Where AI changes work—and where evidence matters

AI can classify, predict, generate, retrieve, and recommend. Whether that changes a business outcome depends on the task, data, workflow, and people around the model.

These five patterns are useful places to look. Each one needs a baseline and a test before it earns a production role.

1. Support a defined decision

A model can find patterns in historical data or estimate a likely outcome. That can help a planner prioritise cases, forecast demand, or compare scenarios.

Test whether the result improves the decision over the current method. Measure error by relevant group and condition, not only an average score. Show users which evidence supports the result and when it may be stale.

2. Reduce repeatable manual work

AI can extract fields, classify requests, summarise material, or draft a response inside a workflow. The benefit comes from changing the process, not from producing text quickly.

Measure completion time, rework, exceptions, and human review. Keep deterministic rules for checks that must be exact. Route uncertain or high-impact cases to a person.

3. Assist customer interactions

Search, recommendations, and conversational interfaces can help customers find relevant information. They can also repeat outdated content or expose data through a poorly designed access path.

Ground responses in approved sources. Test access, refusal behaviour, accessibility, escalation, and whether the customer can reach a person. Measure resolution and correction, not only conversation volume.

4. Detect operational signals

Forecasts and anomaly detection can help teams inspect equipment, inventory, logistics, or service performance. A signal has value only when someone can act on it.

Define the action, owner, response time, and cost of false alerts. Run the model beside the current process before allowing it to trigger a material change.

5. Add a product capability

AI can create a new search, assistance, personalization, or analysis feature. Start with the user’s job and the evidence that existing tools do not meet it.

Design for monitoring, feedback, data rights, model changes, and withdrawal. A feature that cannot be evaluated or supported becomes product debt.

Use one evidence card for every idea

Record the user, task, source data, model role, human decision, prohibited action, baseline, acceptance threshold, and owner. Reject ideas that cannot name these fields yet.

The original 2024 infographic PDF summarises the five categories. It is a discussion aid, not evidence that a use case will deliver a result.