Intact sharpens AI strategy across specialty lines
Artificial intelligence has moved from experimentation to execution at Intact Insurance. And in specialty lines – where underwriting is bespoke and risk profiles are complex – the insurer is deliberately embedding AI without diluting human judgment.
“Being able to identify how innovation comes in and makes us better, stronger, and more competitive is something that we’ve spent a lot of time on,” said Lynn A. O’Leary, global COO of Intact’s specialty lines group.
Intact operates across Canada, the US, the UK and Europe, spanning personal, commercial and specialty lines. While AI has gained traction in personal and commercial portfolios, specialty requires a more tailored application.
“There’s not necessarily an obvious correlation when you think about the way we present specialty: unique risks, niche markets, customized and tailored solutions,” she said. “So we’ve really been looking at how AI fits into the specialty lines business, because it certainly does.”
More than 10 years ago, Intact established the Intact Lab. Today, it includes over 600 employees across Montreal, Toronto and Hong Kong. The lab focuses on automation and AI, with Hong Kong providing access to specialized expertise.
Initially, the lab drove innovation in Canadian personal and commercial lines. Over time, that capability expanded into the UK and specialty segments. Applications span pricing, risk assessment, mitigation and claims.
Within specialty, AI is being deployed across underwriting, pricing sophistication, operations and claims. “It is still very much a human-driven process, but by leveraging AI we can ingest submissions, assess a risk quickly, and get a quote out the door,” O’Leary said.
Speed matters. But so does precision. “Not surprisingly, in insurance, the quicker you can ingest a submission, assess that risk, and get a quote out, the better the service to our broker partners,” she said. “And it’s not just about speed; it’s about getting a quote that we feel strongly about.”
O’Leary is direct about underwriting control. “Our process currently benefits from an underwriter's involvement, and we’re focused on leveraging human oversight where it adds the most value” she said. “You need that human interaction, oversight, and engagement.”
“AI’s role was to improve the flow of information into underwriters’ hands to make us a more sophisticated carrier and business partner,” she said.
Claims has provided another use case. AI accelerates ingestion and analysis of data, enabling faster communication with policyholders. “It accelerates our ability to digest information, make decisions, and get back to our policyholders and customers,” she said. The objective is service. Faster responses. Stronger relationships.
O’Leary stresses that innovation does not happen in isolation. “They work hand in hand,” she said of the relationship between the lab and underwriting teams. “If the business is not at the table to talk about the output they need… we miss the mark.”
Underwriters understand risk. Lab teams understand technical possibility. The model only works when both sit together. “The data lab experts sit at the table and say, ‘We could do this, this, and this. What really makes sense for you?’ Then we can build a model off that,” she said. Solutions built without business input would fail to gain traction. “It’s very much driven by: what do you need, and how can we help you be smarter, go faster, and service the customer better?” O’Leary said.
Intact’s instinct is to build core capabilities internally. “Our reflex is to build it and own it internally,” O’Leary said. “We want to be able to control our data and move at the pace that matches our investment priorities.”
Still, she acknowledged the value of external providers. "We are not agnostic to the value of external vendors,” she said. External partners are viewed as accelerators - not substitutes. “At the end of the day, we would never heavily rely on external expertise, but we see it as an accelerator for things already underway internally,” she said.
Technology, she suggested, is not the hardest part. “The biggest challenge is the speed of adaptation and adoption,” O’Leary said. “You’re asking businesses and individuals to work in different ways, in new ways, and to get out of their comfort zones.”
Transformation requires early engagement. “If you just put something in front of them and ask them to adapt to it, that’s very difficult,” she said. Instead, teams were brought into discussions at the outset.
“When you think about transformation, it starts at the beginning with everybody who needs to be involved and who will be impacted by it,” she said. Internal co-pilot tools illustrated the shift. Once employees used them, resistance faded.
“Once people are using it, they see what it can do and what it can deliver, and it’s pretty amazing,” she said. The long-term differentiator is alignment – technical capability matched with frontline expertise. “They may not be experts in AI, but they’re experts in their business,” she said. “You just have to figure out how to build it in ways that match what they see as a differentiator, an accelerator, and a supporter for them.”
