As Canadian insurers weigh how to handle the efficiencies generated by AI adoption, there's no single playbook that applies across the industry, according to Sonia Sood (pictured), national human capital insurance leader at Deloitte. What an organization chooses to do with those gains - whether to reinvest them or use them to reduce headcount - depends heavily on where that company started.
"I don't believe you can make a generalization," Sood said. "I think it depends on the strategy of the organization, their financial performance and their outlook. And it depends on what their starting point is."
Rather than recommending that insurers set broad workforce targets and work backward from them, Sood's advice to C-suite leaders across both life and health and property and casualty insurers is to begin from the work itself.
"I am recommending taking a look at the work that needs to be done and then making the decision, versus widespread statements around what needs to be done to achieve a specific target," she said.
On the broking side, that distinction matters. Insurers that reduce headcount without first examining underlying workflows risk degrading the service capacity - in claims handling, policy processing and relationship management - that broker partnerships depend on. Those that reinvest efficiency gains thoughtfully are more likely to maintain and improve the operational standards brokers rely on day to day.
Sood said some insurers, when benchmarked against industry leaders, may already be operating above average on measures like full-time employee ratios, giving them less room to generate the same scale of efficiency gains from AI as one with more slack in its structure. Others are working from a very different starting position.
That starting point also shapes where efficiencies get reinvested, if they get reinvested at all. Sood gave the example of an insurer with a lean actuarial function choosing to direct newly freed-up capacity there, though she noted that redeploying staff isn't always straightforward, since employees coming from very different roles may not easily transition into another function.
"Maybe their actuarial team is quite lean, maybe they actually reinvest those capabilities there," she said. "If people's jobs change significantly, let's say they're working in technology, it's probably harder to move them into an actuarial role. But there are some places across the organization where you can get a lot of good cross-pollination."
Despite the case-by-case nature of these decisions, Sood said organizations that take a more people-focused approach to transformation are more likely to succeed in the long run than those that apply blanket workforce reduction targets without first examining the underlying work.
Sood does not expect large-scale, sweeping workforce cuts to become the norm across the Canadian insurance sector, at least not in the near term. She pointed to structural differences between Canada and other markets - particularly Europe - as a key reason.
"We are a bit more behind than what's happening in the European market from a digital and AI adoption perspective," Sood said. "We have a different regulatory environment, we have different labour conditions. I would see Canada being much more conservative in any sort of wide-scale workforce reductions."
By "conservative," Sood said she meant that Canadian insurers are less likely to pursue large-scale reductions in the near term compared to their European counterparts, rather than describing a permanent industry-wide stance.
That caution doesn't mean AI-driven change is happening slowly in Canada. Some insurers, she said, are already grappling with difficult decisions shaped by market pressures that predate the current wave of AI adoption entirely.
"The reality is that some insurance organizations, due to the market forces, are having to make some tough choices," Sood said. "But that's actually irrespective of AI. They were already contemplating those things for the last two years in what's been a difficult market."
Sood also pushed back on the idea that decisions about AI-driven workforce change are one-time events tied to a single transformation project. In her view, the pace of change in AI means organizations will need to keep revisiting these questions on an ongoing basis.
"This isn't a one-time event," Sood said. "This is going to be an ongoing transformation. Because the world of AI is moving so quickly that it's hard to know what's going to happen in three years, let alone even what's true today may not even be true by this time next year."
That ongoing uncertainty reinforces the value of insurer partners who are approaching AI transformation deliberately - building capacity rather than cutting it - and who will still be operationally strong on the other side of it.
