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PIPEDA applies to personal information an organization collects, uses or discloses in the course of commercial activity, and Schedule 1 sets out the fair information principles: accountability, identifying purposes, consent, limiting collection, limiting use, disclosure and retention, accuracy, safeguards, openness, individual access and challenging compliance. Feeding customer records into a system to train or run a model is a use of that information, and it has to sit inside a purpose people were told about.

The Office of the Privacy Commissioner of Canada, with its provincial counterparts, published principles for the responsible development and use of generative AI. They include establishing legal authority and valid, meaningful consent; using personal information only for appropriate purposes; showing that the use is necessary and proportionate; being open about what is collected and why across the system's life; limiting collection rather than hoovering up data on the strength of broad claims about future purposes; and maintaining that accountability for decisions rests with the organization and not with any automated system. That last one is the sentence to keep in front of you when a vendor describes a system as self-managing.

Quebec goes further on automated decisions. Under Law 25, an organization that uses personal information to make a decision based exclusively on automated processing must inform the person no later than when it tells them the decision, and must give them the opportunity to submit observations to a member of personnel who is in a position to review it. On request, the person can be told the personal information used, the reasons and the principal factors and parameters behind the decision, and their right to have inaccurate information corrected. If your system will decline, price or prioritise anyone in Quebec, that review path has to exist in the workflow, not just in a policy document.

The single most important clause is what the consultant and the underlying model vendor may do with what you send. Ask in writing whether your inputs and outputs are retained, for how long, whether they are used to improve or train anyone's model, whether human reviewers see them, and how deletion works. If the consultant cannot answer for the model vendor sitting behind their product, they have not read the terms they signed on your behalf.

Location matters too, though not in the way people assume. The Office of the Privacy Commissioner's guidance on transfers for processing says PIPEDA does not prohibit transferring personal information to an organization in another jurisdiction for processing, but the transferring organization stays accountable, must use contractual means to provide a comparable level of protection, and should tell customers in clear language that their information may be processed abroad and may be accessible to foreign courts and authorities. Most AI tooling routes data outside Canada, so this is usually a live question rather than a theoretical one.

Then cover the ordinary commercial ground: ownership of the prompts, configurations, evaluation sets and code produced, what happens to them if you part ways, and a security expectation proportionate to how sensitive the data is. If a breach happens, PIPEDA requires organizations to assess whether it creates a real risk of significant harm, to report qualifying breaches to the Commissioner and notify affected individuals, and to keep records of all breaches of security safeguards for two years.

AI systems drift in ways ordinary software does not. The underlying model gets updated by its vendor, your own data shifts with the season or the product line, and staff invent uses nobody designed for. Something has to be sampled and checked on a schedule, by a person who knows what a good answer looks like, with a written record of what was reviewed and what was found.

Plan for the handover in the contract rather than at the end. You want the prompts and configuration, the evaluation set, the integration code, documentation of every external service in the chain, and administrative access to the vendor accounts in your organization's name. The federal voluntary code of conduct for advanced generative AI systems, which signatory organizations adopt by choice, includes commitments around human oversight and monitoring and around post-deployment incident tracking; even though it binds nobody who has not signed it, it is a reasonable checklist for what your own operating routine should cover.

For a common problem — meeting notes, transcription, document search, customer-service drafting — an off-the-shelf product usually beats a custom build, and the consultant's value is in selection, configuration, policy and training rather than engineering. Paying for a bespoke system that duplicates a mature product is the most common way money disappears in this category.

A consultant earns their fee when the problem is specific to your business: your data, your workflow, your integrations, your regulatory position. Building in-house makes sense once the system is central to how you operate and you can keep somebody on it, but it is a poor first step, because the first attempt teaches you what you actually needed and you would rather pay for that lesson once.