Most of the “AI will replace X” hot takes are pretty tiring.
So I decided to try something more practical (and a bit tongue-in-cheek): what happens if I treat AI as my Product Manager?
Over a couple of sessions, I used an AI model as a structured thinking partner to design ClubHub — a platform for community clubs to manage members, events and payments without being milked by high-fee apps.
This post is a write-up of that process: what worked, what didn’t, and where the human still absolutely matters.
The experiment: AI as PM
The rules of the game were simple:
- I bring intent and constraints: values, context, and the kind of company I want this to appeal to.
- The AI behaves like a very fast, very patient PM partner: propose options, ask clarifying questions, help structure decisions.
I framed ClubHub as both:
- A real product I’d like to exist (for sports clubs, music groups, youth clubs, etc.), and
- A “lure” project: a miniature case study that shows late-stage startups how I think about product, architecture and DevEx.
The initial question was deliberately provocative:
“Could AI replace the product manager on this project?”
Spoiler: no. But it made a fantastic collaborator.
Step 1 – Getting clear on values before features
Instead of starting with features, I asked the AI to help me nail down values and constraints.
We used a simple A/B/C format for each big decision. For example:
Q: When you have to choose, what comes first — affordability or UX polish?
A) rock-bottom cost, B) premium UX, C) a balance
I picked C: it has to be cheap and usable by non-technical volunteers on a phone.
From there, we walked through a series of questions:
- Data ownership and lock-in
→ We landed on: “Easy to join, easy to leave. Clubs fully own their data; exports and portability are a feature, not a threat.” - Advertising stance
→ Clear “no ads, ever.” No tracking, no sponsored clubs, no ad-tech hanging off kids’ activities. - Raffles / lotteries ethics
→ Fundraising and fun only, not gambling. No casino loops, loot boxes or “infinite spin to win”. - Geography and regulation
→ Start with Ireland/EU and treat GDPR + child safety as design constraints, not compliance afterthoughts.
The AI was extremely helpful here: it kept presenting clean option sets and summarising what we’d chosen so far. That made it much easier to see contradictions and refine my thinking.
But the actual choices — especially the ethical ones — were human.
Step 2 – Defining who ClubHub is for
Next up: who this thing should serve.
We knew it was “clubs”, but that’s a big space. Together, we tested different slices:
- Community music / arts groups
- Amateur sports clubs
- School / parent / youth organisations
The answer was: yes to all of them.
Rather than over-optimise for a single niche, we decided ClubHub’s language should be inclusive and generic:
- “club”, not “team”
- “event”, not “fixture”
- “member”, not “player” or “pupil”
The AI helped by sanity-checking wording and pointing out where terminology might exclude certain club types. That’s a small detail that matters a lot when your users are volunteers across wildly different contexts.
Step 3 – Mission and business model, with ethics baked in
Once the values and audience were clear, the AI helped me condense the mission into something I could actually put on a landing page:
Mission:
Help community clubs manage members, payments and events at radically low cost, so they can thrive without being exploited by high-fee platforms.
From that, we derived some concrete product and business principles:
- Core admin (members, events, “who has paid”) should be very low cost or free for small clubs.
- Monetisation only where clubs are already making profit, like merch sales, raffles and fundraisers, and premium features (analytics, integrations, white-labelling).
- Absolutely no lock-in: data export and “easy to leave” are features.
- No ads, ever.
The AI kept proposing standard SaaS pricing patterns (“per member per month”, “take a % of all payments”), and I kept saying no until it aligned with the mission.
This is a good example of the dynamic:
AI is brilliant at recalling patterns that work for many businesses.
Humans still need to decide what kind of business they want to be.
Step 4 – Shaping the MVP and UX
With values and business model in place, we moved to the question: What should v1 actually do?
We narrowed the MVP scope to:
- Clubs and members – basic profiles, roles, child flag.
- Membership plans and subscriptions – who’s on which plan, and their status.
- Events and registrations – simple events, capacity and sign-ups.
- Payment recording – “this person paid for this membership/event”, with method and reference.
- Admin UI (mobile-first) – a treasurer or coach should be able to do 90% of tasks on their phone.
The AI kept me honest by pushing for coherence:
- Don’t attach raffles or merch before the basics work.
- Don’t go “full marketplace” if the core problem is “we don’t know who’s paid”.
- Keep the UI deliberately simple and calm.
We also chose SumUp as the initial payment mindset — because they treat small organisations reasonably well — and decided that v1 would just record payments, not own the entire payment flow.
Step 5 – Technical shape (without drowning in detail)
Finally, we sketched the technical architecture, still using AI as a co-pilot:
- Go backend – modular monolith, multi-tenant by club_id.
- AWS App Runner – one container per environment, low-ops, easy scaling.
- RDS Postgres – shared database.
- S3 – logos and documents.
- React SPA – mobile-first, built with Vite and Tailwind and served from the Go API.
- GitLab CI – a paved-road pipeline with stages for linting, frontend build, security checks, image build, and deploy to App Runner.
The AI was very good at:
- Suggesting plausible stacks for my constraints (cost, EU data, ease of ops).
- Structuring CI stages and naming them coherently.
- Keeping everything consistent with the mission (multi-tenant for low per-club cost, etc.).
The choices still reflected my own experience and goals, especially around designing for thousands of clubs and using ClubHub as a mini case study for late-stage startups who need paved-road CI/CD.
So… can AI replace the Product Manager?
After this experiment, my answer is:
No, but it can be an excellent Product co-pilot.
Where AI shines:
- Generating clear options quickly (A/B/C trade-offs).
- Summarising decisions so you can spot contradictions.
- Keeping the conversation moving instead of getting stuck on the blank page.
- Remembering all the constraints you’ve already set.
Where humans still matter:
- Setting the mission and ethics: no ads, no exploitation, child safety, fundraising-not-gambling.
- Choosing which users to serve and how to talk to them.
- Balancing business reality with values (what you’re willing to charge for).
- Connecting the product shape to your broader story — in my case, a “lure” for humane, late-stage startups who want to move from chaos to paved roads.
In other words: AI can help you think, structure, and explore. But it can’t be the conscience, the strategist, or the one who chooses what kind of impact you want your product to have.
For ClubHub, that combination worked beautifully: AI did a lot of the grinding work; I supplied direction, values, and judgement.
And I’m much more interested in Product Managers who know how to use AI like this than in replacing them.
In a follow-up post, I’ll go deeper into ClubHub’s mission and how it’s designed to keep club costs low while still being sustainable. After that: a hardcore tech deep dive on the Go/App Runner/GitLab CI side for the platform nerds.
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