What if we have been looking at AI from the wrong angle? What if it is not a magic fix for the insurance industry’s legacy issues but is an unlock for the next generation of growth through insurable risks? 

AI is emerging alongside forces already reshaping the global risk fabric: the rise of intangible assets and cyber exposure, mounting climate volatility, shifting global demographics, and an entirely new class of technologies. These are not distant scenarios, they are today’s realities. 

The IIS Innovation Report reflects an industry in transition, a theme underscored during our executive working group session at the Swiss Re Centre for Global Dialogue in Rüschlikon. Leaders recognized that early AI efforts often focused too narrowly on efficiency and missed the broader strategic opportunity emerging across the global economy. 

The discussions made it clear that the next decade will divide the sector between organizations making marginal improvements and those rebuilding their operating models around proprietary knowledge graphs, reengineered data flows, and augmented human judgment. 

These foundations enable stronger risk selection, superior service performance, and loss prevention in a far more dynamic risk environment, while preserving what remains fundamentally human in our business: trust, advice, and long-term client relationships. AI agents can deliver transformative gains, but only for firms prepared to rethink governance, decision rights, talent, and data strategy. 

This is the strategic inflection point. If we mobilize for it, insurance will not simply adapt, it will become one of the defining stabilizers of an increasingly connected and AI-enabled world!

--George Kesselman

Executive Summary

AI transformation is sweeping the insurance industry

The IIS Report on Innovation, which draws from a diverse respondent pool across insurers, reinsurers, insurtechs, and consultancies, finds that, while enthusiasm for AI is high, maturity levels vary significantly by company size and type. Larger firms are generally further along in production deployment, while smaller firms are focusing more on exploration and customer-facing innovation. 

Efficiency remains the primary driver of AI adoption 

Operational efficiency and workflow optimization dominate current AI priorities, with 53% of respondents citing them as top focus areas, followed closely by underwriting, pricing, and claims management. These findings indicate that insurers are initially using AI to strengthen core processes rather than disrupt existing models. Smaller firms, however, show a stronger tendency toward leveraging AI for customer service and market expansion. Metrics of success largely center on productivity gains, data accuracy, and improved customer experience, though formal frameworks for ROI measurement are still evolving across the industry. 

Experimentation is widespread but deployment maturity is limited

Adoption data reveal that about 87% of companies are pursuing GenAI initiatives, though only around a quarter have reached production-level implementation. Budgets dedicated to AI average 3.9% of overall spending. Most firms rely on third-party general-purpose large language models like ChatGPT, while larger organizations increasingly explore first-party or industry-specific models. Leadership of AI innovation typically originates at the executive level – especially CEOs, boards, and CTOs/CIOs – indicating strong top-down strategic ownership of AI adoption.

Key challenges focus on governance, data, and talent

This report also identifies major challenges that can temper progress. Chief among these are concerns over data privacy and integrity, security, and bias management, as well as the difficulty of measuring ROI. Talent shortages and the lack of formal governance frameworks also impede scalable AI integration, especially for small firms. Most companies rely on human oversight rather than structured governance systems, though larger insurers are beginning to formalize processes through ethics committees, audit trails, and explainability standards.

Innovation is balanced with risk in the era of AI agents 

Looking forward, the report highlights both excitement and caution surrounding the rise of autonomous AI agents in insurance. Top concerns – such as hallucinations, validation difficulties, and regulatory compliance – reflect an industry still grappling with trust and accountability in automated decision-making. Overall, the findings portray a sector experimenting, learning, and building the foundations for responsible, scalable AI adoption that enhances both operational excellence and customer experience

To download the full report, click here.

For many in the insurance industry, 2025 was the year of the "AI Reality Check." After a whirlwind of excitement surrounding generative models, many carriers found themselves navigating a landscape cluttered with broken promises and stalled pilots. As we look toward meaningful innovation in 2026, the path forward requires us to address the "key myth" of AI: the seductive, yet ultimately destructive, belief in the end-to-end magic pill.

Believing that AI can or should replace human judgment at scale is disconnected from the reality of what the technology is. It's far more nuanced and, ultimately, more valuable. AI excels at specific, well-defined tasks: parsing documents, extracting structured data, identifying patterns in large datasets. Humans excel at everything else: understanding context, applying judgment, managing relationships, and making decisions that balance competing priorities.

AI in insurance isn't about doing it all at once. It's about deploying AI module by module, connecting thoughtfully, and staying grounded in what the technology can and cannot do today. That's how AI moves from hype to durable business value.

This distinction matters enormously, especially in insurance, an industry that has been swept up in the promise of AI-powered transformation. Over the past few years, insurance companies have invested heavily in "end-to-end AI systems," ambitious platforms that promise to automate entire workflows, from document intake through underwriting decisions to claims processing. The pitch is compelling: let AI handle the complexity so your teams can focus on strategy. The reality, however, tells a very different story.

The Gap Between Hype and Production

The most significant barrier to durable business value has been the industry's obsession with "end-to-end" solutions. We have seen insurers attempt to buy "AI underwriters" with the expectation that the model will handle everything from initial intake and actuarial analysis to final premium pricing.

There's significant noise around concepts like "AGI" (artificial general intelligence) which creates unrealistic expectations about what AI can accomplish today. This prevailing narrative obscures a critical truth: we're nowhere near the kind of AI that can independently manage the nuanced, multifaceted work that insurance professionals do every day.

An AI cannot replicate 20 years of an underwriter's experience or possess the nuanced context of a specific account. When these "do-it-all" systems attempt to underwrite a complex entity like a national car rental fleet, they often produce inaccurate results because they lack the human context to understand the specific distribution of vehicle types or local risk factors.

When these end-to-end systems fail to deliver, adoption plummets, and frustrated teams retreat to their old manual ways of doing things. This is a failure of strategy, not technology. The myth that AI can do it all has led many to overlook the "hidden costs of delay"—the thousands of touchpoints where humans are forced to review the same long documents and messy email threads over and over again.

This observation cuts to the heart of the key myth that has driven billions in insurance AI spending: the belief that you can build a single system to handle everything.

The Human Touch

Another critical truth? People want to know there is a human hand guiding the decision-making, particularly in an industry as important as insurance. Insurity's 2025 AI in Insurance Report revealed that just 20% of Americans say it's a good idea for P&C insurers to leverage AI, and 44% of consumers are less likely to purchase a policy from an insurer that publicly uses AI. In a 2025 Guidewire survey, 40% of respondents said they would feel more confident in insurers' AI if decisions could always be referred to a human when challenged. Finally, a 2025 survey conducted by J.D. Power showed that insurance customers are most comfortable with AI when it is used to automate routine aspects such as sending claim status updates (24%), managing their billing (23%), and answering basic customer service questions (21%).

So what insight can we gain from these numbers? People are more wary of the insurance industry's use of AI when there isn't a human available to speak with or in control of ultimate decision-making. It seems that customers are far more comfortable with insurers using AI in their workflows when it is deployed for automatic, manual processes embedded with human oversight.

The Failure of End-to-End Automation

Many insurers bought AI underwriting or claims products with high expectations. These systems promised to intake documents, evaluate risk, and generate underwriting decisions and pricing. It seemed the entire underwriting process would be fully automated. What happened next was instructive.

In one recent example, a large insurer deployed an "end-to-end" AI system to handle renewal underwriting for a major account. The AI evaluated the client's profile and recommended a specific premium. But when the human underwriter, who had managed that account for years, reviewed the recommendation, the flaws became obvious. The AI had missed critical nuances about the client's composition and risk profile. The underwriter knew from years of professional experience that this contextual information fundamentally changed the risk calculation. The AI system had the same information as the human underwriter, but the AI's recommendation was simply wrong.

The outcome was predictable: the insurer stopped using the system and went back to manual underwriting. With one major near-miss, "people just go back to the old way of doing things," the expert said.

This represents a profound failure in the AI industry. After this experience, the underwriter noted "It's better to do it manually than to use an AI. Something seriously has gone wrong here."

The Real Innovation: Modular AI

If end-to-end systems fail, what actually works? The answer lies in a fundamentally different approach: "modular AI deployment." Rather than trying to automate entire processes, successful organizations break complex workflows into smaller, well-defined components and apply AI where it genuinely adds value.

Instead of attempting to automate every aspect of a human's job, AI initiatives should focus on eliminating one extremely tedious and time-consuming task.

This philosophy is particularly powerful in document-heavy operations like insurance. Rather than developing an AI that promises to fully contextualize an underwriting submission and make complex recommendations, a more effective strategy is to concentrate on a single, crucial pain point such as accurately extracting and classifying documents. This is a genuinely difficult challenge. Insurance submissions often contain mixed document types, irrelevant supplemental data, and complex tables that general-purpose AI models frequently fail to process correctly because they are not designed to do so.

This is precisely where focused AI adds clear, measurable value. Once documents are properly classified and key data is converted into structured formats, human underwriters operate with far greater efficiency. Their time is spent reviewing pre-processed data and applying their judgment, experience, and understanding of company-specific risk appetite, not manually hunting through dozens of PDFs for critical information.

Building Digital Transformation Through Integration

The path to meaningful AI advancement in insurance isn't about finding the perfect all-knowing system. It's about thoughtful integration of specialized components to increase efficiency and letting professionals get back to the real work at hand. Organizations should consider which capabilities to buy (like document extraction), which to build internally (like risk models specific to your business), and how to orchestrate them effectively.

This is building AI one small piece at a time. You might deploy document classification as a module. Then add information extraction. Then integrate those outputs into your downstream systems. Each step is validated, each component is understood, and each addition genuinely improves the workflow for the humans who ultimately make the decisions. No "end-to-end" black box AI.

Admittedly, this approach requires discipline and is less exciting than the promise of end-to-end automation. But it actually works and leads to full adoption, rather than initial experimentation and inevitable abandonment when reality fails to match the pitch.

Since 2019, several casualty lines of business have shown a consistent pattern of adverse development through year-end 2024. Preliminary third-quarter 2025 disclosures signal that this trend is not yet reversing. The underlying experience, however, differs significantly by line and by carrier. This article focuses on where and why reserve shortfalls are occurring. We also provide some high-level suggestions to adjust actuarial methods for more adequate reserves.

Using Annual Statement data from S&P Global Market Intelligence, we examined:

• Commercial auto liability (industrywide), and
• Other liability—occurrence experience for 20 writers concentrating on excess or umbrella coverage.

Across accident years 2016–2024, published ultimate loss ratios have increased almost every calendar year. With the benefit of hindsight, initial and subsequent reserves were inadequate.

From Annual Statement data via S&P Global Market Intelligence Industry Commercial Auto Liability

From Annual Statement data via S&P Global Market Intelligence based on Other Liability Occurrence results from 20 companies that predominately write excess and umbrella business.

What is driving this pattern of inadequate reserves? We believe that the following factors are the most significant:

1) Extended Litigation: The expansion of third-party litigation funding and the improved capitalization of certain plaintiff firms mean more lawsuits proceed to trial. This causes challenges with traditional actuarial methods. Actuaries often use the past patterns to predict future patterns; however, if the environment changes significantly the methods become less reliable. With an increasing percentage of claims being litigated, historical loss emergence patterns are less reliably predictive of the future patterns. The industry has observed both longer cycle times (from claim report to claim settlement) due to more litigation and increased settlement costs as jury outcomes increasingly favor plaintiffs.

2) Backward-looking benchmarks: Actuaries often use older years' loss ratios to estimate loss ratio results for more recent years (after adjusting for premium changes and loss trends). However, if the older years' loss ratios consistently increase, the initial assumptions for the newer years start too low.

3) Under-estimated trend in a rising-cost environment: In an environment of increasing costs, it is difficult to estimate trend factors. For example, if average claim costs are increasing, some companies may believe that case reserves are more adequate and therefore not reflect the higher trends in the projections.

4) Management optimism. After the large rate increases and underwriting tightening during 2019-2022, some management teams find it hard to believe that loss ratios are not dramatically improving. This belief can delay the recognition of continuing adverse development.

The published industry results for the last few years clearly indicated adverse industry development as illustrated in the graphs above. Preliminary data published through the third quarter of 2025 indicates adverse development is continuing for some companies.

The table below displays development through the third quarter for all lines of business, separated by companies that indicated favorable development for accident years 2022 and prior and those that indicated adverse development.

*Based on Accident Years 2022 and Prior

From Annual and Quarterly Statement data via S&P Global Market Intelligence

For the companies we have summarized that reported third-quarter data, this industry composite displayed little change in prior year reserves for accident years 2022 and prior, with favorable reserve development for accident years 2023 and 2024. 53% of the companies indicated favorable development and 47% of the companies indicated adverse development for accident years 2022 and prior. We note that reserve development differs by company in the amount and magnitude due to the lines of business written.

The quarterly data reported to the NAIC is not presented in the same level of detail as the year-end data, as Quarterly Statements display development for all lines of business combined. Therefore, we segregated the companies into different groupings based on our assessment of the type of business the companies write. Additionally, the quarterly development is only available for accident years 2022 and prior, 2023 and 2024.

The cohorts of companies that primarily write personal lines business, workers compensation business, medical malpractice business and mortgage insurance displayed favorable reserve development for accident years 2022 and prior, and also for accident years 2023 and 2024. Personal lines business as well as workers compensation business are lines generally less affected by social inflation. For accident years 2022 and prior, the total combined reserves for these cohorts of companies developed favorably by approximately 3%.

From Annual and Quarterly Statement data via S&P Global Market Intelligence

However, the cohort of companies that write primarily commercial insurance, companies in run-off, and reinsurance companies displayed adverse development for accident years 2022 and prior.

From Annual Statement data via S&P Global Market Intelligence

Drilling down within the commercial lines writers provides additional insights. The following table displays the reserve development for commercial lines writers that:

• write limited amounts of workers compensation;
• write both commercial and personal lines;
• are excess and surplus lines companies; and
• are writers of other commercial lines of business including workers compensation (i.e., "other commercial writers").

The cohort of companies that primarily write commercial lines with limited workers compensation business displayed higher adverse development (2.6% of adverse development for accident years 2022 and prior) compared to their more diversified peers that also wrote either workers compensation or personal lines (these cohorts displayed 0.3% of adverse development for accident years 2022 and prior). It is reasonable to assume that commercial lines carriers that are more diversified (e.g., write workers compensation or personal lines business) are benefiting from the favorable development on these lines which mitigates the development they may be experiencing in their commercial business.

The cohort of commercial companies with limited workers compensation business also write commercial automobile liability. The companies that primarily write commercial auto liability are displaying higher adverse development. We did not separately segregate these companies as the reserve base is limited and the development is driven by a few companies. Commercial auto liability is a line of business more affected by social inflation that has had significant rate increases and re-underwriting over the past few years, which increases the uncertainty in the reserve estimation process.

We note there is variability within the various cohorts and for certain cohorts of companies, a few large carriers had a significant effect. Within the "favorable" cohorts, 41% of companies posted adverse development for accident years 2022 and prior. Conversely, 49% of insurers in "adverse" cohorts reported favorable development for those same accident years. For the lines of business affected by social inflation, prior years' development hinges on how effectively each insurer has captured social inflation effects in past analyses and how aggressively they are recognizing those pressures today.

Favorable cohorts: Companies writing personal lines, workers compensation, medical malpractice and mortgage insurance

Adverse cohorts: Companies writing commercial business, companies in run-off and reinsurers

Given the factors outlined, we expect unfavorable reserve development to persist for certain lines of business and companies. However, favorable and adverse development will affect insurance carriers differently depending on the lines of business they write and their prior recognition of social inflation in the actuarial methods.

Although accident years 2023 and 2024 are generally indicating a favorable run-off, we have a concern that adverse development will occur in these accident years as the historical adverse development may not be fully reflected in the actuarial assumptions.

To reflect social inflation in actuarial methods, we recommend companies:

• Reevaluate the expected loss ratios that are used in actuarial methods to not only reflect historical adverse development but also current claim activity; and
• Separate lines of business into more granular groupings which segregate those segments more affected by social inflation and those less affected by social inflation (e.g., litigated versus non-litigated claims).

After year-end 2025 data is released, we will publish a companion article that presents updated results with more details by line of business, along with greater discussion on how to adjust actuarial methods.

When the Buffalo Bills fired head coach Sean McDermott on Monday, that brought the number of top jobs vacated this season to 10. That struck me as a huge number, out of just 32 such positions. I'm accustomed to six or seven, maybe even eight. 

It turns out that the 10 departures this year are the most since... the end of the 2022 season. And eight of the 10 hires from three seasons ago have already been fired. 

I realize I have the luxury of surveying the frenzied hiring and firing as a lifelong fan of the Steelers, who have had precisely three head coaches since 1969. (There have been six popes in that same stretch.) The first two of those Steeler coaches are in the Hall of Fame, and Mike Tomlin, who resigned last week after 19 seasons, will surely join them when he becomes eligible. 

It's obviously not helpful to tell teams that they, too, should hire Hall of Fame coaches, but I do think lots of teams are getting one thing very wrong — and anyone leading an innovation effort, including at an insurance company, may be tempted to make the same mistake. 

Basically, too many teams don't have a long-term vision. They think they do, but they don't. So they lose patience too quickly. They get twitchy when the results aren't there immediately and move on to the next coach or general manager or both, only to pull the plug on them too quickly, too. As I wrote two weeks ago — in what I thought would be my one NFL reference for the year — the impatience is partly because owners get focused on the outcomes of their choices, rather than on whether they made a good bet. 

Owners ask: Did we win the Super Bowl this year? They don't ask: Did we put ourselves in a good position to have a chance to win? They don't ask: Are we putting together the pieces for the next several years? They ask: Did we win this year?

That sort of thinking is how you become the Cleveland Browns. Since the 1999 season, when the team was reconstituted after the original franchise moved to Baltimore and became the Ravens, the Browns have had 12 head coaches and are about to hire their 13th. In those 27 seasons, they've won 33% of their games and zero titles in the four-team AFC North. They've played in all of four playoff games, winning one.

Yet the Bills have decided to follow suit. They fired McDermott even though he took the Bills to the playoffs in the last seven seasons and in eight of his nine seasons as head coach. They made it to the conference championship twice, losing both times to the formidable Chiefs. The Bills hadn't been to the playoffs in the 17 years before McDermott arrived. Who do they think they'll get who'll be better? 

Firing Pete Carroll as the head coach of the Las Vegas Raiders after one season? Sure, the Raiders were an awful 3-14 this season, but that's only one game worse than their record for the 2024 season, and they'd had losing records in 2022 and 2023, as well. You're telling me they didn't know what they were getting in a 74-year-old coach who, among many other things, had just coached for 14 seasons in Seattle? I have no idea whether he was the right fit in Las Vegas, but either Raiders ownership was too quick to commit to him as the turnaround guy a year ago or was too quick to bail after this season. In either case, the Raiders showed they're dysfunctional.

By contrast, when the Steelers hired Chuck Noll in 1969, he was a 37-year-old with no track record as a head coach and went 1-13 in his first season. He had losing records in his second and third seasons, too. But he was putting the pieces together, the Rooney family stuck with him, and the magic started happening in season four.

When the Cowboys hired Jimmy Johnson as head coach in 1989, he went 1-15 his first season. But he and owner Jerry Jones had a long-term vision largely based on the eight draft picks, including three first-rounders, they got for trading Herschel Walker to the Vikings. By season four, Dallas was winning the first of the three Super Bowls it took in four years.

The Cowboys actually demonstrate both patience and impatience. After Johnson led the team to two Super Bowls, Jones felt Johnson was getting too much credit. So — in what I believe is the dumbest decision ever by the owner of a sports franchise — Jones fired Johnson and brought in another college coach who had won national championships to try to show that just about anyone could win with the juggernaut Jones, the self-proclaimed genius, had put together. The Cowboys did, in fact, win one more Super Bowl, but then got twitchy as Jones took more control and have never recovered. The Cowboys have won five playoff games in the last 30 seasons and have never even made it back to a conference championship game. 

What Should Insurers Do?

Insurance companies have a luxury that NFL franchises don't: They don't have to deal with hundreds of podcasts by rabid fans who want to fire everybody any time someone fumbles a football. 

Still, insurance companies have to answer to shareholders, and they do have to succeed. That means innovation efforts, especially related to generative AI, need to fit into a long-term vision. They can't be one-offs, because those are too easy to kill. And the efforts can't be judged based just on whether they succeeded or on any other short-term indicators. 

The right questions are: Were they good bets? Did we learn something important? What do we do next to build on what we just learned?

Early on, it was at least okay to do broad experiments with Gen AI. People needed to get comfortable with the concept, and the applicability was somewhat nebulous. But we're more than three years into the Gen AI revolution now, so it's time to do more long-term planning about how Gen AI can both make your organization more efficient and about how it might even let you make more radical changes to your business model. 

Once you've laid out that vision, you have to stick with it. None of this firing the coach or heading off in a new direction the first time something unexpected happens. And the commitment has to be communicated from the top of the organization, repeatedly, so people know this isn't just a phase that they can assume will pass them by if they just keep their heads down. 

I can't guarantee success. Even my Steelers haven't won a playoff game since 2017, and there's no guarantee we won't pick a dud as head coach this time. Dan Rooney played a major role in hiring all three of our coaches since 1969, and he died in 2017. But I can guarantee that taking a stable, long-term approach means you won't be the Cleveland Browns. 

Cheers,

Paul

P.S. How committed have the Steelers been to their head coaches for the long term? My father once told me an illustrative story that was passed on to him by a friend who was the PR guy for the Steelers in the 1970s and 1980s. 

Now, my father was a hail-fellow-well-met, Irish storyteller type, but his stories always started out based on something that actually happened, and I choose to believe this took place just as my father described: 

The PR guy said he was sitting in Noll's office at the end of a workday, when Dan Rooney stuck his nose in. 

Rooney said, "Chuck, I put your contract in your in-box. I left the numbers blank because it's your turn to put them in this year." 

Noll responded, "No, no, it's your turn. I put the numbers in last year."

Rooney said, "I checked. I did the numbers last year. Just put the contract in my in-box when you're done, and I'll sign it in the morning. Have a good night."

Cyber incidents created many headlines in 2025 and are still the biggest worry for companies globally in 2026, according to the just released Allianz Risk Barometer. The past year has also been a significant one for accelerated adoption of artificial intelligence (AI), which is reflected in its ranking as the biggest riser in the annual survey at #2. Close to half of survey respondents believe AI is bringing more benefits to their industry than risks. However, a fifth say the opposite.

The Allianz Risk Barometer is an annual business risk ranking compiled by Allianz Group's corporate insurer Allianz Commercial together with other Allianz entities. Now in its 15th year, the Risk Barometer incorporates the views of 3,338 risk management experts from almost 100 countries and territories and identifies the main perils risk management practitioners are expecting in 2026.

Cyber risks by far the biggest concern for companies

In 2026, cyber incidents are the top global risk for the fifth year in a row, with its highest-ever score (42% of responses), and by a higher margin than ever before (+10%). It ranks as the main corporate concern in every region (Americas, Asia Pacific, Europe, and Africa and Middle East).

The continued presence of cyber at the top of the Allianz Risk Barometer reflects a deepening reliance on digital technology at a time when the cyber threat landscape, and geopolitical and regulatory environments, are fast evolving. Recent high-profile cyber-attacks underline the continuous threat for businesses of all sizes. Smaller and mid-sized enterprises are increasingly targeted and under pressure due to a lack of cyber security resources.

AI creates emerging risks as well as new business opportunities

AI has surged into the top tier of global business concerns, rising to #2 (32%) in 2026 from #10 in 2025 – the biggest jump in this year's ranking. It is a big mover in all regions – ranked #2 in the Americas, Asia Pacific, and Africa and the Middle East, and #3 in Europe – and is a growing risk for companies of all sizes too, moving into the top three for large, mid-sized and smaller firms.

As AI adoption accelerates and becomes more deeply embedded in core business operations, respondents expect AI-related risks to intensify, especially when it comes to liability concerns. The rapid spread of generative and agentic AI systems, paired with their growing real-world use, has raised awareness of just how exposed organizations have become.

Business interruption strongly connected to geopolitical risks

2025 marked a shift towards protectionist trade policies and tariff wars that brought uncertainty to the world economy. It was also a year of regional conflicts in the Middle East and Russia/Ukraine, as well as border disputes between India/Pakistan and Thailand/Cambodia and civil wars in Africa – a trend which continues in 2026 with the U.S. intervention in Venezuela.

Geopolitical risks are putting supply chains under increasing pressure, but as risks rise, just 3% of Allianz Risk Barometer respondents view their supply chains as "very resilient". In the past year alone, trade restrictions have tripled to affect an estimated U.S.$2.7 trillion of merchandise – nearly 20% of global imports according to Allianz Trade – fueling companies exploring trends such as friendshoring and regionalization. These developments lead to a high-risk perception – 29% of respondents rank business interruption as a top peril, placing it at #3, although it drops a position year-on-year.

Unsurprisingly, political risks and violence climbs two places to #7, its highest-ever ranking. The closely linked risk of changes in legislation and regulation – which includes trade tariffs – ranks #4 globally, unchanged year-on-year but with an increase in respondents, driven by concerns over growing protectionism. In fact, global supply chain paralysis due to a geopolitical conflict ranks as the most plausible "black swan" scenario likely to materialize in the next five years, according to 51% of the respondents.

The full report is available at: 2026 Allianz Risk Barometer.

One of the continuing and increasing challenges in clinical and cost modeling is translating scientific advances into real-world practice at scale. Years can pass before new evidence meaningfully influences care delivery, benefit design or financial planning that affects insurance premiums. Closing this gap between what is known and what is applied has proven difficult across the healthcare ecosystem.

This is largely the result of medical knowledge that is not inherently computable, which limits precision, transparency and scalability across the healthcare ecosystem. Making medical evidence usable in real-world insurance coverage decision-making requires computational approaches that bridge medical science, clinical practice and economics.

With medical knowledge becoming computational, a new class of solutions is emerging – one that connects the science of medicine with the economics of delivering care and managing risk. This approach structures evidence-based clinical knowledge in a form that can be reasoned over transparently, helping organizations compress the knowledge-to-practice cycle and make more informed decisions under uncertain conditions.

At its core, this methodology supports better risk stratification and management by grounding prediction in clinical understanding. Rather than relying solely on historical usage patterns, organizations can now evaluate patient journeys, assess plausible future trajectories and reason about clinical and financial risk with greater clarity.

Aligning Clinical and Financial Perspectives

Most healthcare in the United States is employer-driven and sits at the intersection of clinical insight, economics and access. Yet these components often remain siloed. Clinical information, claims data and financial models are rarely aligned in a way that supports coherent and holistic risk management.

Risk-bearing organizations routinely navigate clinical and financial decisions that are not intrinsically connected. In the absence of alignment between these perspectives, early risk identification and confident action are challenging.

Introducing a computational layer that connects medical evidence with real-world data helps bridge this divide. Clinical guidelines, care pathways and research are translated into explainable models of clinical logic. When an individual's health history is evaluated against this foundation, organizations gain a more complete and interpretable view of risk.

Instead of a standalone risk score, this approach offers a transparent, evidence-grounded view of risk that informs pricing, underwriting, budgeting, care management and more.

Explainability as a Requirement

Explainability also plays a central role in whether AI can be trusted in healthcare risk management. Decision makers must be able to see how a conclusion was reached, how evidence was connected and why certain outcomes are considered plausible.

When models reflect real clinical reasoning and make that reasoning transparent, they become usable across teams. Actuaries, care managers and leadership can operate from a shared understanding rather than interpreting disconnected outputs.

Research increasingly highlights the importance of interpretable models that align with clinical practice. Predictions that cannot be examined or explained offer limited value in environments where financial and human outcomes are closely intertwined.

A More Precise View of the Future

One of the key advantages of clinical modeling is its focus on individual trajectories rather than broad population categories. A diagnosis alone does not indicate whether a condition is stable or worsening. A procedure does not explain whether it reflects appropriate care or avoidable deterioration. Individuals with similar claims histories may face very different futures.

When these distinctions are made visible to all, organizations can act earlier and with greater confidence. This enables targeted intervention, education or more effective planning, driven by understanding and contemplation rather than hindsight.

This clarity helps align clinical and financial teams. Clinical experts understand how health evolves; financial teams understand how cost behaves. When both are connected through a shared, evidence-based model, organizations can make more confident decisions around pricing, benefit design and care management investment. This shared foundation reduces friction between teams by grounding discussions in the same clinical and economic context.

Moving Forward Responsibly

As AI adoption accelerates in healthcare, responsible use remains essential. Models must address bias, protect privacy and preserve meaningful human oversight. Clinical modeling does not replace professional judgment – it augments it by providing a clearer, evidence-grounded view of uncertainty and risk.

When prediction is grounded in clinical understanding, risk becomes more visible and more manageable. Organizations can see not only what may happen, but why, enabling more responsible action.

By transforming medical evidence into computational knowledge and applying AI to that foundation, this approach enables more transparent, aligned and effective risk management – benefiting patients, employers, insurers and the broader healthcare ecosystem.

Methodology for Choosing Small-Business Insurance Brokers

It’s best to have robust criteria when vetting different insurance brokers. Here are the most relevant factors when searching for and choosing one.

Specialization in the California Market

Businesses should prioritize brokers with deep knowledge of state-specific regulations and carrier networks. The more knowledge they have of California’s laws and business landscapes, the more information they will have to match you with the right insurance policies. You can also discuss the most standard or necessary options. 

Industry Experience

Small businesses often employ a different approach to risk management compared to larger, more established companies. It varies based on the leader’s financial capabilities, comfort level with taking risks and the specific industry they are in. It’s vital to find an insurance broker who recognizes these factors and has the experience to guide you along the way. 

Credibility and Knowledge

It’s crucial to verify whether your chosen brokers are licensed to operate within your state and are authorized to work with the insurance companies there. They should also have enough knowledge to evaluate insurers’ financial ratings and claims processes, as they should only recommend credible organizations. 

Clear Communication and Transparency

Initial consultations and continued sessions of finding the right insurance policies will require plenty of communication. It’s crucial to find brokers who communicate transparently, rather than using intimidating business jargon and tactics. Their level of openness should be clear based on initial discussions about their fee structure and work style. 

Who Is the Best Insurance Broker for Small Businesses in California?

Here are the contenders for the best insurance broker for small businesses in California.

1. Health for California

Health for California is a health insurance agency that has been helping California families and businesses since 2004. It is dedicated to streamlining the application process at no cost, while serving with respect and kindness to make the purchasing process as pleasant as possible.

Key features:

• Offers online services for free instant quotes
• Can help you provide plans with minimal cost
• Helps with applications for Covered California

2. Skyline Benefit

Skyline Benefit is an independent broker that has worked with major insurance companies and vetted numerous policies. It helps you feel comfortable with shopping for the right health insurance coverage. 

Key features:

• Can help with self-funding insurance
• Provides flexible, small-business-focused insurance options
• Prioritizes data security

3. KBI Benefits

KBI Benefits is a benefits consulting and technology service that works with business owners and decision-makers to achieve success. It is prepared to negotiate with insurance service providers to get the best possible price and coverage before signing. 

Key features:

• Implements full-service assistance on benefits
• Ensures safety compliance
• Has helped clients save an average of up to 40% on employee benefits packages

Comparative Table of Insurance Brokers for Small Businesses in California

Here’s a comparison of the recommended insurance brokers for small businesses in California.

Get Your Small Business Insured

Small businesses must be insured for financial protection. Working with the right broker is a straightforward way to find the right policies without exhausting your internal resources. Connect with the people who can search and handle these responsibilities on your behalf.

Sponsored by: Health for California

While our coverage at ITL these days can feel like "all AI, all the time," there's still a massive open question: How quickly can we develop and adopt AI agents that can act on our behalf? 

Most of the articles submitted to me are from enthusiasts, but there are reasons to be cautious, too. We've all seen or read about the hallucinations AI can have, about how AIs can learn to be ugly, and so on. How do we know they won't do something stupid or offensive if we cut them loose? One nasty lawsuit or loss of a major customer can outweigh a lot of efficiency gains.

Insurance will get there with AI agents. The question is just how fast. If you could tell me today what adoption would look like at the end of the year, I could tell you a lot about the state of innovation in insurance. So I'll be watching closely.

Six other areas also seem to me to be key for this year. The two other huge ones are the spread of the IoT, which is letting us monitor so many more issues in real time, and the growing adoption of the Predict & Prevent model, which helps turn that new data into ways of protecting customers. Embedded insurance and parametric insurance are also playing increasingly important roles — and have lots of potential still. And autonomous vehicles have built a launching pad that could let them take off this year, with all sorts of implications for insurers. Finally, I'll focus on the general notion of speed. Sometimes, a quantitative change is important enough to make a qualitative difference, and I think some processes in insurance, such as underwriting, may start moving so much faster that they could change the competitive landscape.

Let's have a look at the seven keys I'll be scrutinizing this year — and think you should be watching, too. 

AI Agents

My caution stems from the sort of problem I've witnessed in years and decades past related to "decision rights." Remember when "Internet refrigerators" were supposed to track what you had in them and order, say, milk for you when you were running low? Well, I didn't want my refrigerator ordering for me. Maybe warn me if I'm running low on something or be able to tell me when I'm at the store whether the thyme is still usable, but that's it. I control what I eat. 

I'd love for AI agents to take repetitive work off my plate, but I give up control slowly. 

My optimism stems from the sort of vision that my old friend and colleague John Sviokla described in a webinar conversation we had recently on AI. He described AI agents as employees that we'll be able to supervise and monitor as we do our current staff and suggested that each of us might have dozens of highly trained AIs whose autonomy is carefully circumscribed. He said job interviews in the future will include the question, "What AI agents do you have? Show me your bots." If you don't have an impressive array, John said, it will be like interviewing for a chef position at a fancy restaurant without having your own set of knives.

AI agents will be a big one to watch.

IoT

The Internet of Things, especially if you include auto telematics, has already made a massive impact on insurance. Drivers are being coached to be safer. Devices are detecting fire hazards so well that carriers are giving them away to customers. Water leak detectors in homes are getting closer to that tipping point, too, where carriers will give them away because they'll prevent so much damage. 

A sort of operating system for homes now lets any sort of device communicate wirelessly with it and have a signal relayed, letting you know about that water leak or that your smoke detector has gone off while you aren't home. Amazon's Sidewalk creates what's known as a mesh network that allows wireless connections to all sorts of nodes, even in public spaces, that can relay a signal to wherever it needs to go. Meanwhile, sensors just keep getting smaller — I wrote in November about how researchers had even managed to outfit Monarch butterflies with trackers weighing .06 gram.

Progress will only accelerate.

Predict & Prevent

When I got involved with Insurance Thought Leadership a dozen years ago, following decades of writing on innovation and technology, the first talk I gave carried the title, "He Who Sells the Least Insurance Wins." My reasoning: Nobody wants to buy insurance, but everybody wants safety. So why focus on selling insurance when the industry can take its massive amounts of data and expertise and provide safety?

That's obviously easier said than done, but I've been delighted to see that there's been so much progress and that the industry is rallying around the Predict & Prevent idea (sometimes called by slightly different names). At ITL — and more broadly at The Institutes, of which ITL is an affiliate — we've tried to highlight some key examples, such as Whisker Labs' Ting, which detects electrical faults in homes and is preventing hundreds of fires a year, and Nauto, whose two-way cameras in truck fleets are drastically reducing accidents. 

In 2026, we'll highlight as many more as we can — while hoping for more breakthroughs.

Embedded Insurance

Embedded insurance had a big year in 2025, to the point that toward the end of the year authors published two major articles with us on the topic. One said embedded insurance was nearing a tipping point and marshaled an impressive amount of evidence about the companies leading the way. Another said embedded insurance wasn't just a way of reducing distribution costs but had become a key part of the customer experience, by simplifying the purchasing process.

There are some complications. For one, agents will resist being cut out of the purchasing process. For another, carriers that offer insurance as part of the purchase of something else risk ceding control of the experience to the seller of that product. The insurance could even be treated as a commodity, meaning one carrier could easily be swapped out for another. 

But the convenience is still so great and the drop in customer acquisition costs so substantial that I expect more and more insurance to become embedded.

Parametric Insurance

Parametric insurance is showing up, in particular, in agriculture and in natural catastrophes, where it's relatively straightforward to find an agreed-upon metric, such as wind speed or lack of rainfall, and where a speedy, partial payout on damages can be key to keeping a business running or to rebuilding quickly. 

We haven't covered it as much as we might have, but I suspect we'll see parametric insurance make inroads in lots of areas in 2026.

Autonomous Vehicles

AVs have had their ups and downs in the nearly 13 years since Chunka Mui and I wrote a book on the topic, but they seem to finally be on a glide path — and an exponential curve at that. While Uber, Cruise and others have fallen by the wayside, Google's Waymo keeps expanding relentlessly. It's up to 450,000 fully autonomous paid rides per week in the U.S. and expects to hit 1 million a week late this year. (Waymo tends to understate its goals and routinely exceeds them, unlike, say, Tesla, where Elon Musk has been promising full autonomy for a decade but only has perhaps 30 robotaxis on the road at the moment, almost always with safety drivers.) Waymo keeps expanding into more cities and will even move into London this year, where it will go head-to-head with China's Baidu. 

Amazon's Zoox has begun offering limited service, as have some startups, including May Mobility and Nuro. Nvidia just announced big plans to supply the technology and even much of the AI for car makers that want to develop AVs, debuting with a slick offering it developed with Mercedes. Tesla, of course, continues to talk big — and much of Musk's potentially $1 trillion pay package depends on meeting aggressive plans for AVs. 

AVs have taken hold enough that an ER doctor — as in, not a techno-optimist — recently wrote an op-ed in the New York Times arguing that we have to move as fast as possible to AVs for public safety reasons. He argued that AVs are now so clearly safer than human drivers that we have no choice.

I think it'll be a big year for AVs, whose effects will eventually trickle down not just into auto insurance but health, life and workers' comp.

Speed

One of the impediments to innovation in insurance has always been that it takes so long to see if an idea will pan out. In Silicon Valley, when they talk about A-B testing, they're talking about testing thousands of different ideas — headlines, offers, etc. — every second. In insurance, if you want to test a new price or new product, regulatory approval alone can take months, no matter how fast you go internally. 

But generative AI has increased the metabolism in insurance, and I think we're just beginning to pick up speed. Because Gen AI can gather so much information so fast and at least pre-process it, claims agents and underwriters can make decisions faster than ever before. Carriers can also start experimenting with automating certain classes of submissions and claims so that a human never even has to touch them. Agents can respond to customers faster, too, and speed everything along.

Those who increase their speed the most will win a competitive advantage, according to all sorts of surveys showing how much customers value quick payments and how carriers and MGAs may lose business if they're slower than the other guy at responding to a submission. 

Increased speed could cause even more fundamental shifts. For instance, here is a piece we published recently on how underwriting could move so fast in some cases that no binder would be needed. Just think about how much work that could eliminate. Or, consider what happens if policies aren't just reviewed at renewal, and underwriting becomes continuous, updating as circumstances change. There are all sorts of implications, as Bobby Touran and I discussed in a recent webinar that carried the confident title, "Continuous Underwriting Changes Everything."

Speed is such a powerful but broad force that it's hard to see just where it takes us, but I'm sure it'll be somewhere interesting and important.

Here's to a fascinating 2026!

Cheers,

Paul

In an increasingly competitive marketplace, insurers are looking for every edge they can find to enhance operational efficiency and drive profitable growth. Digital payments have had a positive impact in these areas as the transition from traditional payment by check streamlines the disbursement process and reduces costs. Now, new research shows that digital payments are just as important when it comes to retaining customers in the face of rising premiums.

One Inc. commissioned industry analyst Celent to investigate key drivers of policyholder satisfaction in the claims process so insurers could better understand where to focus improvement efforts that would really move the needle. To do this, Celent polled more than 300 auto insurance claimants about their experience.

The good news is that 75% of respondents were "somewhat" or "extremely" happy with their claims experience. Moreover, a good claims experience can also create "loyalists," which Celent defined as policyholders who will stay with their insurer despite a rise in price. Nearly 40% of cat claimants and 25% of non-cat claimants in the study said they would stick with their current insurer, even if it costs them more. Impressive numbers, to be sure, but that leaves a significant majority of policyholders — both cat and non-cat — at risk with every claim.

Insurers must focus on strategies for retaining these price-sensitive customers, and this is where the power of digital payment capabilities can make a major difference in policyholder loyalty.

Speed of Payment is King

While policyholders appreciate quick claims cycles, how fast a claim closes matters less than how fast they receive their payment. For all claimants, speed was the most important aspect of payments. What is more striking is the response from dissatisfied claimants who were much more likely to say that speed was a top priority and were less likely to be satisfied with the speed of payment. These claimants really care about how quickly they're paid, and it's critical for insurers to meet that expectation.

Among dissatisfied non-cat claimants, 55% said payment speed was their top priority, and 62% of dissatisfied cat claimants ranked speed most important. It comes as no surprise that catastrophe claimants place even greater weight on how quickly insurers disburse claims since they are trying to rapidly rebuild their lives. Cat claimants were found more likely to be "extremely" dissatisfied with speed of payment (9% compared to 3% of all claimants) and less likely to be "extremely" satisfied (38% compared to 43%). Insurers must take advantage of this clear opportunity to deliver speedy payment and ensure an experience that creates lasting loyalty.

While slower payments don't necessarily doom the process, faster payments are clearly a driver of satisfaction.

Payment Choice Matters

It is not only the speed of payment that matters, but also policyholder control over how claims get paid.

The research found that paper check was the most common form of payment, at 34%, and most claimants (57%) don't have the ability to choose how they receive their payment. It was also found that when the insurer chose the form of payment, the claimant wished they would have been able to make their own choice in a majority of cases.

Indeed, allowing claimants to choose how they receive their payments leads to high levels of satisfaction. Of claimants who were allowed to choose, 85% indicated they had a positive experience overall, with 50% indicating their experience was "extremely" positive. Just providing choice of payment methods can be a game changer for insurers.

Moreover, the study revealed claimants' familiarity with digital wallet products. Nearly half said they have used Cash App, Apple Pay, or similar products in the past, and over two-thirds of the total survey group have previously used a virtual card. Clearly, the insurance policyholder market is becoming comfortable with digital payments and wants the ability to choose between traditional methods and the proliferating number of digital payment options.

Growing the Loyalists

While digital payments are not the only factor in claims satisfaction, based on this new data, partnering with providers who can facilitate faster payments and more flexible payment options for policyholders is critical for insurers to build more loyal customers.

Digital payment solutions such as our ClaimsPay are delivering on the promise of insurance, enabling insurers to disburse claims the way people are transacting more and more in their everyday lives. Leveraging modern technologies may have seemed esoteric just a few years ago, but today, virtual cards, electronic funds transfers, and digital wallets such as Venmo, PayPal, and Apple Pay can take a stressful and rare process and make it a familiar one.

Digital payments are a critical tool that gives insurers more control over the customer experience when premium increases are inevitable, over time. Taking advantage of this financial technology is critical to transforming claimants from being at risk with every claim to "loyalist" policyholders who will stick with their insurer even if their costs increase.

AI is rapidly reshaping the insurance industry, from underwriting and claims processing to customer service and fraud detection. What once required manual review and human judgment is now increasingly handled by technology that promises speed, efficiency, and scale. And with the rapid influx of new and exciting AI tools, it's easy to get swept up in the momentum.

But like any powerful tool, AI also comes with potential risks and challenges, such as algorithmic bias, data privacy, lack of transparency, and overreliance on automated decision-making. Navigating these issues requires careful human oversight. According to a study by McKinsey, 92% of companies plan to invest more in GenAI over the next three years, underscoring both the scale of the opportunity and the potential disruption in the coming years.

For insurers, the stakes are especially high. Decisions made by AI systems in insurance can directly affect an individual or small business's access to essential coverage, affecting everything from whether a claim is approved, to how much a policy costs, to whether the business is deemed insurable at all. That's why one of the most critical and consistently overlooked steps in this transformation is building ethical safeguards into AI systems from the very beginning.

Why AI Ethics is Critical for Micro-Businesses and Solopreneurs

For micro-businesses, the solopreneurs, neighborhood shops, and gig-based enterprises that make up the backbone of our economy, insurance isn't just a product. It's a lifeline. These entities often operate with minimal safety nets, meaning a single denied claim or an unfair pricing model can determine whether they stay afloat or shut their doors.

AI-driven systems have the potential to make underwriting faster and smarter, but they can also unintentionally reinforce biases that put these vulnerable businesses at risk. When algorithms rely on incomplete or unrepresentative data, they can exclude or misprice small operators who don't fit neatly into traditional risk models. That's why ethical, technically sound AI design is not a "nice-to-have" in this segment—it's a moral and operational imperative.

Principle 1: Embedding Ethical AI Considerations from Design (Ethics by Design)

Ethical AI doesn't begin at deployment; it starts at the whiteboard. So what does this mean and look like? Embedding ethical considerations during the earliest stages of AI design and development is crucial. That means asking not just "Can we build it?" but "Should we?" and "Who might be impacted?" before a single line of code is written. What may seem like a simple reframing is in fact a profound shift, as this mindset shift lays the foundation for every other safeguard that follows, starting with the data itself.

Principle 2: Ensuring Fairness, Transparency, and Explainability in AI Data

AI systems are only as fair as the data they're fed. In insurance, where models dictate access, pricing, and protection, fairness is foundational. For micro-businesses, whose financial resilience often hinges on small margins, data quality and explainability can mean the difference between inclusion and exclusion.

Clearly showing customers how and where AI is applied is essential for building trust. When a small business owner understands why their premium is what it is, or how their risk was assessed, they're far more likely to view AI as a partner rather than an opaque system. The keystone of a strong offering is a system trained on inclusivity that ensures no consumer is left out. For insurers, transparency also supports regulatory compliance, reduces legal and reputational risks, and empowers human teams to make informed decisions and challenge results when necessary, boosting performance overall.

Principle 3: The Necessity of Human Oversight and Control in AI Systems

AI should never operate without human oversight. While automation can streamline processes and improve efficiency, it's critical that people remain actively involved at every stage. AI is simply a tool, not the full solution.

In the insurance industry especially, where decisions can directly affect someone's financial security, human judgment provides a layer of accountability and empathy that algorithms alone can't replicate. A small error in an automated claims decision might devastate a single-owner business. Ensuring ethical AI requires close collaboration across all functions, including legal, compliance, product, and customer experience teams, so standards are upheld consistently and proactively.

Principle 4: Continuous Monitoring and Auditing for Responsible AI Governance

Ethics isn't a "set it and forget it" exercise. Responsible AI requires continuing attention and care, long after a model goes live. That means continuously monitoring systems to detect issues like model drift, bias, or unintended consequences. Regular audits, feedback loops, and a culture of continuous learning are essential to ensure AI systems remain fair, effective, and aligned with evolving standards and expectations. For complex, dynamic segments like micro-business insurance, this vigilance is non-negotiable.

The Road Ahead: Ethical AI as a Smart Business Strategy for Insurance Leadership

As AI continues to transform the insurance industry, success won't come from being the fastest to adopt new tools; it will come from being the most thoughtful and responsible in how those tools are implemented, used, and monitored. In the micro-business segment, where vulnerability meets complexity, AI must be both precise and compassionate—powered by technology and guided by human expertise. Embedding ethics into every phase of development is a smart business strategy that prevents real-world harm, earns customer loyalty, and builds market leadership on a foundation of protection and trust. Insurers who prioritize it today will be better equipped to meet regulatory demands and lead with credibility.