"We're using AI to personalize emails. But honestly? I have no idea if we're allowed to do that under PIPEDA."
When a Canadian CMO said this to me in January, it captured the national mood better than any white paper. Polls consistently show that more than four in five Canadians want strong rules and oversight for AI—even as Ottawa races ahead with its "next chapter" strategy.
Last month's federal AI consultation—the largest of its kind, with more than 11,300 submissions processed through a four-model LLM pipeline—should have been a trust-building moment. Instead, it triggered a "people's consultation" from civil society and a wave of criticism about how the government itself used AI to process public input.
This matters for you right now. The 2026 AI strategy will be shaped in the next few months. Civil society is organizing. The Privacy Commissioner has weighed in. And scrutiny on how organizations use AI—including in marketing—will increase, not decrease. If you wait for final regulations before building guardrails and workflows, you'll be re-architecting under pressure instead of capturing safe early advantages.
I Called It (And Wish I'd Been Wrong)
In January, I published "AI in Canada Is Different: A Risk-First Roadmap for Marketing Leaders" flagging a critical disconnect: while federal policy pushed growth-first AI adoption, Canadian voters were increasingly skeptical. I noted that even the government's own 26-question consultation seemed skewed toward commercialization, with minimal focus on the rights and transparency concerns that actually drive public sentiment.
This week, the government released its summary—and it confirmed the pattern I predicted. A 30-day "national sprint" that processed 11,300+ submissions through a four-model LLM pipeline, with minimal transparency, no published prompts or review criteria, and no clear opt-out for respondents. Civil society groups, academics, and even the Office of the Privacy Commissioner raised red flags.
I'm not documenting this to dunk on policy staff. I'm documenting it because the same failure mode—black-box AI over stakeholder input—is playing out inside marketing organizations right now.
What Actually Happened (The Receipts)
Innovation, Science and Economic Development Canada (ISED) ran its AI strategy consultation from October 1–31, 2025. The compressed timeline yielded over 11,300 individual submissions plus 32 commissioned expert reports.
To manage the volume, ISED deployed a four-LLM classification and summarization pipeline: Cohere Command A (Canadian), OpenAI GPT-5 nano, Anthropic Claude Haiku, and Google Gemini Flash—three of four models are U.S.-based. The official summary states plainly that outputs from this pipeline were "used in drafting this report, with elements paraphrased or taken directly" from the LLM-generated text.
What ISED didn't publish: the prompts used, the classification thresholds applied, which specific passages are AI-generated versus human-authored, how human reviewers verified accuracy, or whether respondents could opt out of having their submissions processed by commercial LLMs.
The backlash was swift. Blair Attard-Frost, an Amii fellow at the University of Alberta, called the methodological details "scant" and a "bad start" for building public trust. A coalition of 160+ civil society organizations—including the Canadian Civil Liberties Association—issued an open letter protesting that the consultation prioritized economic benefits over rights and had responses "assessed by AI rather than public officials." The Privacy Commissioner submitted formal concerns about the process.
How Canadian AI Marketing Differs From the Typical US Playbook
Before we get to the lessons, it's worth making this contrast explicit—because importing a Silicon Valley playbook into a Canadian market is exactly how organizations end up replicating Ottawa's mistake.
| Dimension | Typical US Framing | Canadian-Fit Framing |
|---|---|---|
| AI narrative | Speed, disruption, first-mover advantage | Trust, safety, proof of value |
| Regulatory lens | "We'll catch up later" | "Prove it's safe first" |
| Marketing angle | Aggressive personalization at scale | Clear consent and visible guardrails |
| Public sentiment | Optimism-dominant | Skepticism-dominant (52% distrust AI oversight) |
| Data residency | Rarely questioned | Increasingly audited by procurement |
| Winning positioning | "We're fastest" | "We're safest—and we can prove it" |
This is why a Canadian-specific approach—one that treats governance as a growth lever, not a compliance cost—isn't optional. It's the competitive differentiator.
Why This Matters for Marketing Leaders: Three Lessons
This isn't a policy story. It's a playbook for what happens when speed and scalability override governance. Here are three lessons every CMO should internalize—each one maps directly to how I work with clients.
1. Black-box summarization destroys stakeholder trust
If your team uses LLMs to digest customer feedback, focus-group transcripts, NPS surveys, or qualitative research without transparent criteria and human verification protocols, you're replicating Ottawa's mistake.
Stakeholders—customers, employees, partners—expect that when they share input, it will be read, understood, and acted upon by humans who can exercise judgment and accountability. Running their words through an undisclosed AI pipeline, then publishing outputs without clear attribution or review trails, signals that you value efficiency over understanding. That destroys trust faster than any single campaign can rebuild it.
In practice, this is one of the first things I map in an AI Marketing Audit: where AI is allowed—and not allowed—to touch your marketing workflows and decision points. Governance-first isn't a constraint; it's the foundation that makes every AI use case defensible.
2. Consent isn't optional in a risk-first market
Canadians already distrust AI. Recent polling shows 52% don't trust federal oversight of AI systems. Running AI over customer or stakeholder input without explicit, informed consent amplifies that skepticism and invites regulatory scrutiny.
The consultation provided no apparent opt-out mechanism, and the released dataset still contains rich quasi-identifying detail: job titles, employers, project anecdotes. For B2B marketing teams, this creates two risks: reputational blowback if customers discover their feedback was LLM-processed without disclosure, and potential issues under PIPEDA if you're handling personal or business-sensitive data through third-party AI vendors without proper safeguards. If your consent language doesn't cover AI-driven profiling or cross-border processing, your most creative campaign ideas may never make it past legal.
This is why my Audit starts with your funnel and data flows, not a shiny tools list. If your inputs and consents are off, every AI use case is built on sand.
3. Sovereignty theater undermines positioning
ISED positioned this consultation as part of "Canadian-centred" AI leadership, then routed every submission through a pipeline where three of four LLMs are U.S.-based. That contradiction isn't lost on civil society groups, data sovereignty advocates—or your customers.
If your brand narrative emphasizes Canadian values, data sovereignty, or responsible innovation, your AI stack needs to align. Prospects and procurement teams are increasingly auditing vendor jurisdictions, data residency, and cross-border data flows. If your messaging says "trust and transparency" but your systems say "we're using whatever's fastest," your competitors will exploit that gap.
Those alignment checks become the backbone of a 90-day AI roadmap and, when needed, the execution plan I run as a Fractional Director.
Where Are You on the Canadian AI Readiness Curve?
The lessons above aren't theoretical. Here's how they manifest at each stage of AI maturity—and what to do about it in the next 30 days.
Getting Started
Symptom: AI shows up in board decks, but not in any live campaigns or workflows.
Next 30 days: Run one stakeholder listening session (customers or internal) focused purely on AI hopes and fears, and document three "red lines" you won't cross.
Experimenting
Symptom: You've bought three AI tools in the last 12 months and can't say, in one sentence, which revenue problem each is supposed to solve.
Next 30 days: Pick one segment of your funnel (for example, MQL → SQL) and design a single, low-risk AI pilot around it with a clear owner and success metric.
Operationalized
Symptom: AI shows up in monthly reporting, but no one owns the playbook or the risk register.
Next 30 days: Assign a single cross-functional owner for "AI in marketing," and build a one-page register of use cases, risks, and safeguards.
Leading
Symptom: You're already using AI across the funnel, but you still rely on vendors to explain governance to your board.
Next 30 days: Publish a short internal "Responsible AI in Marketing" memo that your legal and comms teams can stand behind.
My AI Readiness Scorecard formalizes this into a structured assessment with a personalized action plan. Eight minutes, no cost, no email required.
What to Do Instead: The Governance-First Checklist
Turn the consultation's failures into your competitive advantage.
Transparency: Publish method summaries for any automated qualitative analysis. Disclose which tools you used, what human review steps occurred, and what decision criteria guided classification or summarization. If your process can't survive public scrutiny, it shouldn't touch customer data.
Consent: Offer clear opt-outs and data-handling disclosures before running stakeholder input through LLMs. Make consent active, not assumed.
Sovereignty alignment: If your brand or client base values Canadian control, audit your AI vendor stack and hosting jurisdictions. One Canadian model in a four-model pipeline doesn't make the process Canadian.
Human verification: Set and disclose minimum review thresholds. Clarify which sections of reports, dashboards, or briefs are machine-drafted versus human-authored. If you can't trace a claim back to a human decision-maker, don't publish it.
Stakeholder inclusion: Don't mistake speed for legitimacy. If you're gathering customer or employee feedback to inform AI adoption, build timelines that allow meaningful participation, not just checkbox compliance.
Your Next Step
My work with Canadian B2B teams follows a simple operating model: Audit → Roadmap → 90-day execution. The goal is to design systems—policies, workflows, and metrics—that make AI safer and more profitable, not just launch one-off campaigns and hope compliance catches up.
If you just want a baseline, start with the AI Readiness Scorecard to see where you sit on the Canadian curve. If you need a structured plan, an AI Marketing Audit will give you prioritized, revenue-tied next steps with a 90-day roadmap. And if you're ready for a senior operator to ship those guardrails and pilots without waiting to make a full-time hire, a Fractional AI Marketing Director engagement gets it done.
The federal government's consultation shows that even well-intentioned AI adoption can backfire when transparency, consent, and sovereignty take a back seat to speed. Don't wait until those lessons land on your desk as a crisis.