Building AI Features People Actually Want to Use

Adding an AI feature to a SaaS product is easier than deciding whether the feature should exist in the first place.

A chatbot can be added to almost any application. That doesn't necessarily make the application better.

What interests me more is how AI fits into an existing workflow.

Take three different products.

In a sales automation platform, AI can help research prospects and personalize outreach.

In a document management platform, it can answer questions using information from uploaded files.

In a CRM, it can summarize customer interactions and help identify opportunities that need attention.

The technology may be similar, but the user expectations are quite different.

Start with the problem

I prefer starting with a specific task that takes too much time or requires too much manual effort.

Only then does it make sense to decide whether an LLM is the right solution.

Some tasks need language understanding. Others are better handled by ordinary application logic.

A useful AI feature should also fit naturally into the interface. Users shouldn't need to understand prompts, embeddings, or model configurations to benefit from it.

The details that matter

Response speed, output quality, cost, and reliability all affect the experience.

For tasks involving important business decisions, users may need to review or edit AI-generated results before taking action.

It's also worth measuring whether the feature actually saves time. A technically impressive AI integration doesn't mean much if people rarely use it.

I think the most successful AI-powered SaaS products will be the ones where AI feels like a natural part of the software, rather than a separate feature competing for attention.