Where Should a Business Start with AI?

A good start does not begin with picking a model or tool. It begins by finding work that is time-consuming, repetitive or prone to avoidable errors.

1. Narrow the problem. Instead of “use AI across the business,” select one task such as searching internal documents, classifying incoming requests or drafting a report.

2. Establish a baseline. Record current completion time, error rate, request volume and cost. Without a baseline, it is hard to determine whether a solution creates value.

3. Review data access and quality. Required data should be authorized, accessible, current and relevant to the goal. Do not send confidential information to unknown services.

4. Build a small pilot. Test a controlled set of representative cases. Track incorrect answers, edge cases, cost per run and where human review is required.

5. Define acceptance criteria. Accuracy alone is not enough. Consider speed, cost, explainability, security, user experience and the impact of expensive errors.

6. Assign an owner before launch. Decide who monitors quality, when a case is routed to a person and how the team can fall back to the previous process during an incident.

The best first pilot is usually small, measurable and reversible. Once value is demonstrated, it can be connected to more systems and workflows in stages.