The first decision is whether the information belongs in the tool at all. Purpose, permission and accountability come before convenience.
AI can help with parts of a consulting assignment. It can organize public material, suggest a structure or help a team examine a question from several angles. The difficulty begins when convenience moves faster than the decisions about information.
An interview transcript is a record of something a person agreed to share in a particular setting. It may contain personal details, confidential organizational information or knowledge that carries restrictions on further use. Uploading it to another service introduces a new decision about access and processing.
Canada’s privacy commissioners have made clear that generative AI sits within existing privacy obligations. Their guidance calls for attention to appropriate purposes, safeguards, transparency and accountability. The rules that apply depend on the organization and its activities.
For a project team considering AI-assisted analysis, we recommend a short decision record before any upload.
• Purpose. What specific task will the tool perform, and is there a less intrusive way to do it?
• Authority. What permissions, agreements and applicable requirements govern this information?
• Service. Where is it processed, who can access it, how long is it retained and can it be used for model training?
• Review. Who will verify the output, correct errors and remain accountable for the final analysis?
Removing a name does not automatically make a record safe to share. A role, location or distinctive account may still identify someone. Summaries can also lose context or turn a qualified observation into a confident conclusion.
A practical starting point is to work with public or purpose-created material while assessing the tool and the proposed use. For restricted records, resolve the permissions and service controls first. An enterprise subscription alone does not settle those questions.
The project still needs human judgement. Check the output against the source, retain disagreements where they matter and give participants an appropriate opportunity to validate how their contributions have been interpreted.
The value of AI should be assessed against the quality of the work and the responsibilities attached to it. Faster processing is useful only when the project remains worthy of the trust people placed in it.
