Insurance has traditionally been a business built around risk. That makes the rise of InsurTech somewhat unusual: startups are expected to move with the speed of software companies while operating in an industry where regulation, compliance and long-term risk management remain fundamental to the business model.
The opportunity for technology is considerable. Global insurance premiums reached an estimated $8.3 trillion in 2025, according to McKinsey, while insurers continue to deal with large amounts of structured and unstructured data and workflows that remain partly manual.
AI is increasingly being positioned as a way to change that. McKinsey estimates that generative AI could unlock $50 billion to $70 billion in insurance industry revenue, with potential applications across sales, customer operations and software engineering.
The technology is also moving beyond individual tasks. Agentic AI is emerging as the next layer, with systems designed to manage more complex workflows across the insurance value chain.
But faster technology does not necessarily mean simpler operations.
Scaling an InsurTech without scaling the risk
GetCovered.io provides a risk and compliance management platform for property managers in the rental market. Its work sits at the intersection of insurance operations, technology and regulatory requirements, making the company’s own ability to scale a central part of its strategy.
“But in InsurTech, growth without discipline isn’t momentum—it’s risk,” said Rick Folgmann, COO of GetCovered.io.
For Folgmann, that means building processes before they become difficult to change.
“At GetCovered.io, we focus on building repeatable, scalable workflows early, while the organization is still flexible enough to change.”
The issue becomes particularly important as companies expand across customers, states, carriers and revenue streams. In insurance, those additions can bring new requirements around eligibility, enrollment, billing, renewals, compliance and customer service.
AI can help automate parts of those workflows, but it also introduces another layer of operational responsibility.
McKinsey’s research suggests that insurers deploying generative AI across their operations could see 10% to 20% productivity gains, while customer onboarding costs could fall by 20% to 40% in some applications. The firm also expects agentic AI to increasingly manage end-to-end processes rather than isolated tasks.
That makes the infrastructure surrounding the technology just as important as the technology itself.
For Folgmann, compliance should be embedded into that infrastructure rather than treated as a separate function: “Insurance regulation isn’t a checklist—it’s an environment.”
He argues that licensing, filings, disclosures, audits and regulatory responses need to be incorporated into everyday operations.
The same principle extends to customer experience. As insurance companies automate more of their operations, the quality of the final service still depends on the processes supporting it.
“Customer experience is often framed as a product or support issue. In reality, it’s the output of dozens of operational decisions.”
For InsurTech companies, the next phase of growth may therefore be less about moving fast at all costs and more about building systems that can absorb that growth without creating new risks. As AI becomes more deeply embedded across insurance, operational discipline could become one of the factors separating experimentation from sustainable scale.
