ecosystems. Apple does this by allowing other platform partners to build on top of it, a specific example is much of the consumer Internet of Things (IoT) industry, such as Philips Hue and other connected device product lines. Another important example is commercial blockchains, which are poised to become major category ecosystems in their own right, highlighting a path towards a major new digital future. Short version: Ecosystems are as much about their community of business partners, not just the technologies or platforms within them.

Digital Strategy: The Story of Disruptive Co-Evolution

Is this list of strategies an oversimplification? Almost certainly. Is it incomplete and will it grow. Absolutely. Yet it also provides a clear lens through which to look at the heart of an existing organizations and making momentous changes. Smart organizations will grow digital competency — largely through talent — that can quickly execute from the start to the end of this list.

How will such changes be made in large organizations? I’ve been exploring that and grappling with the means of digital transformation and the future of technology enablement in my work for years and some broad outlines are emerging. So in the meantime, brush up on these and get ready for one of the most interesting and challenging times in business history.

Additional Reading

In Digital Transformation, Culture Change Goes Hand in Hand with Tech Change

Vital Trends in Digital Experience and Transformation Today

22 Power Laws of the Digital/Social Economy

Old but still interesting: Fifty Essential Web 2.0 Strategies

Filed under Analysis, Digital Transformation, Strategy, The Enterprise

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