The UK insurance sector is in the middle of a genuine step change. Underwriting, which for decades meant experienced professionals working through paper submissions and applying judgment built over careers, is being redesigned from the ground up. Machine learning models are now assessing risk in seconds, pulling in data from satellite imagery, IoT sensors, Companies House filings, claims histories, and weather models simultaneously. In the Lloyd’s market, algorithmic underwriting platforms are processing submissions that once took days to quote. At the other end of the market, motor and home insurers are building dynamic pricing engines that adjust in near real-time based on behavioural data from telematics devices.
Claims are changing just as fast. Straight-through processing, where AI handles the entire journey from first notice of loss to settlement without human intervention, is now live at multiple major UK carriers for high-volume, lower-complexity claims. AI-powered fraud detection is identifying patterns across millions of claims that no team of investigators could spot manually, at a point in time when generative AI is simultaneously making it easier for fraudsters to fabricate documentation at scale. The industry is fighting that battle on both sides at once.
None of this is theoretical. According to Accenture research published in early 2026, ninety percent of insurance executives plan to increase AI investment this year. A quarter of them say skilled talent shortages are the single biggest obstacle to extracting value from that investment. Those two facts sit uncomfortably next to each other.
The gap between ambition and delivery
The problem is not a shortage of AI strategy. Every large insurer has one. The problem is the gap between the strategy and the operational layer that makes it real. Building and maintaining the data pipelines that feed a machine learning model requires data engineers. Training, validating, and monitoring those models requires data scientists and ML engineers. Integrating AI tooling into core policy administration systems, whether Guidewire, Duck Creek, or a bespoke legacy platform, requires platform engineers and integration specialists who understand both the technology and the insurance domain it sits inside.
These are not senior, high-cost roles. They are the mid-tier technical workforce that every transformation programme depends on. And right now, more than seventy percent of UK insurers say difficulties securing specialist talent are directly limiting their operational capacity. The talent market for data and AI roles in financial services is extremely competitive, and insurance is competing against fintech, banking, and the technology sector itself for the same people.
The ageing workforce compounds the problem. The Chartered Insurance Institute estimates that a quarter of the sector’s workforce will retire within the next decade. In underwriting and claims, the people leaving are not just headcount, they are carrying institutional judgment built over thirty years that does not exist in any database or training dataset. Automating their workflows before capturing that knowledge is not transformation. It is a different kind of risk.
“The gap I keep seeing when I talk to technology leaders in insurance is not ambition, it is execution. The strategy is in place. The investment is committed. What is missing is a sustained pipeline of technical people who understand the environment they are walking into and can contribute to it from day one. That is exactly what we built the Academy to provide.“
Rick Hughes
Director
What getting this right actually requires
McKinsey’s research on insurance AI leaders identifies a consistent pattern. The firms doing this well are building seventy to eighty percent of their digital talent in-house rather than relying on external consultancies or contractors for delivery. They are building insurance-specific expertise into junior roles from the outset, not hiring generic data graduates and expecting them to learn the domain on the job. And they are treating talent as a long-term infrastructure investment, not a short-term headcount decision.
That is a materially different approach from how most insurers have tried to solve this problem so far. The default has been to bring in experienced contractors and consultants, get the project delivered, and move on. That model works for point-in-time delivery but it does not build the capability the organisation needs to operate and evolve what has been built. An AI-powered claims triage system that nobody in the business fully understands is a liability, not an asset.
At Corecom Tech Academy, we build the technical teams that sit underneath the strategy. Associates are recruited for aptitude and trained specifically for the client’s environment, including the insurance-specific tools, data structures, and regulatory context they will be working in from day one. They deploy on a two-year contract with no FTE commitment, no employment liability, and no recruitment fee at conversion. The knowledge builds inside the organisation rather than leaving when a contract ends.
The insurance sector is at a point where the investment in AI is real and the intent is genuine. What determines whether that investment produces anything lasting is the operational layer underneath it. That is the problem we exist to solve.
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