Your AI Lab
Get ahead on what AI makes possible - without asking your team to find the time.
Most product & engineering teams know AI is changing what's possible, both in their product and in how they build it. But exploring it happens in spare time: experiments squeezed in between deliveries, promising ideas staying with one curious person, and nobody actually owning the question "what does this make possible for us?"
Some AI-forward companies solve this with a dedicated AI lab: a small group whose job is to explore, experiment and bring what works back to the teams. Smaller and mid-size companies rarely have the size or budget for a permanent one - but they face the same pressure to keep up.
I help teams get the benefit of an AI lab - focused, structured exploration - without building a permanent one, and hand it over to someone on your team once it's running.
Why now
I thought I was keeping up with AI. As a product and engineering leader at scaling SaaS companies, I followed the developments, tried things out and brought ideas to my teams. When I later had real, structured time to go deep, I found I had been much further behind than I'd assumed. Not for lack of interest or skill. Exploration simply kept losing to the day job.
That gap grows quietly. Models and tools change faster than any team's spare time can follow, and the distance between what's possible and what your team has actually tried gets wider every quarter.
🧪 Set Up
A short start to agree on what's worth exploring, and how results become decisions.
- Priorities across two areas: your product, and how your teams work
- A backlog of small, concrete experiments, ranked by potential value
- A demo-and-decide cadence that fits your existing planning rhythm
- A named person on your side who will take over the lab
🔬 Run
Focused experiments, with your team watching the work as it happens.
- Small, time-boxed experiments, each ending in a clear decision: adopt, adapt or drop
- Full transparency on priorities, experiments and findings as we go
- Your internal owner increasingly runs experiments alongside me
- Capped at 2 days a week, so it stays lightweight
🤝 Hand Over & Step Out
The point isn't for me to run your lab. It's for your team to own it once I leave.
- Your internal owner runs the lab, with me stepping back to occasional sparring
- Promising findings are handed to your teams to make their own - implementing them is their work, not the lab's
- Protected time for the internal owner, agreed up front - without it, the lab becomes another "when we find time" initiative
- A defined end point, agreed up front
Who this is for
- Founders, CEOs and board members asking "what are we actually doing with AI?" - this gives you a concrete answer that doesn't depend on your team finding spare time.
- CTOs and CPOs who sense they're not moving as fast as they'd like - this isn't a verdict on your team. It's structural: exploration loses to the day job, even for leaders doing that job well.
- Heads of product who want to put this on the agenda - this gives you a concrete, bounded way to raise it.
Usually a fit when AI keeps coming up in leadership conversations, but nobody has had the time to find out what it would mean for you, or what it would take.
How It Works
Explore What's Possible
I show what I've seen work with AI in practice, and we discuss where it might fit for your team.
Agree Priorities & Scope
A clear, fixed plan: what the lab explores, who takes it over, and over what period.
Run, Hand Over, Step Out
The lab runs alongside your team, ends on a defined date, and stays with you after I'm gone.
Built from operating experience
Over the past decade, I've helped numerous B2B SaaS companies scale their product and engineering organizations successfully:
Learn365 SaaS Platform
As Chief Product Officer and Head of Engineering at Zensai, scaled from early-stage to 3,000+ customers and 2.5M+ users globally
Wizdom Digital Workplace
As VP of Product & Engineering at Wizdom (now Omnia), led development supporting 1M+ daily users across global enterprises
GRC SaaS Solutions
As VP of Product & Engineering at Wired Relations, led cross-location teams establishing scalable product organization and processes
From nothing to marketable products
As Chief Product & Technology Officer at Adactit, built the first products and the product & engineering organization from zero to mature department
Questions worth asking your team
- When did we last run an AI experiment that wasn't squeezed into someone's spare time?
- Who owns exploring what new models make possible for our product - as an actual responsibility, not an interest?
- When something promising turns up, how does it get from one person's experiment into the team's way of working?
- If a competitor started doing this seriously six months ago, would we know?
Is AI exploration still happening in spare time?
That's usually the sign it's worth a short, honest conversation.
Let's talk about itIf the bigger question interests you
The lab explores what AI makes possible for your product and your teams today. If you're also asking bigger questions about team structure, roles and decision-making as AI changes what's possible, that's a different conversation - see AI-Native Product Organizations.