Data Privacy Considerations in AI Implementations

Evaluating an AI vendor requires looking well past the polished demo to questions about data handling, real accuracy on your own data, and what happens if the relationship ends. Beyond the demo, ask how the vendor handles your data, whether it is used to train their models, and what happens to your data if you cancel the contract.

Also check the actual accuracy on your own data through a pilot, not just published benchmarks, since performance varies significantly by industry and use case.

We run structured pilots for clients evaluating AI vendors, comparing real outputs on their own data before any long-term commitment is made.

Data handling questions deserve specific, written answers rather than vague assurances: where is data processed and stored geographically, is it used to improve the vendor's models for other customers, how long is it retained, and can you get a full export of your data and configuration if you decide to leave. Vague or evasive answers to any of these are a warning sign worth taking seriously.

Published benchmarks are a starting filter at best, not a decision-making tool, since a vendor's reported accuracy is almost always measured on a dataset that does not resemble your specific documents, terminology, or edge cases. We have seen vendors that score well on general benchmarks perform noticeably worse once tested against a client's actual, messier real-world data.

We also recommend evaluating the vendor's support and update cadence, not just the product itself: how often do they change model behaviour without notice, what does their incident communication look like during an outage, and how responsive is support when something genuinely breaks in production. These operational factors often matter more to long-term satisfaction than a small difference in headline accuracy.

Our standard evaluation process runs a structured pilot with a fixed, representative set of real client data and tasks, scoring each vendor against the same criteria side by side, so the final decision is based on comparable evidence rather than impressions from separate demo calls.