AI in MVPs: Where It Actually Helps (and Where It's a Trap)

Every discovery call in 2025 includes the same question: "Can we add AI to this?" Sometimes the answer is yes. Sometimes the honest answer is: not yet.

After integrating LLMs into five client projects over the past year, we've developed a clear framework for when AI adds real value to an MVP and when it's just burning runway on a feature that sounds good in a pitch deck.

When AI Actually Helps

1. Replacing Manual Workflows That Already Exist

The best AI features replace something a human is already doing manually — and doing slowly. We built an AI-powered job matching system that replaced a recruiter spending 4 hours per day screening resumes. The LLM wasn't perfect, but it was 80% as good and 50× faster.

The pattern: If a human is doing repetitive cognitive work with clear inputs and outputs, AI is probably worth it.

2. Making Search Actually Useful

Traditional keyword search fails when users don't know the right terms. We added semantic search to a knowledge base product and saw search success rates jump from 35% to 72%. The user just describes what they need in plain language.

The pattern: If your users are searching and not finding, AI search is a genuine improvement — not a gimmick.

3. Generating First Drafts

AI is excellent at producing a starting point that humans then refine. We built a proposal generator for a consulting firm that creates 80% of a client proposal automatically. The consultant then spends 15 minutes polishing instead of 2 hours writing from scratch.

The pattern: AI generates, humans verify and refine. This workflow actually ships.

When AI Is a Trap

1. When Your Core Product Doesn't Work Yet

We've talked founders out of adding AI chatbots to products that don't have basic user flows working. If your signup funnel is broken, an AI assistant won't fix it. Get the fundamentals right first.

2. When You Can't Define "Good Output"

LLMs are probabilistic. If you can't clearly evaluate whether the output is correct, you can't build a reliable feature. We passed on building an AI-powered legal document generator because the cost of a bad output was too high and validation required a lawyer anyway.

3. When the API Costs Will Kill Your Unit Economics

A client wanted AI-generated personalized recommendations on every page load. At 10,000 daily users, that's $3,000/month in API costs for a feature that could be replaced by a simple algorithm. We built the simple version first. It worked fine.

Do the math before you build. Estimate API costs at your target user count. If AI costs more than the revenue it enables, it's not ready for your MVP.

Our AI Stack in 2025

After a year of real-world integration, here's what we actually use:

• Claude API for structured outputs and complex reasoning (our go-to for most features)
• OpenAI for embeddings and when clients specifically request GPT
• Vercel AI SDK for streaming responses in Next.js apps
• Pinecone or Supabase pgvector for RAG pipelines
• No agents yet — we've experimented but they're not reliable enough for production client work

The Real Question to Ask

Don't ask "Can we add AI?" Ask: "What's the most tedious thing our users do repeatedly, and can AI do 80% of it?"

If the answer is clear, build it. If you're reaching for a use case, ship without AI and add it in v2 when you understand your users better.

The best AI feature is one your users don't even think of as AI. They just think the product is fast and helpful.