AI headlines promise transformation. In practice, the businesses getting value from AI are doing something humbler: they are removing repetitive work from people’s days.
Start with a boring, measurable problem
The best first AI projects share a pattern: high volume, clear inputs and outputs, and a human who currently spends hours on it. Examples include classifying support requests, extracting data from invoices, drafting first-pass replies, summarising long documents and generating structured reports.
Choose one, define what a good result looks like, and measure time saved before and after.
Reliability beats novelty
A demo that works 80% of the time is impressive. A production system that fails 20% of the time is a liability. Dependable AI needs engineering around the model:
- Retrieval over your own data so answers are grounded and cite sources.
- Evaluation sets that catch regressions when prompts or models change.
- Rate limiting, queueing and retries so spikes do not break the system.
- Human review for low-confidence cases.
- Cost and latency monitoring from day one.
Then expand
Once a first workflow proves value, teams trust the approach and the next use case is easier to justify. Save time first. The bigger ambitions earn their place afterwards.