Managing general agents (MGAs) are, in many ways, better positioned than traditional insurers to capitalize on automation and the integration of artificial intelligence into their operations.
With leaner structures and fewer legacy systems, they have the flexibility to move and innovate more quickly. But while their tech agility gives them a head start, it’s still far too soon to declare a clear winner – especially with the AI race only just beginning.
That is according to Billy Walsh, partner at Deloitte Canada, who shared insight into how MGAs and traditional insurers compare when it comes to technology stacks, innovation priorities, and the evolving insurance landscape.
When it comes to the most discussed innovation of the past couple of years – artificial intelligence – Walsh says it’s not yet clear whether MGAs or traditional insurers have a decisive edge.
While MGAs are often seen as more nimble and capable of experimenting with new tools, carriers bring something equally powerful to the table: scale and deep pockets.
“I have not seen substantial differences myself. Carriers have a big advantage as they have scale and capital they can invest. They can run many pilots at once. MGAs have the advantage of being smaller, more agile, nimble, and able to experiment with things,” he said.
Walsh said that MGAs tend to be built around narrower products and more focused customer segments, which allows for greater experimentation and quicker pivots.
According to him, MGAs are typically smaller, more specialized entities targeting specific customer segments and coverage areas. This focused approach allows them to build technology and operational processes tailored to precise needs, rather than adapting systems meant to serve a broad range of products.
“This [characteristic] allows them to build fit-for-purpose and specific solutions for the markets they want to serve.”
In contrast, traditional carriers are responsible for managing vast, diverse portfolios developed over years. Their technology infrastructure has often evolved in tandem with this expansion, resulting in legacy systems that weren’t necessarily designed with modern needs in mind.
“They are not necessarily built for now. It’s [infrastructure] built for the needs as they’ve evolved, and that makes it challenging to progress from there,” Walsh said.
Legacy infrastructure often brings with it legacy processes. As Walsh pointed out, systems and workflows are closely linked – and it’s difficult to modernize one without modernizing the other. This can slow innovation and limit a carrier’s ability to quickly test or scale new offerings.
On the personal lines side, Walsh said that carriers have already invested heavily in technology and modernized many of their systems.
“It would be hard for MGAs to be a ton more advanced… Most [carriers] have API-forward architectures, modernized cores, and advanced digital offerings.”
However, the story changes when looking at commercial insurance. In this space, MGAs have carved out a clear lead in some domains – especially those involving digital capabilities and underwriting platforms.
He attributed this in part to the historical investment patterns of carriers, who until recently had not prioritized commercial lines for tech upgrades.
That said, Walsh cautioned against sweeping generalizations. With different carriers and MGAs pursuing different tech strategies, the degree of innovation varies widely across organizations.
“There are exceptions,” he said. “It depends where you are and which part of the value chain you’re looking at.”
While structure and legacy systems shape how MGAs and carriers adopt technology, Walsh pointed to two deeper factors that ultimately determine success: data quality and organizational culture.
On both sides of the industry, the pursuit of high-quality data sources is a common trend. Walsh said that both carriers and MGAs are searching for ways to bring accurate, integrated data into underwriting workflows – which would minimize the need for manual input.
When asked whether deep pockets or agility provide the greater advantage, Walsh sidestepped the binary. Instead, he emphasized the importance of having the right tech infrastructure, skilled teams, and a culture that fosters experimentation.
While MGAs may be structurally better positioned to experiment, Walsh said that many carriers, particularly their data teams, are also building the capabilities needed to compete in this space.
The deciding factor, he argued, is less about size or speed and more about whether an organization is culturally ready to innovate and has a tech stack that can support it.
“What you need to have is the ability to quickly spin up a test – pilot for the data or for the AI process that you’re trying to work through – run it, and be willing to fail,” he said.
