Summary: Canada’s artificial intelligence (AI) rulebook kept evolving after the Artificial Intelligence and Data Act (AIDA)¹, part of Bill C-27, died when Parliament was prorogued on January 6, 2025. Bill C-36² was introduced on June 15, 2026 and is currently at second reading, while Ottawa launched the National Artificial Intelligence Strategy³ and an AI-transparency consultation platform⁴.
Federal policy is taking shape, but the practical implication for business leaders goes beyond preparing for a unified AI law. Business leaders also need to weigh ethical concerns around fairness, privacy, transparency, accountability, and human oversight, because even a legally permitted AI application can create reputational risk and erode trust if stakeholders view its use as unfair, intrusive, or insufficiently accountable.
This article explains what governs AI in Canada now, which guardrails matter, what to adopt first, and how to pilot and scale responsibly.
Why AI Governance Is a Board-Level Question Now
The AI debate has shifted. The question is no longer what the technology can do, but where it earns the investment, where risks outweigh rewards, and how far the business should go.
Poor controls can expose personal data, amplify bias, make decisions harder to explain, deepen vendor dependence, and leave promising pilot projects stuck in testing and never scale. Canadian privacy regulators⁵ stress that organisations using generative AI remain responsible for complying with privacy law and for decisions their systems support.
That is why AI governance should be part of your business’s digital transformation strategy. Clear ownership, approval rules, and risk boundaries make AI easier to adopt with greater confidence. AI governance is the set of rules, roles, and controls used to select, deploy, monitor, and retire AI systems.
So, what does responsible AI adoption look like in practice? It comes down to four strategies: know the rules, set the guardrails, choose your bets wisely, and scale without outrunning your controls.
The Canadian Rulebook: What Actually Governs AI Here
Canada’s AI rulebook has a gap where a federal AI law might be. AIDA, Part 3 of Bill C-27¹, died when the parliamentary session ended on January 6, 2025 and has not been reintroduced. That leaves businesses without one rulebook, but not without rules.
- Privacy: The Personal Information Protection and Electronic Documents Act (PIPEDA)⁶ applies within its federal scope; Alberta, British Columbia, and Quebec have substantially similar private-sector laws⁷ that may apply instead. Quebec’s Law 25⁸ adds rights when a decision is based exclusively on automated processing.
- Sector Rules: The Office of the Superintendent of Financial Institutions⁹ published an AI-risk framework for financial services, while Health Canada¹⁰ has guidance for machine learning-enabled medical devices.
- Models to Borrow: The federal Directive on Automated Decision-Making¹¹ uses risk-tiered Algorithmic Impact Assessments for covered government systems. Ontario’s enacted Bill 194¹² establishes a framework for the use of AI by prescribed public-sector entities.
The takeaway: the rulebook is still taking shape, but guardrails already exist. Canada’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems¹³ offers a non-binding governance model; the European Union AI Act¹⁴ may apply to some Canadian businesses; and PIPEDA and Quebec’s Law 25 already impose duties. Bill C-36² signals further federal change.
A Governance Framework: Guardrails Before You Scale
Start with an AI use policy that answers the questions your employees face before opening a tool: What uses are allowed? What data may be entered? Who approves systems? Which uses are prohibited?
Build disclosure rules for customer or employee uses where transparency is appropriate or legally required.
However, an AI application can comply with the law and still raise ethical concerns. This is especially important when AI could influence significant decisions about people, including their opportunities or access to services.
Your governance framework should therefore address both legal requirements and ethical standards such as fairness, transparency, accountability, and human oversight. These principles are reflected in Canada’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems, introduced in September 2023.5
For higher-risk applications, consider a cross-functional AI ethics or governance committee that reports to executive leadership and reviews whether proposed uses are not only legally compliant, but also ethically responsible.
An AI governance checklist and policy put those principles in place, with oversight rising alongside risk.
- Inventory and risk-tier use cases. Borrow the logic of Canada’s Algorithmic Impact Assessment¹⁵: assess potential harm to determine the oversight a use case needs. A drafting assistant should not be subject to the same controls as a hiring, credit, or safety system.
- Govern the data. Document what data feeds the system, whether it contains personal or confidential information, where it is stored, who accesses it, and how long it is retained. Canadian privacy guidance⁵ recommends documenting legal authority, limiting collection, and setting retention schedules.
- Oversee models and vendors. Whether internally developed or externally procured. Build documentation, testing, and monitoring requirements, and checks for bias, accuracy problems, security weaknesses, and model drift. Your vendor supplies the tool, but the organization deploying it remains responsible for its governance, oversight, and compliance obligations.
- Assign ownership. Assign an executive to oversee your company’s AI risk and governance, with well defined roles, and a reporting path to leadership and board. Include ethical and reputational risk in that mandate, and outline when higher-risk uses must be escalated for additional review.
- Use a recognised framework. The Canadian Cyber Security Skills Framework¹⁶ provides guidelines to help businesses navigate cybersecurity. ISO/IEC 42001¹⁷ provides a certifiable management system standard for governing AI throughout its lifecycle.
The Decision Framework: Build, Buy, and What to Adopt First
Governance sets the guardrails. The next challenge is deciding where AI is worth the investment.
- Start with readiness, not a product demo. Test whether your business problem is worth solving, the data is reliable, systems can support the technology, and someone from your team can own implementation.
- Sequence opportunities by value and risk. Drafting, summarization, internal knowledge search, and forecasting support can be sensible starting points, as people can review the outputs before acting. Uses influencing employment, credit, health, safety, or people’s rights need stronger controls.
- Build-versus-buy decision framework. Building offers control and customization, while buying can move faster with less in-house burden. The right choice depends on strategic value, resources, and flexibility. Ask four questions:
- Is the capability genuinely strategic or differentiating?
- Do you have the data, talent, budget, and time to maintain it?
- Can a vendor meet control requirements faster?
- What happens if you need to change vendors later?
For many mid-market firms, buying and configuring an established platform is the practical default unless proprietary AI is central to competitive advantage.
- Evaluate more than features. Review security, data handling and residency, model transparency, contractual protections, indemnities, retention, portability, and whether your prompts or data can train the vendor’s model.
- Right-size for smaller firms. A short AI-use policy, an approved-tool list grouped by risk, and one well-governed pilot can be enough to start responsibly.
A Staged Roadmap: From Pilot to Scale, Safely
Once the policy and decision framework are in place, implementation should advance in clear stages. The point is to earn the right to scale rather than assume every pilot deserves it.
Stage 1: Assess and set policy. Begin with an AI readiness assessment covering business value, data, technology, people, and risk. Approve the AI-use policy, choose one use case, name its owner, and define success before implementation begins.
Stage 2: Pilot small. Run a scoped, lower-risk pilot with human oversight, a time box, and measurable targets such as accuracy, turnaround time, cost, or customer outcomes. Keep the test narrow enough to learn what fails as well as what works.
Stage 3: Govern and monitor. Before deployment, put controls in place for accuracy, privacy, security, bias (where relevant), access, and logging. Maintain an audit trail and an incident-response plan that explain how employees should escalate harmful, inaccurate, or unexpected outputs. The Principles for responsible, trustworthy and privacy-protective generative AI technologies, released by the Office of the Privacy Commissioner of Canada,⁵ recommend ongoing monitoring and regular reassessment, with stronger safeguards where impacts may be significant.
Stage 4: Scale through gates. Expand only when agreed metrics and control requirements are met. Use go/no-go checkpoints for each new process, dataset, user group, or geography, and preserve the ability to suspend or roll back the system if performance drifts or rules change.
AI ethics becomes practical when you keep people in view. Be open with your employees and clients about what they need to know, train users on system limits, preserve human judgment on consequential calls, and revisit the framework as technology, business needs, and regulation evolve.
Final Thoughts: Lead AI Adoption with Confidence
Strong AI governance gives businesses the confidence to move with purpose. Know where AI is used, understand the data, match controls to risk, and scale when the evidence supports it. As Canada’s rules continue to evolve, a clear governance framework can adapt with them. The leadership opportunity is to turn AI potential into disciplined, responsible growth.
References
- Parliament of Canada, “C-27 (44-1) – LEGISinfo”
https://www.parl.ca/legisinfo/en/bill/44-1/c-27 - Parliament of Canada, “C-36 (45-1) – LEGISinfo”
https://www.parl.ca/legisinfo/en/bill/45-1/c-36 - Innovation, Science and Economic Development Canada, “Canada’s National Artificial Intelligence Strategy: AI for All”
https://ised-isde.canada.ca/site/ised/en/canadas-national-artificial-intelligence-strategy-ai-all - Innovation, Science and Economic Development Canada, “Have your say on advancing AI transparency in Canada”
https://ised-isde.canada.ca/site/ised/en/have-your-say-advancing-ai-transparency-canada - Innovation, Science and Economic Development, “Principles for responsible, trustworthy and privacy-protective generative AI technologies”
https://ised-isde.canada.ca/site/ised/en/voluntary-code-conduct-responsible-development-and-management-advanced-generative-ai-systems - Office of the Privacy Commissioner of Canada, “The Personal Information Protection and Electronic Documents Act (PIPEDA)”
https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/ - Office of the Privacy Commissioner of Canada, “Provincial laws that may apply instead of PIPEDA”
https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/prov-pipeda/ - Ministry of Employment and Social Solidarity – Quebec, “Act respecting the protection of personal information in the private sector”
https://www.legisquebec.gouv.qc.ca/en/document/cs/p-39.1 - The Office of the Superintendent of Financial Institutions, “FIFAI II: AI Risks and Opportunities: Adopting an AGILE Framework in Canadian Financial Services”
https://www.osfi-bsif.gc.ca/en/about-osfi/reports-publications/fifai-ii-ai-risks-opportunities-adopting-agile-framework-canadian-financial-services - Government of Canada, “Pre-market guidance for machine learning-enabled medical devices”
https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/application-information/guidance-documents/pre-market-guidance-machine-learning-enabled-medical-devices.html - Treasury Board of Canada Secretariat, “Directive on Automated Decision-Making”
https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592 - Legislative Assembly of Ontario, “Bill 194, Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024”
https://www.ola.org/en/legislative-business/bills/parliament-43/session-1/bill-194 - Innovation, Science and Economic Development Canada, “Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems”
https://ised-isde.canada.ca/site/ised/en/voluntary-code-conduct-responsible-development-and-management-advanced-generative-ai-systems - European Commission, “AI Act”
https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai - Government of Canada, “Algorithmic Impact Assessment tool”
https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html - Government of Canada, “Canada Cyber Security Skills Framework”
https://www.cyber.gc.ca/en/education-community/cyber-skills-development/canadian-cyber-security-skills-framework - ISO/IEC 42001:2023 – AI management systems
https://www.iso.org/standard/42001 - Advisory Services
https://www.aprio.com/advisory-services/ - Data & AI Solutions
https://www.aprio.com/technology-digital-transformation/data-analytics-ai/ - Risk & Compliance
https://www.aprio.com/risk-compliance/ - Aprio Accredited to Independently Certify Organizations Against the Leading International AI Governance Standard
https://www.aprio.com/insights-events/aprio-accredited-to-independently-certify-organizations-ins-firmnews/
