Why Ontario's New AI Principles Matter for Your Business

by Michael Pascu, Senior Manager, Artificial Intelligence and Michelle Niazi, INQ Law Intern

Businesses across Ontario are rapidly adopting AI tools in pursuit of efficiency and to drive competitive advantage. But speed without oversight comes at a cost. Building and maintaining consumer trust in AI requires deliberate care, and when that trust is lost, the consequences can be severe, including discrimination claims, privacy breaches, regulatory penalties, and lasting reputational harm that may far outweigh any short-term operational gains.

In January 2026, Ontario’s Information and Privacy Commissioner and Human Rights Commission released joint principles for the responsible use of AI. Although developed for the public sector, these six principles also provide a practical framework for private businesses seeking to manage legal and financial risk while strengthening stakeholder trust. The question is not whether AI will transform your operations, but whether it will be implemented responsibly enough to earn and keep that trust. 

The Business Case: Trust as a competitive advantage

The risks are concrete: discrimination lawsuits, privacy violations, regulatory penalties, and reputational damage. Canada does not have a federal AI regulation after Bill C-27 died on the order paper in January 2025, but provinces are moving ahead: Ontario passed Bill 194 in November 2024, and Quebec has its own legislation in force. The EU AI Act and other international frameworks are setting global standards. Organizations that build AI governance now avoid scrambling to retrofit systems later when regulations tighten.

But compliance alone isn't the goal. The trust gap between businesses and consumers using AI is real, and trustworthy AI represents a competitive advantage rather than a cost centre. 82% of Canadian consumers say they would trust a brand less if it intentionally concealed AI use within the organization, and 96% of Canadian executives believe consumer trust in their AI will be critical to the success of new products and services.¹ This isn't about ethics alone; it's about protecting your organization from legal and financial consequences while positioning trust as a strategic differentiator. To stay informed on the rapidly evolving regulatory landscape, the IAPP’s Global AI Law and Policy Tracker provides comprehensive updates on AI legislation worldwide. 

The Six Principles: What They Mean for Your Business 

Ontario's framework identifies six interconnected principles: Valid and Reliable, Safe, Privacy Protective, Human Rights Affirming, Transparent, and Accountable. 

Valid, Reliable, and Safe

What it means:

The AI used by your organization must work correctly, produce accurate and consistent outputs across different scenarios and diverse populations, and not cause harm such as discriminatory treatment of certain groups. 

Why it matters:

Systems that fail under real-world conditions expose your business to operational failures, lawsuits, and regulatory action. An AI customer service system that hallucinates policy details or provides contradictory information about coverage can bind your organization to incorrect commitments while simultaneously eroding customer confidence in every interaction.

What to do:

Start with an inventory of your current AI systems; you can't manage what you don't know you're using. Prioritize high-risk applications such as hiring tools and customer-facing systems. Test rigorously before deployment across different scenarios and populations. Monitor continuously for unexpected outputs and accuracy drift with automated alerts. Establish clear performance benchmarks and the authority to shut down problematic systems immediately. Document all testing results to protect yourself when regulators or courts ask questions. 

Privacy Protective and Human Rights Affirming

What it means:

AI must embed privacy protections by design and actively prevent discrimination against protected groups under the Ontario Human Rights Code.

Why it matters:

Privacy breaches can trigger regulatory notification obligations and investigations in the event of non-compliance. Discriminatory outputs expose organizations to human rights complaints and litigation. An AI hiring tool trained on historically discriminatory data or a system processing customer information without proper consent creates substantial legal exposure that erodes trust quickly.

What to do:

Conduct privacy and human rights impact assessments before deployment; the OHRC and IPC provide free tools for this, including Human Rights AI Impact Assessment (HRIA) and the Privacy Impact Assessment Guide. Embed privacy protections from the outset using privacy by design principles. Test proactively for bias across diverse populations and protected groups. Minimize data collection to what's strictly necessary and ensure clear legal authority for processing personal information.

Transparent and Accountable

What it means:

Your organization should be able to explain AI decision-making in plain language where decisions materially affect people and designate who's responsible when things go wrong. 

Why it matters:

If you can't explain why your AI denied someone's application, you can't defend that decision or maintain stakeholder confidence. When things go wrong, stakeholders need to know who's in charge and how to seek recourse. Concealed AI use destroys trust quickly. When AI systems are rated as highly transparent, they’re 1.6 times more likely to share personal data, 8.5 times more likely to express high trust in the brand.²

What to do:

Establish clear governance with designated individuals responsible for AI oversight who have authority to pause systems. Maintain lifecycle documentation in plain language that describes AI outputs and decision processes. Notify people when they're interacting with AI. Build human oversight into processes, especially at critical decision points. Create accessible mechanisms for people to challenge AI-generated outcomes. Ensure vendor contracts include explainability requirements.

For organizations looking to develop comprehensive AI governance frameworks, our AI Governance Series offers in-depth guidance on implementing responsible AI practices across your operations.

Building these protections now is far easier and cheaper than retrofitting later under regulatory pressure or during litigation, and positions your organization as a trustworthy partner when AI trust is scarce and valuable.

Conclusion

Ontario's AI principles provide a clear roadmap for deploying AI responsibly. Adhering to them isn't an obstacle to innovation; it's how you build the trust necessary for AI to deliver sustainable business value. Trust is the currency of AI adoption. Organizations that embed these principles into their AI governance don't just protect themselves from legal and reputational risks, they position themselves as trustworthy partners to customers, employees, and regulators. In a market where consumer confidence in AI remains fragile, responsible AI practices become a competitive differentiator. The choice is clear: build trust-centered AI governance now, or scramble to rebuild stakeholder confidence later when regulatory pressure intensifies and trust has already eroded.

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¹ IBM Canada. “Canada’s AI Moment: Five Trends Redefining Business Confidence, Speed and Trust in 2026.” IBM Canada Newsroom, 2 Feb. 2026, https://canada.newsroom.ibm.com/2026-02-02-Canadas-AI-Moment-Five-Trends-Redefining-Business-Confidence,-Speed-and-Trust-in-2026. Accessed 3 Feb. 2026.

² IBM Canada. “Canada’s AI Moment: Five Trends Redefining Business Confidence, Speed and Trust in 2026.” IBM Canada Newsroom, 2 Feb. 2026, https://canada.newsroom.ibm.com/2026-02-02-Canadas-AI-Moment-Five-Trends-Redefining-Business-Confidence,-Speed-and-Trust-in-2026. Accessed 3 Feb. 202