5 AI Product Strategy Traps I See Founders Fall Into

I get it, AI is exciting. The demos are impressive. Your friends are shipping AI features. You feel the pressure to build something before the window closes. But I've watched this pattern play out enough times now to see where it leads: founders who spend six months building something nobody actually needs.

Here are the five traps I see most often, what they look like in practice, and what to do instead.

Trap 1: Starting with the Model, Not the Problem

A founder comes to me and says: "I want to build an AI assistant for [insert profession here]. It'll use GPT-4 and fine-tuning to understand their specific needs."

I ask: "What problem are you solving?"

They pause. "Well, it'll save them time."

"How much time? On what specific task?"

Another pause. "I'm not sure yet. But the AI can do loads of things."

That's the trap. You've chosen the technology before you've defined the problem. You're now working backwards, trying to find a use case that fits your solution. It's like buying a JCB and then looking for something to dig up.

Founders need a specific, repeatable problem that real people are currently solving in a way that's painful, slow, or expensive. If you can't describe the before-state in concrete detail — how long it takes, what tools they use, where it breaks down — you're not ready to talk about AI yet.

What to do instead: Spend two weeks watching people work. Not asking them what they need, watching them. Find the task they do every Tuesday that makes them swear. That's your starting point.

Trap 2: Ignoring the 80/20 of AI Capability

Most AI products need the model to be right about 95% of the time to be useful. But the technology is realistically good for about 80% accuracy on most real-world tasks without significant fine-tuning and guardrails.

The gap between what founders assume AI can do and what it actually does reliably is where products die.

I worked with a founder building an AI tool to generate technical documentation from code. The demos looked great. But when we tested it with real codebases, it hallucinated function names, invented parameters that didn't exist, and confidently described logic that wasn't in the code.

The founder's response: "We'll fine-tune it."

Fine-tuning doesn't fix fundamental reasoning gaps. Even if it did, you've now got a six-month data collection and training problem before your product works.

What to do instead: Design for 80% accuracy from day one. Either pick a problem where 80% is good enough or build human-in-the-loop workflows where someone checks the output before it goes live. Don't bet your product on the AI getting magically better.

Trap 3: Building Features Nobody Asked For

This one's not unique to AI products, but AI makes it worse because the technology can do so much.

A founder shows me their product roadmap. It's got 15 features. I ask which ones came from user requests.

"None yet, we haven't launched."

"So where did these come from?"

"Well, the AI can do all of this, so we thought..."

That's the problem. You're building what the AI can do, not what users need it to do. You end up with a Swiss Army knife product where every feature is half-useful and the core use case gets lost.

I've seen this play out with an AI email tool. The founder added sentiment analysis, suggested replies, calendar extraction, task detection, and priority scoring. When I asked which feature users wanted most, they didn't know — because they'd never asked.

When they finally did ask, users said: "I just want it to summarise long email threads."

One feature. The rest was noise.

What to do instead: Launch with one AI feature that solves one specific problem. Get 100 people using it. Ask them what they need next. Build that. Repeat. The AI's capability is not your product roadmap.

Trap 4: Underestimating the Operational Reality

AI products don't just need to work in a demo. They need to work at 3pm on a Thursday when your user is tired and the input data is messier than your test cases.

Most founders test their AI with clean, structured data. Real users give you:

• PDFs with broken formatting
• Voice notes recorded in a car with the window open
• Emails where half the context is in a reply chain from six months ago
• Screenshots of spreadsheets instead of actual spreadsheets

Your RAG system that worked perfectly in testing? It falls over when someone uploads a scanned document from 1987.

I worked with a team building an AI research assistant. In demos, it was flawless. In production, users kept uploading massive PDFs that exceeded the context window, asked follow-up questions that referenced previous sessions the AI had no memory of, and expected it to work offline.

None of that was in the original spec.

What to do instead: Build a v0.1 and give it to five real users for a week. Watch what breaks. The operational gaps you discover in week one will save you three months of rework later. And accept that some problems might mean you need to change the product, not just fix the AI.

Trap 5: Believing AI Will Save You from Strategy

The most dangerous trap: assuming that if you just ship something with AI in it, users will figure out the value.

They won't.

I see this with founders who've shipped an AI product in two weeks, then... crickets.

The problem isn't the build. It's that there's no strategy underneath it. Who is this for? What job are they hiring it to do? Why would they switch from what they're using now? How will they find out it exists?

‘It's better than the alternatives’ isn't a strategy. Most users don't even know the alternatives exist.

What to do instead: Before you build, answer these:

• Who is this for, specifically?
• What are they doing now to solve the problem?
• Why would they stop doing that and use your thing instead?
• Where do these people hang out, and how will you reach them?

If you can't answer those, you don't have a product strategy. You have a demo.

Most AI product failures aren't technology failures. They're strategy failures dressed up in impressive demos.

The traps: starting with the model instead of the problem, assuming 95% accuracy when you've got 80%, building features the AI can do rather than features users need, underestimating operational messiness, and believing that shipping something with AI in it is enough.

The fix: talk to users before you write code, design for the AI's actual capability, launch with one feature and iterate, test with real-world chaos, and build a distribution and retention strategy alongside the product.

If you're building an AI product and want someone to challenge your assumptions before you waste six months, book an MVP Clarity Session. I'll tell you what I'd cut, what I'd test first, and whether the thing you're building is actually the thing you should build.