What does it really take to reskill AI?

October 29, 2025Last Updated: October 29, 2025

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What does it really take to reskill AI?

By Faisal Hoque | Published: 2025-10-21 08:00:00 | Source: Fast Company – leadership-2

What does it really take to reskill AI

When Accenture announced its plans To lay off 11 thousand workers Deemed unable to reskill in AI, the tech consulting giant framed the decision as a training issue: some people simply cannot learn what they need to learn to succeed in the world of AI. But this narrative misunderstands – and greatly underestimates – the deeper challenge.

Walmart CEO Doug McMillon pointed to this biggest challenge recently when he said: “Artificial Intelligence will literally change every job“Now, if this turns out to be true, every role will have to be reimagined. And when every role changes, it is more than just a change in every job or even in a particular field. It involves a deep and systematic change in the nature and meaning of work itself.

For example, when a customer service rep’s job changes from answering questions to managing AI escalations, they are no longer providing the old-fashioned customer service — they are supervising the AI in the context of customer service. Their supervisor doesn’t manage people anymore; They coordinate a hybrid intelligence system consisting of humans and artificial intelligence. HR does not evaluate communication skills; They are evaluating the ability of human-AI collaboration. The job titles remain the same, but the actual work is completely different.

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You can’t prepare people for this disruption by sending them to a three-day workshop on how to claim more effectively. When change is so systemic, the real question is not whether it is possible to re-skill individual individuals. Rather, it is whether organizations are able to transform themselves at the scale and speed of AI requirements.

Two types of transformation

To understand the reskilling requirements resulting from AI transformation, it is useful to distinguish between limited and unlimited transformations.

LIMITED TRANSFORMATIONS Organizational changes that follow a predictable path, starting with specific areas of operations with well-defined capabilities for development. It unfolds in distinct phases, allowing companies to master one phase before moving on to the next.

Limitless transformationsOn the other hand, they are comprehensive changes that affect all parts of the organization at the same time, without a single point of origin. Because they simultaneously change functional tasks, competencies, processes and performance measures in interconnected ways, they cannot be addressed piecemeal or rolled out sequentially – they require a comprehensive, coordinated strategy.

The AI revolution is a paradigmatic example of limitless transformation, as it is reshaping how we think, work, and create value in fundamentally every industry, function, and level of organization—redefining not just individual tasks, but the nature of human contribution to work itself.

This means that it is not enough to simply retrain employees in AI. Instead, business leaders will need to transform the entire business system – the infrastructure, interconnected roles, and culture that enables change. They will often need to do all of this across the entire organization at once, not sequentially, not department by department, but everywhere at once.

There are three key dimensions that organizations need to address if they want to successfully transform themselves and reskill their employees for the AI revolution.

1. Rebuilding the business infrastructure

Most reskilling budgets cover workshops and certifications. And almost none of them cover what actually defines success: rebuilding the systems within which people work.

For example, AI now often handles routine inquiries in call centers while humans handle complex cases. like Mackenzie Successfully implementing this transformation, he argues, requires much more than simply teaching agents to use AI tools. Companies must rethink operating models, workflows, and talent systems, create escalation protocols that integrate with AI triage, metrics that measure human-AI collaboration rather than the number of individual tickets, and training that builds the judgment needed to handle ambiguous cases that AI cannot decide. Career paths and team structures must evolve to support hybrid human-AI capabilities.

Very little of this work is “training” in any classical sense—rather it is organizational structure and system building. Organizations that do not do this will find that their AI reskilling programs will inevitably fail.

2. Network effect: Why roles must shift together

Organizational roles do not exist in isolation. They are interconnected nodes in an organizational network. When an AI transforms one role, it also transforms every other role it touches.

For example, when AI chatbots handle routine customer inquiries, front-line agents typically shift to managing only complex situations, which may be more emotionally charged for the customer. This immediately shifts the role of their coaches and trainers, who must now redesign their curriculum away from teaching effective delivery of scripted informational responses toward teaching de-escalation techniques, empathy skills, and complex judgment calls. Furthermore, team supervisors will now no longer be able to evaluate performance based on call handling times and throughput, but must instead develop new frameworks for assessing emotional intelligence and problem solving under pressure.

The upshot is that comprehensive and comprehensive role redesign is essential if we are to successfully retrain employees in AI. Transforming AI requires simultaneous change across interconnected roles – when one piece of the network changes, every piece connected with it must change.

3. Cultural transformation

As Peter Drucker almost said, culture eats reskilling for breakfast. It is important for organizations to understand that cultural transformation is not a beautiful thing that comes after technical change. Indeed, this is the fundamental condition that determines whether artistic change will take hold at all. Without the right culture, training budgets become write-offs, and transformation initiatives turn into expensive failures.

Consider a financial services company training analysts in AI tools. If culture punishes mistakes made by AI more harshly than human mistakes, adoption dies. If success metrics continue to reward “heroic individual effort,” collaboration with AI will be undermined. If executives do not explicitly use AI and acknowledge their own learning difficulties, teams will treat it as optional theater rather than a strategic necessity.

The culture that enables AI reskilling is one built on curiosity, not certainty. This culture prioritizes experience over perfection, and treats failure as data, not shame. In fact, because AI tools are evolving so rapidly, the defining capability of an AI-ready culture is not mastery but continuous learning. Relatedly, psychological safety is essential: people must feel free to test, question, and sometimes get things wrong openly.

The reference to all this comes from above. When leaders use AI openly, admit what they don’t know, and share their learning process, they make exploration possible. When they don’t, fear takes over.

In short, successful AI cultures don’t celebrate efficiency, they celebrate learning.

conclusion

AI reskilling is not a training challenge, but rather an organizational transformation imperative. Companies that recognize this reality will rebuild their infrastructure, redesign interconnected roles, and cultivate learning cultures. Those who won’t will continue to announce layoffs and blame workers for failures that have always been about systems, not people.

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