Where AI actually fits in your product

Most conversations about adding AI to a product start in the wrong place: with the model. Which one is best? Should we fine-tune? Do we need a chatbot? The better starting point is the work itself. Where do people in your product, or your team, spend time on tasks that are repetitive, text-heavy and tolerant of a first draft?

Start with the workflow, not the model

Write down the steps a user or employee actually takes to get a job done. For each step, ask three questions:

• Is it mostly reading, writing, sorting or matching? Those are the jobs language models are good at.
• What happens if the output is slightly wrong? If a person reviews it anyway, a good draft is valuable. If an error is silent and costly, be careful.
• How often does it happen? A small saving on a step done hundreds of times a day beats a big saving on something done once a month.

Steps that score well on all three are your candidates. In our experience they are rarely the flashy ones.

Patterns that usually work

• Drafting: a first version of a summary, a reply, a description or a bullet point that a person then edits.
• Extraction: pulling structured fields out of messy text, such as invoices, emails, resumes or forms.
• Classification and routing: tagging incoming requests and sending them to the right place.
• Matching and scoring: comparing two documents, such as a resume and a job description, and explaining the gap.
• Search over your own content: answering questions from documents the user is allowed to see, with sources.

A concrete example from our own work: in BrainUp, AI rewrites weak resume bullet points and scores a resume against a job description. The person stays in charge, every suggestion is editable, and the feature saves time on exactly the part of resume writing people find hardest.

The best AI features feel like a shortcut through work the user was already doing, not a new place they have to go.

Patterns that usually don't

• A generic chat window added to a product with no clear job to do.
• Fully automated decisions with no review, where a wrong answer is expensive or hard to notice.
• Anything that needs exact arithmetic or guaranteed facts with no way to check them.

Prove it small, then build it properly

Before committing to a full build, test the riskiest assumption cheaply. Take twenty real examples, run them through a model with a simple prompt, and have the people who do the work today judge the results. If most outputs are useful, you have a feature. If they aren't, you have saved yourself a project.

Once a use case passes that test, it deserves real engineering: clear inputs, validated outputs, sensible fallbacks, logging, cost limits and a way to measure whether it keeps working. That is the difference between a demo and a product.

Where to go from here

If you are not sure where AI fits in your product, that is exactly the question we help answer. We map the opportunities, separate the useful from the fashionable, and build the ones worth building. Start a conversation through the contact page.