What Canada Is Missing In Its AI Strategy
Canada is sprinting to refresh its AI strategy. A month of consultation sounds decisive, but speed without depth creates blind spots.
Here's the core risk that keeps getting sidelined: our digital infrastructure is built on foreign-controlled platforms. That dependency can override domestic law, public expectations, and service standards in a single policy turn outside our borders.
The sprint problem
Thirty days is enough to gather opinions, not to test assumptions. Short processes produce broad slogans, vague commitments, and vendor-led roadmaps.
This moment needs disciplined scoping, staged decisions, and independent challenge. Without that, government will ship risk to the public, and costs to the future.
Critical dependencies we can't ignore
- Core services: cloud, content delivery, app stores, identity, and payments are largely controlled by non-Canadian firms.
- AI stack: model providers, GPUs, and tooling pipelines sit outside our jurisdictional reach.
- Corporate obligations: large platforms prioritize home-country directives. Even executives have acknowledged they will align with their government regardless of laws elsewhere.
Data "in Canada" is not the same as data under Canadian control. Ownership, contractual terms, and legal reach matter as much as postal code.
Data residency is a floor, not a moat
Public servants are already signaling this. In a recent survey, most respondents wanted Canadian data stored domestically and worried about public trust if it wasn't.
That instinct is right. But residency alone doesn't solve extraterritorial exposure, model training leakage, or vendor-side telemetry. Treat residency as a baseline, then build actual control above it.
Procurement: prevent conflict, buy outcomes, not hype
When service providers help decide where AI should be used, conflict of interest is baked in. Vendors sell adoption; government must buy outcomes and safeguards.
- Separate advisory from delivery. If a company proposes use cases, they shouldn't be the ones implementing them.
- Mandate code/data escrow, exit clauses, and portability from day one.
- Prefer open standards and auditability over black boxes with glossy demos.
Service quality, liability, and privacy
If a public-facing chatbot gives bad advice, who pays? We already have a case study. A court held a major airline responsible for its chatbot's incorrect guidance, and ordered compensation.
Translate that to immigration, benefits, or tax. One wrong answer can have life-changing consequences. Every AI-assisted decision needs traceability, a clear appeal path, and a human fallback.
Air Canada chatbot ruling (CBC)
Public trust is conditional
Canadians are fine with AI for simple tasks. That tolerance drops fast for health, financial, and legal advice.
Trust follows performance and accountability, not press releases. Build the safeguards first; then scale usage.
Use the right words for the right tools
"AI" is an umbrella that hides risk. Rules for document search or fraud triage are not the same as rules for generative systems that can produce confident errors.
- Classify systems by function and risk: retrieval, prediction, generation, decision support, or automated decision.
- Treat generative outputs as unverified by default. Design services so a wrong answer can't quietly pass as fact.
A practical plan that fits the duty to serve
- Set a policy floor: data residency, access controls, logging, and red-team testing for any AI touching the public.
- Adopt risk tiers: low, medium, high. Tie each tier to required testing, security, and human oversight.
- Publish a model and vendor register: purpose, data sources, known limits, and contact for redress.
- Mandate human-in-the-loop for high-impact areas (immigration, taxation, security, health).
- Create incident reporting: prompt disclosure, remediation timelines, and user notification for harmful errors.
- Stand up independent assurance: pre-deployment audits and periodic reviews by third parties.
- Plan the workforce: no silent attrition. Retrain, redeploy, and define new roles before automating legacy work.
- Run cross-border legal reviews: confirm how foreign laws could compel access to Canadian data and models.
- Budget for exit: switching costs, data portability, and replatforming are part of total cost of ownership.
- Measure value: pick 3-5 service metrics (accuracy, timeliness, appeals, satisfaction, cost per case) and publish them.
On partnerships and infrastructure
Domestic data centers run by foreign firms are a start, not an end state. Prioritize contractual control, encryption with customer-held keys, and clear limits on background data collection.
Where feasible, build Canadian capacity: shared government clouds, sovereign key management, and consortia for model evaluation. Independence is built, not rented.
Process over posture
A sprint can open the door. It can't carry the house. Move from announcements to an iterative program with quarterly releases, public scorecards, and real-world pilots that can be paused or rolled back.
If that sounds slower, good. Safer systems survive contact with reality. That's what the public expects.
Useful references
Upskilling your teams
Capability beats slogans. If you're building internal fluency in AI risk, procurement, and service design, a structured catalog can help you map roles to skills and courses.
Bottom line: Build control, then add capability. Buy outcomes, not hype. Earn trust, don't assume it.
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