The MGA market is outpacing the broader P&C sector, fueled by specialized expertise, speed, and innovation that help close the protection gap. But as next-gen, AI-enabled MGAs raise the bar, many still rely on legacy systems that slow growth and limit agility. To stay competitive, MGAs need modern foundations that drive scalability, efficiency, and innovation.

Read Now

Sponsored by ITL Partner: Majesco

Paul Carroll

At ITL, we've been encouraging the insurance industry to move to a Predict & Prevent model and away from the traditional repair-and-replace approach. Workers' compensation has been a poster child as organizations make remarkable strides in reducing workplace injuries. But there's significant complexity below the surface. What are the key challenges around volumes, documentation, staff shortages, and legacy systems?

Connor Atchison

I think you summed it up right there. It's the culmination of all of these things over decades that are making things slower and more cumbersome. We have gaps in knowledge as we strive for better care outcomes—to get that worker back to work and make sure we're spending the right amount of money on the right treatment to make that happen.

There are definitely issues around legacy systems. Workers' comp, even more than other insurance lines, is still a little bit behind. But they're catching up and adapting, and they're seeing the need, which is great.

The volumes are certainly high. When I was doing adjustment work during my time in health administration serving in the Canadian Armed Forces, I'd be sitting there highlighting and tabbing documents. You can't scale that, and that's why we need more technical innovation.

Then there's the knowledge loss. How can we retain the knowledge from someone who's been in the industry for 30 years and is retiring? Younger generations aren’t filling the gap, so technology needs to help retain that knowledge.

We're also seeing a lot of changes at the state and federal legislation level. They're getting more stringent on costs and more stringent on audits and understanding where the money's going.

You put all those together, and it's almost like a perfect storm. Technology is definitely needed right now, more than ever.

Paul Carroll

You recently commissioned a survey of claims professionals with PropertyCasualty360 “AI in claims and the 4x trust effect of human oversight” focusing on AI’s rising role in claims and wrote an article for us about how important human oversight is for generating trust. While I’ve certainly heard lots about the importance of a human-in-the-loop, your survey still surprised me.

Connor Atchison

Yes, we found that human oversight increased trust in AI by up to 4X. People are concerned about quality, accuracy, outcomes, and liability if human oversight isn’t there. Once you have an expert-in-the-loop, you build trust for the end user. They can see why the machine learning is processing the document the way it is, while being trained on industry domain knowledge and over 100M+ claims documents. That allows them to say, "This makes sense. I can understand it, and I can make my own inferences."

The other thing that really stood out to us was that 75% of the claims professionals we surveyed said they believe AI can improve efficiency through better speed and resource optimization. So on one hand, they’re clearly seeing the upside. But at the same time, there’s this capability–trust gap: they know AI can help them, they’re just not fully comfortable trusting it on its own yet.

What they do trust is themselves — and human oversight. When an expert is in the loop, they feel confident that the AI is being guided, validated, and grounded in real industry knowledge. That’s why the combination matters so much. As our Head of Machine Learning always says, we use AI for scale and humans for accuracy. That pairing is what closes the gap and ultimately builds trust.

Paul Carroll

In insurance, in general, and workers' comp, in particular, there are numerous silos—employers, bench adjusters, nurse case managers, medical examiners, treating physicians, vocational rehab specialists, and legal counsel. How does AI help resolve the silo problem in workers' comp?

Connor Atchison

I don't believe it's a silver bullet. There’s no point solution that can do it all. But AI can plug into your day-to-day workflows and take 15, 16, 20 steps and shrink that process to half or maybe a third. 

No one wants to sit there for eight hours going through a 2,000-page document. It just doesn't make sense to do this clerical work any more. It makes sense to elevate the human to make better use of their time and spend more time analyzing and making expert decisions. 

Paul Carroll

You said at a recent conference with the Division of Workers’ Compensation in California that AI can cut claims processing time in half, making claims documentation platforms essential. But speed raises the stakes—how do you ensure compliance and trust keep pace with automation?

Connor Atchison

I think there are two answers to that question. First is the human in the loop, which we’ve already talked about. Beyond that, it's about building really good technology.

Going back to what I said about point solutions: Just putting data into an LLM [large language model] or SLM [small language model] or a foundational model isn't going to give you a result that helps the injured worker or the adjuster. It's going to give you something very high-level. When you go deeper and build all the different configurations around that data and the workflows, that's where you actually start getting leverage.

When you go to the next step, you have to look at the compliance standpoint. You have to know, and be able to demonstrate: Why is the model providing that information the way it has, based on the data it's been trained on?

I remember talking with the Honorable Judge Rassp from California about this. A judge doesn’t want a black box. They need to know objectively why something happened. If you can't objectively define where you've gotten the information, you run into problems. 

That's why point solutions are not the definitive answer. It's about building the entire workflow—creating transparency, understanding what legislation means, and why you have to follow different audit guidelines, time periods, or reviews.

Paul Carroll

Large language models started as generalists, ingesting all available information. Now it's possible to train AI on specialized information to produce vertical AI, providing more precise results in complex fields like workers' comp. What does this verticalization involve?

Connor Atchison

We don't have to use an LLM to do everything. We can find models that train on extractive data and give a higher confidence score and better outcomes. If all you have is a hammer, everything looks like a nail. But sometimes there's an easier, more cost-efficient way.

When we train in a very vertical way, you have to start looking at what the governments and states are doing. With on-prem deployments, we're starting to see regulated industries needing more and more of their own models on their own data to understand outcomes more effectively. There are a lot of reasons for that—compliance, security, risk—but there's a huge opportunity here if you have the data and know how to orchestrate it the right way and build it with the right outcomes.

These organizations can actually accelerate, and I don't think we can use generalist models. If you don’t fine-tune your technology and don't understand what it's trying to do, you're just going to have garbage coming out. Garbage in, garbage out.

Paul Carroll 

What does the feedback loop look like for humans to refine AI over time when it doesn't get something quite right?

Connor Atchison

Without getting technical, you can think of the top, middle and bottom of a workflow. You ingest information at the top and spit out a result at the bottom. The question is: Where do you want the human to come in?

That really depends on what you're trying to build and on the complexity of that data. At the ingestion point, for example, is the data curated? Is it clean? If you have chicken scratch from a doctor and can’t read it, the machine can’t read it, either. 

Maybe it's in the middle where you want things fine-tuned. If your models are confident and you understand the scoring—the actual mathematics—then you can have a human review at the bottom.

I think you need a combination of all of them.

Paul Carroll

Given the unique dynamics of workers' compensation—from complex injury types to vocational factors and return-to-work timelines—how do you see AI reshaping the medical record review process? And what are the key operational or regulatory hurdles insurers and TPAs must overcome to realize those efficiency gains?

Connor Atchison

The way I see it, you need a platform, not a point solution. You can’t just focus on workflows and configurations. You have to understand your data and use those unique datasets to get cross-case analysis and different inferences on how current injuries are being treated. Are they being efficiently treated? Is there waste? Are there better treatment outcomes? When you can surface all of that—and there are so many states and organizations sitting on this information but not really getting it surfaced—that could help immensely.

To really stay in front, you need to always see where things are moving from a state-by-state jurisdiction and also from a federal level. Texas has new laws coming into place. Louisiana just passed a few bills. And so on. You don't want to build something and then realize, "Whoa, this doesn't work," and have to retool your solution stack. 

I think this is where users and buyers are getting fatigued. It's so noisy out there, and only a handful of companies really know what they’re doing. Everyone's got an AI solution with an LLM. 

Wisedocs put together a buyer's guide to evaluating AI-powered claims documentation platforms to help people ask the right questions. Some issues are really important to understand, such as security and compliance primers or whether you should build vs buy. You need to get this right the first time, not the second or third time.

Paul Carroll

Returning to the recent survey you commissioned of claims professionals with PropertyCasualty360, yit found that 75% of respondents believe AI can improve efficiency in claims, yet 58% aren't using it. As we wind up here, what's your vision for where the industry could be in two to three years in terms of both technology and adoption?

Connor Atchison

I've seen studies showing that anywhere from 60% to 90% of AI initiatives are still in pilots and proofs of concept. The adoption isn't there. AI is great, but it's not connecting with the actual business need.

If I buy a Bugatti, I don’t want it to drive like a golf cart, and I think many people have been disappointed because they need to better understand their needs and the parameters of how to use AI. There are different needs for every business and use case.

But this is a really exciting time. Success is all going to come down to data. Understanding your data and then building around that—that's what's going to win, and that's why I think there will be organizations that are the haves and others that are the have-nots. 

Paul Carroll

Thanks, Connor. 

About Connor Atchison

Connor Atchison is the Co-Founder and CEO of Wisedocs, the AI-powered claims documentation platform purpose-built for medical records and insurance files, trained on 100 million+ documents. Connor is an experienced founder with a demonstrated history of working in health services, information technology, and management consulting. He aims to digitize a formerly manual industry through the adoption of artificial intelligence with expert human oversight to support manual claims processes — turning complex unstructured records into defensible outputs. As a former veteran with 12 years of military service under the Department of National Defence, he strives to change the process for filing health insurance and disability claims for top-tier carriers, federal agencies, legal defense firms, healthcare providers, and their claimants.

Matteo Carbone's latest look at a high-profile, full-stack insurtech demonstrates a point that is too often missed amid the adulation of breakout start-ups: 

Incumbents should beat insurtechs in the innovation battles far more often than they do.

While Matteo actually congratulates the insurtech, Root, for profitably growing its number of auto policies 16% over the past six quarters, he notes that Progressive grew nearly twice as fast, largely based on the same telematics innovations that are Root's calling card. 

Why should other incumbents win more often?

As Chunka Mui and I wrote in our 2013 book, "The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups":

"Yes, small and agile beats big and slow, but big and agile beats anyone — and that combination is now possible.... Big companies have everything they need to continue to dominate: unmatched people, resources, supply and distribution capabilities, brand power and customer relationships.... Incumbents have growth platforms that would take start-ups years to build."

Although the insurtech glow has faded some over the past decade, as we've all realized that insurance won't be turned upside-down in the way that, say, retail has been, a sort of "arms dealer" model has developed. Insurtechs don't overwhelm incumbents but develop novel weapons that incumbents buy, often by acquiring the whole company. The model is akin to what happens in pharmaceuticals, where little guys develop new drugs and Big Pharma then jumps in, buys them and runs them through the incumbents' massive marketing and distribution networks. 

But Progressive shows how incumbents can even out-innovate start-ups in-house. It followed the model that Chunka and I have long referred to as "Think Big, Start Small, Learn Fast." (He writes about our mantra in some detail here.) 

Progressive had huge plans when it introduced its Snapshot telematics offering in 2008 but didn't try to do everything at once. It experimented at low cost, letting it learn fast and adapt as technological capabilities developed — for instance, when it became possible in the mid-2010s to use smartphones as data sources and dispense with the dongles that previously had to be plugged in underneath cars' dashboards. 

In his LinkedIn post, "When the 'Dinosaur' Laps the 'Disruptor,'" Matteo estimates that a third of Progressive's auto business now comes through its telematics offering and says it grew its base of policyholders by 28% over the past year and a half (to Root's 16%). Progressive greatly outpaced Root even though its base of policyholders is about 80 times the size of Root's.

I realize that Progressive has long been a shining example of innovation in the insurance industry, but you can also look to Geico, which has mostly caught up on telematics, far faster than most competitors, including start-ups. Or look at the failures of Amazon and Google in insurance, which Matteo dissects here and ascribes to the power of incumbents. 

I'd also suggest investigating Walmart, whose CEO recently announced retirement plans following a career that will be taught in business schools for years to come. Chunka and I actually singled out Walmart a dozen years ago as a behemoth that would have to work hard to defend its market in the face of technological change. Our book came out just as Doug McMillon was becoming CEO and blew away all expectations through super-smart use of technology.

He continued to lean into the sorts of supply chain technology advancements that Walmart has long been known for, while also experimenting with technologies that let Walmart keep pace with the ease-of-use, product selection and home delivery that gave Amazon such an edge and that it has continued to drive as hard as it can. I don't know about you, but I get a lot delivered from Walmart these days — not as much as I get through Amazon, given its hold on me through Prime's free shipping, but still plenty.

Yes, an awful lot of work has to be done to overcome the internal antibodies that can kill innovation and that become more potent the bigger and more successful a business becomes. But by now have a lot of examples of companies that have fallen by the wayside because of corporate atherosclerosis. We also have some examples of companies that, like Progressive and Walmart, have put themselves at the cutting edge of innovation despite their heft. 

Many insurance companies will do just fine by relying on insurtechs to innovate and then absorbing those new capabilities into their products, services and operations, but there is also bigger game afoot, as Matteo shows. It's possible to produce breakthrough innovation in-house if you think big, start small and learn really fast.

Cheers,

Paul

P.S. Here is a piece on the methodology that Chunka and I have developed over the decades about how to identify and handle the strategic options that can turn into breakthroughs. 

Auto insurance in the United States is under immense pressure. As 2026 approaches, auto insurers face a perfect storm of rising repair costs, increased claims severity, inflationary pressures, and shifting consumer expectations. Many consumers are questioning the value of their policies, while insurers are struggling to maintain profitability.

This evolving crisis presents both a challenge and an opportunity—one that requires bold innovation, operational agility, and a renewed focus on customer-centric strategies.

The Cost of Claims is Surging

One of the most pressing issues affecting auto insurance in 2026 is the rising severity of claims. This is not just a short-term spike—it's a structural shift.

Key contributors include:

• Advanced vehicle technology: Modern cars now feature complex sensors, cameras, and electric systems that are expensive to repair or replace.
• Labor and parts inflation: Supply chain disruptions and skilled labor shortages have driven up repair costs dramatically.
• Medical inflation: Healthcare costs tied to bodily injury claims continue to outpace general inflation.
• Litigation and legal trends: Increased legal involvement in personal injury claims has led to higher settlements and longer claim durations.

As a result, insurers are paying significantly more per claim—even as the frequency of claims remains stable or slightly declines.

Affordability Is Reaching a Breaking Point

As insurers attempt to recoup losses, premium hikes have become unavoidable. For many American drivers, especially those in urban areas or lower-income brackets, auto insurance is becoming unaffordable.

By 2026:

• The average auto premium has risen by over 20% since 2023.
• States like California, Florida, and Michigan report even steeper increases due to regulatory restrictions or localized risk.
• Many consumers are shopping around more frequently, increasing policy churn and straining insurer retention efforts.

The core problem: The gap between cost and perceived value is widening. Consumers are paying more, but don't feel better protected or supported.

Technology-Driven Solutions

To address affordability and fairness, insurers are turning to usage-based insurance (UBI) powered by telematics. These programs base premiums on driving behavior—such as speed, braking, and mileage—rather than traditional demographic factors alone.

Benefits of UBI include:

• Lower premiums for safe or low-mileage drivers
• Enhanced pricing accuracy and risk segmentation
• Greater transparency and engagement for customers

However, adoption has been uneven. Privacy concerns, lack of customer awareness, and inconsistent user experiences have slowed broader acceptance.

In 2026, the opportunity lies in making UBI the default for personal auto policies—combined with stronger education, clearer benefits, and seamless onboarding.

Another key solution lies in AI-driven claims automation, which improves efficiency and customer satisfaction while lowering costs.

By 2026, leading insurers are:

• Using computer vision to assess vehicle damage from photos in minutes
• Automating first notice of loss (FNOL) through mobile apps and virtual assistants
• Implementing fraud detection algorithms to flag suspicious claims
• Streamlining repair approvals and payments through connected platforms

The result is faster resolutions, lower operational costs, and better experiences. However, the human touch remains vital in complex or emotionally charged situations—highlighting the need for a balanced, hybrid model.

Regulatory Pressures and Market Disparities

Auto insurance affordability is also a regulatory issue. Several state governments are imposing tighter oversight on rate filings and premium increases, attempting to protect consumers from excessive pricing.

Challenges include:

• Balancing insurer solvency with consumer protection
• Inconsistent regulation across states, creating fragmented market dynamics
• Political pressure to curb rising premiums during economic downturns

Regulators and insurers must work together to create sustainable pricing models, promote innovation, and ensure equitable access—especially for high-risk or underserved drivers.

Climate Change and the Future of Mobility

Climate change is now a factor in auto insurance. In 2026, the rise in extreme weather events—from floods to wildfires—has increased vehicle damage claims.

Insurers are responding by:

• Adjusting risk models to include geographic climate data
• Offering weather-linked alerts and early warnings to policyholders
• Revising underwriting criteria in high-risk areas

While the focus has traditionally been on property and catastrophe insurance, auto insurers must now account for environmental volatility as a growing risk driver.

Emerging trends in mobility are also reshaping the risk landscape:

• Electric vehicles (EVs), while environmentally friendly, are costlier to insure due to expensive battery systems and limited repair networks.
• Autonomous driving technologies have not yet delivered the expected reduction in accidents, and liability questions remain unresolved.
• Car-sharing and subscription models are complicating ownership-based insurance frameworks.

Insurers in 2026 must adapt their products to match new patterns of vehicle use—offering flexible, modular, and pay-as-you-go options that align with the future of mobility.

Restoring Trust and Rebuilding Value

At its core, the crisis in auto insurance is about trust. Consumers feel they're paying more for less. Insurers, meanwhile, are battling rising costs, regulatory scrutiny, and customer churn.

To succeed in 2026, insurers must:

• Invest in transparency: Clear communication about pricing, claims, and policy changes
• Improve digital experiences: Easy-to-use apps, quick claims processes, and responsive service
• Embrace innovation responsibly: Use technology to enhance—not replace—human-centered care
• Prioritize fairness: Personalize pricing while protecting vulnerable customer groups

By redefining their value proposition, insurers can move from being seen as a financial burden to becoming trusted partners in mobility safety and risk management.

Conclusion

In 2026, the rising cost of claims, growing affordability concerns, and changing mobility trends will present serious challenges—but also a chance to rethink the system from the ground up.

The insurers who emerge stronger will be those who embrace digital transformation, personalize their offerings, improve transparency, and work collaboratively with regulators and consumers alike.

In this time of disruption, innovation is not a choice—it is a necessity. 

Insurers are racing to modernize their operations, yet the greatest constraint isn't the technology itself. It's whether teams are prepared to use it. According to KPMG, 62% of insurance CEOs believe talent gaps could hinder growth over the next three years. Automation, data tools, and AI only move the needle when employees understand how to work with them, make informed decisions, and adapt to new ways of delivering value.

That's why workforce development has become central to every corporate transformation effort. Carriers can't rely on hiring alone. To grow sustainably, insurers must inspire and engage existing employees to grow into the roles today's advanced technologies require. How to do this? Focus on strengthening digital confidence, designing clearer pathways for career advancement, and creating a culture of learning.

Building digital confidence

Modernizing the underwriting, claims, and operations functions begins with preparing employees for the new responsibilities and capabilities that come with AI, analytics, and automation. These technologies are reshaping how insurers operate, but they only deliver value when employees feel equipped to use them effectively.

In underwriting, for example, AI can complete a first pass on risk assessments, while analytics highlight patterns across portfolios, but underwriters still need the knowledge to interpret those insights and determine how they influence pricing and policy structure. Claims teams face similar changes. Image-recognition tools, natural-language systems, and fraud-detection models can streamline intake and flag anomalies, yet adjusters must develop the ability to read alerts, investigate exceptions, and focus on cases that call for negotiation or empathy.

Operations teams are moving from executing individual tasks to overseeing automated pipelines. Renewals, notifications, compliance checks, and payment routing now run in the background, which means employees need both the skills and assurance to monitor workflows, troubleshoot issues, and work with data or IT teams to refine rules. This shift from doing to overseeing requires a new kind of capability. One built on understanding systems, not just completing tasks.

All of this makes workforce development essential. Employees need learning opportunities that include guided practice, coaching, and real-world examples to boost their confidence and ability to use these tools to best help the business overall.

Designing clearer paths for career advancement

The confidence employees develop with AI and digital skill-building only translates into business value when it connects to career growth. Insurers need structured career and education pathways that prepare their talent for critical roles.

A strong approach is to create role-specific learning tracks to make it clear to employees what they need to know. Programs that focus on AI, business acumen, and strategic thinking help employees build the exact competencies needed for senior underwriting roles, claims management positions, or operations leadership. For example: an underwriting analyst who completes a structured program in AI-driven risk assessment and predictive modeling positions themselves for advancement into roles that shape pricing strategy and portfolio management.

These programs work best when they combine university rigor with practical application. Employees learn foundational concepts, then immediately apply them to real business challenges in their departments. Cohort-based learning supports better learning outcomes too, creating room for peer collaboration and support. More employees will enroll in the development, and more will finish, when they are part of a group doing it together. When designed well, these learning experiences can deliver capability building at a pace fast enough to address urgent needs, while thorough enough to prepare people for genuine responsibility.

Creating a learning ecosystem

Structured education programs can be important for addressing AI-related talent gaps, but they work best when supported by a broader learning ecosystem. Hiring employees is expensive, and insurers often get more value from developing the people they already have across all levels of the organization. The first step is acknowledging that learning isn't a perk. It's a foundational business strategy that helps teams adapt to modern tools and workflows.

Position learning as part of a larger culture of growth. Mentorship programs, internal mobility pathways, and clear recognition for the development of new skills or earned credentials show employees that the organization values them and their long-term development. When people see that their effort leads to new opportunities, learning becomes something they pursue willingly rather than something assigned to them.

Making modernization work

The pace of digital adoption inside many carriers today reminds us that insurance isn't as slow to evolve as some might think. As companies integrate AI, automation, and new data tools, success depends on whether employees feel prepared to use those tools in meaningful ways. Technology can accelerate decisions and streamline workflows, but people translate those advances into better service, stronger risk assessment, and more efficient operations.

Investing in workforce development is the key to these modernization efforts. Existing teams already understand the business, the market pressures, and the needs of policyholders. When they have access to learning programs that help them grow into new responsibilities, insurers strengthen talent retention and avoid costly rehiring. A workforce equipped to adapt moves ahead with the industry, while building a stronger foundation for the future.

As the insurance industry enters 2026, AI is no longer a futuristic concept; it's embedded in underwriting, claims, fraud detection, and customer engagement. Yet, as technology accelerates, the real differentiator for insurers will not be how advanced their algorithms are, but how effectively they keep people at the center of innovation.

The Core Insight: AI Isn't the Strategy—Human Experience Is

For years, insurers have focused on digitization and automation to reduce costs and improve efficiency. Those gains are now table stakes. The next frontier is strategic integration of AI that enhances—not replaces—the human experience. Policyholders expect empathy, transparency, and tailored solutions. AI can deliver these outcomes only if deployed thoughtfully and with ethical rigor.

Why People-First Matters in an AI-Driven World

Insurance is fundamentally about trust. Customers rely on insurers during moments of vulnerability—after an accident, a health crisis, or a natural disaster. If AI-driven decisions feel opaque or impersonal, trust erodes. Conversely, when technology empowers human judgment and improves responsiveness, it strengthens relationships and loyalty.

Consider claim processing: AI can triage and flag anomalies in seconds, but the final conversation with a policyholder should reflect empathy and clarity. Similarly, predictive analytics can identify coverage gaps, but agents must translate those insights into meaningful advice.

Three imperatives for insurers in 2026

1. Reframe AI as an enabler, not a replacement

AI should augment human expertise, not eliminate it. Automation can handle repetitive tasks—document verification, fraud scoring, risk modeling—but complex decisions require human oversight. This hybrid approach ensures accountability and preserves the human touch that customers value.

AI-driven underwriting systems for small commercial policies now routinely process the majority of submissions autonomously, with underwriters stepping in to review edge cases and maintain direct communication with brokers. This approach has led to faster turnaround times while still preserving essential human judgment.

2. Invest in ethical and explainable AI

Regulatory scrutiny is intensifying, and consumers demand fairness. Black-box algorithms won't cut it. Insurers must prioritize models that are transparent, auditable, and bias-tested. Explainable AI isn't just a compliance requirement, it's a trust-building tool.

Action Steps:

• Establish governance frameworks for AI deployment.
• Conduct regular bias audits across demographic and geographic data.
• Provide clear explanations of automated decisions to customers and regulators.

3. Design for empathy at scale

Personalization is more than product recommendations—it's about anticipating needs and communicating with care. AI-driven insights should empower agents and brokers to deliver proactive, humanized interactions.

Example: Predictive analytics can flag life events—such as home purchases or family changes—that trigger coverage needs. Instead of sending generic emails, insurers can equip agents with scripts and resources for empathetic outreach.

Emerging opportunities

• Generative AI for customer engagement: Chatbots and virtual assistants can handle routine inquiries, freeing agents for complex conversations. But tone and transparency matter—customers should always know when they're interacting with AI.
• AI in risk prevention: Beyond claims, AI can help policyholders avoid losses altogether. Think IoT-enabled sensors for property monitoring or telematics for safer driving. These tools create value by reducing risk and enhancing customer experience.

The bottom line

The winners in 2026 won't be those with the most advanced tech stack, but those who marry innovation with empathy. AI can transform risk management and operational efficiency, but only if insurers remember that trust—not technology—is the ultimate differentiator.

As we look ahead, the mandate is clear: build systems that serve people first. In doing so, insurers will not only harness the power of AI but also reinforce the human values that define the industry.

Insurance has always been about adapting to uncertainty, but the pace of change sets today’s risk landscape apart. Economic volatility, behavioral shifts, geopolitical tensions and technological disruptions now evolve in real time. Insurers aren’t just assessing risk anymore — they’re trying to keep up with it.

That’s where artificial intelligence steps in. More than just a technology investment, AI is a response system; it can help carriers adapt at the speed of risk itself.

However, AI’s impact isn’t uniform across all lines. How AI adds value depends on whether an organization’s goal is efficiency or effectiveness.

Personal Lines and Small Commercial: Boosting speed and accuracy

In personal lines and small commercial volume is king. Profitability is highly dependent on a number of factors including speed, accuracy and consistency across thousands of transactions each day. For these carriers, AI delivers measurable results by streamlining operations. By automating repetitive tasks and enhancing decision consistency, AI enables personal lines insurers to boost throughput, cut costs and elevate service levels without compromising accuracy.

A U.S.-based digital insurer recently deployed AI across policy issuance, claims and customer service. The results were dramatic:

• Policy turnaround times dropped by more than half, allowing near-instant processing.
• AI-led fraud detection improved claims accuracy while accelerating payouts.
• Chatbots reduced call wait times by 70%, freeing up human agents for more complex needs.

These changes didn’t just boost efficiency — they reshaped the customer experience. AI helped the insurer achieve scale without sacrificing precision, turning standardization into a strategic advantage.

Large Commercial and Specialty Lines: Sharpening human expertise

Unlike low complexity, fast issue lines, large commercial and specialty insurance operate not on speed but rather on insight. Whether it’s large property, marine, energy or cyber risk, each policy is unique, high-stakes and data-intensive.

An Australian specialty insurer integrated AI into its workflow, reducing underwriting cycle times by 35%, improving pricing accuracy across multi-jurisdictional portfolios and accelerating regulatory reporting with automated compliance tracking.

Rather than replacing human expertise, AI sharpened it, aggregating disparate data, modeling complex scenarios and providing context-aware recommendations. The result was not simply faster decisions but smarter ones, making complex risk more manageable and measurable.

The new equation: Efficiency + effectiveness

The real transformation in insurance won’t come from choosing between efficiency and effectiveness. It will come from knowing when to lead with each and support with the other.

• Efficiency ensures scale, speed and consistency.
• Effectiveness ensures sound judgment in complex, high-stakes decisions.
• AI’s true potential lies in bridging the two, creating systems that adapt to both.

As governance and compliance frameworks evolve, insurers must ensure that AI-driven acceleration doesn’t outpace accountability. The leaders of tomorrow will be those that use AI to enhance human decision-making.

The future of insurance is adaptive

The next generation of insurance will be defined by who enables smarter decisions. Personal lines and small commercial will continue to harness AI for operational leverage. Specialty and large commercial insurers, including brokers and MGAs, will rely on it for analytical depth and precision. But the real winners will be those who blend both approaches, creating hybrid models that can flex between speed and sophistication as the situation demands.

The goal isn’t just faster insurance: It’s smarter insurance built on systems that think, learn and evolve alongside the risks they’re designed to protect.

Partnering for intelligent ops

For insurers seeking to harness AI without overhauling their internal infrastructure, Cogneesol offers a scalable bridge between innovation and implementation. Our insurance solutions support everything from underwriting and policy administration to claims and analytics. By combining data, automation and industry expertise, Cogneesol helps insurers reduce operational friction, enhance compliance and turn digital transformation into measurable performance gains.

About the author

Ilya Filipov is a strategy-driven insurance and technology executive specializing in growth, partnerships, and operational transformation across the P&C and legal ecosystems. As Head of North America at Cogneesol, he leads go-to-market, alliance development, and client success for brokers, MGAs, carriers, TPAs, and law firms — helping them modernize operations through AI-enabled intake, back-office automation, system integration, and scalable BPaaS solutions.

With more than 15 years of experience spanning carriers, insurtechs, and distribution networks — including leadership roles at Total Expert, Talkdesk, and Westfield — he brings deep expertise in product strategy, commercial partnerships, revenue operations, and complex service delivery. Filipov is known for simplifying operational chaos, architecting data-driven transformation, and building durable growth engines for mid-market and enterprise clients.

Sponsored by Cogneesol

Robert Marshall is the founder and CEO of Whisker Labs. Whisker Labs, a spinout of Earth Networks, delivers next-generation home energy intelligence technology to realize the full potential of the connected home. In 1992, Marshall co-founded AWS Convergence Technologies, the company that would become Earth Networks, by pioneering the networking of weather sensors and cameras using the internet. By developing groundbreaking technology to find "signals" — valuable, meaningful intelligence — in big-data "noise," Marshall improves people's lives and protects their livelihoods. He has appeared on CNN, BBC World News and ABC Nightly News and has been quoted in major news outlets that include the New York Times, the Washington Post, Nature and Scientific American.

Paul Carroll

One of my goals for the Predict & Prevent movement is that it will be able to lay out a clear economic argument, showing that the savings are greater than the cost of the investment in prevention. You and the Insurance Information Institute, the Triple-I, recently reported on a study that found significant savings from installing your Ting devices in homes. Would you start us off by telling us what you found?

Bob Marshall

We document that Ting prevents 0.39 electrical fire claims per 1,000 home-years. If you multiply that by the severity, which has gone up considerably over recent years, then you get to $81 per year per home in savings from Ting. 

That's obviously greater than the cost of a Ting, and that's why insurers love the idea. Not only does it protect their customers and create a great experience and good engagement, but it delivers a clear ROI, paying for itself and beyond.

Paul Carroll

And the benefits are actually greater than the cost savings on fire damage, right? Preventing a fire keeps a family out of danger and saves them from a huge amount of hassle and dislocation.

Bob Marshall

A fire is often devastating for the family. You could lose pets, you could lose lives, the whole thing.

The savings on the insurance side are higher than what's calculated there, too. There is also the cost to the agents, who often have to work with families every week for a year or more to try to itemize all the losses and damage from a catastrophic fire and help them recover. 

The best claim is one that never happens. To the extent we can prevent fires, it's good for everybody.

Paul Carroll

You’ve said that people who install a Ting may become more open to other Predict & Prevent initiatives. I'll share a Triple-I blog on the topic, but would you briefly explain how that works?

Bob Marshall

Homeowners have an innate fear of fire, so when a carrier partner offers them Ting, they're very motivated to say, “Yes. I want that.”

We've worked really hard to deliver a simple and seamless experience for the homeowner. You just plug the Ting into the wall. Setup takes two minutes. Then we deliver valuable information every week with summary reports, power outage notifications, and other beneficial insights.

If you lead with Ting and the homeowner opts in and has a great experience, then when you follow with, say, water, they're much more likely to say, "Hey, I like this fire thing the carrier offered me. I think I'll do the water thing, as well."

Paul Carroll

What’s the latest on the number of homes you’re in?

Bob Marshall

We currently have over 1 million active homes in our network. We're consistently adding 40,000 to 50,000 homes per month, so we're growing very rapidly.

The ROI report was super important for us. Gathering enough data to document results is never easy when you're dealing with low-frequency perils such as fire and even water damage. You have to have a lot of data to properly document the loss prevention, but we have that now. We overcame a number of obstacles with that research and paper to make the results really clearly documented, which is awesome.

Paul Carroll

If you do the math, based on the current number of homes you serve and the prevention of .39 fire claims per 1,000 homes, you’re preventing some 400 fires a year. And the number will only grow as you expand your reach.

The last time we talked, a few months ago, 30 carriers were working with you to provide Tings to their customers. Where do you stand now?

Bob Marshall

I think we're at 34 now, and obviously going up. At this point, it's pretty clear most every carrier is going to work with us because Ting is proven to work. 

We're trying to make the experience more seamless and easier for the carrier, because partnering and distributing loss-prevention devices isn’t something they naturally do. And I think we're pretty much there. 

Paul Carroll

I assume it’s important for insurers that you automatically verify that a Ting is plugged into a wall socket and active, not just sitting in a box, unopened. I know home insurers struggle to not just know that an owner has a security system but that it’s activated.

Bob Marshall

Yes, absolutely. The way we structure our partnerships with carriers, Whisker Labs doesn't get paid if the Ting is not installed and active. We're structured in a way where we're 100% aligned.

Paul Carroll

What progress have you made in your international expansion efforts, and what challenges are you encountering given the different electrical standards globally?

Bob Marshall

We are working on opportunities to expand outside North America, though I can't talk about it too much. I think I'll have more to say on that in the coming months.

The electrical problems and fires are worse in many parts of the world. The electric codes are not as rigid. The buildings are older. The homes are older. The wiring is older. The voltage is higher, which creates more potential for the arcing that can cause fires.

The opportunity for us to prevent fires is even higher outside North America than it is here.

Paul Carroll

How does your technology help monitor electricity quality, particularly for data centers and other situations where reliable power is critical? I’ve read that increased demand is degrading quality.

Bob Marshall

We are doing a ton of work in that regard. Bloomberg actually did a comprehensive analysis a few months ago using our Ting data along with a database of data centers. What's clear is that the power quality for homes in the vicinity of data centers is materially worse.

With bad power quality, your large appliances like air conditioners, water heaters, refrigerators—anything with a motor—their energy efficiency is materially reduced. Air conditioners are half of the energy used in a home. If you reduce their energy efficiency by 15% or 20%, that's a material cost to the homeowner that is hidden. We also see that other power-quality problems—outages, power surges, brownouts—happen much more often where the grid is stressed in the vicinity of data centers. Our preliminary analysis suggests that costs to homeowners from poor power quality can be up to $1,000 per year. 

It's not exclusively near data centers. In general, with the grid becoming more stressed because of the demands and complexity, we're seeing a decrease in the power quality that is very clear and unambiguous.

Paul Carroll

Your network of sensors is proving to be useful in pinpointing grid problems that could lead to wildfires, such as the Lahaina and Eaton Fire disasters. What progress have you made in delivering this critical information to utilities ahead of time rather than retroactively?

Bob Marshall

We are working extraordinarily hard on solving the problem, and we are making some progress.

One key issue is trying to pinpoint the exact source of any given fault that could cause a wildfire. We can do that reasonably well, though we still have work to do. 

When you look at cases like Lahaina and Eaton, our data shows that the entire grid was under incredible stress and was experiencing a high frequency of faults for many hours in advance of the wildfire ignitions. Faults occur when tree limbs touch a wire or wires touch each other, and each incident can produce a spark that ignites a wildfire. Most don't, or we'd have wildfires everywhere.

What our data could help utilities with very quickly is seeing when their grid is stressed and making better decisions about shutting off the power. If you shut the power off, there's no energy to create the spark that causes the fire.

For some of these devastating wildfires, the only solution is to prevent the spark, because when you have 70 mile-an-hour winds and dry brush, there's no way to stop a fire once it starts. There's no amount of water or firefighters that can contain it. But that's a tough decision to turn off power to any community, and utilities have for decades focused on keeping power on essentially at all costs.

Paul Carroll

Any closing thoughts on the industry’s move toward a Predict & Prevent model?

Bob Marshall

We're excited, and we really appreciate that The Institutes, Triple-I, and the insurance sector are embracing the Predict & Prevent future.

I think that vision is so key, and the direction that you all have helped establish is truly taking hold. We're pleased to be able to make our contribution to it and hopefully help drive it forward.

Paul Carroll

Thanks, Bob. I always feel more encouraged after we talk.