AI Consultancy for UK Insurers, Brokers and MGAs
Insurance has been using models to make decisions for far longer than most sectors, which makes the arrival of AI both easier and harder: easier because the idea of a model in the decision path is not new, harder because the standards of explanation and oversight are already set and AI does not meet them by default. Our founder held group CTO and CIO roles in insurance before starting this consultancy, so this is the sector we know from the inside rather than from a market report.
Our founder has run the technology function, not just advised one
Most AI advice offered to insurers is written by people who have never had to keep a policy administration system running while the business grows around it. That shows up in the recommendations, which tend to assume clean data, a modern core and a delivery team with spare capacity, none of which describe a typical mid-sized insurer or broker.
We start from the estate you have, including the parts of it nobody wants to touch, because that is what determines which AI use cases are realistic this year rather than after a core replacement. Our founder spent 30 years leading technology in regulated businesses and has carried the accountability for these systems personally, which changes the advice you get.
We are paid by the client, and only by the client. We take no commission, referral fee or resale margin from any vendor, we have no implementation arm that needs feeding, and we are not tied to a single platform. So if the sensible answer is a narrower programme than you were expecting, that is the answer you will get.
Where AI actually earns its place in an insurer
The use cases that pay for themselves in this sector are rarely the ones that make the trade press. They cluster around claims, where triage, document handling and fraud signals absorb an enormous amount of skilled human attention on tasks that do not need it, and around underwriting support, where the work is assembling and summarising submission material rather than making the risk decision itself.
Both of those have a common shape, which is that AI does the assembly and a person still makes the call. That shape is deliberate, because it is the version most likely to clear a risk committee and to hold up under a file review. It is also the version that delivers, since the time saved is real even when the decision authority never moves.
The harder conversations are about pricing and anything that touches customer outcomes, where the oversight expectations are highest and the appetite for an unexplainable model is close to zero. We are candid about which of those are worth starting now and which are worth waiting on, and that judgement is usually the most valuable thing we bring.
How we work with insurance businesses
Breathe is our 5 to 10 day discovery sprint, assessing readiness across your data, your core systems and your governance appetite, then returning a scored set of opportunities and a costed roadmap with a three-year view of the spend. For insurers it also separates the use cases that clear your existing model oversight from the ones that would need a new control framework.
Flow is implementation, taking one problem such as claims triage or submission handling and building a working capability over 4 to 8 weeks with the audit trail, human checkpoints and measurement designed in from the first day.
Grow is a fractional Chief AI Officer, holding strategy, governance and board reporting together for insurers who need senior AI accountability without adding a permanent executive to the establishment.
Firms in the wider sector may also want our financial services page, and law firms our legal page. We work across the UK from a base in Leeds.
Related reading
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AI Strategy for Insurance Companies: Where the Real Opportunities Are
Insurance is not a monolith. AI creates different opportunities across claims, underwriting, customer onboarding and reporting. Here's where to focus.
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AI Governance in Financial Services: What Good Looks Like
Governance is not the opposite of innovation. The firms moving fastest with AI in financial services are the ones that invested early in getting their governance right.
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Introducing AI in a Regulated Business: A Risk Officer's Practical Checklist
You are not against AI. You are against AI that nobody has thought through. Here is a practical checklist for introducing AI in a regulated business.
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