The insurance claims industry sits at an inflection point. Medical records are more complex, nuclear verdicts are rising, and the workforce is changing faster than most organizations can adapt. AI promises to help — but most implementations have fallen short. We sat down with Mark Tainton, senior vice president of data solutions at Wisedocs, to talk about what's actually working, what isn't, and why the industry needs to move from document management to true decision intelligence.
Paul Carroll
The insurance claims industry has been talking about digital transformation for years. What's actually changed in the last 18 to 24 months, and what's still stuck?
Mark Tainton
Having worked in the insurance industry for over 30 years at the intersection of technology and claims operations, I've certainly seen infrastructure change. But the bigger question now is the operating model that can actually leverage that infrastructure. And the operating model is not so much around storing documents in claims management systems or document management systems—it's about how we take advantage of that data asset. We’re essentially moving from document storage into effective decision-making.
Over the last five years, there has been an acceleration in the technology, in particular with large language models. Technology is not the problem.
It's really about taking advantage of the individual pieces of information in the world of unstructured data. That's the next wave we should be focusing on: How do we operationalize the assets so they’re part of the DNA of insurance processes?
Paul Carroll
Medical record review is at the heart of so many claims decisions, yet it still appears remarkably manual at most organizations.
Mark Tainton
I’ve certainly seen large carriers that have introduced AI but haven't introduced the process changes or changed how people can take advantage of the insights as the claim goes through its lifecycle. Carriers are using ineffective decision making approaches that continue to mirror what we saw 10, 15, 20 years ago.
There needs to be a conversation around how adjusters work, especially because of the change in their age demographic. New people coming into the claims industry consume data completely differently. We have to adjust.
You have to also understand the psychosocial aspects of the workforce, where COVID accelerated change. You need to cut across multiple claims at any given time and look for triggers that are prevalent by a treatment provider, or at risk indicators that suggest psychosocial issues—they are top of mind for a lot of claims teams right now.
Paul Carroll
There's always a tension between speed and defensibility in claims, especially given the high stakes. How do insurers resolve that tension?
Mark Tainton
Claims are getting more complex, and we've seen a lot of legislation that makes it very clear that if someone's making a decision solely based off AI output with no human in the loop, that's going to be a problem.
When you tie that concern into the expansion of traditional fraud and increases in nuclear verdicts, the defensibility question becomes critical. There needs to be a human in the loop.
Several states are already drawing that line legislatively. California's SB 574 and a growing number of AI governance frameworks now require that AI-assisted decisions in insurance and legal contexts be documented, auditable, and explainable. That is not a future concern; it is a present operating requirement for carriers doing business in those jurisdictions. The organizations that build defensibility infrastructure now will not be scrambling to retrofit it later.
Paul Carroll
There are a lot of solutions out there these days, but they seem to largely be point solutions—summarization tools, triage tools, document processors, and so forth. What's missing from the point solution approach?
Mark Tainton
First, they don't fit into the ecosystems of clients and large carriers. They don't work alongside platforms like Guidewire where they can function as a module and help make those decisions effective.
The point solutions also aren’t really end-to-end. They're focusing on a point in time on a particular claim. That produces what I call a silent failure. The AI processes the document and returns a summary, and the claim moves forward. But the anomaly that should have triggered a flag, the treatment pattern that does not match the diagnosis, the billing inconsistency that signals a problem: None of that surface because the tool was never designed to look across the lifecycle. The claim does not fail loudly. It just quietly travels in the wrong direction for months.
Think about first notice of injury as a claim goes through the life cycle, and all of a sudden you get a demand package or a treatment package coming in. What are the decisions you want the adjuster to make?
You need intelligence that cuts across the full lifecycle of the claim in terms of other claims with certain characteristics. And I think that's where point solutions really come up short.
Paul Carroll
I assume that thinking is why you took a platform approach with WiseShare.
Mark Tainton
Very much so. We have the sorting and summarization solution that we just renamed WisePrep. It includes WiseChat, where users can save all the insights they generate from a large language model. We've introduced WiseInsights looking at litigation trends, looking at treatment patterns and how they develop, looking across claims that an adjuster who's got a workload of 200 or 300 claims cannot identify on their own. These insights reveal similar characteristics across claims. For example, we looked at one portfolio and identified that a particular treatment provider, over a 12-week program, consistently prescribed a higher and more severe medication at the four-week timeline.
WiseShare is important, too. Far too often, a summarized document gets passed from the adjuster to inside counsel, then to external counsel, and eventually to an IME [independent medical examiner]. A lot of the time, we see slip-ups—documents go missing, misinterpretations occur, different versions of the truth emerge. WiseShare brings everything together into one consolidated environment where all of those entities can actually share, review, and export the claim file.
From a legal defensibility standpoint, that consolidation is not a convenience; it is a chain-of-custody argument. The defense bar needs to see a complete, unbroken record: the medical record chronology, the time series of decisions made, and documented consistency in how AI processed the underlying materials. When a claim ends up in litigation, the question is not just what decision was made; it is whether that decision can be reconstructed, sourced, and defended at deposition. WiseShare is built for that standard.
You have to be able to wrap intelligence around a decision, and that requires a platform.
Decision intelligence needs to be comparative. You have to be able to see the claim you're dealing with in the context of other claims. The intelligence also needs to be sequential. Are we seeing similar patterns starting to develop on other claims in certain jurisdictions? Are we starting to see certain seasonal trends? Are we starting to see different types of treatment coming through? Finally, the intelligence must provide accountability. Is every inference sourced and every decision point documented?
The defense bar needs to see that audit trail. They need to see the medical record chronology, the time series, and the consistency in terms of best practices for how AI actually processes documents and insights for better outcomes. From 2023 to 2024, nuclear verdicts rose 52%. Thermonuclear verdicts are up 81%, and overall verdicts are up 116%.
You need one single environment where you store the materials, one single process that's consistent across an organization.
Bottom line: if you can't show defensibility, you're in a world of trouble.
Paul Carroll
There's discussion about AI replacing many human workers in the insurance industry. What is your perspective?
Mark Tainton
There's this notion that AI is going to replace people at the desk. From my perspective, that's totally inaccurate. And I think that mindset sets back adoption.
But here's the inflection point: We're dealing with an aging workforce. Insurers and TPAs are struggling to attract talent. Why? Because some of the tools and technology have not evolved as quickly as in other industries. When you can walk hand in hand with AI and the person at the desk and show them all the benefits, that’s exciting.
Paul Carroll
If you could change one thing about how the insurance industry is currently approaching AI adoption in claims, what would it be?
Mark Tainton
For me, it's what I call the evolution framework. AI is a journey, not a one-time event. Far too often, what I've seen is large organizations—mid-tier, tier two, tier three—treating this as basically an implementation. It's almost like they're going in, turning the light switch on and walking out.
I spend quite a bit of time working with clients all the way from inception to asking: Where are we actually going to implement this? What's the impact we're expecting? How does this align with strategic objectives? What are some of the key measurements we want to see in terms of adoption, change, and, ultimately, having the AI start to hit the hard dollars—reduction in litigation, average duration, and things like that.
I'll give you an example. I worked with a large carrier that wanted to implement AI across the entire organization. But they have an aging demographic in certain lines, and getting them to adopt AI would be difficult. They've also captured a lot of information very poorly in their systems—it's very much in their heads.
I said, Let's focus on the younger generation. They’ll adopt AI, and we’ll create a best practice, one that we can use when we bring in new talent. So we built a three-year program focused on them. Ultimately, the program was so successful that the older generation said, We want to be part of that, too.
For me, the next window for anyone embarking on an AI journey is to focus on embedding it upfront—knowing, of course, that the process will evolve over time.
Begin with what we call an EDA—exploratory data analysis—to determine what the baseline is. That way, you can prove that you’re opening and closing claims far more quickly and can see the change quarter over quarter. That data helps sell the journey. We've also done quite a bit of work around what we call data quality programs, where we assess the quality and change behavior at the desk in terms of how people are capturing data—all the way from structured to unstructured and, more importantly, in the adjuster call notes. That program embeds the solution into the fabric of the organization.
I think that's the next wave.
Paul Carroll
Thanks, Mark.
Sponsored by Wisedocs
About Mark Tainton
Mark Tainton is the SVP of Data Solutions at Wisedocs, bringing over 30 years of AI, data and analytics transformation expertise in insurance and financial services. Having served as Chief Data Officer at multiple leading organizations, Mark understands the critical intersection of medical intelligence, litigation strategy, and claims outcomes. He advises Wisedocs on data and product strategy, go-to-market positioning, and the deployment of AI-powered solutions that address the most pressing challenges facing claims and legal professionals today.
