Pick a repetitive, high-volume task
Customer questions, document handling and lead qualification are good candidates: lots of examples and clear success measures.
Check your data first
AI is only as good as the information it can access. Clean, organised knowledge makes a bigger difference than the choice of model.
Keep a human in the loop
Start with AI drafting and people approving. Increase autonomy as accuracy is proven.
Measure time saved and quality
Track both. Speed without accuracy creates more work, not less.
Putting it into practice
Make a shortlist of tasks your team repeats every day, such as answering the same customer questions, sorting documents or qualifying leads. For each, note how often it happens, how long it takes and how you would know if an answer was right. Choose the one with the most volume and the clearest test of quality.
Before choosing a model or vendor, gather the information the AI will need: help articles, policies, past answers and product data. Clean it up and fill the gaps. Then run the AI as a drafting tool for a few weeks, with a named person approving every output and noting mistakes.
Agree in advance what level of accuracy and time saved would justify giving the AI more freedom. Review a sample of outputs each week, and only reduce human checks once the results support it.
Key takeaways
- Start small and specific
- Prepare your data
- Keep people in control at first
- Measure accuracy as well as speed