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AI tool evaluation: define the task before choosing the tool
An AI tool should be evaluated against a specific business task, not against an impressive demonstration. Are you drafting public marketing copy, summarizing internal notes or searching customer records? Those uses carry different risks. Write down the expected output, who will review it and what a serious mistake would look like. A tool that performs well on general writing may be unsuitable for sensitive support cases or a regulated decision.
Classify the data you intend to upload
Separate public information, ordinary internal material, confidential commercial details and sensitive personal data. Decide which categories are prohibited without additional approval. A useful default is to test with invented or properly anonymized examples first. Removing a person’s name is not always enough: account numbers, dates, location details or unusual facts can still identify them. If you are working for a client, their permission and policies take priority over your convenience.
Read training and retention policies separately
A statement that data is not used for model training does not mean it is never stored. OpenAI’s business privacy documentation, for example, distinguishes training practices, retention controls and API data handling, with exceptions and eligibility conditions. Other providers and consumer plans can differ. Verify the exact product, plan and settings you intend to use. Do not transfer assurances from an enterprise product to a personal account or assume that “private” means zero retention.
Check account and integration access
Find out who can see conversations, export information, change settings and connect external sources. Review permissions requested by browser extensions or integrations. Give the tool only the access needed for the approved task. If an assistant can read a shared drive or send messages, the review must cover those connected systems too. Access decisions should be owned by an authorized person, not delegated to an enthusiastic team member without a clear policy.
Test quality with representative cases
Build a small evaluation set that includes ordinary tasks, incomplete inputs and ambiguous requests. Compare the output with a checked reference or a human reviewer’s criteria. Record factual errors, unsupported claims, missed instructions and inappropriate disclosure. Do not judge only the best result after repeated prompting. A business process needs predictable quality across realistic inputs, and it needs a clear fallback when the tool cannot produce a reliable answer.
Estimate the whole workflow cost
Include review time, subscriptions, usage charges, integration work and maintenance. A cheap model can become expensive if every output needs extensive correction. Document who handles failures and how you can switch tools or export the work. Start with a supervised pilot and an explicit stop condition. Avoid granting autonomous payment, deletion or publishing permissions simply because the product makes those actions technically possible.
Record an approval decision you can revisit
Create a short record naming the task, approved data categories, product and plan, permissions, retention assumptions and required human review. Include a date for checking the policy again. Vendor terms and features can change. The practical goal is not to prove that AI is universally safe or unsafe; it is to make a bounded, informed decision about one use. If you cannot establish the relevant controls, do not upload the sensitive material.
Sources and further reading
OpenAI: business data and enterprise privacy
Related reading
Local AI or Cloud AI: Privacy, Cost and Practical Trade-Offs