The useful bar for AI in a business is simple: does it return time, reduce errors, or improve decisions — inside tools people already open every day?
Look for high-volume, low-judgment work first
Strong early candidates include:
- Document classification and extraction
- Draft responses that a human reviews
- Search across internal knowledge
- Forecasting where you already have clean historical data
Prototype with cost and risk in mind
A pilot should measure accuracy, latency, unit cost, and failure behaviour — not just a happy-path demo. Define human oversight before you scale.
Integrate, don’t isolate
A chatbot that lives outside your CRM and ticket tools becomes another silo. Embed features where work already happens, with clear audit trails.
Improve in production
Logging, evaluation sets, and feedback loops turn a launch into a system that gets better. Without them, quality drifts and costs climb.
If you want AI that ships rather than slides, start with one measurable workflow — then expand.