In this exclusive insight for IFA Magazine’s New Insurance and Protection, Will Watling, Insurance Solutions Director, UK & EU at Equisoft, brings together a range of industry experts to explore how AI is being adopted across the UK insurance sector, cutting through the noise to reveal what’s delivering real value.
Every organisation in the UK insurance industry is on their own journey from AI curiosity to use cases to proof of concepts, and, when things go well, to something that actually runs in production and delivers measurable value.
Some organisations are moving with careful deliberation, building governance frameworks before they scale. Others are leaning hard into a fail-fast philosophy, running experiments quickly, accepting that some won’t land, and learning faster as a result.
Both approaches address the same underlying reality: the rules of AI adoption in insurance are still being written, and no one has a complete map.
To understand what’s really happening on the ground, Equisoft, a global insurance and financial services technology company, spoke with three leaders from distinct areas in the UK insurance world: a market intelligence expert tracking AI adoption at scale across insurers, a consulting director embedding AI into client engagements daily, and a trade body chief executive helping mutual insurers navigate the opportunity without getting burned.
Their perspectives differ, and that’s precisely what makes them worth reading.
How AI is Reshaping the UK Insurance Market?
“To unlock AI’s full value, insurers must improve data governance, standardisation, and centralisation” – Michał Litwiński, General Insurance Lead, Sollers
The UK insurance industry began adopting AI around four years ago, mainly through external providers and focused on claims triage using optical character recognition. By 2022, insurers expanded AI use into risk management and operations, and AI in underwriting has since become a major trend.
No other global insurance market currently invests more in AI expertise, and the London Market is rapidly catching up. About one in three IT roles now requires AI skills, with a strong focus on claims and underwriting.
Underwriting system vendors increasingly embed AI, accelerating adoption. As Intact UK CEO Ken Norgrove noted in the Sollers CEO Voices series, Canadian operations have long leveraged predictive AI to improve pricing and segmentation. QBE CEO Andrew Horton highlighted how AI tools are already boosting underwriting speed and accuracy, especially in cyber.
Major insurers are modernising infrastructure to scale AI; Aviva, for example, implemented Azure and GitHub Copilot to modernise legacy platforms and improve efficiency and customer service.
In a London Market project, Sollers helped a leading Lloyd’s syndicate implement an underwriting workbench, which later proved difficult to enhance with AI due to insufficient data readiness. This reflects a broader industry challenge: complex IT landscapes create data silos, limiting AI’s impact.
To unlock AI’s full value, insurers must improve data governance, standardisation, and centralisation. A recent Sollers AI survey confirms that dispersed data and weak governance hinder AI-driven automation across organisations.
How AI Is Impacting Consultancy?
“Fail fast is one of our mantras. If you never start, you never learn.” – Carl Woodward, Director, Simplify Consulting
As a consultancy, we know that to accelerate outcomes for our clients, we either need to employ more people or be more efficient in how we deliver. It’s not a scalable model in this age to grow without investing in technology innovation, and the progress that’s been made in the last ten years has really opened the door to new ways of working.
So, alongside helping our clients embed AI and become more efficient themselves, we’ve introduced it into our ways of working, both operationally and in how we deliver engagements for our clients. We started small, ensured appropriate governance was in place, created guardrails, but incentivised our team to explore new ways of using AI to accelerate benefit realisation – it’s now a formal part of our team meeting each week to understand how everyone has been embracing it and benefitting from it.
What are you currently doing with AI?
There are classic use cases where we’ve deployed AI to complement our usual day-to-day activities – for example, in taking meeting notes, in helping us craft initial drafts of thought leadership, in producing artwork for marketing campaigns and in helping us leverage previous client engagements in pitch documents.
We’ve also used it to make us more operationally efficient, in terms of how we onboard new clients, mobilise engagements and set projects up for success.
However, we’ve gone further — we’ve created some agents to help accelerate analysis of client documentation, including client correspondence, process maps, and procedure guides, to really do the heavy lifting so our consultants can focus on the value-add analysis that sets us apart from our competitors. We’re using it daily, it’s adding value regularly, and it will feature as a key part of our consulting proposition going forward.
What lessons have you learned along the way — and what would you do differently?
Our model of testing and learning comes with an acceptance that not everything succeeds. A waterfall approach to implementation simply won’t cut it, you need to accept that a percentage of proof of concepts simply won’t deliver the outcomes you’re hoping for.
For example, the data may not be robust enough, processes may be too fragmented across different technology stacks, or AI simply doesn’t learn the way you expect it to. So we’ve had to accept that for every five AI use cases we explore in practice, one or two may not reap the rewards you expect. But that’s ok. Fail fast is one of our mantras; it requires a different way of thinking, slightly less upfront analysis and indeed governance and an expectation that benefits may materialise incrementally.
Though we’ve learnt that if you never start, you never learn, so you mobilise quickly, test and evolve regularly and benchmark results often. With that model, we’ve been able to focus on the real value-add agents that will help drive our proposition forward.
How is AI Impacting Mutual Insurers?
“Working with established and trusted partners with a proven track record and who understand our business and operations, and especially the distinctive character of the mutual sector, will be vital.” – Andrew Whyte, Chief Executive, AFM (Association of Financial Mutuals)
You’d have to have been living under a rock for the last few years not to be aware of the impact of AI on all our lives. But as a trade body, we were keenly aware that AI would have a huge impact on the insurance industry.
Our members are all mutual insurers, many of them smaller firms, but all operating in a highly competitive market. So, our initial steps were designed to enable our members to discuss and understand the potential impact on their business, the opportunities and risks that AI would bring.
AI featured in our regular webinar sessions, our professional network groups and our major conferences, for example, exploring the emerging use cases or looking at decision-making and governance, as well as at our Board and Committees, where we considered the policy implications. That process, of creating space for shared learning, will continue to be a key part of our role as a representative body going forward.
What aspects of AI adoption are unique to smaller mutual insurers?
In common with many other sectors, one of the first uses of AI in practice was in customer services, but increasingly we are seeing it applied in other areas of core business.
There are particular challenges for smaller mutual insurers who, while there are potential savings to be had from implementing AI in routine processes, may lack the resources to invest in new technologies upfront, which could put them at a disadvantage. But there are some upsides to this, as one AFM Member CEO commented:
“We may benefit from ‘second-mover’ advantage – learning the lessons from early adopters, seeing what works and what doesn’t, and so being able to assess what would add the most value for us.”
What lessons have you learned along the way – and what would you do differently?
The main lesson is that things are changing at lightning speed. That applies obviously to the range of products on offer, but also means that corporate policy or usage guidelines are soon outpaced by technological developments. So we, and more importantly, our members, need to be agile in responding to the rapidly shifting context.
And sadly, we’ve learned that, like so often with ‘next big things’, there are a lot of snake-oil salespeople out there. Working with established and trusted partners with a proven track record and who understand our business and operations, and especially the distinctive character of the mutual sector, will be vital. We have been grateful for the contribution that specialist firms from amongst our network of Associate Members have made to helping AFM and our members build our understanding and develop our thinking.
Key Takeaways
UK insurance’s AI adoption is further along than many realise — but progress is uneven, and the hard work is just beginning. Data readiness remains the single biggest barrier to unlocking real value, while the organisations making the most headway are those willing to experiment quickly, accept failure as part of the process, and iterate without ego. For smaller players, a deliberate second-mover strategy can be a genuine advantage rather than a weakness.
And across the board, one thing is becoming clear: as AI vendors proliferate and claims multiply, choosing the right partners, ones who actually understand insurance, may matter as much as choosing the right technology.
About Will Watling
Will Watling is Insurance Solutions Director, UK & EU at Equisoft, a global provider of advanced digital solutions for the insurance and wealth management industry. For more than 3 decades, Will has supported digital service providers across the financial services sector in go-to-market strategies, business development, and system implementation.
