AI is easy to add to a product and easy to add badly. The products that get real value from it usually start from a specific task, not from a desire to "have AI."
Start from a task, not a technology
Instead of asking "where could we use AI," look for tasks in your product or workflow that are:
- Repetitive — something a user or your team does the same way, often
- Time-consuming relative to its value — small but frequent friction
- Pattern-based — the kind of task where past examples predict what's needed next
Search, summarization, document processing, drafting, and recommendations are common categories because they tend to fit this shape.
Check whether a simpler approach already solves it
Not every repetitive task needs AI. If a fixed rule, a better default, or a small workflow change solves the problem just as well, that's usually the better first move — it's cheaper to build and easier to maintain. AI is worth reaching for when the task is genuinely too varied or unpredictable for simple rules to handle well.
Design for trust, not just capability
Especially with LLM-based features, usefulness depends on more than raw capability:
- Show where an answer or suggestion came from when possible
- Make it easy to correct or override the AI's output
- Design for the AI being wrong sometimes — because it will be
A feature that's occasionally impressive but unpredictable is often worse for trust than one that's consistently useful in a narrower way.
Prototype against real data before committing
Before building an AI feature into the product, test it against real examples from your actual use case — not a generic demo. This is usually the fastest way to find out whether it will genuinely help, and where it's likely to fail.
Where this fits into your roadmap
Adding AI to an existing product is a normal, common project for us — not a separate track from regular development. If you have a specific task in mind, let's explore whether AI is the right fit.