When I taught my older daughter to drive, my (overly) nervous tendency to constantly kibitz caused her to blurt out: "You have to let me make my own mistakes." 

As she drove my sportscar on a winding backroad with narrow lanes and no shoulder, and as she occasionally drifted toward the middle even though oncoming traffic could appear suddenly over a rise, I assured her that she was, in fact, not allowed to make any mistakes. 

Yes, I would try to take a hands-off approach about school, friendships, soccer and so on. But driving? Nope. That was off-limits.

While I'm happy to report that, at age 32, she has never had an accident — not even a moving violation — the tension we worked through springs to mind as I think about the deployment of AI. There is a fundamental tension between having AI improve as fast as possible — to get as many benefits out to as many people as soon as can be done  —and a legal system that will hold the AI accountable for each and every harm it causes, no matter if that loss is in the service of a long-term gain for society writ large.

The underlying tension between statistics and accountability goes even beyond the usual issues that accompany the rise of a revolutionary technology. So AI faces a hard question: When is good enough good enough?

Let's have a look.

The clearest example of the tension is probably in autonomous vehicles, where Tesla took the speed route while Google's Waymo took the careful one.

Tesla relies just on cameras and radar as the sensors in its AVs, largely because they are much less expensive than the Lidar that Waymo and others use, meaning that Tesla could deploy what it calls Full Self Driving (Supervised) quickly. (The "supervised" label refers to the fact that, while drivers are told they can trust their cars fully, they are legally required to stay alert and be able to take control of the car instantly.) CEO Elon Musk also encouraged aggressive use of the technology so he could gather as much data as possible on problems that needed to be ironed out before the cars could be fully autonomous. 

The result is that Tesla's cars have logged nearly 13 billion miles in FSD mode — but also that there have been all sorts of complaints about problems with the software, as well as numerous accidents and even fatalities. Tesla has mostly avoided legal liability because of the fine print about drivers retaining responsibility for their vehicles even in FSD mode. Musk has said that problems are inevitable but that, in the long run, his rapid deployment of AI will reduce the total number of car accidents and fatalities.

While Musk has been promising that full autonomy was just around the corner for a decade, his vaunted fleet of robotaxis has logged just 380,000 paid miles without a safety driver in a car. He and his supporters still argue that his advantage in generating real-world data on driving, based on all the cameras and radar systems he has in the nearly 10 million Teslas on the road, will eventually make him the winner. But recent declines in the stock price for Tesla are being attributed to growing concern among his investors about his ability to deliver, after so many promises went nowhere. We'll see. 

Meanwhile, Waymo took the go-slow approach, using a full array of sensors on the assumption that they would ride the exponential Moore's law cost curve and become inexpensive enough soon enough. Prices have, in fact, cut the cost of a Lidar from about $75,000 per vehicle to $7,500, and technologies are out there that could take the price down into the hundreds of dollars per car. Waymo has rolled out the cars cautiously enough that it only has about 4,000 robotaxis on the road, but they have logged 200 million paid, unsupervised miles — so more than 500 times Tesla's total. There have been glitches with Waymo, such as with misunderstanding signs warning of construction zones, but nothing like what Tesla has faced, so Waymo is the clear leader on AVs at this point.  

Software developers take an approach like my daughter's: focusing on learning quickly even if the speed leads to some mistakes along the way. The legal system, however, takes my approach: Even if fast learning reduces the total number of lives lost and total damage that occurs over the lifetime of an AI, those who deploy it are liable for each and every mistake along the way. 

Basically, the legal system says: Don't tell me that you constructed a model and optimized it for gains to society writ large. If an AI hurt my parent/child/friend, I demand accountability.

Watch Our Demo: 

Learn More

Are you interested in learning more about Fenris and this product? Please visit these sites to learn more:

• https://www.fenrisd.com/
• https://www.linkedin.com/company/fenris-digital

Why We Are the Right Solution For Your Needs

The quality of every underwriting decision traces back to the quality of data at intake. Fenris delivers real-time data enrichment APIs that return verified, multi-sourced information the moment an address or vehicle is entered. Carriers and MGAs receive accurate, comprehensive data from the first step of the workflow. When the right data is present at intake, everything downstream improves: quotes move faster, underwriters work from a stronger foundation, and risk assessments reflect the full picture.

Fenris covers the full scope of property and auto risk intelligence: residential and commercial property characteristics, hazard and peril profiles, replacement cost estimates, vehicle and driver data, and predictive analytics to optimize acquisition workflows, reduce risk, and personalize customer experiences. Across all of it, the principle is the same: comprehensive, accurate data returned instantly so carriers and MGAs can make better decisions earlier. From intake to quote to underwriting, Fenris equips every step of the workflow with the risk intelligence needed to write business with confidence.

Three Main Benefits of the Product: 

• Real-time data at intake reduces drop-off, accelerates quotes, improves early decisioning, and creates a better experience for agents and applicants alike.
• Predictive AI improves risk assessment, optimizes acquisition, and delivers personalized experiences that drive higher conversions and reduce losses.
• Non-contributory across all products, so enriched data never feeds a shared pool. Predictive models stay specific to your book and your customers.

What Part(s) of the Insurance Industry Can Benefit From Our Product: 

• Customer Experience
• Distribution
• Operational Efficiency
• Underwriting efficiency

A head of claims operations walks the executive team through the results of an AI-assisted document review rollout: a sharp, real cut in average review time. The room comes alive over it: not skeptical, enthusiastic. Someone's already running expense-ratio math out loud before she's finished the slide. Someone else wants to know if the same approach works in underwriting. The head of claims tries to steer the conversation toward what else those hours might be worth doing instead of cutting them, but there are three more items on the agenda, 15 minutes left in an hour that's already running long, and a room half-checked-out toward the next meeting.

The cost-savings read isn't wrong. It's just the only idea that got any airtime.

By the following week, the number is sitting in the forecast as a bottom-line improvement, and HR has scheduled a meeting to talk through the actual headcount reductions. Nobody chose this outcome over another one; it's just the one with momentum, and momentum in a room with an hour on the clock beats an idea nobody had time to finish developing. The room did what rooms do under time pressure: it ran with the fastest, most defensible read, and by the time anyone might have asked a second question, the decision was already operational.

This is the mechanism I keep running into with insurance and financial-services leaders working through AI at scale. It isn't that no one owns the question of what to do with freed capacity. Someone owns it, in exactly the moment described above: usually whoever's in the room when the result lands, or Finance once it's in their model. The problem is they're answering a question nobody asked out loud: is this time worth more as a saving, or as something redirected? By default, the answer is savings, because savings is the fastest, most comfortable story a room on the clock can agree on.

There's a harder truth underneath the reflex, too. Even if the head of claims had been able to keep the floor, she probably couldn't have made the case: nobody had set up a way to measure what those hours would be worth doing something else. Under real forecast pressure, with downside risk already sitting in the numbers, the room isn't choosing the fast story over the slow one. It's choosing the only story it actually has data for.

The default doesn't hold up, and two firms now say so independently

Gartner surveyed 350 business executives this spring at companies with at least $1 billion in revenue. Eighty percent of the organizations that had piloted an AI or autonomous technology followed with a workforce reduction. But there was no meaningful ROI difference between companies that cut staff and those that didn't. The strongest returns weren't at the companies that cut deepest; they were at the ones using AI to make people more productive, not to replace them. Gartner's Helen Poitevin put it directly: chasing value through headcount reduction alone leads most organizations toward limited returns, not the returns they modeled.

Deloitte's research puts a number on the other half of that same gap. Eighty-four percent of organizations are increasing their AI investment. Only 20% report meaningful revenue impact. But organizations that redesign the work itself, rather than just removing roles, are roughly twice as likely to exceed their AI ROI expectations, and nearly two and a half times more likely to see real financial gains.

Two firms, two survey populations, the same finding: cutting the role doesn't reliably convert a technology gain into a business gain. Redesigning the work does, by a wide margin. This isn't a values argument about protecting jobs. It's a return argument, made with the same data the cost-saving case leans on.

Why the default survives unquestioned

Part of why this default never gets challenged is that the dashboard isn't built to challenge it. A rollout dashboard answers one question the week the tool launches: did the tool work? It was never built to ask the second question: now that the tool freed something up, what's the best use of it? Nobody updates the dashboard to ask that, because nobody built a version where the question has a place to live.

The result is a familiar split. Leadership sees a rollout that delivered the projected time savings and reads that as ROI, declared and closed. The people managing the work six months later are living inside whatever happened to that time, usually nothing or usually cost-cutting, and see a different number entirely. Both readings are accurate: they're reading two different moments, off two different instruments, and only one of those instruments ever looks past launch week.

This lands harder in insurance and financial services for a specific reason: the executive team is tracking results weekly, which is exactly the speed that produces a room like the one above. A real redesign case takes longer than a week to build. By the time underwriters or claims reviewers could show what redirected hours would be worth against loss ratios or retention, the quarterly external reporting cycle has already locked in the launch-week number as a win, and the budget conversation has moved on.

What interrupts the default

Naming the pattern doesn't fix it, so here's what I'd tell a claims or underwriting leader sitting inside this right now.

Put the redirection case on the roadmap when the rollout is designed, not after the results land. That claims leader's slide only had one story on it because only one story had data behind it. Building the other one is real work: further experimentation and analysis, beyond what it takes to simply validate that the tool works. Naming that work up front, as part of the rollout plan rather than an afterthought raised in the meeting, is what gives the room something to weigh against the savings read before momentum decides for everyone.

Reuse a review cycle you already trust. Regulated organizations already run disciplined quarterly and annual reviews for compliance and audit. Add one standing question to that cadence: what did we say this rollout would free up, and which path, saved or redirected, did it take? You don't need a new process. You need one more question inside a process that already has teeth.

Track what changed in the work, not what changed in usage. A login count says the tool got turned on. It doesn't say whether a reviewer, underwriter, or claims handler is doing anything differently on Thursday than in January: the real measure of whether freed time went anywhere.

The open question underneath this one

There's a third factor sitting under both of these that I don't think the data above fully answers, and I'd rather name it honestly than force a tidy conclusion: why doesn't anyone stop to ask the redirection question at all? Do organizations reward speed and visible activity over the slower work of finding a better use for freed time? Is the savings number just easier to defend than a redirection bet that might not pay off for multiple quarters? I don't have a clean answer, and I'd rather leave the question open than manufacture certainty. It's worth its own room, not a paragraph tacked onto this one.

What the data above does settle is narrower, and it's enough: cutting the role may feel like the safe, obvious way to capture value from AI. Two independent studies now say the opposite is the better path. The next time a rollout result lands in a room, and everyone reaches for the cost-savings read in the same breath, it's worth asking whether anyone had a number for the alternative, or whether the room just did what rooms do.

The insurance sector is, understandably, quite risk-averse. Insurance companies don't endure and thrive by taking unnecessary risks in an environment defined by stringent regulation and nuanced decision-making. This is why it is so concerning to see companies within the sector taking unnecessary risks as they face up to the mounting pressure of demonstrating progress with generative AI.

It's easy enough to understand the urgency behind the industry-wide scramble to leverage new tools like autonomous agents and AI-powered applications. AI isn't new to the insurance business — use cases revolving around risk modeling and data forecasting were becoming commonplace in the sector before the deep learning and LLM boom in 2023. However, those technologies took years to find a place in insurers' technology stacks. They were heavily tested with strong, clear use cases. The race to adopt generative AI tools is not the same.

EIOPA's 2024 Digitalisation report found that 50% of non-life insurers and 24% of life insurers were already using AI in various areas of the insurance value chain, with applications including pricing and underwriting, fraud detection, and claims management. When it works, it really works. Research from McKinsey found that the insurance sector's AI leaders have created 6.1 times the total shareholder return compared with AI laggards. That figure doesn't just make the case for adopting AI. It makes the case that, if generative tools can be successfully integrated with insurers' tech stacks, the results are outstanding. McKinsey's report found that, in other sectors, AI leaders were generating at most two to three times the shareholder return.

Across the insurance space, there is an increasingly common fear that everyone else is somehow ahead. Companies see their competitors announce new AI pilots and products, vendors make new promises of transformative AI tools, and employees are already experimenting with agents and chatbots. No one wants to be caught standing still while the rest of the market races ahead.

The result is a growing wave of AI FOMO. Insurers are launching pilots, funding multimillion-dollar integration plans, and generally jamming AI into any perceived gap in their workflows. The problem is, many are doing this before interrogating and identifying where AI will actually create any meaningful impact for their business.

The dangers of putting AI before the business case

Last year, a study from MIT found that 95% of AI pilot projects "failed to deliver any discernible financial savings or uplift in profits," the data from which supports an earlier report released by Capgemini in 2023 that found 88% of AI pilots never reached production.

A more recent report from Simplifai found that, while 99% of insurers now have some form of generative AI in place and 83% of carriers are spending more than £3.75 million every year on tokens, subscriptions, and infrastructure, just 42% of insurers had taken the next step towards actually deploying AI into active business functions.

There is an undeniable appetite in the insurance sector for AI, but not a great deal of understanding when it comes to what the technology can do, or where it fits within existing business systems (not to mention the tech stack, which for an insurer is more likely to be some form of legacy system). This issue presents symptomatically as an abundance of AI pilot programmes that never mature into real world business solutions. Insurers know they need to act on AI, but they don't have a clear idea of where to start, which processes to prioritise, or how to evidence the value it creates.

How insurers can distinguish AI opportunity from AI hype

This raises an important question for insurers: what will it take to shift AI from isolated innovation projects to something with tangible business applications?

Scaling AI effectively can lead to substantial business value — the data supports it — but for insurers mired in expensive pilot programmes that never seem to translate into finished products, or who feel as though they're in danger of being left behind, it's essential to approach AI from a business perspective, not a technological one.

Successful AI adoption in insurance might just mean embracing more of the risk averse, methodical behavior for which the sector is sometimes criticized. It means making informed decisions regarding where AI can create real, sustainable impact. Success depends on identifying the use cases with the strongest commercial and productivity outcomes, rather than increasing the volume of AI initiatives in hope of reaching a magical, unspecified tipping point.

Insurance industry-specific AI tools are accessible across the market, doing away with any competitive advantage gained by adopting a particular platform or model. As access to the technology becomes more uniform, competitive differentiation will depend upon how organisations apply it. Successful approaches start with workflows, operating models, and business outcomes. They deploy AI across complete business processes instead of isolated point solutions and establish governance from the outset so that risk, compliance, and accountability are embedded throughout implementation.

The insurance industry doesn't have an AI problem. It has a strategy problem. FOMO is pushing insurers into suboptimal decisions, when the real challenge lies in executing a coherent transformation strategy. The next phase of insurance AI adoption won't be about buying and using more AI. It will be about making better decisions about where AI belongs.

A World Cup soccer tournament wouldn't be a World Cup without controversies, and the just-concluded event had its share. 

Then Argentina said, Hold my Fernet con Cola. 

Following the team's 1-0 loss in the finals to a clearly superior Spanish team, an Argentine player picked a fight on the field that included grabbing a Spanish player by the throat and throwing him to the ground, and Argentine teammates backed him up. Just about the whole Argentine team then acted churlish during the awards ceremony, even turning their backs as the Spanish players were awarded their gold medals and the team trophy. 

Within minutes, reporters and fans were revisiting every untoward thing Argentina had done during the tournament, then during prior tournaments, then on the team bus, then.... 

Argentina provides a great example of how actions taken even by a few in the heat of the moment can sour masses of people on a group or a brand. It's a lesson that I think insurers, in particular, should take to heart, given that our most consequential actions tend to come when dealing with people in situations where their emotions are running hot. 

Let's have a look. 

Argentina had been a possible feel-good story coming into the tournament this year. It had finally won the World Cup in 2022 for its captain, all-time great Lionel Messi. If Argentina had repeated as champion, it would have been the first to do so since Brazil in 1962. Messi, who had won the Golden Ball award in 2022, given to the best player in the tournament, was in the running for the award again. Going into the final, he also had a shot at the Golden Boot, given to the top scorer in the World Cup. At 39 years old, a beloved player was putting in a remarkable performance.

Now, Argentina is known for being chippy, even dirty, and it played to form throughout the tournament, including by having a player sent off in the final after a violent tackle. Spain, while hardly free of fouls, played a classic style that contrasted sharply with Argentina and led any number of people to post at the conclusion of the game some variant of, "Football won today." 

The history of writeups about the Argentine team suggests that would have been about the extent of the complaints about Argentina's tactics. 

Then the Argentine players started knocking around some of the Spaniards after the final whistle, and all bets were off. 

Someone quickly shared a clip of the Argentine player instigating the post-game brawl. Then people started going back through the whole game, pointing out everything even borderline that Argentine players did — here is one-such 13-minute clip. But why stop there? Here is a 5 1/2-minute clip of transgressions by Argentina that weren't penalized in the semifinal against England. Of course, there was group play, too — here is nine minutes of uncalled fouls against Switzerland. 

Earlier incidents became fair game, as well. A video surfaced in 2024 of Enzo Gonzalez, the Argentine player who drew a red card in the final, and teammates chanting racist slurs on the team bus, as posts such as this one quickly noted over the weekend. Gonzalez had apologized profusely, including personally to Black players on his club team, and surely thought the incident was behind him. No longer. Many on social media also noted that the Spanish goalkeeper had been classy in accepting the Golden Glove award, for the best at his position in this year's tournament, while the Argentine keeper had used the award to make an obscene gesture when he won in 2022.

Analysts were universally brutal about Argentina after the final. The New York Times ran a story under the headline, "Argentina disgraced themselves, and the World Cup final, with their charmless petulance." In case that wasn't enough, the NYT ran another story, full of images, under the headline, "How Argentina turned the World Cup final dirty with shoves, skulduggery and squealing."

My point being: Once sentiment turns against you, even based on an incident by one person or a small group, things can go downhill fast — and keep going.  

This surely isn't news to insurance companies, which understand that claims are the moment of truth. Everyone and everything has to line up just right when you're dealing with longstanding, loyal customers in their moment of need. They've earned compassionate, professional excellence — and they'll react in horror if they don't get it. 

But I still think object lessons like those provided by Argentina are worth noting and spreading, because it only takes a few people, or even a single person, to undercut what so many other people are doing to earn loyalty. Social media can broadcast bad actions incredibly fast these days and seems to relish doing so, especially if there is compelling video. 

And narratives are hard to shake once they take hold. The Argentine team is being cooked especially hard because it was already known as a dirty team. In insurance, if you're not known for great customer service, complaints will find an especially alert audience — I'm sure State Farm, for instance, is being incredibly careful these days, given the controversy over its handling of claims from last year's wildfires in California.

I won't suggest buying the jersey of Leandro Paredes, the Argentine player who ran up on a Spanish player from behind after the game and knocked him over, because some of the money would find its way back to him. But maybe he can be an anti-hero for anyone dealing with insurance customers. Whatever you do, people, don't earn us a reputation like that guy....

Cheers,

Paul

P.S. When I think back on the World Cup, I'll prefer to think about the positive surprises. Who knew that Costco and ranch dressing would be such delights for those visiting the U.S.? Erling Haaland? I've spent years hating on him in a Man City kit but found him impossibly charming both in a Norway jersey and in his experience with U.S. culture. Then there was Spanish star Lamine Yamal's three-year-old brother, Keyne, who stole every scene he was in throughout the tournament. 

And I'll especially cherish a moment that Jude Bellingham and Bukayo Saka and their English team had in their third-place game against the French. 

When England earned a penalty kick, Bellingham prepared to take it. He had emerged as a full-on star for England and had already scored six goals; seven would be unworldly. But he knew that Saka had scored twice against France, knew that concerns about injury had (unwisely, in my view) kept Saka out of the semifinal that England lost against Argentina, and may have been thinking about how Saka and two Black teammates had missed penalty kicks in a tournament in 2021 and had endured wildly racist criticism. 

Bellingham told Saka, "Go on and get your hat trick," and handed him the ball. Saka converted with a kick that the keeper wouldn't have touched even if he had guessed right, rather than diving in the opposite direction. Saka's goal turned out to be the winner. 

Bellingham, by the way, got his seventh goal a few minutes later with an extraordinary display of technical virtuosity. So nice guys finish.... first?

AI has moved past the hype stage—it's reshaping cost structures, competitiveness, and growth across insurance. Carriers who hesitate are locking in cost and risk profiles that only get harder to unwind. Drawing on original research with insurance executives, this report shows how AI, GenAI, and Agentic AI are separating Leaders from Followers and Laggards—and why 2026 is the point of no return.

AI is now a boardroom priority, tied to insurers' top 2026 goals: cutting costs, streamlining operations, and improving customer experience. Across underwriting, claims, servicing, billing, distribution, and loss control, carriers are moving from talk to active pilots, targeting friction in paperwork-heavy areas like claims and service. But appetite is outpacing the data foundation needed to support it, raising scalability and reliability risks without stronger governance.

The center of gravity is shifting from "AI as a data tool" to "AI as a workforce multiplier," powered by the Frontier Firm—companies built on on-demand intelligence and human-agent teams, where staff act as "agent bosses." Leaders are already scaling GenAI and Agentic AI with mature data capabilities behind them; laggards risk losing ground on performance and cost.

Download this report to explore:

• Why modernized data and an Intelligent Core are essential for scalable, responsible AI
• How to bring the Frontier Firm and Agent Boss models into your organization
• How AI is reshaping cost, competitiveness, and growth across the insurance value chain

Get Started>>

The industry no longer doubts whether AI benefits submission intake in commercial property and casualty (P&C) lines. Recent AI rollouts by Zurich, AIG, Markel, and other market leaders show impressive gains come very quickly: intake timelines for complex submissions compress from hours to minutes, straight-through processing (STP) rates increase from 10% to 95%, underwriter productivity grows by 100%+, and submit-to-bind ratios improve by 35%.

Yet many P&C carriers are still figuring out how to make AI work for their business. Which parts of their submission intake workflow can realistically be automated? Where to start so that we can move out of pilots fast? How can we explain, audit, monitor, and defend what the AI did? And what AI solution design should we actually pursue to scale well?

These are the questions I hear most often in ScienceSoft's engagements with commercial P&C clients. In this article, I'll share my perspective on where AI works best in submission intake today, why many initiatives never move beyond pilots, and what insurers should focus on if they want to launch submission intake AI over the next six to 12 months.

Where AI Delivers the Fastest Value in Submission Intake

From my experience, the quickest wins come from automating the parts of insurance submission intake that are high-volume, repetitive, document-intensive, and largely governed by business rules. Strong starting points in commercial P&C include:

• Capturing and classifying submissions.
• Extracting data from ACORD forms, loss runs, schedules of values, and other submission documents.
• Checking submissions for completeness and identifying missing information.
• Summarizing and triaging risks for underwriters.
• Prefilling policy administration and underwriting systems.
• Drafting underwriting files and follow-up questions for brokers.

These activities create a large operational workload but require little underwriting judgment, which makes them ideal candidates for AI automation. Modern AI systems can handle these connected intake operations almost entirely, only involving humans in complex cases. In this first half of the intake pipeline, STP rates of 90–95% are a realistic target.

They are also the safest processes to automate from both a business and regulatory perspective. In each case, AI prepares data and doesn't touch high-impact decision-making.

Anything involving decision-making is a different story. Commercial P&C underwriting is rarely standardized. It often requires negotiation, exceptions, and collaborative expert judgment, with every submission carrying its own nuances. Most of the cases are just too complex for reliable straight-through AI decision-making.

That doesn't mean AI has no role here. It can absolutely assist in eligibility assessment, pricing, and coverage decisions — for example, by highlighting relevant risk factors, surfacing similar cases, or recommending next steps — but I'd keep it away from making those decisions autonomously. Human experts should continue to own the final judgment.

Moreover, even when AI acts only as an assistant, the governance bar remains high. You need mature controls, explainability, and human oversight to defend AI suggestions to brokers, policyholders, auditors, and regulators. That's why I usually recommend that clients avoid complex assistive use cases during early AI deployments. A safer path is to first confirm AI accuracy, auditability, and workflow impact across lower-risk data intake tasks, then gradually scale into underwriting decision-support areas.

Overall, I don't think anything close to fully autonomous underwriting should be the near-term objective for commercial P&C insurers. The "AI assists, humans decide" approach, where AI prepares files and experienced underwriters make the final decisions, has proven the most practical operating model. It lets insurers preserve human accountability where it matters most while still delivering huge business returns. I recently came across a case study where deploying AI for submission intake routines alone brought a 646% ROI for a large property carrier through faster, more accurate, and more efficient data processing.

Data Foundations for AI-Powered Submission Intake

First of all, you can effectively start with whatever data you have. That's an important point because I've seen many insurers unnecessarily delay AI initiatives out of fear that their existing data is too scarce or too low-quality for AI processing.

The first necessary data foundation you need in commercial submission intake is a clear document taxonomy. However strong AI may be at data tasks, it still struggles with unstructured, consequential data containing insurance-specific terminology and context. One classic example is loss runs: they vary by provider. Claim descriptions often contain abbreviations. Severity indicators may be buried in narrative text. The AI may read the document correctly but fail to determine what's material from an underwriting perspective.

A clear taxonomy creates enough structure around the data so AI can distinguish between document types and apply the right extraction logic, validation rules, and review requirements to each. It also helps determine which documents AI should treat as authoritative when data conflicts across sources.

What Might Keep AI Stuck in Pilot Mode, And How You Avoid It

By far the biggest blocker is AI integration into existing submission intake workflows.

Most commercial P&C insurers operate heavily fragmented environments. Documents sit in one place, policy data in another, rating logic somewhere else, and underwriting notes in spreadsheets or workbenches. People have built manual workarounds over the years because their legacy core systems do not support the connected workflow.

Making AI work with these core systems doesn't mean you need to modernize or replace all of them. But you do need a practical integration pattern to avoid building numerous point connections. Off-the-shelf AI architectures often hit a wall at this point: most mass-market tools, by design, introduce AI as another isolated interface sitting alongside the existing process. AI product vendors can't possibly account for every system or data format their clients may still be using to build a product that integrates well with legacy stacks.

Another blocker is what I call "pilot thinking." During pilots, people often focus too much on proving that the AI model can accurately classify, extract, summarize, and answer questions. That's necessary, but it's only one part of the equation. Moving to production requires proving that the AI can operate reliably inside a real insurance workflow: integrate with existing systems, handle exceptions, support human review, produce audit evidence, and scale across thousands of submissions. That's where many otherwise successful pilots stall.

The goal of a pilot should be validating not just AI accuracy but rather the operating model around the AI. Can the workflow route low-confidence cases for review? Can underwriters easily verify and override AI outputs? Can the solution recover from missing or conflicting information? Can it integrate with core systems without creating manual workarounds? Those capabilities determine whether the AI can become part of daily operations.

The third blocker is poor ownership models. Someone has to review AI performance, define business rules, validate compliance, and measure outcomes. When ownership is unclear, issues randomly fall between teams. The technology may work, but if nobody takes responsibility for adoption, governance, and continuing improvement, your promising AI initiative may quickly lose momentum after the pilot stage.

You'll need a board of AI governance owners from underwriting, IT, data or AI engineering, and compliance or risk before moving beyond the pilot. The board should regularly review AI performance, override rates, exceptions, user feedback, and regulatory risks, and decide when the solution is mature enough to expand into new workflows or business lines.

Addressing AI Risks That Insurers Often Underestimate

A lot of insurers immediately think about AI hallucinations, and that's a real risk.

To address that risk, the AI reasoning should be grounded in source documents, and the system must show where each important field or summary statement came from. Adding deterministic validation of AI outputs after each processing iteration also helps prevent error creep. These are rule-based checks that verify required fields are present, figures are consistent, references match the source documents, and the output complies with predefined business rules before the workflow continues.

Yet, in commercial P&C submission intake, I think one of the most overlooked risks is silent workflow bias.

By that, I mean AI may not be making the final underwriting decision, but it may still influence which submissions move faster, which are routed to senior underwriters, which are treated as gapped, and which receive follow-up. Those workflow decisions can create different outcomes over time, even if the model never explicitly uses protected-class data.

Another risk is silent portfolio drift. The process gets faster, productivity looks better, and everyone is happy. But over time, the mix of business may change. Maybe more borderline risks get through because the process feels smoother. Maybe underwriters stop asking certain follow-up questions because the AI-produced summary looks complete. None of this looks like a major failure on day one, but it shows up later as leakage, adverse selection, and portfolio quality issues.

The way to manage this is through continuing outcome monitoring. During pre-launch tests, compare AI-assisted cases versus manually processed ones to establish execution benchmarks. After rollout, monitor trends in quote-to-bind rates, referral rates, missing-data rates, post-bind corrections, override rates, and downstream loss performance. Also regularly sample fast-tracked cases for expert review and ask underwriters: would we have handled the submission the same way without AI? The goal is to detect when AI begins influencing portfolio quality in unintended ways.

Regulatory compliance is a known source of risk, and it requires governance from the start. In the US, regulators increasingly look beyond underwriting outcomes and examine whether AI affected submission routing, triaging, eligibility assessment, and broker interactions. The NAIC Model Bulletin on AI, adopted by 24 states and the District of Columbia, calls for controls against AI discrimination. New York, California, and Connecticut have issued their own AI guidance for insurers. You need explainable AI logic, audit trails, mandatory human reviews for high-impact decisions, and evidence that you regularly monitor for bias and drift to withstand regulatory scrutiny.

An Actual AI Solution for Commercial P&C Submission Intake

In an end-to-end commercial submission intake scenario, we would be looking at a multi-agent system coordinated by an orchestrator. We need specialized AI agents to perform different tasks: one classifies incoming documents, another extracts and validates data, the third drafts broker follow-up requests, and so on.

ScienceSoft prefers this pattern because submission processing involves many distinct activities with different accuracy requirements, permissions, and risk profiles. An input classification agent doesn't need access to the same data as a risk file preparation agent. An agent responsible for drafting emails shouldn't be reasoning on eligibility. Separating responsibilities reduces the blast radius of errors and makes governance much simpler.

And then, the orchestrator acts as a control layer of the agentic workflow. Unlike narrow agents, it doesn't perform submission-intake tasks itself. Its only job is to coordinate how work moves between AI agents, business rules, systems, and people. It decides which agent should act next, applies predefined routing rules, manages confidence thresholds, sends exceptions to human reviewers, and maintains an audit trail across the entire process. Think of orchestration as an AI traffic controller. Without it, you have just a set of AI capabilities.

Must-Haves of a Production-Ready AI Architecture

Three engineering principles separate a production-ready agentic AI system from a one-off pilot: decoupling AI from core insurance systems, implementing a multi-level AI authorization model, and building agents in a modular way.

Avoiding tight coupling between the AI workflow and the insurer's existing systems is essential for interoperability. Otherwise, adding AI would require rework across existing systems, and even small changes to those systems would trigger changes to AI-supported operations. In an ideal setup, the orchestrator and AI agents act as a back-end operational layer between your current systems without replacing them or requiring expansion. A layered architecture with a dedicated agentic layer and an integration layer sitting between the AI workflow and other insurance systems works well for that.

Consider applying event-driven integration patterns, where business events (think submission arrival or document upload) automatically trigger the next AI task. This keeps workflows synchronized across multiple systems without creating tightly coupled point-to-point integrations and allows AI to react immediately as an event occurs, making submission intake faster. Another major advantage is easier integration with legacy systems. Older apps that don't support APIs can often participate in the workflow by sending or receiving event messages, removing the need for custom integrations.

One more practical move is to expose the integration layer through a single gateway. This way, you don't need to integrate every AI agent separately with your existing systems and get a single place to capture audit logs. This approach simplifies integration across fragmented environments and supports a consistent audit trail required for compliance. It also minimizes integration maintenance overhead: if you later change your core platforms, the AI integration contracts remain stable.

The second principle is multi-level AI authorization. This approach aims to limit AI autonomy where business and regulatory risks are high while maximizing overall automation. We typically use three authorization levels. The first level allows fully automated actions for low-risk tasks like document classification or completeness checks, provided the AI outputs pass predefined deterministic checks. The second allows AI recommendations but requires human approval before execution, for example, when drafting broker follow-ups or preparing underwriting summaries. The third covers decisions that may affect eligibility, pricing, or other material underwriting outcomes. Here, AI prepares the supporting analysis and data, but humans remain the decision-makers.

You also need the system to log every action, including inputs, outputs, agent actions, source references, validations, and underwriter overrides. With this complete log, you can explain how decisions were reached, trace outputs back to evidence, review AI behavior long after a submission pack is processed, and prove regulatory-aligned controls during audits.

The third principle is modular agent design. With a modular architecture, each agent is built as an independent component. Such a design lets you add, improve, and replace task-specific agents without redesigning the whole system. This means you can first deploy agents for only a few processing tasks, one product, or one submission channel, prove accuracy and adoption, and then expand. That ability to scale gradually lets you start your AI journey with moderate upfront investment and with minimal implementation risk. If you're ready for a broader rollout from the outset, the modular architecture still makes the solution easier to maintain and evolve as business needs change.

The same modularity also allows you to use the best-performing and cheapest technology for each agent. For example, agents that assess eligibility and summarize risks benefit from the reasoning capabilities of large language models (LLMs). Implementing them with tailored retrieval-augmented generation (RAG) pipelines ensures outputs are grounded in actual submission documents. For document classification and entity extraction agents, fine-tuned encoder models trained on a fixed set of documents typically deliver very high accuracy at a fraction of the cost of LLMs. Plus, these models do not generate anything, so there's no hallucination risk at early intake steps.

If you're wondering what a workable automation scope, AI architecture, and data foundation would look like for your submission volume and current intake process, don't hesitate to reach out to discuss your case.

Contributing to this article were: Vadim Belski, head of AI, principal architect, ScienceSoft, andStacy Dubovik, financial technology & AI researcher, ScienceSoft.

One of the greatest challenges facing insurers is making sure that every claim is documented consistently, regardless of who is performing the inspection or where it takes place. Smart glasses offer a clear path to standardizing this process by relaying information, guidance, and documentation tools directly within the wearer's field of view.

Rather than relying solely on handwritten notes, memory, or photos taken after an inspection, smart glasses enable adjusters and field representatives to capture POV evidence in real time while following predefined inspection workflows. Step-by-step prompts, often oral, help ensure that required photographs, measurements, and observations are collected in the same order and according to the same standards across every inspection.

The result is greater transparency for insurers, policyholders, and auditors alike. Claims files become more complete, inspection procedures become more repeatable, and documentation is captured as events occur rather than reconstructed later. This not only improves confidence in claim decisions but also creates a stronger evidentiary record should questions arise later in the claims process.

As AI capabilities continue to mature, AI smart glasses can also assist by identifying missing documentation, recognizing objects or damage patterns, and helping ensure that inspections meet established company guidelines before they are submitted.

Is it possible that AI-enabled remote assessments can reduce the need for adjusters to travel on-site? If so, could this help lower costs or improve overall response times?

In many situations, yes. While some claims will always require an experienced adjuster to visit a site in person, smart glasses make it increasingly practical to conduct guided remote assessments for a wide range of property, vehicle, and equipment claims.

Using a live video connection, a policyholder, contractor, or local representative wearing smart glasses can share a first-person view with an adjuster located anywhere in the country. The adjuster can observe conditions in real time, ask questions, request additional views, and guide the inspection without the delays associated with scheduling travel.

This approach offers several advantages. It can significantly reduce travel time and associated costs, accelerate response following widespread events such as severe storms or natural disasters, and allow experienced adjusters to assist more customers each day. It also improves access to expertise in secure, remote, or difficult-to-reach locations where specialized personnel may not be immediately available.

Faster assessments often translate into faster claims decisions, helping policyholders begin repairs sooner while enabling insurers to manage resources more efficiently during periods of high claim volume.

Can smart glasses better provide guidance to policyholders when resolving claims or billing issues?

Smart glasses have the potential to transform customer interactions from reactive conversations into guided experiences.

Rather than describing damage over the phone or attempting to follow written instructions, policyholders can receive visual guidance while completing important tasks. AI-generated prompts or remote experts can direct them to photograph specific areas, locate serial numbers, inspect equipment safely, or verify documentation without leaving the inspection process.

This guidance helps reduce misunderstandings while making the experience less stressful for customers who may be navigating an insurance claim for the first time.

The same capabilities can extend beyond claims. Customer service representatives may use smart glasses during virtual support sessions to help policyholders understand equipment, review billing questions, or verify information required to resolve an issue more efficiently. By providing contextual information directly within the user's field of view, smart glasses make complex interactions easier to complete while reducing the need for repeated follow-up calls.

How can smart glasses create a more consistent experience to guide policyholders through the documentation process when building claims?

Incomplete documentation remains one of the most common reasons claims require additional review or follow-up. While wearing smart glasses, users can be guided through a structured documentation process from beginning to end.

Instead of expecting policyholders to determine which photos to take or which details are important, the system can provide step-by-step instructions tailored to the type of claim being filed. Users can be prompted to capture specific rooms, damaged assets, identification numbers, receipts, or supporting evidence before moving to the next step.

Because the guidance is delivered in real time, the likelihood of missing critical information is significantly reduced. AI can also identify gaps in the documentation, recognize whether required images have been captured, and recommend additional photos when necessary.

For insurers, this creates a more standardized claims package regardless of who submits it. For customers, it reduces uncertainty and increases confidence that the necessary information has been provided the first time, helping claims move through the review process more quickly.

What's the hidden benefit of the insurance industry beginning to adopt smart glasses, and why is it happening now?

The most significant benefit may not be the technology itself—it is the ability to capture data and make it available anywhere.

Insurance organizations face continuing challenges associated with workforce transitions, increasing claim complexity, and rising customer expectations. Smart glasses enable experienced adjusters to extend their knowledge beyond physical travel by supporting multiple inspections remotely, mentoring less experienced personnel, and guiding policyholders through complex situations from virtually any location.

At the same time, artificial intelligence is making these systems increasingly valuable. AI can assist with documentation, recognize objects and damage, summarize inspections, retrieve policy information, and provide contextual recommendations without interrupting the inspection process. Together, AI and smart glasses create a powerful platform that augments human expertise rather than replacing it.

For the insurance industry, the opportunity extends beyond operational efficiency. Smart glasses have the potential to improve consistency, strengthen documentation, reduce unnecessary travel, accelerate claims resolution, and deliver a more transparent and supportive experience for policyholders. As insurers continue to seek ways to improve both customer satisfaction and operational performance, AI smart glasses are emerging as a practical technology capable of advancing both objectives simultaneously.

With so many smart glasses flooding the market, which is the right choice for insurers?

Not all smart glasses are designed for the same purpose. While consumer AI glasses like Ray-Ban Stories, Virtue, and Xreal have generated significant attention for features like voice assistants, messaging, and media capture, insurance organizations require a very different set of capabilities. Claims assessments, inspections, and customer interactions demand devices that are built for reliability, security, and extended professional use.

Enterprise smart glasses, such as the Vuzix M400, are specifically engineered for these environments. They offer extended-wear battery life, hands-free operation, high-quality cameras, and displays that remain visible in a wide range of lighting conditions. Just as importantly, they integrate with enterprise software platforms, support secure device management, and can be deployed and updated across large organizations.

For insurance professionals, this means adjusters can access claims information, inspection checklists, policy details, and remote expert assistance without interrupting their workflow or reaching for another device. Enterprise platforms also support compliance requirements through encrypted communications, secure authentication, and centralized IT management—capabilities that are often essential when handling sensitive customer information.

Durability is another important consideration. Field adjusters routinely work in challenging environments, including construction sites, disaster areas, industrial facilities, and severe weather conditions. Enterprise smart glasses are designed to withstand these demanding settings while delivering consistent performance throughout a full workday.

Perhaps the most important distinction is purpose. Consumer smart glasses are primarily designed to enhance everyday personal experiences, while enterprise smart glasses are built to improve business processes. In insurance, success depends on accurate documentation, standardized inspections, secure data handling, and efficient collaboration between policyholders, adjusters, and remote experts. Enterprise smart glasses are purpose-built to support those objectives.

As AI capabilities continue to evolve, the most valuable deployments in insurance will likely combine enterprise-grade hardware with intelligent software that assists users in documenting claims, identifying missing information, guiding inspections, and connecting field personnel with subject matter experts. Choosing a platform designed for professional workflows ensures insurers can take advantage of these innovations while meeting the security, reliability, and scalability requirements of the modern enterprise.

In the world of mergers and acquisitions, representations and warranties (R&W) insurance has become a near-standard tool for managing transactional risk. Buyers and sellers across industries routinely use these policies to bridge gaps in negotiation, allocate liability, and move deals to the finish line with greater confidence.

Yet in the reinsurance sector, specifically within large and complex block transactions, R&W insurance has been conspicuously absent from the conversation. As one transactional attorney puts it, a block reinsurance deal is functionally "like an asset deal but in the insurance space."

It's a missed opportunity, leaving significant value on the table for both ceding insurers and reinsurers.

A Tool That Has Been Hiding in Plain Sight

Block reinsurance transactions carry a unique set of risks. Whether the deal involves a loss portfolio transfer, a full novation of policy liabilities or a multibillion-dollar block of long-dated, asset intensive life or annuity liabilities, both the ceding company and the assuming reinsurer must navigate a unique set of representations about the underlying book of business. Such matters can be further complicated if the block reinsurance transaction also provides for a transfer of a policy administration platform to the reinsurer, thus requiring additional representations about the platform. These representations typically cover the accuracy of financial statements, methodology for computing reserves, the validity of policy data, regulatory compliance, and a host of other material facts that inform the economics of the deal.

For a ceding insurer, a fundamental motivation for entering a block deal is to achieve certainty and finality. That goal is undermined when the cedant is saddled with long-tail indemnity obligations post-closing. This is precisely the friction point R&W insurance is designed to solve. It allows the ceding insurer to achieve a 'clean exit' with no significant post-closing obligations with respect to inaccurate R&Ws (outside of fraud) while the assuming reinsurer receives meaningful financial protection.

Despite this complexity, R&W insurance has traditionally not been a feature of block transactions, historically relying instead on traditional risk allocation mechanisms. These alternatives, however, have significant downsides:

• A cedant indemnity can lead to protracted and contentious negotiations over the drafting of R&Ws and the size and survival period of the indemnity itself. This not only delays closing but can sour the post-closing relationship if a claim arises. Post-closing relationships are particularly important in reinsurance transactions where, even though the economics of the block are being transferred, the parties will remain tied together for the duration of the block of business.
• Self-insuring the risk leaves the reinsurer holding all the exposure for losses from a breach, an atypical and often undesirable position.

The gap in R&W adoption has been fueled by a perception that reinsurance deals are lower risk or that R&W policies were not designed for their nuances. However, these views overlook the primary benefit: superior deal efficiency and a cleaner allocation of risk for all parties.

Speed, Simplicity and Reasonable Cost

For those involved in block transactions, the practical benefits of R&W insurance are compelling and worth serious consideration.

One of the most attractive features is the speed at which coverage can be bound. Block transactions often operate on tight timelines, particularly when they are driven by regulatory deadlines, fiscal year-end targets, or strategic portfolio management objectives. R&W policies can typically be bound in a matter of weeks, and in some cases even faster, once the underwriting process is underway. This timeline aligns well with the pace at which many deals need to close.

Beyond speed, R&W insurance can simplify the negotiation process between the parties. In any transaction, the allocation of risk around representations and warranties can become one of the most contentious and time-consuming points of discussion. The ceding company wants to limit its post-closing exposure, while the assuming reinsurer wants robust protections in case the underlying information relied upon when entering into the transaction turns out to be different from what was represented.

When an R&W policy is in place, much of that friction is removed. The policy effectively backstops the representations, giving the assuming reinsurer confidence that it has recourse in the event of a breach, while simultaneously allowing the ceding company to reduce or eliminate its indemnity obligations. The result is a cleaner, faster negotiation with fewer sticking points.

While major indemnity claims are infrequent, industry data shows that roughly 20% of R&W policies have claims submitted. This suggests that buyers are more willing or able (e.g., because a broader suite of representations are made) to seek recourse from an insurer than from a counterparty, making the protection more practical. In this context, that means preserving the commercial relationship between the cedant and reinsurer, turning a potential dispute into an unemotional claim against the R&W insurer with minimal adverse impact on the parties' continuing relationship.

Cost is another consideration that often surprises parties who are new to the product. Premiums for R&W insurance in the current market are reasonable and generally in line with what organizations pay for other financial and transactional insurance products. For example, in a block deal with a $500 million ceding commission, a customary 15% policy limit ($75 million) would cost approximately $2.25 million - $3 million in premium (i.e., a 3 - 4% rate-on-line).

There is also an important qualitative benefit that is easy to overlook. The presence of R&W insurance can signal to both parties that the transaction has been conducted with a high degree of diligence and good faith. The underwriting process for an R&W policy involves a thorough review of the deal's representations, the underlying data, and the due diligence that has been performed. This independent layer of scrutiny can increase all parties' confidence in the integrity of the transaction.

Placing the Policy: What to Expect

Mechanically, crafting an R&W policy for a block deal is very similar to a standard M&A transaction. The underwriting process involves the vetting of the reinsurer's due diligence. Although there may be less detailed third-party due diligence reports on reinsurance transactions than on corporate M&A, so long as appropriate levels of due diligence are conducted in light of the scope of the representations and warranties being insured, fulsome coverage will be available.

However, there are unique aspects to consider. For example, certain representations around actuarial data accuracy are notoriously difficult to insure. Insurers are not in the business of guaranteeing reserves. Therefore, the "specified data rep" will be heavily scrutinized and may be knowledge qualified or excluded depending on the specifics of the transaction and supporting diligence information and sufficiency of reserves is likely to be excluded from coverage. An experienced broker and legal advisor can help navigate these nuances to secure the broadest possible coverage.

Looking Ahead

With the uptick in block deals, the demand for tools that reduce transactional risk and accelerate deal timelines will only grow. R&W insurance is well positioned to meet that demand, offering a practical, affordable, and efficient solution that more reinsurance professionals should have in their toolkit. For those considering their next block transaction, exploring how to leverage R&W insurance most effectively could be one of the most valuable conversations to have before the deal gets underway.

Claims handling is one of the most visible cost lines in insurance. Industry estimates consistently place 70% to 80% of claims handling costs inside routine, repeatable processes: status inquiries, documentation requests, coverage confirmations, first-notice-of-loss intake. These are the categories that make the AI business case straightforward to build and difficult to execute without hurting accuracy.

Most insurer AI triage deployments begin with a proof of concept on controlled test data. What they encounter in production is a different environment, with a different risk profile, and different failure modes. Three design decisions determine whether the transition from pilot to live operation succeeds or stalls.

The Claims Categories Ready for AI Triage (and the Ones That Are Not)

The categories that perform reliably in production share a characteristic: the resolution requires accurate information retrieval and a rule-based decision, not adjuster judgment.

First-notice-of-loss intake for standard peril types (vehicle collision, water damage, property theft) follows a structured data-collection process that maps cleanly to what AI agents do well. The agent gathers required fields, confirms coverage against the policy record, generates a claim reference, and routes to the appropriate handling queue. Intake time drops significantly with AI. Early-stage accuracy is high when the agent has direct, live access to the policy management system.

Policy status and coverage inquiries are a second reliable category. Policyholders and brokers need clear, accurate answers about what is and is not covered under a specific policy. These queries have a deterministic answer that the AI can retrieve from the policy record and communicate without ambiguity. When it does so accurately and immediately, satisfaction scores on this category improve, and the insurer avoids the misquote risk that comes from a rushed human response during peak volume.

Documentation status updates on open claims, whether a repair estimate has been received, whether a payment has been processed, where a claim sits in the workflow are the third reliable category. These interactions are high in volume and low in complexity. They consume significant adjuster time. When the agent handles them with real-time access to the claims management system, adjusters recover that time for interactions that actually require their expertise.

The categories that are not ready are those that require genuine coverage interpretation, multi-party coordination, or circumstances the policy language does not address clearly. Deploying AI on these categories in an early implementation is where most accuracy problems originate.

The Accuracy Threshold That Protects Both CSAT and Regulatory Standing

In most service sectors, a triage system that resolves 70% of queries correctly in the first months of deployment and improves from there is considered a successful pilot. Insurance applies a different standard, for two reasons that are specific to the sector.

First, inaccurate coverage information given to a policyholder at claim time creates both a CSAT problem and a potential errors-and-omissions exposure. A claimant told their loss is covered and later finding it is not does not experience this as a minor service inconvenience. Second, insurance regulators in most markets require that specific communications meet accuracy and disclosure standards that a misconfigured AI agent can fail to meet without the insurer knowing until a complaint surfaces.

The practical consequence is that the confidence threshold below which the AI escalates rather than responds must be set higher in insurance than in most service environments. A system that generates a coverage answer when its confidence score is moderate is operationally acceptable in retail support. It is not acceptable in insurance, because the cost of a wrong answer is asymmetric: a small number of incorrect coverage statements create regulatory and customer relationship problems that far outweigh the efficiency gains across the cases the system handled correctly.

Define the escalation trigger conservatively in the early deployment. A narrower AI scope with a high accuracy rate builds the internal confidence and operational track record needed to expand scope responsibly. A wide scope with a moderate accuracy rate generates precisely the incidents that slow adoption and invite regulatory scrutiny.

What Production Looks Like After the Proof of Concept

Proof-of-concept environments test the happy path. Production environments test the edge, at volume, across the full range of policy types and peril circumstances the carrier actually handles.

Three failure modes appear consistently in live insurance triage deployments.

The first is policy variant coverage. A claimant's policy may carry endorsements, exclusions, or carrier-specific modifications that are not reflected in the standard coverage language the agent was trained on. Without direct access to the full, structured policy record, not a summary, the agent falls back to standard language and produces an answer that is accurate for the base product and wrong for that policyholder's specific terms.

The second is multi-party claims. In a commercial property claim or a liability claim involving multiple parties, the intake process requires collecting different information from parties with different roles and interests. AI agents calibrated for personal lines intake do not handle this correctly without specific configuration, and the errors they generate in multi-party scenarios tend to be the most visible ones.

The third is mid-process handoff quality. When a claim requires escalation from the AI to a human adjuster, what the adjuster receives determines whether the customer experience continues or restarts. A handoff record that captures the full interaction context, what the agent understood, what was collected, and what was confirmed allows the adjuster to continue from where the agent stopped. A handoff that returns the claimant to the beginning of the intake process generates the complaint pattern that regulatory affairs teams track.

Keeping Adjusters in Control of What Matters

The framing that produces both operational results and staff adoption is direct: AI handles information retrieval and routine intake so adjusters spend their time on the interactions that require professional judgment, relationship management, and expertise. Not as a threat to the role. As a description of what the role becomes.

The adjusters who see AI triage succeed in their operation are consistently the ones who were involved in defining where the escalation boundary sits. That line is a professional judgment, not only a technical parameter. Involving the claims team in setting it and giving them a clear override path when the system routes something they believe it should not produce better-calibrated systems and faster adoption than any training program.

The insurers getting durable results from AI claims triage are not the ones that deployed the most capable model. They are the ones that were clearest about where human judgment is irreplaceable and built their system around that boundary from the first day of deployment.