When Accenture announced plans to lay off 11,000 workers who it deemed could not be reskilled for AI, the tech consulting giant framed the decision as a training issue: some people simply cannot learn what they need to learn to thrive in the world of AI. But this narrative fundamentally misunderstands—and significantly underplays—the deeper challenge.
Doug McMillon, the CEO of Walmart, pointed to this bigger challenge recently when he said, “AI is going to change literally every job.” Now, if this turns out to be true, every role will have to be reimagined. And when every role changes, this is more than a change in each job or even a specific field. It implies a profound and systemic change in the nature and meaning of the work itself.
For instance, when a customer service rep’s job changes from answering questions to managing AI escalations, they are no longer doing old-fashioned customer service—they are doing AI supervision in a customer service context. Their supervisor isn’t managing people anymore; they are orchestrating a hybrid intelligence system composed of humans and AI. And HR isn’t evaluating communication skills; they are assessing human–AI collaboration capacity. The job titles remain the same, but the actual work has become something entirely different.
You cannot prepare people for this disruption by sending them to a three-day workshop on how to prompt more effectively. When the change is as systemic as this, the real question is not whether individuals can be separately reskilled. It is whether organizations can transform themselves at the scale and speed AI demands.
Two types of transformation
To understand the reskilling demands created by AI transformation, it helps to distinguish between bounded and unbounded transformations.
Bounded transformations are organizational changes that follow a predictable path, starting from specific areas of operation with well-defined capabilities to develop. They unfold in distinct stages, allowing companies to master one phase before moving to the next.
Unbounded transformations, on the other hand, are sweeping changes that affect all parts of an organization at the same time, with no single point of origin. Because they simultaneously alter job functions, competencies, processes, and performance measures in interconnected ways, they can’t be tackled piecemeal or rolled out sequentially—they demand a holistic, coordinated strategy.
The AI revolution is a paradigmatic example of an unbounded transformation, as it fundamentally reshapes how we think, work, and create value across every industry, function, and level of the organization—redefining not just individual tasks but the very nature of human contribution to work itself.
And that means that it is not enough to simply reskill employees for AI. Instead, business leaders will need to transform the entire ecosystem of work—the infrastructure, the interconnected roles, and the culture that enables change. And they will often need to do all of this across the entire organization at once—not sequentially, not department by department, but everywhere simultaneously.
There are three key dimensions that organizations need to address if they are to successfully transform themselves and reskill their workers for the AI revolution.
1. Rebuilding the infrastructure of work
Most reskilling budgets cover workshops and certifications. Almost none cover what actually determines success: rebuilding the systems people work within.
For example, AI often now handles routine inquiries in contact centers while humans tackle complex cases. As McKinsey argues, successfully implementing this shift demands far more than teaching agents to use AI tools. Businesses must rethink operating models, workflows, and talent systems—creating escalation protocols that integrate with AI triage, metrics that measure human-AI collaboration rather than individual ticket counts, and training that builds the judgment needed to handle the ambiguous cases that AI can’t decide. Career paths and team structures must evolve to support hybrid human-AI capacity.
Very little of this work is “training” in any classical sense—rather, it is organizational architecture and system-building. And the organizations that do not undertake this work will find that their AI reskilling programs will inevitably fail.
2. The network effect: why roles must transform together
Organizational roles do not exist in isolation. They are interconnected nodes in an organizational network. When AI transforms one role, it also transforms every other role it touches.
For example, when AI chatbots handle routine customer inquiries, frontline agents typically shift to managing only complex situations, which may be more emotionally charged for the client. This immediately transforms the role of their trainers and coaches, who must now redesign their curriculum away from teaching efficient delivery of scripted informational responses toward teaching de-escalation techniques, empathy skills, and complex judgment calls. Further, team supervisors will now no longer be able to evaluate performance based on call handle times and throughput—they must instead develop new frameworks for assessing emotional intelligence and problem-solving under pressure.
The result is that holistic and comprehensive role redesign is essential if employees are to be successfully reskilled for AI. AI transformation requires synchronized change across interconnected roles—when one piece of the network shifts, every connected piece must shift with it.
3. Cultural transformation
As Peter Drucker almost said, culture eats reskilling for breakfast. It is crucial for organizations to understand that cultural transformation is not a nice-to-have follow-on that comes after technical change. Rather, it is the prerequisite that determines whether technical change takes root at all. Without the right culture, training budgets become write-offs and transformation initiatives become expensive failures.
Consider a financial services firm training analysts on AI tools. If the culture punishes AI-assisted mistakes more harshly than human mistakes, adoption dies. If success metrics still reward “heroic individual effort,” collaboration with AI will be undermined. If executives do not visibly use AI and acknowledge their own learning struggles, teams will treat it as optional theater rather than strategic imperative.
The culture that enables AI reskilling is one built on curiosity, not certainty. This culture prizes experimentation over perfection and treats failure as data, not disgrace. Indeed, because AI tools evolve so quickly, the defining capability of an AI-ready culture is not mastery but continuous learning. Relatedly, psychological safety becomes essential: people must feel free to test, question, and sometimes get it wrong in public.
And the signal for all of this comes from the top. When leaders openly use AI, admit what they don’t know, and share their own learning process, they make exploration permissible. When they do not, fear takes its place.
In short, successful AI cultures don’t celebrate competence—they celebrate learning.
Conclusion
AI reskilling is not a training challenge—it is an organizational transformation imperative. Companies that recognize this will rebuild their infrastructure, redesign interconnected roles, and cultivate learning cultures. Those that don’t will keep announcing layoffs and blaming workers for failures that were always about systems, not people.
