AI adoption is a change management problem

Most companies treat AI adoption like a software rollout.

Install the tools. Run a training session. Send a Slack announcement. Done.

Then they wonder why no one is using AI assistants that cost them hundreds of thousands a year. Team-level adoption reports start circulating. Single-digit usage rates. Team leaders "asked to fix it."

Here's the truth: AI adoption is not a technology problem. It's a change management problem. And it requires the most potent change management playbook you've ever deployed

The real blocker isn't skills. It's fear.

When I talk to marketing teams about AI, I hear the same thing over and over.

• "I know I should be using it more."

• "I tried it once but didn't really get anywhere."

• "I'm just so busy with my actual work."

But underneath these surface excuses lives a deeper fear. The fear that learning AI means admitting your current skills are becoming obsolete. The fear that if you get too good at Ai and automation, you're automating yourself out of a job.

AI adoption fails not because people lack skills but because they lack safety. They need to know that getting better at AI won't cost them their jobs.

Here's the pattern I see repeatedly:

• Junior team members resist the most. They're protecting skills they just spent years developing.

• Senior leaders "support" AI enthusiastically but rarely change their own behavior.

• Middle managers are caught between pressure from above and resistance from below.

The result? Performative adoption. People get access to the tools. Watch a 30-min video or read a guide. They nod along in meetings. But when they're back at their desks, nothing changes.

Treat AI like a fitness journey

You wouldn't expect a six-pack after two weeks at the gym. So why do companies expect AI transformation after a 1-hour training?

Real AI adoption works like fitness:

• Consistent practice matters more than intensive bursts

• Progress is gradual and often invisible until it suddenly isn't

• You need both the skills AND the motivation to show up

• Quick wins build momentum. Big failures don't mean you quit.

• Recognition matters. Nobody keeps going to the gym if no one notices they're getting stronger. 

This is why pairing training with concrete use cases matters so much. Abstract knowledge ("Here's how prompt engineering works") doesn't stick. Applied knowledge ("Here's how to cut your weekly reporting time in half") creates enthusiasm.

10 change management steps that actually drive adoption

I've seen several AI rollouts. The ones that succeed share common patterns.

Here's what works:

1. Leadership walks the talk

Nothing kills AI adoption faster than leaders who delegate it to their teams while never touching AI themselves.

If the COO doesn't use AI, why should anyone else?

Leaders need to share their own experiments. Their use cases. Their agents. Their "I tried this and it was terrible but I learned something" moments.

You can't outsource transformation. If leaders aren't experimenting with AI and share it, they're signaling it's not important enough for them but somehow important enough for everyone else.

And here's the deeper truth: if you don't play with AI firsthand, you'll never feel its power. 

2. Create safety with a clear strategy

None of the other tools work without this one.

If employees believe automation equals layoffs, they'll suppress what they discover. They'll hide their efficiencies. They'll slow-roll their own transformation.

The fastest way to kill AI adoption is to let fear of job loss go unaddressed.

You need two things: a clear AI strategy and a clear people strategy.

The AI strategy answers: 

• Why AI? Why now? 

• What opportunities are we chasing? Efficiency? New products? Better customer experience? 

• Where do we start? What's the priority?

The people strategy answers: 

• What does AI mean for our team's future? 

• How will roles evolve? 

• What new skills do we need? 

• What happens to time saved? More output or new opportunities?

Say it explicitly: we're using AI to grow our company and our people

3. Shift from "Recommended" to "Required"

As long as AI is optional, it will remain an afterthought.

This doesn't mean forcing bad tools on people. It means embedding AI expectations into how work gets done.

At Shopify, teams must demonstrate AI-driven efficiency before they can request additional headcount.

That's not punishment. That's clarity.

Example: some companies establish new project proposal rules 

Before submitting a proposal, employees must attempt the project with AI first. Then they resubmit, factoring in what AI can handle. This bakes experimentation into planning rather than treating it as an afterthought.

4. Invest in use-case driven AI training

A one-hour webinar isn't training. It's a checkbox.

Real AI training covers 4 things. I call it the Ferrari Framework or Applied AI Framework:

• Capabilities: What can AI actually do?

• Skills: How do you use it well? Prompting, context, quality checks. 

• Use cases: Where does it fit your actual job? Not generic demos. Your workflows.

• Mindset: Are you ready to experiment, fail and keep going?

Most companies only cover capabilities. Maybe skills. Then they wonder why adoption stalls.

You wouldn't hand someone Ferrari keys and expect them to race. They need to know what the car can do, how to drive it, where they're going and whether they're ready for the ride.

Training isn't a one-time event. It's ongoing. AI capabilities are doubling every 7 months. Your training should keep pace.

5. Run experiments and celebrate them 

Ask each team to run 10 AI-driven experiments in six months. The outcome doesn't matter. The attempt does.

You're not looking for perfection. You're building muscle memory. Every failed experiment teaches more than another training session.

Celebrate attempts, not perfection

This is crucial. You're punishing inaction, not failed experiments.

The team that tried five AI approaches and found none of them worked learned infinitely more than the team that tried nothing. Make that visible. Celebrate the experimentation, not just the wins.

6. Create public sharing spaces

Knowledge trapped in individual heads is worthless at scale.

• Slack channels like #ai-use-cases

• Weekly Loom videos showing what's working

• Use case and prompt libraries that capture institutional knowledge

• AI "lunch and learns" for peer sharing

• AI hackathons to share your prototypes and agents

The goal is making AI visible. When people see colleagues succeeding with AI, skepticism turns into curiosity.

7. Redesign your talent strategy around AI

Adoption isn't just about your current team. It's about how you reskill, restructure, partner and hire.

Rethink org design 

• Map the "day in the life" across roles. Where does AI add most value? 

• Insert agents where they multiply human impact.

• The new model: one human supported by multiple specialised AI agents.

Reskill your team 

• Identify skill gaps between where your team is and where AI needs them to be. 

• Invest in ongoing training, not one-off workshops. 

• Build AI fluency across the board, not just in technical roles.

Partner with AI-native companies

• You don't have to build everything in-house.

• Find partners who already think AI-first.

• Learn from them. Borrow their speed.

Hire with AI built in 

• Rewrite job descriptions with AI in mind. "Conduct analysis and reporting with AI support" instead of "Conduct analysis and reporting." 

• AI curiosity becomes a hiring criterion. You want learners, not resisters. 

• Candidates must demonstrate AI skills in interviews. Not "have you used ChatGPT" but "show me how you'd approach this task with AI tools."

This isn't about replacing people. It's about redesigning how work gets done.

The companies that figure out human + AI teaming first will outpace everyone still hiring for yesterday's roles.

8. Appoint AI champions in every team

Adoption doesn't spread from the top down. It spreads peer to peer.

AI champions are the people on the ground who: 

• Experiment first and share what works 

• Answer the "how do I do this?" questions 

• Turn skeptics into believers through small wins 

• Bridge the gap between leadership vision and daily reality

One champion per team changes the dynamic. Suddenly there's someone to ask. Someone who makes it feel possible. Someone who proves it works.

Training reaches minds. Champions reach hearts.

9. Measure what matters

What gets measured gets done. What gets shared gets repeated.

Track three levels:

Leading indicators (are people actually using it?) 

↳ Adoption rate: What % of your team is using AI weekly?

↳ Experiments: How many AI experiments has each team run? 

↳ Leadership behavior: Do managers ask "Have you tried AI?" in every conversation?

Efficiency gains (is it saving time / money?) 

↳ Time saved per employee or team 

↳ Productivity gains on specific workflows 

↳ Tasks eliminated or reduced

Business outcomes (is it generative revenue?) 

↳ Revenue impact 

↳ ROI/ROAS improvements or CAC reduction 

↳ Conversion rate lifts 

↳ Traffic and lead growth

Start with leading indicators. They tell you if adoption is real. Then connect to efficiency. Then tie to outcomes.

Most companies skip straight to ROI and wonder why they can't prove anything. You can't measure business impact if no one's using the tools.

Make it visible. Share wins publicly. Adoption spreads when people see proof.

10. Incorporate AI into objectives, reviews and rewards

What gets measured gets done. What gets rewarded gets repeated.

Some companies are getting creative here:

↳ Weekly cash prizes for the most effective AI automation

↳ "AI Champion" titles and conference sponsorships

↳ Recognition in all-hands meetings for smart use cases

↳ Bonuses tied to documented time savings

Example: some companies establish cash prizes for agents and automation 

Instead of punishing efficiency gains with job cuts, reward them openly. One company gave $10,000 weekly to whoever showed the most effective AI agents and automation. Cheaper than consultants. Better for culture.

My pick: AI is the tool. You're the edge.

AI capability matters. But human skills fuel adoption. Curiosity keeps people trying new approaches. Resilience helps them push through failures. A culture of experimentation makes it safe to be wrong.

Human skills like systems thinking, critical judgment and willingness to experiment are what make AI actually useful.

Many people don't need deep technical skills. But they do need foundational AI literacy and psychological safety to experiment.

The companies winning at AI adoption aren't the ones with the best tools or the biggest training budgets. They're the ones treating this like what it is: the most significant change management challenge of their careers.

And they're running at it accordingly.