Will GenAI and Agentic AI transform insurance claims management for insurer's competitive advantage?, Mike Daly

Not by itself, despite all the hype.

I do NOT write as an expert with proven capabilities of deploying AI, especially Gen AI, LLMs, and Agentic AI. I have learnt some hard lessons as an end user of consultancies and software companies that over-hyped their capabilities. They read the brief before turning up with their ‘A-Team’ with impressive demos and conversations. We chose the best of the shortlist for our global transformation project. Who turned up? Might even have been a ‘C-Team’. You can guess- utter failure as they tried to fit their off-the-shelf services to deliver our required outcomes. ‘Pilot Purgatory’, which we discuss below.

We terminated the POC and went to the second option who turned up trumps. Listened and understood our brief, explained the challenges they would have to overcome, and the impact. Promised and delivered a large A-Team, including the same guys for leadership, continuity, and true collaboration. Even put skin in the game, which is a real commitment.

The POC was successfully deployed, but we had wasted a whole year, added considerable cost, and worst of all, suffered from losing customers, revenues, and profits to competing global hotel groups. All came good as we leapfrogged the competitors and achieved even better customer satisfaction and ROI than the approved business case. Better late than never but best avoided.

This is key to answering the title question. I cover the following topics today with what I hope you find actionable insights.

Lessons from a major transformation project I managed.
Which use cases are best suited for the current maturity of GenAI & AgenticAI?
Augmenting claims handler teams
Claims settlement needs far more than just AI.
How to effectively implement GenAI in the claims business
1. In the core platform
2. Build in-house.
3. License a modern digital claim platform.
Why do so many AI POCs fail?
McKinsey survey ‘Seizing the agentic AI advantage’.
How to manage pilots successfully and improve the GenAI paradox.
AI technology partners who collaborate for successful pilots
Further Reading

Lessons learnt

Whilst I have also suffered some POC failure in my time I can offer you lessons learnt in a successful global transformation of a major and well-known hotel chain. I was the VP of Channel Development, responsible for transforming our business model to serve our consumer and business clients. With what they wanted and not what we decided they wanted. Why?

Customers wanted to book rooms easily, safely, and gain good value. Our problem was that customers found it easier to book via TPAs and received lower prices. Remind you of PCW sites and insurers competing on price rather than on service and customised products for changing needs?

Claims data stored by insurers is so incomplete, across so many incompatible tech stacks, made worse by 80% or more being hidden in the data siloes across those stacks. GenAI that is deployed across those unconnected layers will suffer from LLMs that hallucinate. Adding AgenticAI adds to the nightmare, errors, and compliance-busting outputs.

MACH-architected core & PAS platforms could answer that issue, but most insurers are reliant on platforms like Guidewire that are not MACH (micro-services, API rich, Cloud-Native, and Headless). More on MACH platforms later. Let's stick with Claims.

What use cases are best suited for the current AI maturity of both GenAI and insurers?

“Really proud that we are pioneering in the industry with our talented team & partners. We’ve 6 scaled GenAI use cases creating value and benefit for our customers today and will have more than double that by year-end. Time to change the industry for the better!”

Source Chris Pearce COO, esure.

One area considered ripe for improvement is claim handlers ability to answer questions from customers. Having to key into multiple systems to find information wastes time for the claims team and the customer. Frustration all round and possible damage to reputation and employee morale.

Augmenting the claim handlers with all the information to have a real conversation (not a scripted one) faster is a good use case for GenAI. Compliance challenges are minimised a the legal and claims teams can check the accuracy of data using their experience.

Rory Yates describes that in the business model transformation, #esure has implemented. In the home & contents market, which included the integration and deployment of the RightIndem claims management platform with the EIS #MACH architected core platform.

“Yates, global strategy lead at EIS, says the automation of customer knowledge gathering is among the best use cases he has seen for Gen AI, helping call-handlers quickly understand a customer’s context and respond in a more informed, personalised way.”

Source Insurance Post

GenAI is also being used to translate languages in real-time, to triage claims more accurately, and to suggest the most relevant claims pathway based on the likely size and complexity of the claim. It can help claims handlers with drafting correspondence and assisting in valuations. GenAI’s ability to manage correspondence and digest complex documents is also transforming the day-to-day work of claims teams.

Claims can be very complex; not just CAT events, extreme weather, and the tortuous subrogation process in motor and home claims that can leave claimants in a black hole and very unhappy. And we know where unhappy customers go- to another insurer.

Augmenting professional claim handler teams

As a part of the business transformation model, GenAI could augment human claims handlers just as Remy Gounel states-

“I used to be a claims handler, and I know how much correspondence and complex documents they face – the capacity of GenAI to summarise it means a huge gain of time. Rémy Gounel, subject matter expert, claims at Shift Technology,” Insurance Post

Avoiding hallucinations

#GenAI, #LLM technology mimics the information it finds from the data it is trained on, and, therefore, when the large models like #CHATGP #Mistral #Claude.ai et al are trained on practically every internet data source, they ingest both accurate and inaccurate data. Having crawled through practically every digit of data available, they have had to create synthetic data, which heightens the potential for hallucinations and, therefore, outcomes. A warning comes from a current test case between Toyota North America, Progressive Insurance, and disgruntled motorists.

Filed 'in the U.S. District Court for the Eastern District of Texas, the lawsuit centres on vehicles manufactured by Toyota from model year 2018 onward. Plaintiff Philip Siefke, a Florida resident who owns a 2021 Toyota RAV4 XLE, claims the automaker's tracking systems collect extensive data such as location, speed, acceleration, braking behavior, voice commands and even call logs, which are then shared or sold to third parties like Progressive Insurance.

According to the complaint, “tens of thousands” of Toyota owners across the U.S. may have been impacted by what the lawsuit describes as the unauthorized surveillance and monetization of driver behavior.'

Autobody News June 24th, 2025

See further reading for the link to the article.

There are many other class action lawsuits against insurers and GenAI vendors, so insurers must be able to see, control, and test the sources of data used to train the models.

The highly regulated insurance industry cannot rely on AI for complete automation and straight-through processing #STP without rigorous testing, learning, and measuring results accuracy continuously until insurers can be sure that they achieve compliance standards.

That is made possible with help from data and AI partners who have proven success in that area. They may create private specialist language models #SLM from internal data sources and trusted external sources, applying and combining domain knowledge from the insurer with their own data and AI technology and models. That demands a cooperative and collaborative relationship that can apply the hard yards required to exploit GenAI.

Claims require far more than just GenAI.

Claims platforms must deliver a vital benefit- be able to target the many audiences involved in settling claims, e.g., brokers, fleet managers, supply chain partners (both prime and subcontract) and the many specialists such as loss adjustors, engineers, repair networks. That is where many a delay occur when these are unconnected with the claims management platform. The longer the elapsed time, the higher the cost. This year the motor and homes markets have softened, ` leading to reduced premiums but that may be an illusory comfort when markets harden again. Cost of living pressures and the predominance and preference of price comparison websites make it easy for consumers to shop around for the cheapest price. And that trend is increasing and not helped by so many policies being similar and therefore more a commoditised product than a differentiated product tailored to different customer’s needs.

Insurers held back by legacy technology are product-centred, focused on policies rather than customers. Newer #MACH architected core platforms can solve that issue, but it takes courage for an insurer to replatform. Without that investment they become increasingly vulnerable to competition that has taken that leap. The acquisition of esure by Ageas was stated to be partly to implement the technological innovation achieved by esure, which includes the successful leveraging of GenAI. See further reading for more on that.

Genasys has just published a well-researched blog entitled Future Insurance Trends: Navigating the Evolving UK Insurance Sector in 2025. No hype, just good analysis with references, diagnosis, and prognosis, including a section on claims: -

Claims Process: This is a critical battleground for customer loyalty. Customers of leading insurers are 20% more likely to find the claim submission process clear and 30% more satisfied with the settlement amount, resulting in a 30% higher likelihood of renewal. This underscores the importance of the insurance customer experience in driving retention.

The journey to CX excellence requires insurers to embed customer experience within their broader business strategy, design seamless multi-channel journeys, and leverage data and AI for optimisation. This includes reducing friction in key journeys like claims and renewals, enhancing first-contact resolution, and improving engagement through digital accessibility and personalisation. A single customer view for frontline employees is crucial for providing faster, more informed service, reducing frustration, and improving retention within the insurance sector. This focus on insurance customer experience is a continuous insurance industry trend.

Underlying Trend: The emphasis on clarity and ease reflects a deeper understanding that customer satisfaction in insurance is built on frictionless interactions, not just product features. This is a direct response to evolving customer expectations shaped by digital experiences in other sectors, impacting the future of insurance.
Causal Relationship: Investing in streamlined processes and clear communication directly leads to higher customer satisfaction, which in turn drives increased loyalty and renewal rates within the insurance industry. Conversely, legacy systems and siloed data hinder real-time integration, limiting personalisation and seamless service delivery, leading to customer dissatisfaction and churn.

How to deploy GenAI and other AI tools in claims operations

In the core platform
Build in-house.
Licence a digital claim platform

In the core platform

Modern MACH architecture core and PAS platforms include claims modules as part of an integrated ecosystem in which data flows across the whole value chain. Examples include (alphabetical order):

EIS- describes its deployment of AI for esure. Chief Strategy Officer Rory Yates is well known in the industry for his insights and advice, including the pros and cons of deploying all AI tools, including GenAI. Describes the success with esure on its website.
FintechOS- strength and focus initially on banking; now expanding into insurance and AI deployment. Scott Thompson is the insurance lead
Genasys- clients include Simply Health and many MGAs. Group CEO is Andre Symes; the CRO is Gavin Peters
ICE has many insurance customers listed on its website. ERS Motor Insurer, Ticker, Provident Insurance, The Hood Group (travel insurance). Part of the Actuaris Group. CEO Andrew Passfield
Instanda has`` many insurance clients, including Vitality for its core platform and transformation services. CEO Tim Hardcastle and Group CRO Derek Hill

For single lines of business, this can work well, whilst when looking for an enterprise solution across multiple LOBs, insurers may deploy a specialist claim platform like RightIndem or Five Sigma. See section 5 .

Chris Pearce, COO of esure, states on its website that it has deployed 6 use cases to date, with more in the pipeline and has deployed both from their claim module for motor and integrated RightIndem for home claims.

The AA has the claims module from its core platform, ICE, whilst in parallel deploying motor claim digital FNOL and claim processing from RightIndem.

The number of lines of business is one deciding factor, as claim journeys must be customised, easy to use, and adopted by customers. eFNOL across all LOBs must be consistent if customers adopt digital channels. This must include MGAs, brokers, fleet managers, and TPAs if real customer satisfaction is to be achieved.

Build in-house.

This is a common line to follow for many insurers, especially Tiers one and two. Run a POC with some innovative insurtechs, adopt the learning points, and assign the task to IT to build. Too many insurtechs take the bait without qualifying that the insurer understands the problem(s) to be solved and why. And, importantly, that there will be a budget made available for the chosen short-listed insurtechs. Without this in place, pilot purgatory is the most likely outcome for all parties involved.

With so many low-code, no-code options from insurtechs that have proven success, I wonder why insurers would take this route. They could build the claim solution in Unqork, which would offer a MACH architecture claim module, but that does not come cheap, and you still have to know what, why, and when of the problem. Central and LOB, IT departments tend to jump straight into solutions before the problems are prioritized and understood across all participants impacted.

In my experience, the likely outcome will be a combination of over time, under spec, or over budget, and sometimes all three!

License a digital claim platform.

Esure, The AA, AIOI Nissay Dowa and esure are all part of a larger group of insurers that have taken this journey. On its website, RightIndem describes, with client references, the benefits of the integration with EIS

We needed to solve the complexities within claims supply chain and go beyond simply receiving a digital new claim notification. We chose RightIndem as our strategic partner as their solution gives us the opportunity to coordinate the claims solution ecosystem via a single platform. Their advanced technology, existing supply chain integrations, and customisable solutions impressed us, offering the flexibility we need, giving our customers a simpler and smarter experience.

Mark Ferguson, Head of Claims, Suppliers & Partnerships @ AIOI Nissay Dowa

Practical digital claims management partners

There are various options available, including those below (in alphabetical order):

360 Globalnet
Claims technology with Synergy Cloud
- Home and Property focus
Cotality, the CoreLogic rebranding
- Home, contents, property, and supply chain ecosystem
Five Sigma- transforming insurance with its AI claims adjuster is similar to RightIndem, and years of research made me list the two as the best for insurance to shortlist
RightIndem launched its AI-powered claims assistant that guides claimants to more accurate and detailed eFNOL solututions faster. It names WNS Group as a strategic partner delivering Rightindem embeded as part of its outsourced services for insueres like \howden Group and Zurich. CEO is Julie Rodilosso and CSO Rich Massingham

My research over the last few years suggests that RightIndem and Five Sigma offer the ultimate option for multiple lines of business, digital FNOL, fullim management from FNOL to settlement, and the ability to deploy GenAI and other AI tools. Combined with the MACH architecture core platforms, the combination offers cost-effective solutions.

Why do so many AI POCs fail?

McKinsey published a valuable survey on June 13^th entitled

‘Seizing the agentic AI advantage’.

It highlights the reasons POCs fail and how to combat that issue. In summary

Traditional AI already carved a prescience across enterprises.
- Estimated value $11T to 18T globally
- Mainly Marketing, Sales, Supply Chain
- With expert specialists rather than rank & file
  - Latter slow to adopt
50% companies deploy AI & GenAI in just one LOB
GENAI extended AI in 3 key areas.
- Information synthesis
- Content Creation- status updates
Human comms- but be careful as that will turn customers off if they want personal help on sensitive and complex matters. Simple answers maybe.
McKinsey estimates GenAI has the potential to unlock another $2.6 to $4.4 trillion extra value over traditional AI. See the justification in the full survey.
2 & half years after launch, ChatGPT has reshaped how companies engage with AI
- Democratised access to AI
- Increased awareness
McKinsey states 78% companies using GenAI in one LOB
BUT 80% report no material contribution to earnings -The GenAI paradox
- Similar to the slow adoption and visibility of measurable benefits of the first PCs, SaaS, and Blockchain.
Imbalance between horizontal & vertical use cases
- 70% Fortune 500 companies use MS CoPilot to
- Save time on routine tasks
- Access & Synthesise information
- Whilst a real improvement, GenAI is spread thin
- Not easily visible top & bottom line
Vertical use cases
Specific use cases & processes- easier to deploy and faster to deliver and prove benefits
Seen limited scaling in enterprises
Less than 10% make it past the pilot stage
PILOT PURGATORY. See chart 2 in full survey
Why imbalance?
- Horizontally accessible off-shelf solutions relatively easy to use
- Requires no redesign of workflows or major change management
- Risk mitigation concerns
- Many organisations implement internal secure alternatives
Must ensure COMPLIANCE with Corporate security policies and industry regulations
Limited deployment can be attributed to 6 primary factors
- Fragmented initiatives
- Lack of mature packaged solutions
- Technical limitations of LLMs
- Siloed AI teams
- Data accessibility and quality gaps
- Cultural apprehension and organisational inertia
BUT not time wasted- experimenting now safeguards an insurer's future
- Enriched employee capabilities
- Enabled broad experimentation, accelerated AI familiarity
Helped build capabilities in
- Prompt engineering model evaluation
- Governance
- Lays groundwork for integrated transformative 2nd phase
Emergence of Agents & AgenticAI
From paradox to payoff
- Automates complex business workflows
- To realize the potential of AI agents, companies must reinvent the way work gets done, not just automate it.
- Must redefine human roles
- Build agent-centric processes from ground up
- Accomplishing this requires a new AI architecture and business model
The Agentic AI Mesh
- Challenge not technical but human
- Earning trust to drive adoption & establish proper governance protocols

Johnson and Johnson Group in the USA suffered initial pilot purgatory and by focusing on a limited number of well-managed pilots, overcame that problem

See the Further Reading appendix for a link to the article.

How to manage pilots successfully and improve the GenAI paradox

I am indebted to Chris Surdak for this checklist, which we used together at my hotel transformation project, which was successful.

1. Executive adoption decisions based on something other than FOMO
2. Compelling use cases – will tackle real problems and/or leverage significant opportunities
3. Honest and complete business case ROI
4. Systems engineering thinking
5. Effective test campaigns
6. Users buy into the vision and the experiments
7. Understanding of the technology's true capabilities & the technology's true limitations
9. A backup plan if the experiment fails
10. Self-reflection over prior failures; what has changed in our approach this time?
11. Have the technology partners you chose ‘eaten their dog food’ and proved they can deliver the outcomes you desire using the tools they promote?

Collaborative AI technology partners who deliver successful POCs

This list is not exhaustive; rather based on personal engagement and my perceptions. I have added key people to contact. Where there are no contact details given, I am going on reputation alone.

For large insurers, especially global ones that need to scale up operations and deployment, they need global resources and support. Clients might complain about the cost, but these orgs are restructuring to address that

EY- proof is in the esure use case quoted on its website- Chris Payne heads up the insurance practice; you'll see him at many industry events
Service Now acquired Movie Works to extend generative and agentic AI. Able to scale and operationalise across the whole enterprise like EY. Nigel Walsh heads up the insurance practice
Capgemini publishes valuable survey insights and has partnered with AI-powered pricing and underwriting insurtech Hyperexistenial, which claims to validate every data source used to allow insurers to validate for adherence to compliance and regulatory standards before deploying. Luca Russingan is Senior Director Insurance Intelligence.
Accenture acquired Altus Consulting, which analyses an insurer's digital maturity and how to fill gaps for future innovation- Mark McDonald heads the Insurance practice in the UK
BCG is well known, though I have no direct experience.
KPMG- Hew Evans is Head of Insurance in the UK and Matthew Smith is the Partner for Strategy & Transformation and Global Claims Lead
Salesforce.com- acquired Informatica to deliver AI across insurers; good fit with customers of its CRM, Sales, SaaS, and marketing platforms
Publicis Sapient- growing its insurance customer base. Was the implementation partner that I chose in the hotel project from a short list, as they proved more collaborative and put skin in the game. Dan Cole runs the Insurance Practice
Hitachi Digital Services- Hitachi is part of a massive global implementer with strong experience in Building Management Systems (integrate with insurers for preventative maintenance) and strong AI capabilities. Many other capabilities. Stuart Reeder heads up the Insurance Practice
Palantir made its name and dizzying valuation delivering AI to governments, particularly defence in the USA and the NHS in the UK. Licensing a tad expensive even for Tier One insurers, but they offer some compelling solutions and a test-learn-iterate-prove culture and workshops
CGI- well respected in government, CGI has ten of the top UK insurers as customers of its Ratebase Insurance rating and pricing, and its Underwriters Workbench. Darren Rudd is Head of Insurance and technology business consulting, and will listen to your requirement and be realistic about satisfying them.
PCW's insurance practice includes Automation, AI, and digital ecosystems: product design, underwriting, pricing, and claims.

For Mid-Size Insurers, Brokers and MGAs

Aiimi Limited; Aiimi made its name enabling Water Utilities tocate and reduce water by accessing unstructured data using its AI-powered data management platform that will also make data AI-ready. It counts the FCA as a client and Riverstone International as an acquirer of legacy books of business. JLR and The Cabinet Office are named clients. It names PwC as a business partner. The CRO is Mark Drayton
Kore.ai is a US-headquartered Agentic AI partner with a European presence and support in London. Johnson and Johnson and Morgan Stanley are two of many global clients. The Chief Strategy Officer is Cathal McCarthy
AI Risk: a community of innovators that develops and deploys AI. It has built an agentic AI platform for a broker and is demonstrating a production version of this. On its website an operational deployment is described in a case study. I have a feeling it's a smallish broker ( 6 or so humans). The CEO is stated as being unable to recruit key people (humans) and has replaced all with digital agents. I have a feeling that can work better for a broker or MGA, whilst large insurers data issues will be a challenge. AI Risk certainly advises large insurers so worth talking with Simon Torrance is the founder and CEO
Outlier Technology Limited. A blog on LinkedIn caught my eye. 'The best thing a business can do is forget AI exists' - tech CEO I called CEO David Tyler called CEO David Tyler and found he had delivered a major data and AI project for a large utility that had spent £4m on a POC to meet regulatory compliance demands by a fixed date- a real problem. The POC failed despite it being a technical success. The human end users found it useless and rejected the deployment. Outlier was retained to make it work and ensure the users embraced it, which they did successfully. Outlier can collaborate to deliver even large projects cost-effectively as long as you share domain knowledge and collaborate with all your stakeholders, so Outlier can build out a test, learn, and iterate POC process until you are happy with the result. Worth a chat with David and his team.