AI-Generated Contracts: Legal Risks GTA Businesses Should Understand in 2026

Where AI is showing up in contract workflows

In 2026, GTA businesses are using generative AI tools across most stages of the contract lifecycle:

• Drafting first-cut agreements from a plain-language description.
• Marking up counterparty drafts against an internal playbook.
• Summarizing long contracts into plain-language briefings for non-legal decision-makers.
• Extracting key terms — renewal dates, liability caps, indemnity language — from executed contract portfolios.
• Generating first-cut NDAs, MSAs, statements of work, and vendor terms on demand.

Used carefully, these workflows can save time and improve consistency. Used carelessly, they introduce a specific set of legal and operational risks that GTA business owners should understand before AI-generated documents start moving through their organization.

Risk 1 — Unreviewed clauses that do not fit the transaction

Generative models produce contract language by predicting what usually appears in similar-looking documents. The result is often plausible on its face but does not reflect the specifics of the actual transaction. Common patterns:

• A limitation-of-liability clause capped at fees paid in the prior 12 months, imported without regard to the actual risk profile of the deal.
• Indemnity language cross-referencing definitions that do not exist elsewhere in the document.
• Governing-law and forum clauses that default to a US state when the deal is entirely between Ontario parties.
• IP-assignment language that assumes an employment relationship when the counterparty is actually a contractor.
• Termination-for-convenience windows that do not match what was actually negotiated.

These are not new problems — cut-and-paste from prior deals produces similar issues. What is different is the pace at which unreviewed language can now be generated and inserted. Without a lawyer-reviewed workflow, this pace outruns the ability of the business to catch mistakes.

Risk 2 — Hallucinated legal citations and phantom statutes

When AI tools are asked to justify a clause or draft an argumentative provision, they sometimes cite statutes, sections, or cases that do not exist, or that exist but say something different from what the model asserts. This is a well-documented failure mode of generative language models and has led to sanctioned filings in Canadian and US courts over the past two years.

In a contract, a fabricated citation is usually less catastrophic than in a court filing, but it still matters:

• A recital that misstates a statute can be used against the drafter later.
• A defined term that references a non-existent piece of legislation may be unenforceable.
• A boilerplate compliance-with-laws clause that references the wrong regulator gives false comfort.

Assume that any statute, section number, or case citation appearing in AI-generated contract text has to be checked against a primary source before the document is signed.

Risk 3 — Confidentiality, privacy, and third-party model exposure

When contract text is submitted to a third-party AI service, several categories of risk arise depending on how the service is configured:

• Contractual confidentiality obligations. Many contracts contain confidentiality clauses that prohibit disclosure to third parties without consent. Submitting a counterparty’s draft to a public AI service can be a technical breach even if no human ever sees the input.
• Personal information under PIPEDA. Contracts often contain personal information — names, contact details, sometimes financial details — that is subject to Canadian privacy law. Submitting that content to a third-party service based outside Canada implicates cross-border data-transfer rules and, in some cases, consent obligations.
• Confidential business information. Pricing, customer lists, and negotiation history embedded in a draft can be exposed if the AI service retains input data or uses it for model improvement.

The Office of the Privacy Commissioner of Canada has issued principles for the responsible use of generative AI that address these concerns, including the need for meaningful consent, purpose limitation, and appropriate safeguards on cross-border transfers of personal information.

Practically, this means the AI tool your business uses matters. Enterprise-configured tools with data-retention controls, contractual data-processing terms, and Canadian or SOC 2-audited hosting are a different risk profile than a free consumer chatbot. Both may produce useful contract text; only one is safe to feed sensitive material into.

Risk 4 — Audit trail and evidentiary questions

If a contract is later disputed, courts and arbitrators may ask questions about how the document was produced and who reviewed it. This is not new — the same questions arise with template contracts and drafts produced by junior staff. What is new is the scale of AI-generated language in circulation and the possibility that a party will argue the document was signed without informed human involvement.

For GTA businesses that use AI tools in contracting, it is worth building a light-weight audit trail that captures:

• Which parts of a contract were AI-generated versus human-drafted.
• Which internal person reviewed the AI output before it was sent to the counterparty.
• Which lawyer, if any, reviewed the final version.
• The date and version of the tool used.

Risk 5 — Professional-conduct implications when lawyers are in the loop

When lawyers use AI tools in contract work, professional-conduct duties still apply. The Law Society of Ontario’s Rules of Professional Conduct require competent representation and confidentiality with respect to client information. Regulators in Canada and internationally continue to emphasize that AI tools do not shift the underlying professional responsibility of the human lawyer. For businesses that engage external counsel, the practical implication is that you should be able to ask your law firm how it uses AI, what safeguards apply to your matter, and how the firm records that AI was used.

The Office of the Superintendent of Financial Institutions issued sound-practices guidance for financial-sector AI use in 2026, and while that guidance is directed at federally regulated financial institutions, several of its themes — model risk, human-in-the-loop expectations, and third-party oversight — are useful reference points for any GTA business building an internal AI policy.

A practical checklist for GTA businesses using AI in contracting

Before an AI tool becomes part of your contracting workflow, work through this checklist with your lawyer:

1. Decide which categories of contracts are eligible for AI-assisted drafting or review, and which are not (e.g., material customer agreements, financing documents, shareholder agreements — human-drafted only).
2. Confirm the AI tool’s data-handling terms. Read the vendor’s data-processing addendum. Confirm whether inputs are retained and whether they are used for model training.
3. Identify a human owner for every AI-assisted contract. Every document that goes out the door has a named human reviewer.
4. Update your NDAs and vendor contracts to address AI use. If you are on the receiving end of a counterparty’s AI-generated draft, you may want language on quality, accuracy, and confidentiality.
5. Review your internal privacy notice. If personal information is being submitted to an AI service, confirm your privacy notice and consent language reflect that.
6. Build a lightweight audit trail. Record which contracts used AI, which human reviewed them, and which lawyer signed off.
7. Train the team. The people generating contract text using AI need to understand these risks. Half of contract risk is drafting culture, not the tool itself.

Frequently asked questions

Are AI-generated contracts enforceable in Ontario?

Yes. A contract is not less enforceable because it was drafted with the help of an AI tool. The same rules on offer, acceptance, consideration, capacity, and intention to be bound apply. The risks discussed here relate to the accuracy, appropriateness, and confidentiality of the content — not to a legal question about whether the resulting agreement is valid.

Can our law firm use AI in drafting our contracts?

Yes, subject to professional-conduct obligations. Under the Law Society of Ontario’s Rules of Professional Conduct, lawyers must provide competent representation and maintain client confidentiality; those obligations do not change when AI is involved. It is reasonable to ask your law firm about its AI-use policy.

Is it a problem to feed a counterparty’s draft into an AI tool for review?

It depends on the tool and on the confidentiality obligations attached to the draft. If the draft is subject to an NDA or a confidentiality clause, submitting it to an AI service that does not have appropriate data-handling terms may be a breach. Enterprise-configured tools with strong retention controls are a different risk profile than public chatbots.

Does Canadian privacy law affect our AI contract workflow?

Yes, where personal information is involved. PIPEDA and provincial privacy laws apply to personal information handled by commercial organizations; submitting personal information to a third-party AI service — particularly one hosted outside Canada — implicates purpose-limitation, consent, and safeguards obligations.

What about hallucinated statutes and case citations?

Assume any legal citation appearing in AI-generated text has to be checked against the primary source before the document is signed. This is a well-documented failure mode of generative language models and there is no reliable way to eliminate it from the model itself.

How Mann Law helps

Mann Law works with GTA businesses on commercial contract workflows — from template design and playbook creation through counterparty negotiation and dispute triage. Where AI tools are part of the client’s workflow, we help design a lawyer-reviewed process that captures the efficiency of AI-assisted drafting while managing the confidentiality, accuracy, and audit-trail risks discussed here.

A short contract-process review typically identifies which contracts are appropriate for AI-assisted work, which are not, and what changes to internal policy make the workflow safer.