In an industry that prizes precision and efficiency, commercial lines workflows remain a stubborn outlier. For all the progress in digital transformation, quoting and submitting policies can still feel “universally clunky,” says Steve Whitelaw (pictured), senior vice president and general manager at Applied Systems Canada.
The inefficiency isn’t limited to one class of business – it runs across the board – but it’s particularly acute in the small business segment, where slim margins leave little room for time-consuming manual work. Whitelaw points out that smaller premiums mean smaller profits, so every additional administrative step eats into what’s already a tight return. That imbalance makes automation not just attractive but essential.
Whitelaw said that Applied wants to help brokers grow in that small business space, which he calls “the backbone of Canada.” Roughly 90 percent of businesses in the country employ fewer than 99 people, making it crucial for brokers to serve this market efficiently. By removing friction from submissions, Applied aims to make small commercial accounts as streamlined to process as they are important to the economy, he added.
The company’s approach centers on eliminating duplicate data entry, minimizing re-keying, and supporting real-time quoting where possible – all within the broker’s existing workflow. The goal is a smoother experience that allows brokers to focus on advice and customer service rather than administration.
Access to quality data is the next major hurdle in modernizing commercial lines – and one Whitelaw believes Applied Systems can address. He said the company’s AI and analytics work is grounded in three pillars: strong data science expertise, deep insurance domain knowledge, and, crucially, access to robust datasets.
In a sector where information often sits in silos or legacy systems, gathering usable data is no small task. Applied collects anonymized data from its customer base, but only with explicit permission built into client agreements. That data comes from multiple platforms.
Recent acquisitions, he noted, have strengthened that foundation. The addition of Planck and Cytora brought not only specialized data talent but also access to new data sources – both public and proprietary – that enrich Applied’s existing intelligence ecosystem. Combined, these resources enable the company to build models that are genuinely insurance-specific rather than adapted from other industries, he said.
Still, Whitelaw is quick to note that acquiring data is only half the challenge. The other half lies in how it’s used.
Applied takes what Whitelaw describes as an “ethical by design” approach to data usage. He points to guidance from the Registered Insurance Brokers of Ontario (RIBO), which urges brokers and vendors to fully understand and disclose their data sources and any assumptions used in AI models.
That principle has become part of Applied’s design philosophy. Instead of layering AI on top of existing systems, the company embeds intelligence directly into the workflow – so that recommendations, summaries, and suggested next steps are surfaced within the process itself. Each recommendation is traceable to its data source, ensuring transparency for the broker and compliance with evolving standards.
This emphasis on explainability is both a regulatory safeguard and a matter of trust. As Whitelaw notes, transparency is essential to maintaining confidence in AI-driven decisions. Without it, brokers could be left exposed to compliance or reputational risks.
Applied’s ambition to make commercial lines faster and smarter ultimately depends on that trust – not only in the output of its tools but in the integrity of the data that powers them.
For Whitelaw, automation isn’t about reducing headcount – it’s about unlocking capacity. He describes it as a growth enabler, not a cost-cutting exercise. While automation can create efficiencies, its real value lies in freeing people to focus on higher-impact, customer-facing work.
“The goal isn’t to eliminate people from the equation,” he says. “It’s to surface better recommendations so that brokers can act on them.” Applied’s vision, he explains, is to evolve from a system of record – where brokers simply document activity – to a system of action, where data and AI work together to prompt the next best move.
That still requires human oversight. Brokers validate the suggested actions, apply their judgment, and ensure that each decision aligns with the client’s needs. The automation running in the background – whether processing submissions, reconciling payments, or managing workflows – simply creates the breathing room to do that more effectively.
Whitelaw sees these efficiencies as an opportunity to redeploy staff, not replace them. “It’s about giving people more time with customers,” he says, “and helping brokers differentiate themselves from what clients might experience in the direct channel.”
