Most conversations about AI focus on what is possible. For a growing business, the more useful question is: what is worth doing first?
Look for repetitive, text-heavy work
The best early AI projects share three traits: they happen often, they involve reading or writing text, and a human can easily check the result. Think support replies, invoice processing, proposal drafts or summarising customer calls.
Start with your own knowledge
An assistant grounded in your policies, product documentation and past tickets is far more useful than a generic chatbot. Retrieval-augmented generation lets the model answer from your content and cite its sources, which builds trust with staff and customers.
- Define what a good answer looks like before building anything.
- Create a small evaluation set of real questions and expected answers.
- Measure accuracy, cost per request and time saved every week.
Keep humans in the loop
For anything customer-facing or financial, design the workflow so AI prepares and a person approves. As confidence grows, you can automate more steps with clear guardrails.
Done well, a focused AI project can save dozens of hours every week and give your team more time for the work that genuinely needs them.
Tags
- #AI
- #Product Strategy
