The AI Distribution Playbook for UK Insurers · 2026
Your customers are asking AI where to buy insurance. Are you in the answer?
Research, infrastructure, and a practical view on what UK insurers should do as AI assistants become a primary route to discovery.
Marrow
Research and analysis by the Marrow research team. Methodology: 200+ queries across four AI platforms, March and April 2026.
There is no shortage of writing on AI in insurance. Most of it sits at the level of frameworks and forecasts. This document takes a different angle: what AI assistants are actually saying about insurance products today, where the gaps are, and what the practical response looks like for an insurer's CMO, CTO, and compliance team.
In March and April 2026, the Marrow research team ran more than 200 insurance-related queries across ChatGPT, Claude, Perplexity, and Gemini. The queries covered motor, home, travel, life, and pet, framed the way real customers ask them: "what's the best home insurance for a first-time buyer?", "recommend car insurance with breakdown cover", "I'm 24 and just bought a flat in Leeds, what should I get?". We then tracked which insurers appeared in the responses, how often, and with what accuracy. The picture varies by line and by platform. The direction of travel does not.
On this page
01
Who this is for
This is for senior leaders in an insurance business who can see the platform shift happening, but don't yet have a clear picture of what it means for distribution.
CMO
You own customer acquisition and your brand is largely absent from AI answers
You've built the comparison site strategy, the SEO playbook, and the paid media engine. A new channel is emerging that your team has no way to participate in yet, and the visibility gap compounds the longer it stays open.
CTO
You've been asked to have an AI strategy and don't want to build it wrong
Direct LLM integration sounds appealing but introduces compliance and underwriting risk that legal teams typically won't sign off on. You need an architecture that works without changing core systems.
Head of Distribution
You're watching new channels emerge that you have no access to
Your job is finding new routes to customers. AI assistants are becoming a primary route, and most insurers have no presence in those environments today.
Outside those titles: if you are responsible for how your insurance business reaches customers in the next five years, this is for you.
02
The next distribution shift
Every generation of insurance distribution is defined by the platform customers use to find products. The 1990s belonged to the phone book and the broker's desk. The 2000s, to the search engine. In the 2010s, aggregators such as MoneySuperMarket, Compare the Market, and GoCompare became the dominant intermediary. Each shift created winners and reshaped the economics for everyone else.
The insurers who won comparison sites weren't the ones with the best products. They were the ones who got their data architecture right first.
AI assistants are becoming the interface through which a growing proportion of customers discover and evaluate insurance products. The channel already exists; the question is whether your products appear in it.
Hundreds of millions
Weekly users across ChatGPT, Claude, and Gemini combined, with a growing share asking insurance queries
<2%
of UK insurers can currently transact natively through an AI assistant
Across the queries we ran, most major UK insurers did not appear in the answers. Of those that did, the conversation typically ended at the brand mention, with no path through to a live quote.
03
How buyers find insurance now
A growing share of customers no longer start their insurance journey on a comparison site. They start with a conversation: "ChatGPT, what's the best home insurance for a first-time buyer?". The behaviour is most pronounced among digitally native buyers, and it is moving up the age curve faster than most teams expect.
The old funnel
Step 01
Awareness
TV, outdoor, and paid search build brand familiarity. The customer already knows your name before they shop.
Step 02
Consideration
They visit an aggregator. Your price and features compete on a grid they can see and sort.
Step 03
Purchase
They click through to your site or buy via the aggregator. Transaction complete.
The new funnel
Step 01
Conversation
The customer asks an AI assistant what they need. The AI recommends two or three options. If your brand isn't in those recommendations, the funnel never reaches you.
Step 02
Instant qualification
The AI agent asks qualifying questions, collects details, and structures the risk inside the same conversation.
Step 03
Transaction
The AI passes structured data to your infrastructure, retrieves a live quote, and the customer binds, without ever visiting your website.
What's different
In the old model, you competed on price once the customer arrived. In the new model, you compete on discoverability before they arrive. Visibility becomes a prerequisite for the sale, not a step in the funnel.
04
What your stakeholders need to hear
Most of the work in moving on this is internal alignment. Different stakeholders come at it with completely different questions.
| Stakeholder | What they're worried about | What they need to hear |
|---|---|---|
| CMO | Losing brand equity in a channel they can't control or measure | This isn't brand dilution. It's the next aggregator. You either have a feed into it or you don't. |
| CTO | Integration complexity and legacy system exposure | The infrastructure sits in front of your existing APIs. No core system changes. One integration unlocks all AI channels. |
| Compliance | FCA risk, AI hallucination, auditability of AI-driven decisions | Direct LLM integration is the risk. Compliance-ready middleware is the solution. Every transaction is auditable end to end. |
| Head of Distribution | Channel conflict with existing broker and aggregator relationships | AI is an additive channel. It surfaces customers who would never have come through your existing routes. |
| CFO | ROI justification for a new infrastructure investment | GWP at risk if competitors move first is measurable. The question is whether you want to be first mover or catch-up. |
The most important internal dynamic: this decision is typically championed by a CMO or Head of Digital, then evaluated by the CTO. Both need to be convinced. The CTO's concerns about legacy integration are legitimate and addressable; so are the compliance team's. Naming them directly is a faster route to alignment than working around them.
05
Why direct AI integration is risky
The obvious move, integrating directly with an LLM provider's API to build something in-house, carries risks that most insurance technology teams haven't fully mapped.
The hallucination problem
Large language models are probabilistic. They predict what a plausible answer looks like, rather than calculate what the correct answer is. In most domains a slightly wrong answer is tolerable. In insurance it isn't: if an AI assistant tells a customer they're covered for something they're not, that is a liability problem, and potentially an FCA one.
AI is probabilistic. Insurance is deterministic. That gap is where compliance risk lives.
The missing data structure problem
Insurance underwriting requires structured data; AI conversations produce unstructured data. The customer says "I live in a Victorian terrace in Leeds." The underwriting system needs "Property type: terraced. Construction: stone. Year built: pre-1900. Location: LS postcode." Translating reliably between those two, at scale and in a compliant way, is non-trivial.
The audit trail problem
FCA regulations require that insurance sales processes are auditable. A direct LLM integration typically produces no audit trail by default.
- →AI hallucinations in coverage limits or policy terms = FCA risk
- →Unstructured conversational data ≠ structured underwriting data
- →No audit trail = regulatory exposure on every AI-assisted sale
- →Bespoke direct integrations are brittle, expensive, and model-dependent
- →One integration per AI platform means multiple codebases to maintain as the landscape evolves
06
What the right infrastructure looks like
The solution is a compliance layer that sits between the AI assistant and your existing systems. It handles translation, compliance, data structuring, and the audit trail, so your core systems do not have to change and the AI platform does not have direct access to your underwriting engine.
It is the same idea as a payment gateway. Stripe doesn't rebuild your banking relationship; it sits in front of it and handles the complexity so the product team doesn't have to. The same pattern applies to AI distribution.
Layer 1
AI Platform
ChatGPT, Claude, Perplexity, Google AI. The customer conversation happens here. This layer asks qualifying questions, collects risk data, and constructs a structured query.
Layer 2
Marrow: Compliance-Ready Middleware
Validates and structures incoming data, generates the audit trail, and routes the structured request to your pricing engine. Returns a compliant, formatted quote. Designed to support FCA compliance requirements throughout.
Layer 3
Your Existing Systems
Your pricing APIs, policy management system, and underwriting rules. These don't change. One Marrow integration unlocks all AI distribution channels simultaneously.
A single integration covers every AI platform, with no core system changes, the same way one Stripe integration handles every payment method.
This is also the answer to the CTO's question about maintenance. A direct integration with ChatGPT requires a second one for Claude, a third when Gemini becomes dominant, and a fourth when the next challenger emerges. One middleware integration updates centrally as the AI landscape shifts.
07
The four phases of AI distribution readiness
There is no single go-live moment. AI distribution readiness builds in phases. Sequence matters more than speed, though speed matters more than most insurance businesses currently appreciate.
Phase 1
Visibility
Your brand appears in AI-generated answers to insurance queries. You're in the conversation, even if you can't yet transact.
Phase 2
Discoverability
Your product information is structured so AI systems accurately represent your products, pricing range, and eligibility.
Phase 3
Transactability
A customer can receive a live, compliant quote through an AI conversation and bind a policy. The full transaction happens in the AI environment.
Phase 4
Optimisation
You are measuring AI distribution volume, optimising product data for AI performance, and expanding across platforms as they grow.
Most major UK insurers sit somewhere between Phase 0 and Phase 1 today. A small number, including Aviva, are actively investing in AI distribution capability. The gap between Phase 1 and Phase 3 is not a technology gap; it is an infrastructure decision.
Phase 4 is where compounding begins. Insurers operating here can see which product descriptions convert best, which qualification flows reduce drop-off, and which AI platforms produce the strongest risk profiles. That data advantage is hard for late movers to buy back.
08
The questions you should be asking right now
The visibility question
Ask someone in your team to open ChatGPT, Claude, and Perplexity and run ten insurance queries relevant to your product lines. Count how often your brand appears. Count how often your top three competitors appear. The gap is the business problem.
The integration question
Ask your CTO: if we wanted to make our pricing API available to an authorised AI distribution partner, what would that require? If the answer is "six months and a core system change," the constraint is the architecture assumption, not the technical reality. A well-designed middleware layer can connect to a standard pricing API in weeks.
The compliance question
Ask your Head of Compliance: what would an AI-assisted insurance sale need to look like to satisfy our FCA obligations? This is a solvable problem. The answer is an audit trail, clear disclosure of the AI's role, and a compliant data collection layer, all of which can be built into well-designed AI distribution infrastructure.
The first-mover question
Ask your leadership team: if our largest direct competitor launches AI distribution in Q3, what does that mean for our new business pipeline? Insurers who were fastest to get their pricing onto aggregators in 2004 to 2005 captured disproportionate volume for the next decade. The ones who waited until 2008 spent years buying back market share they had given away.
The honest answer
You don't need to be first. The historical pattern is clear: the window between first-mover advantage and category normalisation in distribution channels is typically 18 to 24 months. Insurers who acted on aggregators in 2004 to 2005 captured disproportionate volume for the next decade.
09
Your 90-day action plan
Here is what we'd recommend for any insurer who has decided this is a priority.
Days 1 to 14
Audit your current AI visibility
Run a structured audit of 50 to 100 insurance queries across ChatGPT, Claude, Perplexity, and Google AI. Document which brands appear, at what frequency, and with what accuracy. Benchmark against your top five competitors. This is your baseline and your internal business case.
Days 15 to 30
Map your existing API infrastructure
Identify your current pricing API endpoints: what data they accept, what they return, and how they are authenticated. These don't need to change. The aim is to understand what a middleware layer would connect to. A CTO-led conversation that typically takes a week.
Days 31 to 60
Run a pilot on one product line
Don't try to solve all of insurance distribution in quarter one. Pick one product line, typically motor or home, and build the pilot on that. Define the qualifying questions, map them to your underwriting data requirements, connect the infrastructure, and test end to end in a sandbox environment.
Days 61 to 90
Go live and measure
Move the pilot to live with a defined scope. Instrument your measurement from day one: query volume, quote completions, bind rate, policy data quality. The data from the first 30 days of live operation will tell you more than any pre-launch planning session.
Ninety days from kickoff to live is achievable with the right infrastructure in place. The bottlenecks are almost always internal alignment and compliance sign-off, not technical complexity. The teams that move fastest treat this as a priority with a senior sponsor, rather than a project.
© 2026 Marrow. Marrow is a trading name of Project Malcolm Limited.