TELUS study finds North American publics demand inclusion, safety and regulation as AI use surges

TELUS released a large cross-border public opinion study that captures what users expect as AI moves from novelty to everyday utility. The poll, conducted across Canada and the United States with intentionally boosted samples from underrepresented communities, documents growing familiarity with AI alongside clear demands for participation, transparency and safety. Large majorities told researchers they want companies to seek customer perspectives before deployment, assess systems for harms prior to launch, and communicate how AI is used in accessible language. There is also overwhelming support for formal oversight: nine in ten respondents in both countries favor regulation with defined accountability. Those signals matter beyond ethics statements because public confidence emerged in the survey as a practical lever for adoption — trust is framed as a determinant of whether people will accept and use AI-powered services. For organizations, the findings imply that investments in explainability, harm-assessment pipelines and meaningful user engagement are not optional PR tactics but core components of market strategy. The report concludes with a road map aimed at policymakers, industry and academia that emphasizes education to raise AI literacy, inclusion during system design, human supervision for consequential decisions, and multi-sector standards to safeguard innovation. TELUS positions these recommendations alongside its own initiatives to build secure compute capacity and deploy privacy-aware customer tools, using the study to validate a governance-first posture. For regulators the survey opens a policy window: strong public backing reduces political friction for concrete rules and could accelerate harmonized frameworks across borders. For competitors and buyers, the message is tactical — those who integrate robust governance and clear user communications will likely face lower friction in rollout and higher uptake. Ultimately, the study reframes AI governance as a commercial force multiplier: trust-building actions reduce adoption barriers and help capture the productivity and economic gains promised by AI while lowering systemic risk.