For six decades, Bruce Tuckman's model of group development has given leaders a reliable map for a messy process: turning a collection of individuals into a functioning team. Teams form, they storm, they eventually norm, and if they are fortunate, they perform (Tuckman, 1965). The model has proven durable because it describes something true about human groups regardless of industry or era. What has changed is the composition of the team itself. A growing share of leaders now manage groups that include people as well as AI agents capable of planning, adapting, and contributing to shared goals (Lou et al., 2025). Applying Tuckman's stages to this new configuration offers a practical way to think about what changes, and what does not, when a machine joins the roster. A Model Built for People, Now Tested by Machines Tuckman's original 1965 paper synthesized fifty studies of group behavior into four stages: orientation, conflict, cohesion, and functional role-relatedness, more memorably known as forming, storming, norming, and performing (Tuckman, 1965). A 1977 revision added a fifth stage, adjourning, to account for how groups disband once their purpose is fulfilled (Tuckman & Jensen, 1977). The model's staying power comes from its simplicity and its accuracy, and its trajectory from an HR practitioner favorite into a widely cited academic framework is itself well documented (Bonebright, 2010). Teams really do need time to establish trust, work through friction, and settle into shared norms before they reach full productivity. Nothing about the introduction of AI into a team changes that underlying psychology. What changes is who, or what, the team is establishing trust with. A Fifth Teammate Who Never Sleeps Researchers studying human-AI teaming describe a shift already underway in many organizations. AI systems are no longer confined to the role of tool, quietly executing instructions in the background. They are increasingly functioning as active collaborators that learn, adapt, and operate with a degree of autonomy inside shared workflows (Lou et al., 2025). This shift requires new thinking about interaction protocols, delegation, and how responsibility gets distributed between human and machine contributors (Lou et al., 2025). For a leader applying Tuckman's framework, that means treating the AI system as a team member whose onboarding, integration, and performance deserve the same deliberate attention given to a new hire. Forming: Defining the Agent's Role Before the Work Begins The forming stage has always been about orientation. Team members test the boundaries of the group, look to the leader for direction, and try to understand how they fit. When an AI system joins a team, that same orientation needs to happen deliberately, not by default. In practice, that means leaders deciding up front which decisions the AI supports, which decisions it should never make unilaterally, and how its output will be verified before it becomes team knowledge. Skipping this step moves the confusion downstream, typically into the storming stage, where it is more expensive to resolve. Storming: Conflict Now Includes a Trust Problem Every team hits a period where politeness gives way to friction. With a human-AI team, that friction often centers on a different kind of conflict: whether the AI's output can be trusted, and how much oversight it actually requires. Current research on human-AI teaming identifies exactly this tension as one of the field's central gaps, pointing to the ongoing difficulty of aligning AI systems with human values and objectives, and the tendency of teams to underutilize the AI's genuine capabilities even once it has proven itself (Lou et al., 2025). Left unmanaged, this friction shows up as a new source of workplace strain. A recent Harvard Business Review study found that intensive oversight of AI tools drives measurable cognitive fatigue among employees, a condition researchers call AI brain fry (Bedard et al., 2026). Leaders who recognize storming as a normal, necessary stage, rather than a sign that the AI integration has failed, are better positioned to work through it deliberately. Norming: Building a Shared Mental Model Teams reach the norming stage once they develop shared expectations about how work gets done. For human-AI teams, this means building what researchers call a shared mental model: a common understanding, held by both the human and the AI system, of goals, roles, and the standards output must meet (Lou et al., 2025). In practice, this looks like documented prompts, agreed-upon review checkpoints, and a clear record of which tasks the AI now owns outright versus which remain under close human review. Norming is also where trust gets calibrated rather than assumed. Teams that skip this step tend to oscillate between over-trusting AI output and second-guessing every line it produces, neither of which reflects a mature working relationship. Performing: When the Combination Outperforms Either Alone The payoff for working through the earlier stages is a team that performs at a level neither its human nor its AI members could reach independently. This is the central promise of human-AI joint cognitive systems: an approach that raises joint performance beyond what either humans or AI achieve on their own, while working around the known limitations of each (Xu & Gao, 2024). Reaching that point is the product of a team that has genuinely formed, stormed, and normed around its newest member, rather than adding a chatbot to an existing workflow and calling it a transformation. What Leaders Should Take From This Tuckman's model was never meant to be applied once and forgotten. Teams reform every time membership changes, and adding an AI agent to a workflow counts as a membership change, even if no one updates the org chart. Leaders who name the stage their team is actually in, rather than assuming everyone has already arrived at performing, will spend less time firefighting avoidable conflict and more time building the kind of shared understanding that lets human judgment and machine capability genuinely compound. References Bedard, J., Kropp, M., Hsu, M., Karaman, O. T., Hawes, J., & Kellerman, G. R. (2026). When using AI leads to “brain fry.” Harvard Business Review Digital Articles. Bonebright, D. A. (2010). 40 years of storming: A historical review of Tuckman's model of small group development. Human Resource Development International, 13(1), 111–120. https://doi.org/10.1080/13678861003589099 Lou, B., Lu, T., Raghu, T. S., & Zhang, Y. (2025). Unraveling human-AI teaming: A review and outlook. arXiv. https://doi.org/10.48550/arXiv.2504.05755 Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63(6), 384–399. Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group & Organization Studies, 2(4), 419–427. Xu, W., & Gao, Z. (2024). Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems. Interactions, 31(1). Copyright © 2026 by Severin Sorensen. All rights reserved.

Dwight Eisenhower's insight about urgency and importance has outlived every technology cycle since he first shared it. In a 1954 address at Northwestern University, Eisenhower quoted an unnamed college president's observation that urgent problems are rarely important, and important problems are rarely urgent, an idea Stephen Covey later formalized into the four-quadrant tool now known as the Eisenhower Matrix (Covey, 1989). The framework has always been about protecting judgment from the tyranny of the inbox. That mission has only gotten harder now that AI has multiplied the number of plausible options in front of every decision-maker. A Framework Born From Genuine Stakes Eisenhower's authority on this subject came from lived experience, not theory. His career as a wartime commander and later as president exposed him to decisions with consequences that gave his observation about urgency and importance real weight, and it is that context Covey drew on when he built the matrix into his influential guide to personal effectiveness (Covey, 1989). That history matters for how leaders should read the matrix today. It was never meant as a productivity hack for clearing inboxes faster. It was a filter for protecting attention when the cost of misallocating it was genuinely high. AI has now changed the volume of inputs competing for a leader's attention before those decisions get made. A Simple Tool for a Persistent Problem The Eisenhower Matrix sorts tasks along two axes, urgency and importance, producing four categories: do now, schedule, delegate, and eliminate. Its enduring appeal lies in forcing a distinction that busy leaders routinely collapse: the difference between what demands attention right now and what actually moves the organization forward. Covey built this distinction into the third habit of his influential management text, arguing that effective people organize their time around importance rather than urgency (Covey, 1989). That discipline was already difficult to sustain in an analog world of ringing phones and stacked memos. It is more difficult still in a workplace saturated with AI-generated options, drafts, and recommendations. AI Was Supposed to Clear the Queue The pitch for workplace AI has always centered on time saved: fewer manual tasks, faster first drafts, more capacity for the work that actually requires human judgment. In practice, that promise is only partially holding up. A 2026 Harvard Business Review study of nearly 1,500 full-time workers found that intensive AI oversight increases rather than decreases cognitive strain, contributing to a condition the authors term AI brain fry (Bedard et al., 2026). The study found that AI reduces burnout when it automates routine, repetitive tasks, but increases mental fatigue when it requires constant monitoring and judgment calls of its own (Bedard et al., 2026). In other words, AI has changed the shape of decisions, and in many workplaces, multiplied their number. When Everything Feels Urgent, Nothing Sorts Itself This is precisely the terrain the Eisenhower Matrix was designed for, and precisely where it is now being tested. The pattern shows up wherever AI gets added to a workflow without a filter: it can sharpen decision quality when deployed with intention, but it just as easily piles onto cognitive load when there's no clear framework for what deserves attention. The failure mode is familiar to anyone managing a marketing or content function today: a growing stream of AI-assisted drafts, options, and suggestions, all requiring review, none of them automatically sorted by what actually matters. Without a deliberate filter, urgency wins by default, because urgent items announce themselves while important ones wait quietly. Rebuilding the Matrix for an AI-Assisted Workflow Applying the Eisenhower framework to an AI-augmented team starts with treating AI-generated output as raw material for the matrix, not an exemption from it. A draft press release, a competitive analysis, or a first-pass email campaign produced by AI still needs to be sorted by importance and urgency before it earns a leader's attention. Practically, this means building review habits around a few principles. AI should be assigned primarily to the quadrant it serves best, important-but-not-urgent work, where it can draft, research, and prepare material well ahead of deadline pressure, precisely the category human attention tends to neglect under normal workloads. A competitive scan of the market, a first pass at quarterly messaging, or a rough outline of an executive presentation are all examples of work that matters a great deal but rarely feels urgent enough to get done, which makes it exactly the kind of work AI should be handling in the background before it becomes a fire drill. Urgent-but-unimportant tasks, the category Eisenhower's framework recommends delegating, are now genuinely delegable to AI in a way they were not before, freeing human time for judgment calls that AI cannot make. Routine status updates, first-draft responses to common questions, and formatting work all fall into this category. Leaders also need an explicit standard for what still requires direct human review before anything produced by AI is treated as final, so that oversight itself does not become the newest urgent-and-unimportant task on the list. Without that standard, reviewing AI output becomes its own source of urgency, and the matrix loses the very discipline it was built to provide. Protecting the Quadrant That Matters Most The real risk of AI in a business environment is that the sheer volume of AI-generated material will quietly crowd out the important-but-not-urgent work that strategic thinking, coaching, and long-range planning depend on. That is the same quadrant Eisenhower identified as the one people most reliably neglect, decades before AI existed. The tools have changed. The discipline required to protect that quadrant has not. Leaders who build AI into their workflow with the matrix in mind, rather than letting AI dictate what feels urgent, put themselves in a stronger position to spend their attention on the decisions that genuinely warrant it. References Bedard, J., Kropp, M., Hsu, M., Karaman, O. T., Hawes, J., & Kellerman, G. R. (2026). When using AI leads to “brain fry.” Harvard Business Review Digital Articles. Covey, S. R. (1989). The 7 habits of highly effective people: Powerful lessons in personal change. Free Press. Copyright © 2026 by Severin Sorensen. All rights reserved.

A RAND Corporation study published in 2024, based on interviews with sixty-five data scientists and engineers with at least five years of experience building AI and machine learning systems, set out to understand why so many AI projects fail. The researchers noted that by some estimates, more than 80% of AI projects fail to deliver the business value they were built for, roughly twice the failure rate of ordinary IT projects. The most important detail is what the interviewees identified as the primary cause: 84% pointed to leadership-driven problems rather than technical ones, citing a persistent gap between what executives believed AI could do and what the teams building it were actually equipped to deliver (Ryseff et al., 2024). All of which goes to say: the models mostly work, but the organizations around them are not changing fast enough to use them well. An Old Problem in a New Package This is not a new problem wearing a new name. It is the oldest problem in change management, arriving in an unfamiliar package. Leaders who would never announce a merger, a restructuring, or a new strategic direction without a change plan are routinely rolling out AI tools with none at all, treating adoption as a training question when it is really a change question. Two frameworks that predate the current AI cycle by decades explain much of why that approach fails, and together they give coaches a useful diagnostic for clients stuck somewhere in an AI rollout that has quietly gone nowhere. Kotter's Eight Steps, Applied to AI The first framework is John Kotter's eight-step model, built from his research on why large organizational changes so often collapse (Kotter, 1996). Kotter's sequence begins with establishing a sense of urgency, a step most AI rollouts skip because the urgency feels self-evident to a leadership team that has been reading about AI for two years. It rarely feels as self-evident to the employee whose daily workflow the tool is about to disrupt. From there, Kotter's steps move through building a guiding coalition, forming a strategic vision, enlisting a volunteer army to communicate it, removing barriers to action, generating short-term wins, sustaining acceleration, and finally institutionalizing the change so it survives once the initial push fades. Skipping urgency leaves an initiative without the organizational will to survive its first budget review, echoing RAND's finding that leadership misalignment on what problem AI is meant to solve is the most common failure driver. Skipping the guiding coalition produces tools that live entirely inside one department, resented rather than adopted anywhere else. Skipping short-term wins leaves projects that technically launch and then fade because no one can point to a specific result that justified the disruption, a pattern RAND traces to underinvestment in the deployment infrastructure that would let early wins actually surface. Skipping institutionalization explains why so many AI pilots that succeeded quietly disappear a year later when the champion who drove them changes roles. ADKAR and the Individual Level of Change Kotter's model operates at the organizational level, and that is precisely its limitation for AI adoption, which depends on whether individual employees actually change how they work. This is where Jeff Hiatt's ADKAR model, developed by Prosci from research on hundreds of organizations, becomes a necessary complement (Hiatt, 2006). ADKAR breaks individual change into five sequential outcomes: Awareness that a change is happening and why, Desire to participate in it, Knowledge of how to do so, Ability to actually execute it, and Reinforcement that makes the new behavior stick instead of reverting under pressure. Most AI training programs start at Knowledge, handing employees a tool and a tutorial, and skip past the two stages that actually determine whether anyone uses it. Awareness without a credible business reason produces polite compliance followed by quiet abandonment. Desire is the stage leaders underestimate most severely, because an employee's motivation to adopt an AI tool is entangled with a genuine, often unspoken concern about what fluency with it will mean for their role. A training session cannot manufacture desire that a leader has not first addressed directly. Reinforcement is the stage almost every organization drops once the launch event ends, which is exactly why usage metrics so reliably spike at rollout and decay within a quarter. Where the Two Models Meet Used together, the two models cover ground that either one leaves exposed alone. Kotter explains whether the organization has built enough structural support for an AI initiative to survive contact with reality. ADKAR explains whether the individuals inside that structure have actually been taken through the psychological sequence required to change how they work. A rollout can execute every Kotter step well and still fail if individual employees never move past Awareness. A rollout can win over every individual employee and still fail if there is no guiding coalition protecting it past the first reorganization. A Diagnostic Conversation for Coaches For a coach working with a leadership team on a stalled AI initiative, this pairing turns a vague complaint (e.g. people just are not using it) into a specific diagnostic conversation. Ask where the guiding coalition sits and whether anyone outside the initial sponsor's own department has a stake in its success. Ask what short-term win the team can point to that took less than ninety days to produce. Then ask the individual-level question underneath the organizational one: for the people closest to the work, has anyone addressed directly what this tool means for their role, moving them from awareness to desire, or has the organization simply assumed that training equals adoption? How This Plays Out in Practice Consider the shape this takes in practice. A mid-sized professional services firm rolls out an AI drafting tool to its client-facing teams. Leadership announces it at an all-hands meeting, points to the productivity numbers from the pilot group, and gives everyone a login. Three months later, adoption has flatlined at the same handful of early enthusiasts who used it during the pilot. Nothing about the tool has changed. What is missing is not a second training session. It is a guiding coalition that includes respected senior staff who were not part of the original pilot, a short-term win specific to the teams who have not yet adopted the tool, and, underneath all of that, an honest conversation about the desire gap, the quiet worry among mid-level staff that visible fluency with the tool is being read by the firm as a signal about which roles it plans to need less of. No amount of additional Knowledge closes a gap that actually sits at Desire. The Bottom Line The number from the RAND report that should reframe how leaders think about AI adoption is 84—the percent of practitioners who pointed to leadership-driven problems, not technical ones, as the primary reason AI projects fail. AI initiatives are failing for reasons that have little to do with whether the model is good enough and everything to do with whether the organization did the unglamorous work of taking people through change in order. Kotter and ADKAR were built to diagnose exactly that gap. The tools involved are new. The way organizations resist changing around them is not. References Hiatt, J. M. (2006). ADKAR: A model for change in business, government and our community. Prosci Learning Center Publications. Kotter, J. P. (1996). Leading change. Harvard Business School Press. Ryseff, J., De Bruhl, B. F., & Newberry, S. J. (2024). The root causes of failure for artificial intelligence projects and how they can succeed: Avoiding the anti-patterns of AI. RAND Corporation. https://www.rand.org/pubs/research_reports/RRA2680-1.html Copyright © 2026 by Severin Sorensen. All rights reserved.

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