"We need to add AI to the product."

I've heard this from founders, product managers, even investors. AI is the feature checkbox of the moment.

But most AI features are solutions looking for problems. They're added for marketing, not users.

Here's how to add AI that actually helps.

The Wrong Reasons to Add AI

Red flags that suggest you're adding AI for the wrong reasons:

"Competitors have it." Following isn't strategy. Their AI features might be useless too.

"Investors want to see it." Building for investors instead of users rarely ends well.

"AI is the future." True, but vague. What specifically will AI improve?

"It'll look innovative." Innovation theater. Users want solutions, not buzzwords.

If you can't articulate the specific user problem AI solves, you shouldn't add it.

The Right Reasons

AI makes sense when:

A task is tedious for users. Summarizing, categorizing, extracting information from messy data.

Human judgment isn't required. Mechanical decisions that follow patterns.

Speed matters. Tasks that are possible but slow without AI.

Expertise is a barrier. AI can provide capabilities users don't have.

Notice the pattern: AI should make users' lives easier in specific, measurable ways.

Starting with the Problem

Before thinking about AI, ask:

What do users struggle with? Watch them use your product. What's painful?

What do they ask for help with? Support requests reveal friction.

What takes too long? Where do users spend time on grunt work?

What requires expertise they lack? Where are users stuck without specialized knowledge?

Find the problem first. Then ask if AI is the solution—often it isn't.

AI Features That Actually Work

Patterns that deliver value:

Smart defaults. Pre-fill forms based on patterns. Suggest settings based on similar users.

Content generation. Draft emails, create descriptions, generate reports from data.

Classification and tagging. Auto-categorize content, suggest labels, organize information.

Summarization. Condense long content into key points.

Search enhancement. Natural language queries instead of exact match. Semantic understanding.

Anomaly detection. Flag unusual patterns. Surface things that need attention.

These are boring. They're also useful.

AI Features That Usually Fail

Common mistakes:

Chatbots for everything. Most users don't want to chat. They want to accomplish a task directly.

AI replacing core actions. "Let AI do it for you" when users want control and understanding.

Generating what users want to create. If creation is the value, AI shortcuts undermine it.

Autonomous agents. Great in demos. Frustrating in production when they do the wrong thing.

AI for AI's sake. Features added because AI can do them, not because users need them.

The Implementation Reality

Adding AI isn't just about features. There's infrastructure:

Cost. API calls add up. Every AI feature has a per-use cost.

Latency. AI takes time. Users notice delays. Design for async where possible.

Reliability. Models have downtime. Rate limits. Changed behavior. Plan for failures.

Quality variance. AI output isn't consistent. Sometimes it's wrong. Handle gracefully.

These aren't reasons to avoid AI—they're factors to design around.

Shipping AI Features

A pragmatic approach:

  1. Start with one feature. Not an "AI-powered product"—one AI feature that solves one problem.

  2. Keep humans in the loop. AI suggests, users confirm. Build trust before automation.

  3. Make it optional. Let users turn it off. Some won't want it.

  4. Show your work. Don't magic. Show what the AI did so users can verify.

  5. Measure impact. Did the feature actually help? Are users using it? Would they miss it?

  6. Iterate based on feedback. First version won't be perfect. Listen and improve.

The Integration Decision

Build vs. buy:

Use APIs (OpenAI, Claude, etc.) when: You need general capabilities, time to market matters, you're testing product-market fit.

Build/fine-tune when: You need specialized performance, cost at scale is critical, you have unique data advantages.

Most solo founders should start with APIs. Don't build infrastructure until you've validated the feature matters.

Related Reading