Insurance Boards Must Rethink Governance as AI Takes Over Tasks, Not Jobs
Agentic artificial intelligence is forcing insurance boards to restructure how they allocate responsibility and oversight as decision-making authority shifts from people to software, according to Paulo Salomao, national lead of strategy and consulting at Accenture Canada.
The shift is not about replacing entire positions. Instead, AI systems are being embedded into specific, repeatable tasks across underwriting, claims and servicing. That changes the fundamental unit that governance frameworks must track.
"AI isn't taking over the job beginning to end," Salomao said. "AI takes over tasks that make up the jobs. And that shift in the unit of accountability is what drives the entire upheaval in governance. Because now you have to govern at the task level as opposed to the overall job level."
The Governance Problem
Traditional insurance governance is built around human roles and titles-who holds the job, who signs off, who owns the end-to-end process. That model breaks down when AI systems own discrete parts of the work.
Boards historically mapped accountability by function: head of claims, chief underwriting officer, CIO. Each leader controlled a relatively coherent block of work. In an agentic setup, boards must understand which tasks are being executed by which classes of models and what controls sit around them.
The difference between passive and active technology matters. "In the past, we used a lot of technology, but it was very passive," Salomao said. "With agentic AI, it's a little bit different because now it's active, it can decide what to do and when to do it. It actually gains decision authority that used to sit with people."
Where This Is Already Happening
The shift is underway now, not a future scenario. In underwriting, AI consolidates information that underwriters once gathered manually. Underwriters now make final decisions based on data presented to them.
Claims processing shows the same pattern. Simple, high-volume loss types are increasingly auto-adjudicated with little or no human touch. Humans focus on complex claims and exceptions.
Life insurance and group benefits illustrate how regulation sets boundaries. In life insurance, a licensed individual must make the final call. In group benefits, carriers can run a higher share of claims straight through with minimal human involvement, depending on what rules allow in each segment.
Why Insurance Is Positioned for This
Insurance ticks three boxes that make it particularly suited for agentic AI: it runs on highly repetitive decisions, it sits on large volumes of data, and it has clear economic signals that make it easier to judge where automation makes sense.
Other industries have messier decision flows. Transportation, for example, has less standardized processes. Insurance operations are comparatively modular and easier to break into discrete, machine-executable tasks.
The technical building blocks are not new. Carriers have used rules engines and workflow tools to automate simple decisions for years. The change now is in the complexity of tasks that can be handed off. "In the beginning, you could automate based on simple rules," Salomao said. "But now you're able to increase the complexity because you have these agents that can perform more complex tasks."
The Next Frontier
Small commercial business is becoming a focus area. This segment has richer data and risks that repeat more frequently than large corporate accounts, creating opportunities for agentic AI in both adjudication and underwriting.
Canadian insurers are moving ahead, though they still trail the US market. The governance challenge-and opportunity-is to map accountability at the task level rather than the job level.
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