If you scroll through LinkedIn right now, it feels like every company is “doing AI.” But when you sit down with founders, CTOs, and Heads of Engineering and ask, “What’s actually in production today that’s making your product better or your team faster?”, you often get a much quieter answer.
Most teams are still in the same place: some cool internal demos, a lot of slides about “AI strategy”, but very little that’s reliably live, measured, and safe.
The good news? You don’t need a giant research lab or a dozen PhDs to change that.
What you need is an AI‑ready engineering team: a team that can turn ideas into small, safe experiments, learn from them, and ship what works.
This post is a sneak peek at what that looks like in practice and how you can start moving in that direction.
(If you want the deeper dive, we’ve put it all into a free guide, The AI‑Ready Engine, from our Chief Innovation Officer, Marc Boudria.)
A common pattern we hear: “We’re going to hire a few data scientists, and then we’ll really get started with AI.”
Six months later, the data scientists have built some impressive models but the core product team hasn’t really changed how they work
The problem isn’t the people. It’s the system they’re dropped into.
If you don’t already have:
Then your new AI hires will struggle to create real product impact.
An AI‑ready team is less about adding a shiny new function and more about upgrading how your existing product and engineering teams think and operate.
In simple terms, an AI‑ready engineering team is one that can regularly turn AI ideas into small, safe, measurable experiments and then ship the ones that actually help your users or your business.
They don’t just collect “use case ideas.” They decide what to ship, shelve, or try next. No magic. Just structure.
From working with startups and scale‑ups, we see a few common traits again and again.
On paper, it’s easy to be impressed by big‑name schools, “AI” in a job title, or a list of tools and frameworks, but in practice, the people who make AI useful inside a product tend to be the ones who can:
In our guide, we talk more about specific behaviors to look for in AI‑ready talent, but the headline is simple: hire for how they think and build, not just what they’ve memorized.
AI‑ready teams don’t start with “Let’s rebuild the whole product with AI.”
They start with questions like:
Then they design small slices:
At the end of that time box, they make a decision: ship, shelve, or iterate.
One of the quiet superpowers of AI‑ready teams is that they leave a trail:
This doesn’t mean huge specs or formal research papers. It can be as simple as a one‑page “experiment log” stored in a shared space (Notion, Confluence, Google Docs).
The benefit is huge:
In The AI‑Ready Engine, we include the Decision Log template we use with teams, plus a few other lightweight tools to boost AI throughput.
You don’t need a massive MLOps stack to get started. But you do need a few basics:
Think of this as lightweight AI governance: enough structure to move fast safely.
The last thing we see in AI‑ready teams: AI is not a separate island.
Instead of a “special AI team in the corner,” you see:
Everyone sees AI as part of how they solve problems, not something one person does after hours.
That’s a big cultural shift, but it doesn’t require a big bang.
It usually starts with one cross‑functional pod working on one meaningful use case, then sharing what they learn.
We’ve only scratched the surface here.
To really make AI part of how your engineering team works, you’ll eventually need to think about:
That’s exactly why our Chief Innovation Officer, Marc Boudria, wrote The AI‑Ready Engine.
It’s a practical, no‑hype guide for tech leaders that covers:
If you’re trying to figure out what your AI‑ready engineering team should look like and how to get there without hiring a dozen PhDs, you can grab the guide here:
👉 Download The AI‑Ready Engine (free guide)