Today we’re speaking with Simon Glairy, a recognized expert in Insurtech with a deep focus on risk management and AI-driven assessment. He joins us to shed light on a fundamental shift happening in the industry: how operational data is moving from the back office to the strategic core of the insurance value chain. We’ll explore how insurers can harness this data to drive faster decisions, navigate complex regulations, and build a true competitive advantage in an increasingly demanding market. Our discussion will cover the critical need for a unified data foundation, the architectural barriers posed by legacy systems, how to ensure fairness and explainability in AI, and what truly separates the ‘data-rich’ from the ‘data-smart’.
We’re seeing customer demand for speed, advancements in AI, and stricter regulations reshaping insurance. How are these three forces changing the role of operational data from a simple reporting tool to a real-time decision-making engine? Please share a specific, real-world example of this shift.
It’s a perfect storm, really. For decades, we used data to look in the rearview mirror—to generate quarterly reports and analyze past performance. Now, those three forces are compelling us to use data as a high-beam headlight. Customer demand for instant, personalized quotes means we can no longer wait days to collate information. AI advancements allow us to process that information in seconds, not weeks. And stricter regulations mean we have to prove our decisions are fair and consistent, which is impossible with messy, siloed data. The result is that operational data has been elevated from a retrospective tool to a live, real-time engine that drives every decision, from quoting a new policy to processing a claim. The entire mindset has to change; it’s no longer about owning data, but about how quickly and intelligently you can put it to work.
Many insurers invest heavily in third-party enrichment and predictive models. Given that, why is the integration of internal data—like submissions, claims, and accounting—on a single platform the critical first step? How does this foundation directly impact the value of those external models?
That’s a fantastic question because it gets to the heart of a common and very expensive mistake. Insurers rush to buy sophisticated predictive models or third-party data feeds, expecting them to be a silver bullet. But those external tools are only as good as the internal data they are fed. If your own house isn’t in order—if your submission, quote, claims, and accounting data are all trapped in different systems that don’t talk to each other—then even the most advanced AI model will fall flat. A unified platform that connects all that internal, operational data is the essential foundation. When you have that clean, structured, and complete view of the policy lifecycle, you gain faster, more confident decision-making and can spot risk trends much earlier. Only then can you truly unlock the value of those external enrichment tools; without that solid core, you’re just building a penthouse on a shaky foundation.
Legacy systems often create data silos, leading to fragmented workflows and manual workarounds. Can you describe the specific operational and regulatory challenges this creates for an insurance team? What are the first practical steps an organization can take to start breaking down these silos?
The challenges are immense and they cascade. Architecturally, you have these legacy systems that were never designed to communicate, creating deep data silos. This technological gap quickly becomes an organizational one. I see teams all the time resorting to heroic, but terribly inefficient, manual workarounds. They’re re-keying data from one system into another, using spreadsheets to bridge gaps, and creating fragmented workflows just to keep the lights on. Operationally, this kills speed and introduces errors. Then, regulation amplifies the pain. Imagine trying to provide a clear audit trail to a regulator when your data is spread across five disconnected systems. It’s a nightmare. The first practical step isn’t just to buy a new reporting tool that sits on top of the mess. The real solution is to invest in a unified operational platform that connects workflows, data, and teams at the core of the business, truly breaking down those walls instead of just painting over them.
As AI becomes more embedded in pricing, ensuring fairness and transparency is crucial. Beyond dashboards, what specific infrastructure elements, like clear data lineage or policy controls, are essential for making a decisioning process truly explainable to regulators and customers? Please walk us through an example.
Explainability has to be baked into your architecture from the ground up; it can’t be an afterthought. A fancy dashboard is nice, but it doesn’t make a black box transparent. To make an AI-driven pricing decision truly defensible, you need several core infrastructure elements working together. It starts with high-integrity, structured data—garbage in, garbage out. From there, you need clear data lineage that allows you to trace a decision from the final output all the way back to every single input that influenced it. You also need granular user permissions to control who can see and modify data, full audit trails of every action taken, and centrally managed policy controls that ensure your rules are applied consistently and compliantly every single time. This infrastructure makes every step of the decisioning process visible, traceable, and defensible, which is exactly what regulators, stakeholders, and even customers are now demanding.
The gap between being ‘data-rich’ and ‘data-smart’ seems to be widening. What does being ‘data-smart’ look like in practice for an insurer, and what key metrics might demonstrate that an organization has successfully bridged this gap, turning its data into a true competitive advantage?
That widening gap is the defining feature of the market right now. Being ‘data-rich’ is easy; most insurers are drowning in data they can’t effectively use. Being ‘data-smart’ is about turning that data into clarity, agility, and a real competitive edge. In practice, a data-smart insurer doesn’t just produce reports; they use a unified data foundation to power every single workflow and decision. They innovate faster because they have a clear, real-time view of market trends. They manage risk more intelligently because they can connect underwriting data with claims data instantly. And they deliver the seamless, personalized experiences that customers now expect. The key metrics aren’t just about efficiency gains; it’s about business outcomes. Are you launching new products faster than competitors? Is your loss ratio improving because you’re spotting risk trends earlier? Are you demonstrating clear value to your industry partners through shared, high-integrity data? Those are the signs that you’ve successfully bridged the gap.
What is your forecast for the role of operational data in the insurance industry over the next five years?
Over the next five years, operational data will become the undisputed central nervous system of every successful insurance organization. The distinction between being ‘data-rich’ and ‘data-smart’ will become the primary determinant of who wins and who gets left behind. We will see a major move away from siloed, legacy systems toward unified platforms that make data accessible and decision-ready across the entire value chain. Insurers who make this transition will not only achieve new levels of efficiency but will also unlock unprecedented capabilities in product innovation, risk management, and customer experience. Those who fail to build this unified data foundation will find themselves unable to compete on speed, intelligence, or trust.
