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AI and Change Management: A Practical Playbook for Leading AI Change

AI Knowhow: Episode

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You might think you know how to ride a bike. But what if you turned the handlebars left, and the wheel went right?

A few years ago, engineer Destin Sandlin of Smarter Every Day tried to learn how to ride a bicycle with reverse steering. It took him—a smart, capable engineer—eight months to ride it just ten feet. His brain literally could not process the change.

Why does this matter to business leaders? Because, we believe, AI is that backwards bicycle.

AI is fundamentally rewriting the rules of business. Most organizations are realizing that learning to “ride” this new technology requires unlearning almost everything they’ve done before. And if you don’t want to crash and burn, you and your team have to master the art of change.

That’s why in this week’s episode of AI Knowhow, we’re kicking off a four-part mini-series that’s dedicated entirely to change management. We’re here to help you learn how to reverse-steer your organization’s AI journey so you can drive your company’s AI transformation without crashing.

The “Definitions” Problem

Before tackling change management, it’s critical to understand where the market actually stands. If you look at the headlines, it’s easy to be confused.

A recent report from G2 claims that 57% of companies already have AI agents in production. The same week, a report from Deloitte came out that puts the same number at just 11%. What gives?

As Pete Buer and Courtney Baker cover, nobody is lying, or just terrible at research. What we’re dealing with is a definitions problem.

  • The Low Bar (57%): G2 counts “AI features” as agents. If you turn on an AI assistant in Salesforce or HubSpot, you’ve got agents running in production. 

  • The High Bar (11%): Deloitte measures enterprise architecture—custom-built, autonomous agents that rewire fundamental business processes.

For leaders, the lesson is clear: Don’t get distracted by efficiency theater. The 11% figure is the real marker of transformation.

The Shift: Deterministic vs. Probabilistic

Why is the “backward bicycle” of AI so hard to ride? Knownwell CEO David DeWolf argues it’s because we are moving from a deterministic world to a probabilistic one.

For decades, business ran on reliable data and analtyics. Two plus two equals four. Algorithms gave us right or wrong answers that we could prove.

AI is different. It is nuanced. The answer you get back from an LLM isn’t a binary “correct” or “incorrect”—it is a probability. It is prose. It is judgment. To succeed, leaders must shift their organizations from asking “Is this right?” to asking “Is this useful guidance?”

The Playbook: Leading Through the Change

How do you help your team ride the bike? Our roundtable discussion outlined critical principles for 2026, including:

  • Anchor to Value Creation: Efficiency is low-hanging fruit. The real needle-movers are effectiveness and value creation plays. Use AI to parse volumes of data to help your team make better judgments, not just faster ones.

  • Model the “Messy Middle”: One of the most effective signals a leader can send is using AI publicly. Be explicit about where it’s still rough. Give your organization permission to learn—and fail—in public.

  • Treat AI Like a Product: As Knownwell CPTO Mohan Rao notes, employees behave like customers. If the “product” (AI) creates friction, they won’t use it. You need to assign ownership not just for adoption, but for model behavior.

Expert Interview: Tom Davenport

Tom Davenport, a world-renowned thought leader, co-author of 25 books and more than 300 articles, and a Distinguished Professor at Babson College, joins us for this week’s expert interview. Tom has spent his career studying and writing about how organizations adopt new technologies, from the internet to big data analytics. His verdict on AI? It is bringing Business Process Re-engineering (BPR) back to the forefront, but with much higher stakes.

While previous technology waves like the internet and cloud computing were transformative, AI introduces a new variable: the potential to eliminate human labor entirely. However, Tom argues that smart companies are resisting the urge to automate everything. Instead, they are focusing on augmentation. Among the tips Tom shares on this episode are:

  • Don’t Demoralize Your Team: Tom points out that leaders who claim they will need “half the knowledge workers” in the future are making a strategic error. It demoralizes the very people you need to drive the transformation.

  • The CHRO’s Dilemma: The C-Suite executive with the hardest job right now is the Chief Human Resources Officer. Workforce planning is nearly impossible when we don’t yet know what AI will be capable of in three years. Tom cites Leena Nair, former CHRO of Unilever and current CEO of Chanel, as a prime example of a leader who successfully prepared a workforce for change by offering resources for upskilling rather than threats of replacement.

Leadership in an Era of Uncertainty

Resistance to change is still real. Many employees are simply “hoping AI will go away,” or that they will retire before it affects them. To combat this, Tom advises senior management to be unequivocal. He points to Walmart CEO Doug McMillon, who declared that AI would change every job at the company, providing a clear signal that opting out is not an option.

The Skill of the Future? Critical Thinking.

Finally, Tom warns against over-indexing on temporary skills like “prompt engineering,” which machines will likely do for us soon. The enduring skill of the AI era is critical thinking. As we rely more on probabilistic systems, the ability to review outputs, verify facts, and apply human judgment is more valuable than ever.

Be sure to tune in to next week’s interview for the second half of our interview with Tom, and the second episode in our mini-series on AI and change management.

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Show Notes

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