The AI Trust Gap: Why 85% of Canadians Use AI Tools But Only One-Third Trust the Companies Deploying Them

Eighty-five percent of Canadians used at least one AI-enabled tool in the past year. Only 34 percent trust the companies that deploy AI.

That gap — between the tools Canadians are using and the organizations they trust to deploy them — is the defining challenge for every Canadian business investing in AI in 2026. It is also the most underappreciated reason why AI adoption rates continue to climb while depth of use and measurable outcomes remain low.

The TELUS 2026 AI Trust Atlas, drawing on a Leger survey of 5,487 Canadians, confirmed what qualitative evidence had been signalling for more than a year: Canadians have normalized using AI tools while remaining deeply uncertain about the organizations wielding them. Only 40 percent of Canadian respondents said they could confidently explain AI's benefits and limitations. Just 34 percent said they knew which products and services incorporate AI. Ninety percent want AI regulated.

For a business deploying AI across its operations, those numbers are not abstract. They describe your employees, your customers, and the regulatory expectations of your operating environment.

The Business Impact of Low Trust

The trust gap shows up in outcomes, not just attitudes.

Intuit's 2026 AI Impact Report, based on surveys of more than 34,000 small and mid-size business owners and anonymized data from over 5.3 million QuickBooks businesses worldwide, found that 69 percent of Canadian SMBs now use AI regularly — up from 52 percent in 2024. A majority (73 percent) say AI has improved their productivity. Both numbers sound like progress.

The report's finding on barriers is where the trust gap becomes concrete. The top obstacle to deeper AI investment among Canadian businesses is not cost. The three most-cited barriers are privacy and security concerns (36 percent), lack of knowledge about how to use AI effectively (30 percent), and fear of bias or errors in AI outputs (26 percent). All three describe trust, not budget: Canadians are not avoiding deeper AI investment because they cannot afford it. They are limiting that investment because they do not fully trust it.

That hesitation is not irrational. IBM Canada's June 2026 research found that only 43 percent of AI initiatives have delivered their expected return on investment over the past two years. Organizations deploying AI without clear governance, training, or measurement frameworks are, on average, failing to deliver the outcomes they promised — which makes skepticism among employees and customers a reasonable response to the evidence they can observe.

Two Trust Problems, Not One

The trust gap in Canadian AI investment is actually two separate problems that require different responses.

Employee trust in AI outputs. Employees who do not trust AI outputs either avoid using AI for tasks where it would genuinely help, or use it without adequate review — accepting outputs they would otherwise have questioned. Statistics Canada's Q2 2026 survey found that 19.2 percent of Canadian businesses now use AI in their operations. But adoption and depth of use diverge sharply. RSM Canada's July 2026 middle market survey found that while 86 percent of mid-market firms have integrated AI in some form, only 36 percent have it fully embedded across core processes. The gap between nominal integration and genuine operational adoption reflects, in significant part, a trust deficit among the employees doing the actual work.

Customer and stakeholder trust in organizations using AI. The TELUS finding that only 34 percent of Canadians trust companies using AI has direct implications for any organization deploying AI in customer-facing contexts — whether that is AI-assisted service, AI-driven pricing, AI-generated recommendations, or automated decision-making that affects customers' financial, employment, or service outcomes. An organization that deploys AI in these contexts without visible governance is deploying into a climate of substantial skepticism.

Ninety percent of Canadians want AI regulated. That figure reflects not just concern about AI generally — it reflects a judgment that most organizations are not governing AI well enough on their own.

What Builds Trust in AI Deployments

Organizations whose AI deployments generate the highest confidence — internally and externally — share three practices.

Transparency about what AI does and does not do. Employees and customers trust AI more when they understand what it is doing, where its outputs are reliable, and what human review is applied before AI outputs take effect. This is not about explaining machine learning to non-technical audiences. It is about clear operational communication: "Our customer service AI handles routine inquiries and routes complex ones to our team" is more trust-building than an AI interface with no disclosure of its nature.

Bill C-36, the Protecting Privacy and Consumer Data Act, tabled in June 2026, will require organizations to make public a general account of any automated decision system that significantly affects individuals, and to explain specific decisions on request. The legal requirement, when it comes into force, will codify in statute what already earns trust in practice: organizations that explain their AI will be seen as more trustworthy than those that do not.

Documented governance that employees can point to. Employees trust AI tools more when they know those tools have been reviewed and approved — when there is a policy that specifies which tools are safe to use with which types of information, and who is responsible for ensuring those tools remain safe. Without that governance layer, every employee is making their own risk judgment on every interaction, and uncertainty produces avoidance.

The OPC's September 2026 guidance on assessing third-party service providers sets out the vendor assessment standard for organizations deploying AI that handles personal information. An organization that has completed those assessments, maintains a vendor register, and has reviewed data processing agreements with its AI vendors has a defensible governance position — and more importantly, has the documented evidence that lets it credibly communicate to employees and customers that its AI deployment has been reviewed for safety.

PIPEDA-compliant data handling, communicated clearly. The 36 percent of Canadian business owners who cite privacy and security concerns as the primary barrier to AI investment are responding to real risk. Organizations that deploy AI without PIPEDA-compliant data processing agreements, that allow employees to input personal information into unapproved tools, or that lack documented data handling policies are carrying real exposure — and their employees and customers, at some level, can sense it.

Organizations that handle AI data appropriately — Canadian data residency where required, signed DPAs with vendors, acceptable use policies that protect personal information — earn the trust that vague assurances cannot produce.

The Governance–Trust Connection

IBM Canada's May 2026 study found that only 18 percent of Canadian organizations have coordinated governance systems spanning the full AI lifecycle, and 63 percent of Canadian executives say governance gaps already make it harder to deploy AI at scale. The 18 percent figure is not just a compliance number — it describes the minority of organizations that have built the internal infrastructure that trust requires.

For Canadian SMEs and mid-market organizations, closing the governance–trust gap does not require a large investment. An AI inventory register, an acceptable use policy, completed vendor risk assessments, and a documented data handling standard are a few weeks of structured work — not a compliance programme. They are the foundation of an AI deployment that employees will actually use and that customers will actually trust.

The Intuit data makes the case plainly: the Canadian businesses that have moved from nominal adoption (69 percent) to genuine, embedded productivity gains are the ones that have addressed the trust barriers — not by promising safety they cannot demonstrate, but by building governance that earns it.

Sources

• TELUS. *New TELUS Cross-Border Study Reveals Canadians and Americans Want Companies to Earn Their Trust in AI.* telus.com (January 2026)
• Intuit QuickBooks Canada. *2026 AI Impact Report.* quickbooks.intuit.com
• IBM Canada. *Can Canadian Organizations Deliver in the Next Era of Business AI? IBM Finds Many May Not Be Ready.* canada.newsroom.ibm.com (June 2026)
• IBM Canada. *New IBM Study: AI Is Moving Faster Than Oversight in Canada.* canada.newsroom.ibm.com (May 2026)
• RSM Canada. *Canadian Businesses Satisfied with AI, But Limited Enterprise Adoption Persists.* rsmus.com (July 2026)
• Statistics Canada. *Analysis on Artificial Intelligence Use by Businesses in Canada, Second Quarter of 2026.* statcan.gc.ca
• Osler. *The Protecting Privacy and Consumer Data Act (Bill C-36): Key Obligations and Enforcement Overview.* osler.com (June 2026)
• Office of the Privacy Commissioner of Canada. *Guidance on Assessing Third-Party Service Providers.* priv.gc.ca (September 2026)

Cloud Forces' AI Advisory service helps Canadian SMEs and mid-market organizations build the governance infrastructure that closes the AI trust gap — covering AI Readiness Assessments, AI Roadmaps, and the acceptable use policies, vendor assessments, and data handling standards that turn AI adoption into AI confidence. As an AWS Consulting Partner since 2019 and a team certifying on Claude, we design AI programs that meet Canadian data residency and PIPEDA requirements. Book a consultation to assess your organization's AI governance posture.