The AI Adoption Framework
For leaders navigating AI without losing their teams
Nobody tells leaders how to bring their engineers along when AI adoption becomes mandatory.
They get the pressure. They get the timeline. They get the expectation that it happens fast, but they don't get a playbook.
This is that playbook.
1. Start with WHY, not WHAT
Engineers are smart. They specially dislike when something is being pushed without a sound reason behind it. And they hate being told to use something “just because”.
Before you roll out any AI tool, be clear on three things:
What specific problem does this solve that we couldn't solve before?
Where in our workflow does this actually create value?
How does this make the engineer's work better, not just the company's metrics?
If you can't answer these clearly, your engineers will sense it. And they'll check out before you've even started.
2. Meet engineers where they are
Kim Scott's Radical Candor teaches that great leaders don't manage everyone the same way. They understand where each person actually is, not where they want them to be. And they care personally enough to act on that understanding.
This applies directly to AI adoption.
Not every engineer is at the same level of skills or enthusiasm when it comes to AI. Think of your team as a spectrum:
Curious and ahead: Already experimenting on their own.
Open but haven't started: They need a low-pressure way in.
Skeptical: They need proof before they invest their time.
Anxious: They need reassurance about what AI means for their role.
Each group needs a different approach. Be empathetic. A strategy that works for the enthusiasts will lose the skeptics. One that nurtures the anxious will bore the ones already ahead.
One important detail: these aren't permanent labels. They are where your engineers are right now. The skeptic today could be your accelerator in six months. The goal isn't to categorize people. It's to understand where they are so you can meet them there.
3. Lean on your accelerators, not top-down mandates
Back to Radical Candor. Scott identifies high-growth, ambitious performers who need to be challenged with new opportunities. Your accelerators are exactly that, in the context of AI.
And here's what most leaders miss: giving accelerators the opportunity to lead peer learning isn't just good for adoption. It's exactly what they need. It challenges them, gives them visibility, and builds their leadership skills.
Everyone wins.
A mandate from leadership says "do this."
An accelerator from within says "here's why I did this, and here's what I learned."
The second one always lands better.
Find the engineers already moving forward. Not because they were told to, but because they were genuinely curious. Give them space to share their lessons learned with peers. Invest in them.
I've found this to be more effective than any top-down mandate. Peers trust peers. The same message carries more weight when it comes from someone doing the work right next to you.
4. Make it engaging
Policy doesn't drive adoption. Engagement does.
The teams moving fastest aren't reading memos about AI. They're building with it. Start with a hackathon: challenge your team to automate a repeatable, boring task or process using AI. Something everyone on the team already knows is painful.
That's the sweet spot. The problem is real, the stakes are low, and engineers get to figure it out on their own terms. That's how adoption sticks.
5. Treat adoption as a journey, not a destination
Adoption is not linear. Some engineers will get it immediately. Others will need time. Some will try, hit a wall, and need to come back to it later. That's not failure. That's normal.
Take small steps. Measure the impact. Celebrate the wins. And then move on to the next level, together as a team.
Adoption that sticks is built in layers, not launched in waves.
Your success as a leader is measured through your team’s. And team success isn’t just business metrics. It’s engagement. Productivity. Job satisfaction.