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Judging AI claims in repair software

The useful question is not whether a product contains AI. Ask what decision it improves, what evidence it uses and what a person can review before acting.

01

Ask for the exact job

A credible feature has a narrow purpose: summarize an inspection, suggest supplier alternatives, flag an unusual price or draft customer-facing language. Broad intelligence claims are hard to test.

02

Inspect the evidence

Recommendations should expose their basis—catalog match, price history, availability, service record or source document—so a user can verify the result.

03

Keep authority with the operator

Define which actions require approval, what is logged and how a user corrects the system. High-impact purchasing, pricing and maintenance decisions should not become invisible automation.

04

Test failure cases

Use incomplete vehicle data, conflicting catalog records, unavailable parts and unusual labor operations. Observe whether the feature signals uncertainty or presents a guess as fact.

05

Demand an operational measure

Choose a measurable target such as sourcing time, avoided price variance, estimate preparation time or follow-up completion. Compare a pilot against the current process before expanding.

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