What if your multi-million-dollar AI investment yields only a fraction of its potential because your workforce can’t or won’t use it effectively? As AI reshapes industries at unprecedented speed, will your organization lead the change, or be disrupted by competitors whose people adapt faster and leverage AI more effectively?
The widespread adoption of Artificial Intelligence presents executive leaders with a transformative challenge, perhaps more significant than any that came before. Its impact on reshaping industries, workflows, and the very nature of work is immense, carrying consequences that demand careful navigation. This isn’t just about implementing new tools. For large organizations, successfully integrating AI is about unlocking billions in economic value, while failure risks staggering financial losses, forfeiture of competitive advantage, and the alienation of talent. As these nascent steps in the journey for digital transformation play out, how do we ensure we don’t lose the unique value and differentiation people bring to the process?
Yet, as we seek to answer this, we carry the heavy legacy of our prior technological shifts, our investments in “digital transformation.” Despite massive efforts over the past two decades, often totaling billions, most digital transformation initiatives remained fundamentally incomplete. Organizations eagerly digitized processes, adopting new technologies with the promise of increased efficiency and agility. However, in their haste, they critically neglected the human dimension. Success was too often measured by the deployment of technology rather than by the meaningful transformation of their people and processes, leading to glossy launch announcements that masked a worrying reality on the ground.
As Chris Heuer, Managing Director of the Team Flow Institute, observed, “We didn’t necessarily get digital transformation wrong—we just didn’t finish it. We digitized processes, but forgot to humanize progress. AI presents our chance to complete the transformation we started.”
Digital Transformation’s Costly Mistake
The evidence of this shortfall is stark and costly. Research consistently shows high rates of digital transformation efforts failing to meet their objectives – a staggering 70% according to McKinsey, with Gartner and others reporting similar figures. These statistics represent more than just missed deadlines; they signify multi-million, often multi-billion, dollar investments that failed to deliver expected returns, leaving organizations without the promised boosts in productivity, innovation, or agility. The reality is that technology was implemented, but the human system wasn’t equipped or enabled to leverage it effectively, turning potential assets into underutilized expenses.
This represents a profound disconnect between the boardroom’s perception of “transformation complete” and the lived reality of employees facing change. Consider the poignant anecdote shared by Gina Debogovich during a recent roundtable: “I’ll never forget the time I stepped onto a floor in our HQ and there was a designer standing up, literally having a meltdown because they were in the middle of actually having to design digital graphics for digital use. And not postcards anymore… And there is the human element having a meltdown on the floor because they were an older employee and they were familiar with postcards.” This isn’t just individual distress. When multiplied across a large organization, this signifies widespread operational friction, lost productivity, and the negative health impacts when change is imposed without adequate support and empathy.
Another telling example comes from Shel Holtz’s proposal for a digital transformation to a major organization. He recounted how he explained that digital transformation was inherently “a change management effort because having the technology requires that you empower people at lower levels to use it to make decisions without having to run them up the flagpole,” leadership demurred. The client ultimately decided against the transformation, stating, “that’s more than we can handle.” This fear of decentralizing control, of enabling the workforce, would have directly translated into a strategic paralysis, preventing the organization from gaining the agility necessary to compete effectively.
In Alan Berkson’s work on The Control Scale, he points to a key challenge of decentralization — “Increased control improves security and privacy, while relaxed control supports collaboration and innovation. There is no right balance. It’s something every organization needs to figure out.”
Dr. Michael Wu adds, “The more powerful a technology is, the more empowering it will be for the frontline workforces. This naturally implies that more powerful technology will drive greater decentralization.”
The People Paradox: Technology First, People Last
This persistent failure to complete the human element of digital transformation points to a fundamental issue: The widely recognized “People, Process, Technology” model – originally intended to prioritize the human element as the foundation for change – was consistently inverted in practice. Technology was implemented first, processes were adapted to fit the tech, and people were expected to simply catch up. This paradox lies at the heart of why so many ambitious digital transformation efforts remain unfinished, leaving organizations vulnerable to AI’s even more disruptive forces if leadership does not finally learn and agree upon these fundamental human lessons first.
Why Transformation Stalled: Key Lessons from the Past
To avoid repeating the errors of the past as we navigate the complexities of AI integration, it is crucial to understand why so many digital transformation efforts stalled or failed to deliver on their intended benefits. The patterns revealed in research and echoed in our recent roundtable discussion point to several root causes:
Ignoring the Human Factor
A pervasive technology-first mentality dominated many initiatives. The excitement and tangible nature of implementing new systems often overshadowed the less immediate, more challenging work of preparing people and processes. This led to viewing technology as the solution itself, rather than as an enabler. As Dr. Wu noted, “many companies take their employees for granted to some extent. Leaders often feel that technology is something that I simply buy and implement – employees just have to use it.” This approach invariably prioritized the technology rollout timeline, leaving human adaptation and adoption as an afterthought, often leading to digital systems operating according to outdated thinking.
Consequently, there was insufficient focus and investment in people. Organizations consistently underestimated the resources, time, and ongoing support required for individuals to truly adapt. The belief that delivering a new system or a training session meant the change was “done” failed to recognize that for the employee on the ground, the real work of changing behaviors, learning new skills, and integrating new tools into daily tasks was just beginning, and would be an ongoing effort. Research confirms that companies prioritizing clear communication and sufficient resources saw significantly higher rates of employee support for transformation. As Gina Debogovich added, “For the individual who’s actually having to live through that change… it’s ongoing. Leaders need to realize that, the deck isn’t the end.”
The Fear of Empowering People
Underlying this was often leadership’s desire for control. By its nature, the digital age facilitates the decentralization of information and empowers individuals who are closer to the actual work. Yet, many leaders were, and remain, uncomfortable with this shift. As Jen McClure stated, “It’s about control and fear. In a top-down organization, the leaders make the decisions and the employees do the work. Many of them are not going to simply put the lower-level people first.”
This fear of relinquishing centralized control, of employees making decisions without constant oversight, or even expressing dissent in newly open digital communication channels, proved a significant, often unexpressed, barrier. This deeply embedded inclination towards control fostered a culture of knowledge hoarding, where information remained a source of power at the top, hindering the collaborative flow essential for true transformation. Few are as conscientious or wise as to recognize Rachel Happe’s belief that “control is for amateurs.”
We saw this historical dynamic play out even before the internet was commercialized. In 1992, Bob Buckman of Buckman Labs had to actively model and declare the necessity of knowledge sharing in internal CompuServe forums to counteract the inherent tendency for individuals to hoard information. This proactive leadership in fostering sharing was the exception, highlighting the default organizational reticence towards decentralized knowledge.
Outdated Approaches to Constant Change
Furthermore, inadequate and outdated change management approaches were the norm. Change management was frequently treated as a bolted-on support function – a communication plan here, a training session there – rather than an integrated, foundational element of the transformation strategy. As the pace and complexity of change accelerate, particularly with emergent technologies like AI, traditional, linear change models designed for predictable, discrete projects are insufficient. As change consultant Caroline Kealey provocatively argues, “change management is dead“ because the assumption of toggling between periods of “change” and “business as usual” no longer applies; “At this point it’s clear that ‘business a usual’ has left the building and it’s not coming back.” Applying outdated methods in this environment is like trying to use a fly swatter against an accelerating force.
Lack of Shared Clarity
This challenge was compounded by a pervasive lack of shared understanding. Even at the executive level, there was often significant divergence on what “digital transformation” meant for their organization, its current state, and the desired future. As Jen McClure shared from her experience as a Chief Digital Officer, “Our initial digital transformation assessments revealed a critical challenge… Each leader held a different perspective… Furthermore, there was a lack of consensus on the fundamental definition…”
Without a clear, collectively understood definition and purpose, initiatives lacked coherence and failed to build the necessary alignment across departments and levels. Investing in this shared clarity was frequently overlooked in the rush to implement technology. To find this clarity of direction and purpose, Alan Berkson suggests developing “narrative infrastructure.” This narrative infrastructure is a foundational framework of shared stories, values, and language that aligns people around common goals and clearly communicates the initiatives’ intended purpose and impact.
Measuring Activity, Not Impact
Finally, the use of Flawed Metrics perpetuated these issues. Success was typically benchmarked against technical outputs – software installed, training sessions completed, systems live – rather than by the actual outcomes of value-creating, human-level change. Was the technology adopted effectively? Did it change how people worked for the better? Did it lead to improved collaboration or innovation? By focusing on deployment instead of meaningful adoption and behavioral change, leaders received a skewed picture of progress, often believing the transformation was complete long before it had genuinely taken root within the workforce. Research from the Boston Consulting Group, for instance, found that a key reason for transformation failure was success criteria focused on technology deployment rather than business value or employee empowerment. Capturing true progress requires “some level of measurement among the employees.”
Bottom Line
These interwoven factors – a technology-first mindset, underinvestment in people, leadership’s fear of losing control, outdated change approaches, lack of clarity, and flawed metrics – conspired to leave the human foundation for digital transformation largely unfinished. These are the critical lessons we must internalize now, as the speed and potential impact of AI make addressing these human dimensions more urgent, and more costly, than ever.
The Path Forward: Building Cultures of Adaptability for Human-Centric AI Deployments
The lessons from our unfinished digital transformation efforts are not merely historical footnotes; they are critical guideposts for the era of Artificial Intelligence. The pace of technological change today is unprecedented, and AI advancements are accelerating it further. This environment of continuous, rapid evolution means that the old model of discrete, project-based “transformations” followed by periods of stability is no longer viable. As Shel Holtz noted, “You can’t roll AI out the same way you roll out Workday, because not everybody’s going to use it the same way.” The risk of falling behind, or of technological change outstripping organizational and human capacity, is greater than ever. To keep pace and harness AI effectively, organizations must cultivate something more fundamental: a Culture of Adaptability.
Why Adaptability is Now Paramount
As described by leading thinkers like Shelley Palmer, a Culture of Adaptability is not about surviving one-off changes but embedding continuous adaptation into the organizational DNA. It’s a mindset and a practice that enables ongoing evolution, allowing the organization and its people to respond fluidly to the opportunities and challenges presented by AI and whatever comes next. This provides immense value by ensuring the organization can effectively harness new technologies, driving productivity, maintaining competitiveness, and supporting the continuous growth of its people. Our research reveals that a people-centric approach to AI isn’t just a moral imperative; Accenture suggests it could unlock an additional $10.3 trillion in economic value by 2038, making it a strategic necessity with clear financial implications.
Harmonizing AI and Human Potential
Creating a successful Culture of Adaptability requires an intentionally human-centric approach. One that reframes our perspective to prioritize how technology can enhance human capabilities. Central to this philosophy is the idea of Augmented Humanity, recognizing technology not as a replacement but as a tool whose ultimate value depends entirely on human adaptation, usage, and evolution alongside it.
To practically achieve this vision, we must actively harmonize technological innovation with human creativity. Jack Myers, author of The Tao of Leadership, describes this harmony as a holistic integration that leverages human ingenuity, intuition, and insight, along with AI’s technological strengths. By embracing the Taoist principles of harmony, leaders effectively alleviate their teams’ fears of displacement, nurturing psychological safety, enabling collaborative growth, and amplifying human potential. In doing so, they position their organizations not merely to survive technological shifts, but to thrive by unlocking greater value creation and sustained competitive advantage.
Building the Foundation for Adaptability
To foster this Culture of Adaptability and enable human-centric AI deployments, several essential prerequisites must be in place:
- Psychological and Sociological Safety: In an environment of continuous change and experimentation with new tools, people need to feel safe to try new things, ask questions without fear of appearing ignorant, challenge assumptions, offer candid feedback, and even “fail fast” without punitive consequences. This individual psychological safety must be complemented by sociological safety – a sense of belonging, inclusion, and trust within teams and across the organization, ensuring diverse perspectives on AI’s impact and potential uses are welcomed.
- Open and Honest Communication: Navigating the uncertainties of AI requires transparent dialogue about its potential impacts, challenges, and the organization’s strategic direction. Leaders must foster environments where information flows freely, concerns can be voiced and addressed, and collective sense-making can occur. As Chris Heuer noted during our roundtable, resistance often stems from “unexpressed and hidden considerations that people have that are often misplaced or based on inaccurate or outdated information.” Surfacing these hidden concerns is vital.
- Co-creation as Practice: Those closest to the work are best positioned to determine how AI can be effectively integrated into daily tasks and workflows. Systematically involving employees in the design, testing, and ongoing adaptation of AI applications ensures practical solutions and builds critical ownership of the change process. As Dr. Wu noted, while you can’t involve everyone in every decision, “you need to create a mechanism to get people involved to co-create these processes early on,” leveraging key leaders and representative groups. Successful co-creation connects AI solutions meaningfully to employees’ roles and organizational goals.
- Bottom Up Leadership: Empowering employees to lead adoption can produce more sustainable and authentic engagement with new technologies. Jaime Schwarz highlighted this with Slack’s early growth: “It was the employees who said ‘I like using this’ and started dumping all their information into it. It was employee-led adoption that forced leadership to follow.” This demonstrates how genuine organic engagement by employees, rather than mandated directives, can drive successful integration.
- From Control to Facilitation: Leaders of adaptive cultures must transition from a command-and-control mindset to one focused on enabling, inspiring, and guiding their teams through continuous evolution. This requires modeling comfort with ambiguity, empowering teams, facilitating ongoing learning, and trusting employees to navigate complexity effectively. Rachel Happe illuminates this essential shift: “Leadership should shift from directing to facilitating, enabling teams to navigate complexity through shared purpose rather than imposed control.”
- Cultivating Critical Thinking and Continuous Learning: AI dramatically changes the nature of work, making uniquely human skills like critical analysis, complex problem-solving, creativity, and emotional intelligence even more valuable. A culture of adaptability thrives when individuals are encouraged and supported in continuously developing these skills and learning to master new technologies effectively. This commitment to lifelong learning is a key behavior leaders must incentivize and facilitate. As Satya Nadella, CEO of Microsoft, said, “The learn-it-all will always trump the know-it-all in the long run.”
By focusing on these human-centric prerequisites – safety, communication, co-creation, adaptive leadership, and continuous learning – organizations can move beyond incomplete, technology-first transformation initiatives to build the fundamental Cultures of Adaptability necessary to integrate AI successfully.
Measuring True Success: Metrics for Adaptability and Human Flourishing
Past transformations faltered because system go-lives and training completions were used to measure success, activity metrics that say little about impact. As Gina Debogovich notes, a project isn’t “done” when the slide deck is finished; employees still have to weave new tools into daily work. Real progress is evident only when people use AI fluently, their work lives improve, and the organization grows more nimble. Leaders must therefore replace technical and activity-based KPIs with measures that capture business value, behavioral adoption, and workforce resilience.
This requires proposing and tracking Human-Centric Metrics that reflect the goals of Augmented Humanity and a Culture of Adaptability:
- Meaningful Adoption & Effective Use: This goes beyond simple login rates. It measures how people are using AI tools – are they integrating them into workflows in ways that enhance productivity, creativity, or decision-making? Are they adapting their standard operating procedures to leverage the technology effectively?
- Collaborative Innovation: Does the new technology facilitate better teamwork and co-creation? Are teams using AI to collaborate more effectively, share knowledge, and collectively solve complex problems? Are meetings more meaningful and creating more momentum?
- Human Experience & Engagement in Change: How does the transformation impact employee well-being, satisfaction, and trust? Are people feeling more empowered, or more anxious? Are they actively engaged in the change process and willing to adapt as needed? Metrics here can include sentiment analysis, psychological safety scores, and participation in co-creation initiatives, capturing the insights Zora Artis notes are crucial for understanding what individuals and teams need to make sense of change and build ownership.
- Organizational & Team Adaptability: This measures the capacity for continuous evolution itself. How quickly and effectively can teams and the organization absorb and leverage new AI capabilities, integrate them, and adjust strategies or processes in response to new information or market shifts?
This measurement shift emphasizes growth through learning, innovation, and collaboration, rather than a narrow definition of productivity tied solely to activity, output volume, or cost reduction. By measuring these human and adaptability-centric outcomes, leaders not only gain a more accurate picture of true transformation but also signal to the entire organization that people and their capacity to adapt are valued above mere technological deployment. These new metrics become essential tools for guiding continuous improvement and reinforcing the desired culture.
Team Flow: The Enabling Framework for Cultures of Adaptability
Embracing a human-centric approach and committing to building a Culture of Adaptability are essential for succeeding with AI and navigating future change. However, these concepts require a practical methodology, an enabling framework, to transform them from aspiration into organizational reality. This is where Team Flow provides the crucial missing link.
Unlike approaches that remain largely theoretical or focus narrowly on process, Team Flow offers an integrated framework that bridges the gap between human-centric philosophy and practical application. It takes the often abstract ideals of psychological safety, co-creation, and adaptive leadership and translates them into actionable practices, offering leaders tangible tools and clear guidance for building the human capacity needed for an adaptable organization. It leads to a culture of adaptability where psychological safety and progress are priorities, where trust enables a holistic view of work, and where communication and collaboration allow teams to achieve a peak state of team flow together.
How Team Flow Drives Adaptability and Business Results
Team Flow operationalizes the earlier prerequisites, making them tangible practices rather than abstract ideals. It achieves this by:
- Building Psychological Safety and Open Communication: Team Flow methodologies create a foundation of trust where team members feel secure in sharing ideas, concerns, and feedback about integrating AI without fear of judgment or reprisal. This open environment is vital for rapid learning and experimentation. When teams feel safe and trusted, they are far less likely to view AI as a threat (like job replacement) and more likely to proactively engage with it to discover its potential benefits, removing a major source of friction and resistance to emergent technologies that stalled past transformations.
- Developing Team Collaboration and Co-creation Capabilities: Team Flow provides structures and practices that enable teams to work together seamlessly. This fosters the collaborative environment needed for effective co-creation of innovative new workflows that are now possible with AI. When teams can collaborate effectively and have a holistic view of their objectives, they can quickly identify how AI can genuinely help them achieve those objectives faster, leading to faster problem-solving and quicker realization of business benefits compared to siloed or resistant teams.
- Empowering Team Leadership Behaviors: Rather than focusing solely on hierarchical control, Team Flow encourages leadership behaviors that distribute ownership, navigate ambiguity collaboratively, and inspire a shared commitment to learning and continuous improvement. This empowers teams to adapt autonomously and take initiative in leveraging AI for value creation.
- Connecting Individual and Team Growth to Technological Change: Team Flow recognizes that adaptation is a continuous process of personal and collective development. It helps individuals and teams integrate new skills and mindsets alongside technological changes, fostering a proactive approach to learning that is essential for keeping pace with rapid and constant change.
- Fostering Trust and Holistic Perspectives: Team Flow builds deep trust that dissolves siloed thinking, prompting team members to see their work—and one another’s—in an integrated, system-wide context. This shared perspective enhances communication, aligns objectives, and leads to mutual commitment, enabling teams to consistently make better-informed decisions and achieve their audacious objectives.
In this way, Team Flow provides the necessary human-centric management structure to move beyond incomplete, discrete technology projects. It focuses on building the core capabilities that enable organizations and their people to not just implement new technology like AI, but to thrive amidst accelerating, continuous transformation. It helps complete the circuit by ensuring technology amplifies human potential and team performance, creating the foundation for a sustainable Culture of Adaptability and delivering tangible business results.
Conclusion: Putting People First to Transform Organizations in the Era of AI
As you guide your organizations into the age of artificial intelligence, you stand at a critical juncture. The lessons from the incomplete digital transformation are stark and undeniable: investing solely in technology and process, while neglecting the human core of change, leads to stalled initiatives, wasted resources, and business risks. Repeating these mistakes with AI, a technology whose speed and potential impact dwarf previous digital shifts, is a risk no leader can afford, as evidenced by Klarna’s rollback of its AI-first strategy.
Traditional, linear change-management playbooks emphasizing technology integration can’t keep pace with today’s nonstop disruption. Business as usual has left the building—and it isn’t coming back. The answer isn’t to abandon change management but to reinvent it around the people-process-technology triangle, placing humans unequivocally at the forefront and the center. Leadership must be intentional, beginning with people and designing every AI integration from their needs outward, creating the foundation and practice for adapting to whatever emerges next.
The Mandate for Human-Centric Change
This means embracing a future built on Augmenting Humanity, where AI enhances, not diminishes, human capability. It requires a deliberate commitment to cultivating Cultures of Adaptability, environments where continuous evolution is the norm, fueled by trust, psychological safety, and a collective capacity to learn and co-create. It demands that we measure success not just by the deployment of AI, but by its meaningful adoption, the innovation it unlocks, and its impact on the human experience of work.
Frameworks like Team Flow provide the practical pathway for executive leaders to champion this new paradigm. They offer the methodologies to build the essential prerequisites for adaptability to empower teams, foster co-creation, and guide leadership behavior in a way that ensures technology investments yield their full human and business potential. Team Flow helps teams move from potential friction and resistance to AI to a state of harmony, in flow, enabling them to quickly determine beneficial AI applications and deliver business benefits faster.
The Stakes: Risk vs. Reward
The choice is clear.
Continue down the path of unfinished digital transformations, risking:
- Vast Financial Waste: Multi-million dollar AI investments yielding little return due to low adoption and operational friction.
- Erosion of Competitive Edge: Stagnating while more adaptable competitors leverage AI to increase efficiency, innovation, and market speed.
- Talent Drain & Acquisition Challenges: Losing top performers and struggling to attract the skilled workforce needed for an AI-driven future.
- Increased Operational Friction & Compliance Risks: Unintended negative consequences from poorly understood or resisted AI tools.
Or, lead with courage and intention, prioritizing the human element to unlock:
- Accelerated Innovation & Productivity: Empowered employees and adaptable teams rapidly identify and implement AI uses that enhance human capabilities and drive progressivity.
- Sustainable Competitive Advantage: A workforce and culture built for continuous adaptation, capable of thriving amidst constant technological evolution.
- Engaged, Resilient Workforce: Reduced burnout and resistance, replaced by trust, psychological safety, and a proactive approach to change.
- Significant Cost Avoidance: Minimizing the expensive friction, rework, and failed rollouts associated with neglecting the human dimension, ensuring a higher ROI on AI investments.
True transformation has never been about the technology itself. It is about enabling people to thrive with technology and to continuously evolve alongside it. When people are empowered, safe, and equipped to adapt, they become the most powerful drivers of progress, ready to accomplish their objectives in any situation that arises. This is the promise of completing the transformation we started – a promise within reach if we are willing to put people first.
Ready to Put People First in Your AI Strategy?
If you’re serious about turning AI into a catalyst for human potential let’s talk. Contact us to request our guide, Executive Action: Steps to Prioritize People in AI Integration. Like our Conversations for Action focused on ethical AI Integration, it is a practical guide that expands on the roadmap above with checklists, starter metrics, and facilitation tips you can put to work immediately.
Step into the next phase of digital transformation—where technology amplifies your people, and your people power your success.
Editor & Contributors
Editor: Chris Heuer, Managing Director
Team Flow Institute Founders:
Chris Heuer, Co-founder
Jaime Schwarz, Co-founder
Team Flow Institute Senior Fellow & Advisors:
Jennifer McClure, Senior Fellow & Advisor
Dr. Jef van den Hout, Senior Fellow & Advisor
Team Flow Institute Research Fellows:
Note: This paper was written with the help of Claude.ai’s generative AI technology to aid with editing and some content creation. Any AI-generated content has been reviewed, adjusted and enhanced with experience and insights by a human editor to ensure accuracy, relevance and authenticity.
