I often tell people I've been watching the same movie for decades — it will be 40 years this fall since I started covering IBM as a young pup of a reporter at the Wall Street Journal. I've watched the disruption that hit IBM spread to the rest of the computer industry, then to commerce in general, thanks to the personal computer, internet, search engines, smartphones and now AI. 

Having watched the movie so often, I have a pretty good sense of how today's story lines will play out.

Today, I'll start even earlier than 1986 and offer a quick history of computing because I think the long view provides useful perspective on where insurance is — and where it's going. Some insurance processes are firmly stuck in the 1950s and 1960s, when batch processing was the only game in town. Others have made it to the 1980s and 1990s, with their PCs and networking. Still others are becoming fully modern, as they take advantage of mobile devices and generative AI.

On the theory that every industry is becoming a technology industry, insurers will eventually catch up on all fronts. Understanding where we lag the most and imagining a world where insurance can operate at the speed of Amazon will, I hope, provide a road map that will help us get to that future faster.

So, yes, I've set myself a rather ambitious goal this week.

To understand the starting point for computing (and insurance), think of my college roommate Mike. He was a computer science major, so he was wedded to the campus mainframe. He'd type out a program on a stack of punch cards, hand them in at the window in the computer center... and wait. When his turn finally came on the mainframe, he'd get a printout with the results. Given the complexity of what he was doing, and that even a typo would derail things, he inevitably had errors. So he'd debug the program, type out some more punch cards, turn them in at the window... and wait some more. 

Because turnaround times were shorter at night, after most students had gone back to their rooms, Mike typically stayed out into the wee hours of the morning, napping on a table while waiting for his latest printout. (The way our habits meshed led to a comical relationship, where we sometimes didn't see other while both were awake for weeks at a time. I'd leave in the morning while he was asleep and, after working a job, not get back until he'd left for the computer center in the evening. He went home on weekends to see his girlfriend, so I'd sometimes find myself asking mutual friends, "Hey, how's Mike? I haven't talked to him in ages.")

Mike's travails were a holdover from the era of batch processing, when a computer could do only one thing at a time. Big efforts, such as processing payroll or reconciling accounting records, were done in a single batch at a time reserved on the mainframe. Mike's programs obviously weren't on anything like accounting's scale, but he still had to run a program in a single batch of cards and wait his turn. 

Even though computing technology has improved by orders of magnitude since Mike and I were in college, a lot of business still operates at the speed of batch processing. You have a meeting on some issue, and a question comes up. Someone is assigned to do some analysis and comes back a week or two or three later with an answer. The issue is discussed again, and another question arises. More analysis over more weeks ensues. The batch processing influence is even stronger in insurance than in most industries because there is so very much data to analyze.

Computer scientists saw early how much better interactive computing would be and spent decades getting us there. By the '60s and '70s, time-sharing became possible. The setup was awkward: You had a keyboard and printer but had to type out a program on special tape that you fed into the machine, and turnaround times were painfully slow because you were queueing up behind all the programs running on a distant mainframe or minicomputer. But time-sharing spread the power of computing far beyond the walls of the data center. (Bill Gates got his career started on a time-sharing terminal at his high school. I, too, had access to a terminal in high school but somehow didn't do as much with it as he did.)

By the late 1970s and into the 1980s, Xerox PARC had worked its magic, and the Apple II and then the IBM PC were putting real power on individuals' desktops. The computers delivered big benefits to business because of the electronic spreadsheet but otherwise proved to be rather limited when used in isolation. Fortunately, Xerox took care of that issue, too, with the Ethernet networking standard that let businesses link their in-house computers. Then the internet took networking into the stratosphere thanks to the World Wide Web's debut in 1989 and the Mosaic browser in 1993. By the late 1990s, search engines were doing a good job of fulfilling Google's goal "to organize the world's information and make it universally accessible and useful." Then smartphones, led by the iPhone debut in 2007, put all the computing power and information in our hands. Generative AI is now letting us gather, process and use far more of the world's data than we humans could ever do on our own.

Big tech has taken advantage of the remarkable progression of technology to gather all sorts of signals about individuals (many of which I wish they didn't have) and target us with ads, with memes that keep us engaged, with dynamic pricing that maximizes their clients' revenue. Progress in other spheres is more uneven, but you can look at big retailers like Amazon and Walmart and see how they sense demand and respond to it in real time.

I'd say insurance has done a so-so job of taking advantage — acknowledging that our situation is complicated by heavy regulation and by the confusion of state-by-state oversight in the U.S. A lot of insurance work is still in a sort of batch mode — the analysis of loss runs, actuarial tables, and so on. While insurers have taken advantage of all the power on the desktop that PCs provide, I'm not sure we've done the best job of internal networking — why, for instance, isn't claims data always fed in real time to underwriters to inform future decisions? Insurers certainly haven't been great about taking advantage of all the information that's out there beyond their four walls; they're starting to figure out what data to trust and how to absorb it, but they've been slow. Insurers are also still figuring out what to do about smartphones. Yes, every company has an app these days, but my impression is that customers still want to be able to do a lot more self-service via phones than is possible today.

I'll withhold judgment on how insurance is doing on gen AI. We're headed in some good directions by gathering and doing initial processing for those in claims, underwriting and agencies, but we clearly haven't figured gen AI out — yet nobody has, so we're in good company. 

The nice thing is that, whatever our inadequacies to this point,  our version of the technology movie can have a happy ending for two reasons. One is that any new computer technology builds on everything that's come before in an exponential way. We're not just adding a gen AI capability alongside an information or networking capability. The capability increases by some exponent what was ushered in by smartphones, which raised what came before to some power, after it did the same to everything that came befoer that. The second reason is that we don't have to build the capability. The tech giants have done that over the past 75 years; we just have to take advantage. They're not done yet, either: The latest figure I saw is that the five biggest AI companies are investing $700 billion on infrastructure in this year alone

To me, the happy ending will come in a decade or so, when insurance can fully switch from batch processing to what I think of as conversational computing. You don't have a question in a meeting and send someone off to study the issue for weeks. You ask a question, and your AI uses all the internal and external information available to provide an answer. Loss runs and actuarial tables don't require massive studies. You converse with your computer and get the answers you need.

You can see glimmers of this sort of conversational future in some things going on today. Continuous underwriting is one great example. Why wait for an annual review of a policy when aerial imaging can tell you that a homeowner has added a pool, when an AI monitoring the internet can tell you that a restaurant has added a drinks menu or delivery options, etc.? Why not take advantage of the ability to sense what's going on among clients and prospects and respond? 

Embedded insurance is another example. Why should selling an insurance policy always be a formal project? Why not just use the ability to sense when a customer might want coverage and respond?

Technology never stops moving. Moore's law made sure of that for decades, with what became a sort of mandate for semiconductor makers to double the power of a chip every year and a half to two years at no increase in cost, and other forces, such as AI, are now amplifying those gains in capability by orders of magnitude. I figure I've gone through six tech revolutions since I debuted on the computer beat in 1986, and we could be in the middle of the next one, with agentic AI.

For insurers, I hope a look at the history of computing identifies some spots where we can and should improve. But I mostly hope the history shows us that we're headed toward a conversational future, where we ask questions and get answers in real time. Just imagine what insurance could look like at the speed of Amazon.

Cheers,

Paul

Recently, there's been talk from AI-native insurance startups telling incumbents they'll never catch up. The argument goes like this: The barrier isn't technology; it's organizational DNA. Boards resist. Agent networks resist. Incentive structures resist. Even superintelligent AI can't rewrite a captive distribution network or a CEO's risk tolerance.

We built one of those AI-native insurers. We've spent nearly a decade learning where AI actually works in insurance - and where it doesn't. So we'll say what most people in our position won't: 

The critics are only half right.

The organizational immune system is real

We've watched it operate from the inside.

AI threatens more than processes. It threatens people, hierarchies, and decades of institutional knowledge that leaders built their careers on. The more powerful the technology gets, the more threatening the disruption feels, and the harder the organization pushes back.

The execution gap is genuine, too. Deloitte surveyed 3,200 enterprise leaders this year and found that executives feel strategically ready for AI but not operationally ready. Every insurance business we talk to confirms this. The board said yes. The pilot worked. But not much actually changed. They tripped in the last mile.

If you're reading those blog posts and feeling uneasy, trust your instincts. Standing still is falling behind.

Where the thesis breaks

The "incumbents are dead" argument assumes the only way to win with AI is to have been born with it. That organizational barriers are permanent. That traditional insurance businesses are evolutionary dead ends waiting for the asteroid.

This confuses two problems.

The first is building AI technology. AI-native startups have a real advantage here. Clean architectures, ML engineers who learned to work alongside actuaries, feedback loops from day one.

The second is having the operational context that makes AI actually work in insurance. Here, traditional businesses have an advantage no startup can replicate.

A startup can build a great claims model. But it doesn't know that your Florida team handles litigation differently than your Texas team because of venue-specific judicial considerations. It doesn't know that your underwriting knowledge base says one thing but your senior underwriters do another - and the deviation is actually producing better results. It doesn't know which of your 50 state regulatory constraints are real compliance requirements and which are institutional habits nobody has revisited in a decade.

That operational context - the messy, human, state-by-state reality of how insurance actually works - is the raw material AI needs to generate value. Technology is the engine. Context is the fuel. Insurance businesses have been accumulating this fuel for decades.

The startup pitch is: "We have the engine, and we'll figure out the fuel." The honest answer is that the fuel is harder to build than the engine.

The real question is speed

Can you close the execution gap before it shows up in your results?

The gap closes by connecting AI to the operational reality of how your business actually runs - across claims, underwriting, distribution, and compliance - in ways that compound over time.

Every month of operational AI data makes the system smarter. Every feedback loop accelerates the next one. This is an exponential curve, not a linear one. The businesses that start building now aren't just catching up. They're beginning a compounding process that gets harder to replicate with every cycle.

We spent nearly a decade building these feedback loops inside our own company. That experience made one thing clear: The distance between an AI demo that works and an AI system that changes how you operate is almost entirely about understanding the insurance underneath.

What I'm telling insurance executives right now

Your data is an asset that will appreciate with use. Your operational context can be youradvantage. The AI-native startups telling you it's over are talking their own book.

Some businesses already know this. The ones investing seriously in operational AI - not pilots, but production systems touching real policyholders - are proving the thesis wrong in real time.

We're seeing this from carriers, MGAs, and specialty businesses alike.

But the window is real. AI feedback loops compound. The businesses that start building them in the next 12 to 18 months will pull away from those that don't. You'll see it first in expense ratios, then in loss ratios, and then in competitive position.

The businesses that win won't become AI companies. They'll stay insurance companies that figured out how to make AI compound inside their operations before the window closed.

For independent agents, the to-do list never gets shorter. New clients to win, policies to place, and revenue to grow. But there's one conversation that doesn't always make it onto the planning agenda, and it might be the most important one of all. What happens when it's time to hand things off?

Succession planning has long carried a reputation as something to worry about later. A conversation for agents nearing the end of their career, not those in the thick of building their business. But that thinking can be costly. The agencies that make the transition most smoothly aren't the ones that started planning at the last minute. They're the ones that built transferable tech operations from day one.

Here's the good news: if you're already using an agency management system to run your daily operations, you're likely closer to succession-ready than you think. The tools that help you manage client account data, track performance metrics, and stay on top of renewals can do double duty. Used consistently, they build the kind of organized and documented operation that makes handing things off far less daunting.

Performance Metrics Tell Your Agency's Story

When it comes time to demonstrate value, data speaks louder than anything else. Potential successors and buyers will want a clear picture of your agency's performance, including which lines of business are driving the most revenue, which producers are performing, and where coverage gaps exist across the book. Those answers need to be readily accessible.

A robust agency management system gives you this performance visibility in real time. Dashboards and reporting tools surface the metrics that matter most, from total annualized premium and active policies per customer to a detailed breakdown of your book of business by transaction type, often presented in intuitive visual layouts.

You can customize these reports too, filtering and drilling down into the data points that matter most. Some systems even let you benchmark your performance against peer agencies, giving you a clearer sense of where you stand. That kind of insight doesn't just serve a future transition. It sharpens your decision-making today, helping you spot growth opportunities and course correct before small issues become bigger ones.

Over time, these reports build a compelling picture of your agency's health and trajectory, one that tells a clear story to a successor and makes you a stronger agency today.

AI Keeps Client Knowledge Transferable

Serving clients without missing a beat is one of the first challenges any incoming leader faces. That means being able to find policy details quickly, understand coverage history, and get up to speed on the relationship without having to track down the person who used to handle it. When information is scattered across inboxes, desktop folders, and spreadsheets, that handoff becomes harder and more costly than it needs to be.

AI-powered agency management tools change that, and not just when a transition is on the horizon. Picture this: a newly onboarded staff member pulls up a long-standing client account in their first week. Rather than digging through months of email threads and agent logs, they get an instant summary of the relationship, enabling more knowledgeable client interactions and a much faster path to getting up to speed.

Clients expect continuity. They don't want to repeat themselves or re-explain their history with their agency. They expect whoever picks up the phone to already know them. AI makes that possible whether you're onboarding a new hire, navigating a leadership change, or simply trying to deliver a better client experience every day.

Renewal Tracking Protects What You've Built

Retention is the metric that tells the clearest story about an agency's health. A consistent renewal process signals that clients are being taken care of and that the book of business is stable.

A good management system gives you and any future leader a single view of every upcoming renewal, what has changed between the current policy and the renewal offer, and which clients are most at risk of shopping around. Predictive analytics flag at-risk policies before they become problems. Automated remarketing workflows retrieve updated rates and surface them alongside renewal details, so whoever is managing the book can act quickly and make informed recommendations.

For a buyer or successor, a clean and consistent renewal process is one of the most compelling things they can walk into.

No Matter Where You Are in Your Career — Start Now

Whether you're just launching your agency, in the middle of a growth run, or beginning to think seriously about the future, the time to invest in technology-enabled workflows is now.

The efficiencies you gain today will compound over time, and when the moment comes to pass the pen and the policies, you'll be glad you started early.

Walk into any bank branch, insurer's office, or contact hub, and you'll see the same challenge playing out: frontline teams juggling conversations, systems, and expectations in an environment where customers expect answers now -- and on the channel of their choice.

Consumers today benchmark banking, financial services and insurance (BFSI) service not against competitors but against the best digital experiences they see anywhere. The organizations that succeed in 2026 won't simply add more tools; they will blend human advice with smart, invisible technology, so their advisors, agents, relationship managers, and service reps can engage faster, personalize better, and build trust at scale.

What Customers Want (And Where Organizations Still Fall Short)

Customers want digital convenience, but they also want expert human guidance for financial decisions that carry risk:

• Choosing an insurance plan
• Applying for a loan
• Completing know-your-customer (KYC) requirements
• Making investment decisions
• Resolving service or dispute issues

McKinsey's customer-experience research across financial services shows why this matters:

CX leaders achieve stronger revenue growth, lower costs, and higher employee engagement than their peers - and generate meaningfully higher shareholder returns.

But many journeys across BFSI remain fragmented.

Customers begin online, switch to chat, walk into a branch or connect with an advisor, only to repeat themselves at every step. Follow-ups may be inconsistent, and context often gets lost between channels.

Yet the human element remains critical: across financial services; trusted advisors and relationship managers consistently rank as the most valued touchpoint, so much so that switching behavior rises when advisors leave or service becomes inconsistent.

The message is universal across BFSI:

Customers don't want to choose between people and technology. They want both, seamlessly connected.

Why Frontline Productivity Is the New Customer Experience

Customer experience and employee experience are inseparable.

When advisors and agents spend time hunting for information, switching screens, or reentering data, customers feel the friction immediately.

McKinsey finds that organizations that engage frontline teams as co-creators in CX transformation see:

• Higher customer satisfaction
• 20% improvement in employee satisfaction
• Faster turnaround time
• Stronger cross-sell and retention

In BFSI, this link is especially clear:

• A banker who has instant access to a customer's full financial picture can advise better.
• A field officer with mobile access reduces turnaround times.
• An insurance advisor with contextual prompts can personalize without delay.
• A contact center agent with unified history avoids escalations.

Technology doesn't replace expertise - it amplifies it.

Accenture's latest research reinforces the urgency: customers increasingly report difficulty reaching a human when needed, or navigating service journeys. In a landscape where attrition is high and expectations are rising, BFSI organizations must remove friction, not add new layers of complexity.

Where AI Fits: Practical, Not Hype

AI's role is not to automate away financial conversations - it is to shorten the distance between a customer question and a confident, compliant answer.

IBM's Institute for Business Value says that:

75% of financial services executives believe AI will improve personalization and CX but warn that fragmented systems and weak data foundations remain barriers.

The promise of AI becomes real when paired with clean data, integrated workflows, and empowered frontline teams.

In BFSI, this looks like:

1. Advisor / agent assist

Real-time summaries, next-best-actions, explanations of product rules, contextual prompts - helping frontline teams spend more time advising and less time searching.

2. Smarter routing

AI triages inbound requests to the right channel:

• Self-service for simple tasks
• Human support for complex or high-value conversations

This reduces drop-offs and improves first-contact resolution.

3. Engagement

Detecting repayment cycles, renewal windows, eligibility changes, life events, and portfolio gaps - then nudging advisors with timely reasons to reach out.

4. Faster, cleaner onboarding

AI-assisted KYC checks, document classification, real-time validation, multilingual support, especially important for banks, insurers, and non-banking financial centers (NBFCs).

Designing the "Human + Tech" Operating Model Across BFSI

To make this transformation real, BFSI leaders are anchoring around a few core principles:

Human-led, tech-accelerated journeys

Map the moments where customers need reassurance (loan approvals, investments, claims, disputes). Then use tech to handle the rest - context-gathering, data prep, routing, and follow-ups.

Modernize the service core

Accenture's "Service on the Brink" findings highlight a striking gap: customers don't feel technology has meaningfully improved service quality yet.

That gap closes only when:

• Data is unified
• Systems are connected
• AI is grounded in real rules
• Handoffs are seamless

Measure what matters

Beyond cost KPIs, leading BFSI organizations track:

• Advisor time saved
• First-contact resolution
• Customer effort scores
• Quality and consistency of advice
• Employee adoption and satisfaction

AI and automation must serve people, not distract them.

The New Standard of Service in BFSI

Picture a loan customer messaging a query.

A virtual assistant validates identity, surfaces repayment options, and flags eligibility for a top-up offer.

The customer asks a nuanced question; within seconds a relationship manager joins, armed with a clean summary of past interactions, risk profile, and suggested talking points.

The conversation finishes quickly, the customer feels understood, and the advisor moves confidently to their next engagement.

This is where BFSI is heading:

Frontline expertise supported, not overshadowed, by technology.

When AI, automation, and connected systems fade into the background, advisors and agents can lead every interaction with clarity, empathy, and insight.

That's the real frontier.

References

● McKinsey & Company. “Elevating customer experience: A win–win for insurers and customers,” 2023.
● IBM Institute for Business Value. “Insurance in the AI era,” 2025.
● Accenture. “Customer Service on the Brink,” 2025.
● McKinsey & Company. “The Human Advantage in Banking Customer Experience,” 2023.

In the legal profession, the work is only as strong as its support. A good argument isn't just persuasive, it's backed by citations. You can point to the contract clause, the case, the exhibit, and the chain of reasoning that got you to the conclusion. That's not academic formality. It's how legal work survives scrutiny in a court of law. 

Insurance is moving in the same direction.

For decades, insurance operations could lean on experience and muscle memory. A tenured underwriter knew what "this form usually covers." A claims leader knew the standard response posture. A broker knew which markets were flexible. That knowledge still matters, but the environment has changed. Regulators, legal teams, and procurement groups now expect decisions to be documented, explainable, and consistent over time.

"Trust me" is no longer a reasonable operating model.

Why legal workflows look the way they do

Legal work is predicated on the ultimate potential that it will end up in front of a judge. This fear shapes all the work lawyers do. 

A motion, an opinion letter, or a contract position might get scrutinized months or years later by a judge, with millions of dollars at stake. The only way to truly prepare for that situation is if the work product is structured to be audited. That's why legal workflows emphasize three things:

Citation. Show exactly where the claim comes from.

Reasoning. Make it possible to retrace the reasoning steps from source to conclusion.

Conclusion. Make it easy for another expert to validate or challenge the conclusion by having a very clear and articulate conclusion.

These practices aren't about slowing work down. They're how the legal industry moves quickly while staying defensible when the stakes rise.

Insurance is discovering the same truth, especially in claims and coverage interpretation.

Insurance is already under similar scrutiny

Insurance has always been regulated, but scrutiny is broader now and comes from more directions, including clients. Decisions can trigger omissions, bad-faith allegations, and liabilities that far exceed a coverage dispute.

State-by-state variability adds another layer. A defensible decision in one jurisdiction may be incomplete or risky in another.

At the same time, the work is deeply document-driven. Policies, endorsements, submissions, claim files, and correspondence are still stored in PDFs, scans, and formats that were created for manual review.

That means insurance decisions are often anchored in unstructured language that must be read carefully, compared across documents, and defended later.

In short, insurance faces legal-like constraints whether it realizes it or not.

The AI factor raises the bar, not just the speed

AI is often discussed as a productivity lever, but in insurance, the real challenge is credibility. When an AI-supported decision gets scrutinized, you need to show the basis for it. If the answer is a black box, you've created a new type of risk.

That's why the industry is increasingly prioritizing accuracy, explainability, and consistency over speed alone.

It's also why "model drift" matters. If a tool's behavior changes over time, it undermines consistency and auditability in regulated workflows.

This is one place where legal has a head start. Many legal technology workflows were designed around precedent and review. The focus is less about generating text and more about interpreting documents with citations and a clear path from source to conclusion.

Insurance now needs the same.

The future of insurance work

This shift isn't theoretical. It changes how teams should define quality.

In an insurance workflow built to withstand scrutiny, a good outcome isn't only correct—it's defensible. That means:

Your conclusions should point back to the policy language. Coverage positions and claim decisions need to be anchored in the actual text, not just institutional memory. Insurance has long depended on expert judgment in how professionals read policies, interpret exclusions, and apply precedent. The next step is making that judgment visible and reproducible.

Your reasoning should be transparent enough for peer review. If a colleague can't follow how you got there, an auditor or regulator won't either. Transparent reasoning isn't a luxury in high-stakes decisions, it's a requirement.

Your process should be consistent across teams and time. Insurance is full of niches and specialized expertise, but inconsistency is costly. As experienced practitioners retire, decision quality can decline if judgment stays trapped in individuals. Understanding the policy is the hardest problem in insurance. You can't solve it with expertise that only exists in people's heads.

Your documentation should be structured for "future you." Claims files, coverage analyses, and underwriting notes should read like work that's expected to be examined later. That's the legal mindset, and it's becoming the insurance mindset.

This is also why many leaders are talking less about flashy AI and more about repeatable operating models. The most valuable AI in insurance will look like consistency, documentation, and fewer surprises.

The practical takeaway for insurance leaders

If you're leading claims, legal, compliance, or brokerage operations, the question isn't whether your teams will use AI and automation. They already are. 

The real question is whether you're building systems that will hold up when scrutiny arrives. That means setting standards for citation, traceability, and reviewability in the work itself, not as an extra step at the end.

It also means resisting tools and processes that optimize for speed while sacrificing transparency. In insurance, "close enough" is often where the risk begins. 

Insurance is learning a lesson the legal industry learned long ago: when the stakes are high, the work must show its sources.

The AI market in the insurance industry is set to hit $80 billion by 2032, yet nearly two-thirds of carriers have a gap between their AI vision and reality. In essence, carriers understand what they want from AI and see the significant value it can drive but are struggling to actually put this into practice, especially in the property sector.

Marrying vision and reality is critical, however, as carriers look to scale automation, re-orchestrate and transform workflows, and fully realize the potential of AI across the lifecycle of a claim. There is a substantial downstream impact on claimants and clients, as well. 

There are a number of steps a carrier needs to take to find success with AI in 2026 and ensure they are future-proofed and ready for the next era of property claims.

The AI Journey for Property Carriers

First and foremost, carriers need to understand where they are on their AI journey. This is important in identifying what the next steps are, what's feasible in the short term, and eventually in the long term, and aligning company communications, operations, workflow, training, and more.

At the moment, anywhere from 58-82% of carriers are leveraging AI tools in their operations, but only 12% claim to have fully mature capabilities, and only 7% have achieved scalable AI success. This means that 93% of carriers are still in the part of their AI journey in which they are identifying how to scale AI to a point at which it is driving real, measurable outcomes. What we've seen so far is that adoption of AI has been popular in areas such as intake, triage, and documentation, but fully integrated technology and end-to-end AI workflows are still far away for most carriers. This, in turn, results in a fragmented technology experience, rife with different tools, vendors, and solutions. This limits AI's impact. It keeps value confined to each step in the lifecycle of a claim, can lead to inconsistent data or silos across systems, and weakens output.

Reliance on pilot programs or point solutions is the first step in an AI journey, but it certainly can't be the last. Most importantly, this technology is rapidly advancing, and the longer it takes carriers to find value and scalability, the further they'll fall behind the competition.

The Challenges Facing Carriers

There are three main challenges facing carriers. First is integration of AI into legacy technology. The majority of claims systems weren't built with API connectivity in mind, which introduces difficulties immediately into scaling this technology across workflows. Before integration even begins, carriers need to ensure that their claims systems can support orchestration.

Second is training a disconnected property workforce. An often-overlooked aspect of AI in the property space is preparing for the challenges that can arise when adjusters are managing heavy caseloads and working in the field. Support systems are critical to success in this area, and AI and any other new tools cannot feel like a burden to them. Training and communication in best-use cases are important in presenting these tools as benefits. This can be streamlined through rollout plans that align with day-to-day workflows, prioritize flexibility, and implement continuing training opportunities.

Finally, expecting AI tools to drive perfection is a key challenge. This technology won't deliver perfect outcomes from day one, but through gradual improvements can drive real change in processes. If too much focus is placed on perfection, then widespread implementation can be delayed. Instead, carriers should prioritize progress first and perfection second when measuring AI against real-world baselines, with the goal of refining capabilities over time.

What Real Impact Can Look Like

When challenges are addressed and overcome, and carriers understand how to progress from point A to point B in their AI journey, real impact can be achieved. This will be seen in a number of ways across operations.

Main points of impact will be evident in faster claims reviews in which AI is helping adjusters summarize claims, extract data, and capture notes more efficiently, as well as in improved program outcomes, smoother workflows across internal and external systems, and smarter claim routing. AI tools can evaluate loss severity, complexity, and fraud risk at intake, assisting in routing claims to the right recovery resource sooner.

In addition, adjusters will see stronger field operations through enhanced drones, sensors, and tablets, which enable faster mitigation, better assessments, and quicker resource mobilization.

The data backs up this impact. Intake automation has reduced average claim processing from 10 days to 36 hours, AI photo analysis boosts claim handling efficiency by 54%, and much more.

Prioritizing Human Expertise

A key consideration in the integration of AI is the increasingly important role that humans play, and will continue to play, in this process. AI should be seen as a tool to augment and support workforce expertise, rather than replace it.

This technology is powerful, but cannot be used to replace human judgment, empathy, or real-world experience in the claims process. Losses can be ambiguous, emotionally sensitive, or require nuanced, complex coverage decisions that AI cannot handle, and require human professionals to consider context, communicate clearly, and advocate for policyholders throughout each stage.

In essence, AI is a catalyst, not a cure-all, and carriers must aim to apply AI selectively while keeping people at the center of their claims decisions. Striking this balance will be the difference in staying ahead and remaining competitive in a rapidly changing technological and regulatory landscape.

For Sedgwick's full report on "Future-Ready Property Claims," click here.

The catastrophe insurance crisis facing American municipalities isn't a crisis of risk — it's a crisis of imagination.

When the insurance industry tells municipal leaders to "invest in resilience" without developing insurance products that reward those investments with affordable coverage, they're asking communities to subsidize the industry's own innovation deficit. When carriers collect premiums from communities while simultaneously withdrawing coverage and providing no recourse to close protection gaps, they leave municipalities to bear the burden of adaptation alone. And when carriers pull away from vulnerable communities while posting record profits, their risk decisions become a way of abdicating their core function of pooling and transferring risk.

This mismatch is unsustainable and dangerous, and the consequences cascade far beyond equity concerns. Low-income residents can't rebuild after disaster. Without business interruption coverage, small businesses close, devastating local employment and sales tax revenue. Affordable housing providers, facing uninsurable properties, stop developing in climate-vulnerable areas, exacerbating housing shortages and displacement. These "equity issues" then become systemic economic failures that threaten the tax base carriers rely upon for commercial lines business.

If carriers don't evolve, they won't just be disrupted by climate change; they'll be made irrelevant by it. To meet the needs of climate-challenged jurisdictions and address these cascading failures, carriers must dramatically increase their R&D investment across product, distribution, and capital structure.

Product Innovation

The catastrophe insurance products available to municipalities today are fundamentally inadequate for current climate realities, let alone future projections. Carriers must invest in genuine product R&D across several critical areas.

Microinsurance for Vulnerable Populations: Carriers should develop lower-limit, lower-premium products specifically designed for low-income households and small businesses. Communities with functioning insurance markets maintain economic activity after disasters while communities without insurance face cascading failures that destroy the commercial premium base.

Advanced Modeling That Provides Incentives for Novel Adaptation: Current catastrophe models treat risk as static or worsening, with no meaningful premium reduction for innovative resilience projects. Carriers must bolster their modeling capabilities, so they can actually quantify the risk reduction from novel adaptation investments. This requires fundamental R&D investment in coupled physical-financial modeling, not incremental improvements to existing cat models.

Parametric Insurance for Rapid Response: Traditional indemnity insurance is ill-suited for disaster response, but parametric products that trigger immediate payouts based on objective measurements (flood depth, wind speed, earthquake magnitude) enable rapid recovery while reducing time and costs. The question is whether carriers will invest in developing parametric products tailored to municipal needs or whether municipalities will turn to specialized parametric providers who will.

Distribution Innovation

Even when adequate insurance products exist, traditional distribution channels systemically fail to reach those who need coverage most. Insurance carriers must invest in new distribution models, particularly embedded insurance mechanisms that integrate coverage directly into existing community touchpoints.

Affordable Housing: Catastrophe coverage can be embedded directly into affordable housing and stay with the property, not the individual tenant or owner. This closes protection gaps for vulnerable residents while creating stable, pooled risk for carriers.

Utility Bill: Municipalities can embed catastrophe coverage directly into customers' utility bills, creating universal protection while drastically reducing acquisition costs and adverse selection.

Employee Benefit: Municipalities and anchor institutions (hospitals, universities) employ thousands of workers, many of whom lack adequate catastrophe coverage. Embedding basic catastrophe protection in employee benefit packages closes protection gaps while creating group purchasing efficiency.

Small Business District: Local business districts and chambers of commerce can serve as aggregators for embedded small business catastrophe coverage. Small businesses often lack business interruption and property coverage. District-level embedded insurance solves the distribution problem while enabling risk pooling across merchants.

Capital Structure

Perhaps the most fundamental innovation required is at the capital structure level. Traditional reinsurance capital is designed to maximize returns for institutional investors and reduce exposure to complex, small-scale risks that don't fit standardized cat bond structures.

Community Development Reinsurance Institutions (CDRIs) — mission-driven nonprofit reinsurers structured similarly to community development banks — offer an alternative capital model specifically designed to support municipal resilience and insurance market function.

Traditional carriers should see CDRIs not as competitors but as catalysts for market development. By providing reinsurance for products serving underserved markets, CDRIs enable primary carriers to write business they otherwise couldn't while maintaining risk tolerance within board-approved limits. This expands the overall insurance market rather than displacing existing business.

Yet most carriers remain unaware of or unengaged with the emerging CDRI sector. If carriers don't invest in understanding, partnering with, and leveraging mission-driven reinsurance capacity, they'll soon discover that CDRIs have enabled an entirely new ecosystem of insurance providers serving municipalities—providers who didn't need traditional carrier participation to succeed.

The Choice Before Carriers: Lead, Follow, or Become Obsolete

The insurance industry faces a stark choice: invest in the R&D necessary to develop products, distribution channels, and capital structures adequate to municipal climate realities or continue business as usual and risk becoming obsolete.

The communities that carriers are abandoning won't simply accept uninsurability. They'll build alternatives — self-insurance pools, parametric coverage through specialized providers, embedded insurance through MGAs, risk financing through CDRIs and innovative bonds that will chip away at carriers' market relevance. Eventually, the "alternative" insurance ecosystem serving municipalities will become the mainstream, and traditional carriers will lose out on large parts of the market.

This isn't speculation; it's already happening. The only question is whether the insurance industry will rise to meet the demands and challenges in the municipal markets.

The insurance industry in 2026 is no longer just policy-driven — it is data-driven. From underwriting and claims processing to fraud detection and customer personalization, data services in the insurance industry are redefining how insurers operate, compete, and innovate.

As insurers face rising customer expectations, regulatory complexity, climate-related risks, and digital disruption, robust insurance data services have become the backbone of sustainable growth and operational excellence.

The Evolution of Data Services in Insurance

Historically, insurers relied on legacy systems and siloed databases. Data was fragmented across underwriting, billing, claims, and customer service departments. Decision-making was often reactive rather than predictive.

In 2026, modern insurers can leverage:

• Cloud-native data platforms
• Real-time data processing
• Advanced insurance data analytics
• AI and machine learning models
• Integrated enterprise data ecosystems

Today's data services in insurance focus not only on storing information but also on transforming raw data into actionable intelligence.

Key Components of Insurance Data Services in 2026

1. Data Management and Governance

Strong data governance in insurance ensures accuracy, compliance, and security. With increasing global regulations and privacy standards, insurers must:

• Maintain clean, validated datasets
• Implement structured data governance frameworks
• Ensure secure storage and access control
• Meet regulatory compliance requirements

Effective data management reduces risk exposure and strengthens reporting capabilities.

2. Insurance Data Analytics and Predictive Modeling

Predictive analytics has become central to underwriting and risk assessment. Using historical and behavioral data, insurers can:

• Assess risk with greater precision
• Improve pricing accuracy
• Identify high-risk policies earlier
• Forecast claim likelihood

Predictive analytics in insurance enables proactive risk management rather than reactive claim handling.

In 2026, AI-powered models also enhance fraud detection by identifying anomalies in real time — reducing losses and improving profitability.

3. AI and Machine Learning in Insurance Data Services

Artificial intelligence (AI) is deeply embedded in modern insurance data services. Applications include:

• Automated underwriting decisions
• Claims triage and prioritization
• Customer sentiment analysis
• Intelligent chatbots powered by real-time data
• Personalized product recommendations

By leveraging AI in insurance, carriers reduce operational costs while improving accuracy and customer satisfaction.

Machine learning models continuously learn from new datasets, making systems smarter and more efficient over time.

4. Cloud Data Platforms and Scalable Infrastructure

The migration to cloud-based ecosystems has transformed data management in insurance. Cloud platforms offer:

• Scalable data storage
• Real-time analytics capabilities
• Enhanced disaster recovery
• Faster deployment of new tools
• Improved integration across systems

Cloud-enabled insurtech data solutions empower insurers to launch products faster and respond to market shifts dynamically.

In 2026, hybrid and multi-cloud strategies are common, ensuring resilience and flexibility across global operations.

How Data Services Improve the Insurance Value Chain

Underwriting Excellence

Advanced data analytics improves risk segmentation and pricing models. Insurers can incorporate alternative data sources such as IoT devices, telematics, and behavioral insights to refine underwriting accuracy.

Faster Claims Processing

Data automation reduces manual intervention, shortens claim cycle times, and enhances transparency for policyholders.

Fraud Prevention

AI-powered fraud detection systems analyze patterns across millions of claims, flagging suspicious activities before payouts occur.

Customer Experience Personalization

Using customer data platforms, insurers can deliver tailored communication, policy recommendations, and proactive risk alerts — increasing retention and loyalty.

Challenges in Insurance Data Services

Despite its advantages, implementing modern data services in the insurance industry comes with challenges:

• Data silos across legacy systems
• Inconsistent data quality
• Cybersecurity risks
• Compliance complexities
• Skill shortages in data science and AI

To overcome these obstacles, insurers must invest in strong data architecture, governance policies, and skilled analytics teams.

The Strategic Importance of Data Services in 2026

By 2026, competitive advantage in insurance will depend heavily on data maturity. Insurers that successfully implement comprehensive insurance data analytics solutions will benefit from:

• Reduced loss ratios
• Improved underwriting profitability
• Higher customer satisfaction
• Faster innovation cycles
• Stronger regulatory compliance

Data is no longer a support function — it is a strategic growth driver.

Forward-looking insurers are building centralized data hubs, leveraging AI-driven insights, and integrating real-time analytics into every operational layer.

Future Trends in Insurance Data Services

Looking ahead, several trends will shape data services in the insurance industry:

• Embedded insurance powered by real-time APIs
• Increased use of IoT and telematics data
• Climate risk modeling using advanced analytics
• Blockchain integration for transparent claims processing
• Responsible AI frameworks for ethical data usage

Insurers that prioritize innovation while maintaining data security and compliance will lead the market.

Conclusion

In 2026, data services in the insurance industry are not just about managing information — they are about unlocking intelligence. From predictive analytics and AI automation to cloud-enabled scalability, data-driven strategies are redefining underwriting, claims management, fraud detection, and customer engagement.

Insurance organizations that invest in modern data infrastructure, governance frameworks, and advanced analytics capabilities will gain a decisive edge in an increasingly competitive landscape.

The future of insurance belongs to insurers who turn data into insight — and insight into action.

While the excess and surplus lines market was once an option of last resort, today it is all too frequently a first step in the process of insuring risk.

A coastal property facing new catastrophe models. A business navigating cyber exposure. A specialized liability account that has outgrown admitted guidelines. For agents and brokers, E&S is now part of everyday operations.

This shift has forced the distribution side of the industry to move faster, communicate more clearly and operate with greater precision. Technology is becoming a bridge to help insurance agencies keep pace while also strengthening relationships with their carrier partners.

Speed matters, but clarity matters more

Unlike admitted markets, where rates and underwriting changes can take time to filter through regulatory processes, non-admitted appetites can shift quickly based on loss trends, capacity and real-time market conditions. A carrier writing a class of business today may pull back tomorrow or adjust pricing as results demand it.

For the retail agent, this reality creates a constant challenge: Where does this risk belong currently? In the past, the answer has required multiple submissions, follow-up emails and trial-and-error market shopping. That cycle slows service, strains staff resources and frustrates underwriters who receive incomplete or misrouted submissions.

Technology can help avoid this cycle of frustration and wasted time, not by replacing relationships but by reducing the friction that can damage them.

Streamlined placements support better partnerships

Modern E&S placement platforms are designed to make submissions cleaner, faster and more consistent. The best tools help agents submit once, validate completeness and route risks to the right markets based on current appetite.

This kind of upfront triage benefits all involved. Agents spend less time chasing dead ends. Underwriters spend less time sorting through half-built submissions. Carriers receive applications closer to their appetites, with clearer exposure data and fewer missing pieces.

The result is a more efficient exchange that respects the time and expertise on both sides of the relationship. We’re seeing that with the deployment of Xchange - Powered by SIAA, which provides our members a faster, cleaner and easier way to access and place E&S business. 

Reducing errors and improving underwriting confidence

One of the most persistent challenges in E&S is submission accuracy. When clients want fast answers, agency teams sometimes make assumptions to move the process along. These seemingly educated guesses can create big delays later when an underwriter must circle back for corrections.

Technology that enriches submissions with third-party data sources can reduce the burden. Property records, hazard data and other verification tools can help confirm details before the submission ever reaches the carrier.

Doing this leads to fewer surprises, fewer resubmissions and a smoother path to a quote. More importantly, it helps carriers trust what they are seeing, which ultimately contributes to stronger carrier-agent relationships.

AI should be an optimizer, not a replacer

Artificial intelligence is playing a growing role in the E&S workflow, but the industry must be clear-eyed about its realities.

AI can help organize information, identify inconsistencies and accelerate routing. It can reduce manual data entry and make it easier for agents to package risks in an underwriter-ready format.

What it cannot do is replace underwriting judgment.

Complex accounts still require human experience, context and expertise. Technology works best when it clears away administrative clutter so underwriters and agents can focus on conversations that matter: coverage structure, risk controls, exclusions and long-term strategy. When positioned correctly, AI supports relationships rather than threatening them.

Strengthening carrier relationships through better submissions

Carrier relationships are built on trust, consistency and professionalism. In the E&S space, where underwriters face heavy submission volume, standout agencies are those that deliver clear narratives and decision-ready accounts. Technology helps agencies meet that standard at scale.

By standardizing intake, improving exposure clarity and managing workflow discipline, agents become better partners to their markets. Carriers benefit from lower acquisition expense per policy, improved risk selection and fewer wasted cycles.

Over time, these operational advantages translate into stronger long-term collaboration.

Carriers tend to prefer distribution partners who can deliver reliable data quality and efficient servicing without requiring carriers to expand headcount at the same rate as submissions.

Agencies that adapt will protect their growth

For agents and brokers, the risk of ignoring technology is not about missing a trend. It is about falling behind both the market and the competition.

As risks become more complex, turnaround time is becoming a competitive differentiator. Agencies relying solely on inbox-driven workflows will find it harder to shift books of business, maintain service levels and compete for talent.

The goal is not to adopt technology for the sake of shiny tech solutions. Rather, the goal is to protect the value of the agency by making the placement process faster, cleaner and easier to hand off to the next generation.

Relationships remain at the center

E&S will always involve more complexity than standard business. But complexity does not have to mean inefficiency. With the right technology, agents and brokers can keep pace with a rapidly evolving market while building better carrier relationships through stronger submissions, smarter routing and clearer communication.

The future of E&S distribution will not be defined by replacing people. It will be defined by empowering them. When technology reduces friction, relationships have room to grow.

We've all had a chuckle about the occasional hallucination by generative AI: the time when it recommended using glue to keep cheese from sliding off a piece of pizza; when an Air Canada chatbot promised a passenger a bereavement fare despite a policy to the contrary, and the airline had to live up to that promise; when a lawyer unknowingly submitted a brief to a judge that was based on citations from court cases that never happened; and so on. 

But a couple of recent stories go well beyond the chuckle level. While generative AI continues to show all the promise in the world, these stories demonstrate consistent problems that, unchecked, would lead to severe consequences. 

Let's start with the one about war games in which large language models almost always recommended escalating to nuclear weapons.

As Axios reports, a researcher at Kings College London pitted three popular LLMs — GPT-5.2, Claude Sonnet 4 and Gemini 3 Flash — against each other in 21 war games in which the AIs acted as the leaders of major nations. The scenarios included threats to survival, but also included lower-stakes conflicts, such as border skirmishes and resource competition. Yet 95% of the time, at least one of the LLMs "used" nuclear weapons, and escalation typically ensued. 

For anyone without a strong Dr. Strangelove streak, those results reflect a scary misjudgment. While the U.S. and the Soviet Union considered tactical nuclear weapons to be legitimate parts of their arsenals in the early years of the nuclear age, those were also the times when the countries casually considered using nuclear weapons for industrial uses such as mining and natural gas extraction. It's been clear for decades that nuclear weapons are simply too powerful for their effects to be limited to legitimate military or industrial targets. 

Even at one kiloton, the smallest payload for what's considered a tactical nuclear weapon, the explosion would be 100 times as powerful as the biggest conventional bomb in the U.S. arsenal. At the top end of the range for a tactical nuclear weapon (generally considered to be 100 kilotons), the explosion would be some seven times as powerful as the bomb dropped on Hiroshima, which destroyed a military target but also killed an estimated 140,000 people, the vast majority of them civilians. The radiation released can also reach far beyond the targeted area. 

While the Kings College researcher noted that no one is handing AIs the keys to nuclear weapons systems, he said, "Militaries are already using AI for decision support — and research suggests those systems may lean into rapid escalation under pressure."

The other article that caught my eye relates to ChatGPT Health. The app, launched in January, is consulted by some 40 million people every day — and a study found the potential for major problems with the app's diagnoses. For more than half of the study's hypothetical patients who should have sought immediate medical care, ChatGPT Health told them they should stay home or wait to schedule a regular appointment with a doctor. 

The article, in the Guardian, said: "In one of the simulations, eight times out of 10 (84%), the platform sent a suffocating woman to a future appointment she would not live to see.... Meanwhile, 64.8% of completely safe individuals were told to seek immediate medical care."

For the study, published in the journal Nature Medicine, researchers created 60 realistic patient scenarios covering health conditions from mild illnesses to emergencies, then presented those scenarios to ChatGPT Health in various ways: changing the gender of the patient, sometimes providing test results, sometimes adding comments about what "friends" advised, etc. Three independent doctors reviewed each scenario and agreed on the level of care needed, based on clinical guidelines.

The study found that ChatGPT Health did well on textbook emergencies such as stroke and severe allergic reactions. But "'what worries me most,'" a doctor is quoted as saying in the article, "'is the false sense of security these systems create. If someone is told to wait 48 hours during an asthma attack or diabetic crisis, that reassurance could cost them their life.'”

Any number of health experts have extolled the potential for AI-based health advice, coupled with wearables and telemedicine, to revolutionize healthcare — providing care to the elderly and to people in rural areas, who would otherwise have difficulty getting access, while slowing the inexorable rise in healthcare costs. And I've bought in: Chunka Mui, Tim Andrews and I included a lengthy scenario about the potential for AI-based healthcare in our 2021 book, "A Brief History of a Perfect Future."

I still think the potential is there, too. As OpenAI, the developer of ChatGPT, told the Guardian, the app is updated and improved all the time, and I hope they keep charging ahead. (OpenAI also said it doesn't believe the study reflects how people actually use ChatGPT Health.)

But I also hope they are constantly checking for problems such as those identified in the study, and anyone else using AI in situations with major consequences should exercise similar care. That includes insurers, and not just in healthcare. As we feel our way toward using AI agents, we need to be very careful to not only vet them before putting them into production but to then supervise them — because they absolutely will make mistakes — and to keep improving them.

Cheers,

Paul