What It Means to Be an AI-Ready Engineering Leader
You don’t need to have a full AI roadmap.
You do need to go first.
That’s what I’ve learned as we’ve started introducing GenAI tools across our teams.
Because it’s not just about the tech.
It’s about the tone.
If you’re leading a team right now and wondering why AI adoption feels slow, scattered, or forced—this might be why:
Your team doesn’t need another platform.
They need a signal.
They need to know it’s safe to try, explore, and fail without looking dumb.
They need to see it’s okay to not “get it” the first time.
They need to see you using it.
So what does it look like in practice? What does it mean to lead a team that’s actually ready for this shift?
Let’s break it down.
What AI-Ready Leaders Do Differently
I got to sit down with Senad Santic on the Product Driven podcast to talk about how real teams are building toward AI maturity. He’s seen it firsthand at TestChimp—and his advice wasn’t about tools. It was about trust.
Here’s what I’ve learned—from my own teams and from leaders like Senad:
✅ You Normalize Experimentation
Senad’s team didn’t start with a top-down plan. They started with bottom-up curiosity.
They encouraged developers to try things. To share their prompts. To show wins.
The habit mattered more than the outcome. And that’s what drove momentum.
Try this: Start a “Prompt of the Week” Slack thread. One person shares how they used an AI tool to make their work easier or faster. Everyone learns. No pressure.
✅ You Show Use Cases By Role
You don’t need a whitepaper. You need real examples.
Like:
A developer using Claude to explain a legacy codebase
A QA lead using GenAI to write variations of test cases
A PM using ChatGPT to summarize customer call transcripts
The more your team sees themselves in the possibilities, the faster they adopt.
✅ You Build Rituals That Support Curiosity
Rituals create safety. And teams need safety to explore.
Try this:
Add a “What did you try with AI this sprint?” question to retros
Host monthly internal demos where team members share their AI workflows
Let one person per week teach a new shortcut or tool
Curiosity spreads fastest when it’s visible and valued.
✅ You Connect It to Real Problems
This isn’t about looking innovative. This is about solving real problems:
Long onboarding times
Manual QA
Tedious documentation
Slow estimates
The AI-ready leader doesn’t pitch a vision of the future. They ask, "Where are we spending time that doesn’t require human brilliance?"
Then they invite the team to find a better way.
The Old Model Won’t Work Here
Top-down AI rollouts won’t work.
Waiting for your team to magically “get on board” won’t work.
Hiring one person to “own AI” won’t work.
This isn’t just a tooling shift.
It’s a leadership shift.
Because being an AI-ready team doesn’t mean everyone’s a prompt engineer.
It means your team knows:
What AI is and isn’t good at
When to trust it—and when to question it
How to improve the way they work because of it
And that starts with you.
The AI-Ready Leader Thinks in Systems—Not Tools
Senad put it best:
“People want a button. But what they need is a mindset.”
You don’t need to be an AI expert. But you do need to create a system that rewards exploration, shares wins, and builds confidence in what’s possible.
You don’t start with use cases. You start by shifting the culture.
If your team is waiting on permission, they won’t move. If your team is afraid to experiment, they won’t grow.
The AI-ready leader’s job is to reduce fear and increase feedback. To make "try something weird with AI" a normal part of how the team works.
And that shift starts with you.
If you're leading an engineering org right now, here’s the question I’d ask:
What signal are you sending?
Are you showing up curious, exploratory, open?
Are you letting your team see you learn—not just lead?
Because that’s what builds readiness.
That’s what builds buy-in.
And that’s what will separate the teams who lag behind… from the ones who leap forward.
🎧 We unpacked this in depth on the Product Driven podcast with Senad Santic from TestChimp—especially the cultural side of AI readiness most leaders miss.