Last week, I spent a couple of hours listening to AI leaders from Adobe, Manulife, Moderna, and other major organizations share their transformation stories. One of the key takeaways from the event was that the companies succeeding with AI aren’t the ones with the best technology. They’re the ones that cracked the code on getting humans to actually embrace and use it.

Don Bennion from Adobe put it perfectly: “The tech is hard, and change management is harder.” He went on to say that if you’re looking at the long pole in the tent for AI transformation, it’s absolutely the change management piece.

This should be a wake-up call for every leader who’s been obsessing over which AI platform to buy or which vendor has the coolest features. You’re optimizing for the wrong thing.

The Expensive Mistake Most Companies Are Making

Here’s a scenario that’s playing out in organizations everywhere: Leadership gets excited about AI, buys enterprise licenses for the latest tools, announces the rollout with great fanfare, and then… watches adoption stall at 5-10%.

Sound familiar? That’s because most companies are treating AI deployment like they treated rolling out Office 365 or Salesforce. They think if they provide access and some basic training, people will naturally start using it.

But AI is fundamentally different. It’s not just a new tool in your existing workflow. It’s a new way of thinking about work itself.

Olya Taran from Manulife learned this the hard way. They started with what she calls “generic road shows” – presentations about AI strategy and capabilities. Everything stayed abstract. People nodded politely and went back to doing things the old way.

The breakthrough came when they shifted from talking about AI to helping people experience it in their actual work context. They created what they called “promptathons” – hands-on team challenges where people solved real problems using AI tools. The difference was immediate and dramatic.

The Four Pillars That Actually Drive AI Transformation

Through trial and error, Manulife developed a framework that took them from low adoption to 50% of their workforce actively using AI. Here’s what they learned works:

Pillar One: Lead from the Top (But Not How You Think)

This isn’t about executives giving speeches about AI being the future. It’s about putting the tools directly in leaders’ hands and having them experience the value firsthand.

Manulife didn’t brief their executives on AI, they made them use it. They hijacked existing leadership meetings and ran live demos where executives had to solve actual challenges with AI tools. Once leaders saw what was possible, they became genuine advocates instead of just saying the right things.

Pillar Two: Treat Prompting as a Business Skill

Most companies treat AI training like software training: here’s how to log in, here’s where to click. But effective AI use is more like learning to communicate well. It requires practice, feedback, and skill development.

Manulife recognized prompting as an emerging business skill and invested in helping people get genuinely good at it. Not just functional, but genuinely skilled at getting AI to produce high-quality outputs.

Pillar Three: Create Stories and Social Proof

People need to see that AI use is becoming normal and expected in their organization. This means celebrating wins, sharing use cases, and making AI adoption visible throughout the company.

But here’s the key: it has to be peer-to-peer learning. When someone sees a colleague in a similar role getting real value from AI, that’s when the lightbulb goes off.

Pillar Four: Make Adoption Someone’s Actual Job

This might be the most important insight: when AI adoption is everyone’s responsibility, it becomes no one’s responsibility. Successful companies assign dedicated people to drive adoption with clear accountability.

At Manulife, Olya’s entire role is focused on adoption, talent, and culture around AI. She’s not building models or managing infrastructure. She’s laser-focused on helping humans successfully integrate AI into their work.

Why Technology-First Approaches Fail

Don’t try to lead with the technology and hope the humans will follow. You’ll spend months evaluating platforms, negotiating contracts, and planning technical rollouts only to end us with a tool that people won’t use.

Don from Adobe described this perfectly as “spray and pray.” You buy licenses, distribute them widely, and pray that somehow everyone will figure out how to get value from them. It almost never works.

The problem is that effective AI use requires behavior change, not just tool access. People need to shift from thinking “I’ll Google this” to “I’ll ask AI about this.” They need to develop new habits around when and how to bring AI into their decision-making process.

That’s not something that happens automatically just because you give someone a login.

The Real 80/20 Breakdown

Based on what I heard from the companies that are actually succeeding, here’s how you should think about allocating your time and energy:

• 20% on technology decisions: which platforms, which integrations, which security protocols. This stuff matters, but it’s not where you’ll win or lose.
• 80% on change management: how to help people experience value quickly, how to build new habits, how to create organizational momentum around AI use.

Companies like Moderna are seeing huge adoption rates among knowledge workers not because they have better technology, but because they invested heavily in the human side of the transformation.

What This Looks Like in Practice

Let me give you a concrete example of what 80/20 change management focus actually means.

Instead of starting with a six-month platform evaluation, start with a two-week experiment. Pick a small team, give them access to AI tools, and focus intensively on helping them find genuine value in their daily work.

Don’t just train them on features. Sit with them as they work and help them identify moments where AI could be useful. Help them develop prompting skills through real work scenarios, not hypothetical examples.

Document what works and what doesn’t. Then use those stories and learnings to inform both your technology decisions and your broader rollout strategy.

The teams that experience genuine value become your best evangelists. The teams that struggle give you insights into what support people really need.

The Questions That Actually Matter

Instead of asking “Which AI platform should we buy?”, start asking:

• How will we help people overcome their initial skepticism about AI?
• What does success look like for someone using AI in their daily work?
• How will we measure whether people are actually getting value, not just logging in?
• Who will be responsible for driving adoption and how will we measure their success?
• What stories and examples will resonate most with our specific culture?

These are change management questions, not technology questions. And they’re the questions that will determine whether your AI investment pays off.

The Urgency You’re Not Seeing

Here’s what’s at stake: the companies that figure out the change management piece first will build sustainable competitive advantages. Not because they have access to better AI models – those are increasingly commoditized. But because they have workforces that can effectively leverage AI capabilities.

Meanwhile, companies that focus primarily on technology acquisition will find themselves with expensive tools that nobody uses effectively. All that potential value will remain locked up because they skipped the hard work of helping humans adapt.

The window for being an early mover on the change management side is closing. The companies that invest now in building organizational capabilities around AI adoption will be much harder to catch later.

This isn’t just about being more productive with current work. It’s about developing the organizational muscle to adapt quickly as AI capabilities continue to evolve. Because the changes we’re seeing now? They’re just the beginning.

Want to dig deeper into what effective AI change management looks like in practice? I’d love to help you think through strategies that fit your specific organizational culture and challenges. Connect with me on LinkedIn to continue the conversation.