AI market forecasting: evaluate before buying

Decide what the forecast is for

A market forecast should support a defined decision, such as purchasing, staffing or pricing. Start with the measure, horizon and cost of being wrong. Large market-size estimates do not tell you whether a forecasting tool will improve that decision.

Evaluate against a simple baseline

1. Keep a historical period out of model development so it can be used for evaluation.
2. Compare the model with a simple baseline appropriate to the problem, such as the previous period or a seasonal estimate.
3. Measure error by product, location or customer group where those differences affect the decision.
4. Include missing data, promotions, unusual periods and changes in the business.
5. Review the operational cost of false alarms and missed changes, not only the average error.

Choose the implementation route

First test whether an existing planning or analytics tool can support the decision. A bespoke model is a separate option where the data, scale or workflow justify it. Confirm access, refresh frequency, ownership, human review and the process for retiring a model that no longer performs adequately.

Read vendor claims carefully

A vendor result does not automatically establish forecast accuracy or a benchmark for your business. Record the organisation, period, measure and whether a number is a target or an achieved result. Use your own pilot to assess transferability.

Prepare a reviewable business case

Estimate implementation and operating costs, define a baseline and assign an owner to measure the outcome. A forecast improvement creates business value only when it changes a decision usefully. The AI business-case template and data-preparation guide provide the next steps. See AI Implementation when a workflow and testable objective are ready.