The innovation unlock is people, not AI
Where AI fits, what teams actually need, and the rhythm that makes it stick.
Welcome to the Experimenter’s Edge. If this is your first issue, the idea is simple. Each month I share what I’m seeing in rapid experimentation work with enterprise teams, what’s shipping inside Rapidly, and a handful of techniques you can take into your week. 1,000+ pretotypers, product leaders, and experimenters read this. Glad to have you here.
This is the first of two letters from me this month. The second will go deeper on the platform side: the pipeline rebuild, the Rapidly v2026 release, and where AI still falls over.
People > AI
I have lots to share about platform work, a rebuilt validation and experimentation pipeline, and where AI has taken some of the friction out. Before we get there, let’s talk about people.
What moves an organisation isn’t a tool. It’s the people inside it making better decisions, faster, with less noise. The teams who already understand the business, who carry the relationships, who feel the friction in the work, who know which ideas have legs and which ones the leadership team is quietly attached to for the wrong reasons.
The most valuable work I do isn’t deploying a platform. It’s helping those teams use what they already have, in a way that compounds.
What I do is knowledge transfer for experimentation and innovation capability inside existing teams. A way of thinking about product decisions that survives after I’ve left the room.
AI is a useful accelerator on top of that, not a substitute for it.
The clients who get the biggest return aren’t the ones with the slickest stack; they’re the ones whose teams can hold a hypothesis, run a real test, and change their minds based on what came back.
I keep meeting leaders who want to skip that part. They want the platform without the practice, the pipeline without the coaching, the AI without the judgement. It almost never works.
Teams who are already good at experimentation get better with AI; teams who aren’t mostly generate more polished bad decisions, faster.
A conversation that captured this well
I had a conversation last week with the product lead of a fast-growing consumer tech company. Engineering capacity had jumped, ideas were stacking up faster than the team could validate them, and the worry on the table wasn’t really about tooling. It was about trust.
How do they make sure ideas don’t disappear into a black hole the way they have at every other company those leaders had worked at? How do they get the rest of the business to believe that “we ran an experiment, here’s what we learned, here’s what we’re stopping” is a credible sentence rather than a polite no?
My answer was the same as it’s always been:
Acknowledge every idea the moment it lands in the system.
Validate against customer data, not against whoever raised it. Disconnect the idea from its owner so the team is reviewing the idea, not judging the person.
Bring in a facilitator whose only job is to ask whether the thing actually moves the needle for a customer.
Treat this as people work, not AI work. It’s where most of the gain actually comes from.
Two schools of thought on AI and teams
Two ways this is playing out right now:
Cut headcount. Run leaner, hand work to agents, smaller teams shipping more.
Keep your people, give each of them five to ten times the leverage. Take the dumb tax off the top of their day so they spend more time on the choices that compound. I’m firmly in the second camp. The best engineers I know don’t want to write yet another login screen or auth flow. They want to be unleashed on the work that actually requires judgement. The best designers and product managers don’t want to slow code production; they want to feed cleaner direction into it.
Celebrate the failures
The most powerful moment I’ve seen in an experimentation team was a senior person standing up in front of their peers and saying “I’m the biggest failure on this team”, and meaning it as a credential. It’s the opposite of how most companies are wired.
Teams who get good at this stop being afraid of the no-result experiment and start treating it as the cheapest possible insurance against the wrong investment.
The thing that gets missed: rhythm
The most powerful piece of any operating model isn’t the tool or the framework. It’s the rhythm:
A 45-minute prioritisation session every Monday.
A short midweek check-in.
A Thursday review of what shipped, what stopped, what we learned. Those standing ceremonies are what make the discipline survive contact with a busy roadmap. Skip them and the whole thing reverts to vibes.
The unlock is people; the tools just remove the blockers.
AI accelerates people
I’ve spent the last few months going deep on tooling, automation, and compression. Work that used to take days now takes hours, and the gains are real.
What hasn’t changed is who does the work. AI accelerates and streamlines; the judgement, the relationships, and the read on what actually matters still come from your people. That’s where my focus sits.
The companies who need me are the ones that already have good people, good systems, and a real business to run, and who want all of those to get better.
Innovation and experimentation drive growth
Innovation and experimentation are fine words inside the practitioner community, but in the boardroom they read as long-horizon and abstract. Leaders are asking more direct questions:
How do we get more growth this quarter?
How do we take cost out without breaking things?
How do we get confidence about where to invest before we commit?
**The **ingredients to innovate and see results rapidly already live inside your organisation. The teams, the relationships, the customer signal, the unused data. My job is helping you use what’s already there, with a small amount of structure and a useful amount of AI on top.
How we’re packaging work this year
Short note on the offer, because we’ve simplified it. Three tiers, all attached to a Rapidly licence so the discipline survives after we leave the room.
Tier 1. Rapid Experimentation Operating Model. A 90-day multi-squad install: discovery, Rapidly deployment, methodology training, a coached experiment cadence, and an internal experimentation team trained to sustain it. Same shape as the engagement that ran 130+ experiments and saved an ASX-listed enterprise group $12M by killing the wrong investments early.
Tier 2. Rapid Experimentation Sprint. A four-week, single-team engagement on one high-stakes idea or one team’s capability lift. Workshop, working sessions, synthesis playback, decision-ready output.
Tier 3. Half-Day Workshop. A focused session for product, innovation, or leadership teams. The fastest way to align a group on what good looks like before any further investment.
If one of those sounds useful, reply and we’ll work out which one fits.
Spotted in the wild
Wrapping a seven-session coaching arc
I closed out a seven-session pretotyping engagement with a CTO at a technology startup last week. The measure of success at the end of every engagement I run is whether they can say out loud, “we ran an experiment, here’s what we tested, here’s what we learned, here’s what we’re doing next”. If they can say that sentence on their own, the work landed.
Three questions worth sitting with
1. What’s stopping your teams from changing their minds based on the evidence?
If the honest answer is politics, or the person attached to the idea, that’s a facilitation problem more than a data problem. Disconnecting the idea from its owner is usually the first unlock.
2. When did you last celebrate a no-result experiment in front of your team?
If you can’t remember, the team is probably still optimising for the appearance of being right. The cheapest insurance against the wrong investment is making “we tested it and it didn’t work” a normal sentence.
3. Does your experimentation rhythm hold up when the roadmap gets busy?
A 45-minute Monday prioritisation, a midweek check-in, a Thursday review of what shipped, what stopped, what we learned. If those three slip the first time things get tight, the discipline isn’t installed yet.
Reading from the blog
Find the best ideas to invest in
We work with teams to go from ideas to evidence in weeks, embedding rapid experimentation using pretotyping as a core capability. Validate fast, stop the wrong ideas early, and back the winners with confidence.
👉 If you’d like to talk through what the Rapid Experimentation Operating Model could look like in your organisation, happy to do a free 15 or 30 minute call. Just reply.
“Data beats opinion. Every time. Across every resource type.”
Until next month, happy innovating!
Leslie