From AI Adoption to a Trusted AI Economy: Where Should Canada Draw the Governance Line?

One useful place to begin is by distinguishing commercial risk from risk imposed on others.

Businesses make decisions every day that can succeed or fail. AI does not change the basic principle that organizations should generally remain free to make commercial decisions and accept the consequences.

If a company uses an AI forecasting system, makes a poor business decision and loses its own money, that does not necessarily create a case for additional AI regulation.

The situation changes when the consequences extend materially beyond the organization.

An AI system affecting employment opportunities, handling sensitive personal information, controlling safety-critical equipment, interacting with critical infrastructure or independently taking consequential actions can create risks for people who did not choose to accept them.

That suggests a useful boundary.

Where AI risk is primarily commercial and borne by the organization making the decision, governance should remain primarily the responsibility of the enterprise.

Where AI-enabled decisions or actions can create material consequences for employees, customers, citizens, public safety, privacy, security, fundamental rights or critical infrastructure, progressively stronger safeguards may be warranted.

The objective should not be to protect businesses from making poor decisions.

It should be to establish reasonable safeguards where AI can impose significant consequences on others.

A consequence-based approach also avoids treating every use of AI within an industry as though it presents the same risk.

Consider manufacturing.

An AI writing assistant helping employees prepare internal communications presents a very different risk from an autonomous AI-enabled system controlling safety-critical machinery.

The same distinction exists in architecture. An AI application summarizing meeting notes is different from a system influencing a life-safety design decision.

In financial services, an internal productivity tool is not equivalent to a system making or materially influencing consequential decisions about individuals.

The industry matters because professional responsibilities, laws, standards and potential consequences differ.

But the application matters too.

The appropriate level of governance should therefore consider what an AI system can do, the authority and access it has, how independently it can act, the effectiveness of human oversight and the seriousness of the potential consequences.

For much of the recent history of generative AI, organizations have focused on outputs.

Was the answer accurate?

Was the content biased?

Did the model hallucinate?

Did someone verify the response?

Those questions remain important.

But increasingly capable AI systems introduce another category of concern: action.

There is a material difference between an AI system drafting an email for a person to review and one independently communicating with customers.

There is a difference between recommending a transaction and executing it.

There is a difference between analyzing code and being authorized to execute code.

There is a difference between providing information about machinery and controlling it.

The existence of these capabilities should not automatically determine regulatory treatment. The more important questions are whether they can be exercised without meaningful human oversight, approval or intervention, what systems and information they can access, and what could happen if something goes wrong.

For agentic AI, the governance question increasingly becomes:

What was the AI authorized to do, what did it actually do, and who remains accountable?

"Human in the loop" can sound reassuring while saying very little about the effectiveness of the control.

Meaningful human oversight requires more.

A reviewer must have sufficient information to understand the decision, enough time to assess it, appropriate expertise, and actual authority to challenge, override, stop or escalate the AI-enabled activity.

A person clicking "approve" on decisions they cannot reasonably evaluate is not necessarily an effective safeguard.

This distinction will become increasingly important as organizations introduce AI into faster and more complex workflows.

Canada does not necessarily need a new AI-specific rule every time AI contributes to a harmful outcome.

Privacy, discrimination, consumer protection, workplace safety, professional responsibility, cybersecurity and product-safety obligations already address many consequences that can involve AI.

A sensible approach is therefore:

Use existing law where existing law adequately addresses the risk. Introduce AI-specific requirements where AI creates a material gap.

This can reduce unnecessary regulatory duplication while preserving protections that already exist.

It also shifts attention toward an important practical question: what is genuinely different about the risk because AI is involved?

Another challenge is that AI systems do not necessarily remain the same after an organization first evaluates them.

Models change. Vendors introduce new capabilities. Integrations expand. Permissions change. Organizations discover new uses. AI agents can be given additional tools or access.

An application that presented relatively little risk when initially introduced could become substantially more consequential later.

This is why AI governance increasingly needs to be continuous.

But continuous AI governance should not mean continuous government supervision.

For most applications, it should mean that organizations maintain enough visibility over their AI environment to recognize material changes and determine whether reassessment or additional controls are necessary.

This principle is consistent with established risk-management thinking. NIST's AI Risk Management Framework treats risk management as continuous and timely throughout the AI lifecycle and organizes its Core around four functions: Govern, Map, Measure and Manage.

For lower-consequence applications, much of this can remain within the organization.

As autonomy and potential consequences increase, the level of evidence and assurance should increase accordingly.

The governance discussion is sometimes presented as though there are only two possibilities: businesses govern themselves, or government regulates them.

There is a substantial space between those positions.

Organizations can conduct internal assessments and maintain evidence. Professionals can review higher-consequence applications. Independent organizations can validate controls. Standards and certification can provide additional assurance where appropriate.

Not every AI system needs every level.

The level of assurance should reflect the potential consequences and the needs of customers, insurers, regulators, procurement authorities and other stakeholders.

This creates the possibility of a broader AI assurance ecosystem - one in which trust can increasingly be demonstrated rather than merely asserted.

Canada's AI workforce conversation understandably concentrates heavily on people who build AI.

But an economy that uses AI at scale will also need people who can govern, evaluate and assure it.

That includes expertise in AI governance, testing, cybersecurity, privacy, risk management, vendor assurance, incident investigation, independent validation, audit and evidence management, as well as professionals who understand how AI interacts with sector-specific responsibilities.

These are not necessarily the same people who develop AI models.

Canada's Strategy anticipates substantial employment effects from AI, including up to 90,000 AI-related jobs and work-placement opportunities for young Canadians and up to 250,000 jobs through AI adoption by 2031.

Canada should also examine the workforce that will be necessary to make adoption trustworthy at that scale.

There is an opportunity to develop an AI assurance workforce alongside the AI development workforce.

That could create opportunities for universities and colleges, professional associations, technology companies, cybersecurity specialists, consultants, independent assurance providers and other professional services.

It could also become expertise that Canadian organizations export.

AI governance is frequently discussed as a cost.

There is certainly a cost to governance. Organizations spend money on assessments, controls, security, documentation, professional review and assurance.

But that is only one side of the economic equation.

The other question is:

What economic value does trustworthy AI make possible?

If effective governance gives executives greater confidence to deploy AI, reduces procurement uncertainty, makes vendor due diligence more efficient, improves customer confidence, supports contracting and insurance, and allows organizations to adopt more capable systems responsibly, governance may enable economic activity as well as constrain risk.

Canada should measure both sides.

That means studying the costs of governance, but also the economic value associated with trusted adoption.

It should also mean developing better information about the economic cost of inadequately governed AI: cybersecurity incidents, privacy breaches, intellectual-property exposure, fraud, erroneous consequential decisions, operational disruptions, unsafe outcomes, failed implementations, litigation, remediation and business interruption.

Without understanding both sides of the equation, it is difficult to determine whether a safeguard creates more economic value than it costs.

Canada's National AI Strategy places trust alongside opportunity and sovereignty and explicitly connects greater AI adoption with productivity and economic growth.

That proposition has important implications for enterprise AI.

Trust cannot depend entirely on promises that an AI system is responsible or safe.

Organizations need visibility into where consequential AI is being used. Someone needs to be accountable for the decisions surrounding it. Appropriate controls need evidence. Higher-consequence controls may require assurance. And that assurance can create confidence among executives, customers, professionals, investors, insurers, regulators and the public.

The progression can be understood this way:

Visibility -> Accountability -> Evidence -> Assurance -> Trust -> Adoption -> Economic Value

Visibility means organizations know which AI is being used and where consequential applications exist.

Accountability means responsibility for AI-enabled decisions and outcomes is clearly assigned.

Evidence means organizations can demonstrate that appropriate controls exist rather than simply asserting that they do.

Assurance means those controls can be tested, reviewed or independently validated in proportion to potential consequences.

Trust increases when organizations and stakeholders have credible reasons for confidence.

Adoption becomes easier when organizations understand and can manage the risks they are accepting.

And economic value emerges when trustworthy adoption contributes to productivity, investment, skilled employment, innovation and sustainable growth.

Canada has already established an international reputation in AI research.

The next opportunity is not simply to build more AI. It is to become equally capable at deploying AI successfully throughout the economy.

That will require technology, compute, investment and talent.

It will also require governance, evidence, assurance and professional expertise.

Canada does not need to choose between innovation and governance.

The more important task is determining where enterprise autonomy should remain paramount and where the consequences of increasingly capable AI justify stronger safeguards.

If Canada gets that boundary right, responsible AI governance does not have to become an obstacle to adoption.

It can become part of the infrastructure that makes adoption possible.