Blab AI
AI Adoption Drives New Liability Risks for Canadian Companies, Says Beazley Expert
On June 23, 2026, Denis Panariti, head of financial lines at Beazley Canada, warned that firms eager to announce artificial‑intelligence (AI) use without solid governance are creating liability exposures that echo the turbulence of the COVID‑19 pandemic and the cryptocurrency boom.
Panariti highlighted that the federal government has committed more than $2 billion to AI initiatives, allocating $700 million specifically for small and medium‑sized businesses. The incentive to signal AI adoption is strong, but the link between spending and measurable returns remains unclear. "A lot of companies are throwing money at AI in anticipation of a return for the investment that's going into it," he said.
The Canadian regulatory landscape adds another layer of uncertainty. No single, unified framework for AI regulation exists, leaving many firms operating in a patchwork of rules that create a governance gap. "Without clear regulation comes a governance gap," Panariti noted, adding that the federal government’s dual role as regulator and market participant creates a tension that will require careful balancing of oversight.
Boards of directors now face a growing duty to oversee AI as an emerging risk. Panariti emphasized that directors must demonstrate an understanding of the risks, ask the right questions, and have governance structures in place—not merely approve a budget. "Disclosure is key," he said. "You want to be able to tell the street and regulators that you're using AI and then what kind of outputs you're expecting from it."
The risk extends beyond internal decision‑making. Companies increasingly employ AI to screen job candidates, evaluate performance, and make workforce decisions. A flawed or biased output can expose the board to liability. "The idea that you're running it through a tool and trusting the output without some of that governance – that could be catastrophic," Panariti warned. He added that deployment of AI in workforce decisions needs the same board‑level oversight as any other material business decision.
When AI tools deliver services to clients, liability follows the tool’s output. Panariti said the exposure is compounded by the tendency to treat AI outputs as inherently correct. If a model hallucines or produces a flawed result and a company acts on it without verification, the liability does not sit with the model. It sits with the people who chose to use it and the board that failed to govern it.
The current situation underscores a need for clear governance frameworks, transparent disclosure, and board oversight that matches the pace of AI adoption. While federal investment signals support for AI, the lack of a unified regulatory framework and the absence of proven return on investment create a fragile environment. Companies that adopt AI without robust governance may face increased legal exposure, regulatory scrutiny, and reputational risk.
The Canadian government is expected to continue developing AI policy in the coming months, and industry groups are calling for clearer guidelines. Until then, boards and risk managers must prioritize governance, transparency, and verification processes to mitigate the liability risks that accompany AI adoption.
Panariti highlighted that the federal government has committed more than $2 billion to AI initiatives, allocating $700 million specifically for small and medium‑sized businesses. The incentive to signal AI adoption is strong, but the link between spending and measurable returns remains unclear. "A lot of companies are throwing money at AI in anticipation of a return for the investment that's going into it," he said.
The Canadian regulatory landscape adds another layer of uncertainty. No single, unified framework for AI regulation exists, leaving many firms operating in a patchwork of rules that create a governance gap. "Without clear regulation comes a governance gap," Panariti noted, adding that the federal government’s dual role as regulator and market participant creates a tension that will require careful balancing of oversight.
Boards of directors now face a growing duty to oversee AI as an emerging risk. Panariti emphasized that directors must demonstrate an understanding of the risks, ask the right questions, and have governance structures in place—not merely approve a budget. "Disclosure is key," he said. "You want to be able to tell the street and regulators that you're using AI and then what kind of outputs you're expecting from it."
The risk extends beyond internal decision‑making. Companies increasingly employ AI to screen job candidates, evaluate performance, and make workforce decisions. A flawed or biased output can expose the board to liability. "The idea that you're running it through a tool and trusting the output without some of that governance – that could be catastrophic," Panariti warned. He added that deployment of AI in workforce decisions needs the same board‑level oversight as any other material business decision.
When AI tools deliver services to clients, liability follows the tool’s output. Panariti said the exposure is compounded by the tendency to treat AI outputs as inherently correct. If a model hallucines or produces a flawed result and a company acts on it without verification, the liability does not sit with the model. It sits with the people who chose to use it and the board that failed to govern it.
The current situation underscores a need for clear governance frameworks, transparent disclosure, and board oversight that matches the pace of AI adoption. While federal investment signals support for AI, the lack of a unified regulatory framework and the absence of proven return on investment create a fragile environment. Companies that adopt AI without robust governance may face increased legal exposure, regulatory scrutiny, and reputational risk.
The Canadian government is expected to continue developing AI policy in the coming months, and industry groups are calling for clearer guidelines. Until then, boards and risk managers must prioritize governance, transparency, and verification processes to mitigate the liability risks that accompany AI adoption.
