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.

With inflation, a higher cost of living, strained budgets, and job market instability, today's consumers are more price-sensitive than ever. One of the key drivers of rising costs in consumer service industries, such as insurance, is abuse of the legal system. 

When the judicial process is unfairly manipulated to generate profit through excessive or unwarranted litigation, the financial fallout extends well beyond the courtroom. With insurance companies forced to spend more defending against these claims, losses are driven up and, therefore, premiums. The result is an inequitable system that places consumers, businesses and the broader U.S. economy at the forefront of absorbing these escalating costs, often out of the public eye but deeply felt in monthly insurance costs.

What is Legal System Abuse and Third-Party Funding?

Legal system abuse is the misuse of laws and judicial processes to increase litigation for profit, often through harassment, control or financial exhaustion and for purposes other than justice. When left unregulated, these practices can drive up costs for consumer services, such as insurance. Defending against these claims increases insurer losses, which in turn drives up premiums for policyholders.

Legal system abuse can include several facets, such as third-party litigation funding, fraudulent claims, exaggerated damages, and deliberately enacted practices to increase costs, delay settlements, and inflate verdicts. Third-party litigation funding is the broad term for providing money to a party to pursue a potential or filed lawsuit in return for a portion of the damages or settlement awarded by the court. Compensation of this nature is often provided by hedge funds, investment firms, individual investors or foreign entities. Since the third-party funders back most, if not all, of the legal expenses associated with litigation, plaintiffs have minimal risk in bringing their claims to court, whether merited or not. According to its 2024 Litigation Finance Report, litigation finance firm Westfleet Advisors disclosed $16 billion in third-party litigation funding (TPLF) assets under management.

This power dynamic puts pressure on insurance companies that are defending against these claims to settle them out of court to avoid trial expenses and drawn-out legal proceedings, even if the claims lack legitimacy.

How Does Legal System Abuse Affect Consumer Finances?

In November 2025, the Independent Insurance Agents & Brokers of America (the Big "I") released a national survey of consumers ages 25 and older who have home, auto or business insurance. It found that 64% of respondents were concerned that excessive lawsuits increase their insurance premiums. According to the survey, 81% believe that the legal system is used in ways that unfairly drive up insurance costs. In addition, eight in 10 (80%) also felt that their premiums would increase due to excessive lawsuits, even if they had never filed a claim themselves.

Consumers' concerns are well-founded. According to A Consumer Guide: How Legal System Abuse Impacts You, released in June 2025 by Triple-I and Munich Reinsurance America (Munich Re US), legal system abuse has resulted in $6,664 in added annual costs for the average American family of four. The excessive litigation prompted by the abuse has cost the U.S. economy 4.8 million jobs and $160 billion in annual tort costs for small businesses.

How Does Legal System Abuse Directly Affect Insurance Losses and Premiums?

Recent findings suggest that legal system abuse drives costs to rise above typical economic inflation rates, ultimately leading to higher prices for average insured Americans. In October 2025, the Insurance Information Institute (Triple-I) and Casualty Actuary Society (CAS) released a report showing that legal system abuse and related litigation trends drove liability insurance losses up by more than $230 billion over the last decade, a figure well over what can be attributed to economic inflation alone.

According to the report's key findings, legal system abuse, inflation-increased losses and defense and cost containment (DCC) for personal auto liability insurance increased by $91.6 billion to $102.3 billion. Commercial auto liability rose by $52 billion to $70.8 billion, property liability by $4.6 billion to $4.8 billion and other liability insurance by $83.4 billion to $103.3 billion (totaling $281.2 billion).

A separate Triple-I report published in September 2025 found that, from 2014 to 2023, an increase in motor vehicle tort lawsuits resulted in $42.8 billion in losses for insurance companies. Data from these findings underscore how litigation dynamics directly influence insurance losses. While the datasets vary, Triple-I's motor vehicle report supports the claim that roughly one-third of "increasing inflation" in auto vehicle insurance losses is due to legal system abuse. These actions, in tandem with mounting legal system pressures, are directly contributing to rising insurance premiums, meaning families are paying more for their coverage at the end of the day, regardless of whether they have filed a claim.

What Can Be Done to Fix an Unfair System?

According to the 2025 report from Big I, 88% of respondents stated that reducing unnecessary lawsuits is important for controlling insurance costs. In addition, 84% reported that they would support reforms if certain legal practices were making their insurance more expensive.

To do this, federal and state reform of tort law and third-party litigation funding must be implemented. But bringing about change to the judicial system is not an easy feat and requires the collaboration of many stakeholders working in tandem toward reform. When asked in the Big "I" survey, a majority (55%) of consumers agreed that the state and federal government should spearhead efforts to address the issue. Many respondents also pointed to insurance companies (34%) and courts (33%) as playing important roles in bringing about reform.

Legal system abuse is a growing financial concern that affects everyday consumers, businesses and the broader insurance industry. Without the necessary intervention to reform tort law, provide oversight of third-party financing, and raise public awareness of these practices, insurance costs will continue to rise, outpacing inflation and putting even more financial strain on American families. Recent data has revealed that consumers are signaling that they want a more just, accountable system. To do this, government officials at the state and federal levels will need to work hand-in-hand with insurance companies and the court systems to bring about change. By continuing to spread information about these practices and to advocate for balanced reform, we can help ease the financial burden on today's policyholders and bring greater transparency and justice to this system.

For decades, insurers have carried the unwelcome reputation of being technological laggards in the financial services sector.

Burdened by enormous, complex legacy systems that are costly to update, insurers have found it challenging to implement new technologies. Add that to cultures and regulations prioritizing strong, stable balance sheets over risky technology punts and insurers have often fallen behind adjacent industries in adopting innovation - or so the story goes.

One of the main reasons legacy systems have hamstrung insurers is that they were one of the first industries to adopt mainframe computers, directly leading to an unfortunate era of monolithic systems. More recently, the industry has been working to change this perception. In a period of rapid transformation, insurers have been quick to adopt new technologies like cloud computing and continue to drive cloud adoption across the value chain.

This reputation of being slow to evolve is especially unfair when applied to claims functions. Over the past two decades, claims operations have undergone significant evolution, shifting away from in-house mainframes, burdensome human-led decision making and inconsistent data capture toward more streamlined, digital-first processes. Insurers are much more data-led today, much more focused on how analytics and AI can improve the efficiency and cost of their operation, while, at the same time actually improving the customer experience.

Six key areas where claims functions can find value from AI:

1. Fraud detection

AI can analyze patterns and anomalies in data to identify potential fraud faster and more accurately than traditional methods. This not only reduces financial losses but also improves the overall integrity of the claims process. For example, AI can analyze structured and unstructured data to uncover hidden fraud patterns and complex relationships within social networks, substantially increasing fraud detection rates.

AI solutions can enhance fraud detection by using machine learning models that continually learn and adapt to emerging fraud patterns. It's important to note that we are not talking about one fraud model here. A network of fraud models working together makes the biggest difference. Insurers have significantly improved fraud detection by implementing a series of fraud scoring models, including supervised models and unsupervised neural networks, within their end-to-end insurance analytics and pricing platforms.

2. Claims triage and allocation

AI optimizes triage and allocation, ensuring each claim is handled by the most appropriate team or individual. This improves operational efficiency and ensures that complex claims receive the attention they need, while simpler claims are processed quickly. AI-driven triage can prioritize claims based on severity, complexity and potential cost, leading to better resource management and faster resolution times. This not only speeds up the claims process but also enhances the accuracy of assessments and decisions.

This is particularly important in markets like the United States, where legal representation in casualty lines is rising, driving up claim costs and closure timelines. Identifying claims that are likely to be represented or litigated early in the process helps triage the claim to the right team, enabling proactive settlement strategies and controlling costs.

3. Predictive analytics

AI-powered predictive analytics can forecast the likelihood of various outcomes, such as the probability of a claim being fraudulent or the potential cost of a claim. This allows insurers to make more informed decisions and allocate resources more effectively. Predictive models can assess the ultimate case value at the First Notification of Loss (FNOL) stage, enabling proactive claims handling and reducing indemnity costs.

Insurers often underestimate the value of powerful predictive claim models. This isn't just about using better prediction in claims, for example, where we have helped insurers route claims that are likely to jump to specialist teams using unstructured data. Insurers need to be thinking more holistically and using the insights their predictive claims models offer in reserving and pricing processes.

4. Customer experience

Enhance the customer experience by providing faster and more accurate service. AI-powered chatbots and virtual assistants can handle routine inquiries and guide customers through the claims process, while AI-driven decision support tools help claims handlers provide timely and accurate responses.

The result? Higher satisfaction and loyalty, as evidenced by NPS scores, and stronger retention.

5. Cost optimization

Automation of routine tasks and improved claims processing accuracy reduce operational costs, freeing resources for high-value activities. For instance, AI can optimize repairer selection based on core claim metrics, leading to cost-effective repairs and shorter settlement times.

AI-driven automation can handle repetitive tasks such as data retrieval, input processing and quality checks, resulting in significant time savings and improved accuracy.

In casualty lines, an integrated generative AI tool can help summarize legal correspondence, medical reports and investigation updates and recommend negotiation strategies, saving claims handlers significant time.

6. Real-time decision support

Real-time AI engines provide claims handlers with instant insights for faster decisions. This includes assessing the likelihood of fraud, determining the best course of action for a claim, and identifying opportunities for cost savings.

These engines can integrate multiple models to provide comprehensive insights and support across the claims process. This real-time capability ensures that claims are processed efficiently and accurately, reducing delays and improving overall performance.

Insurers see enormous benefit by deploying scoring models that can feature in their claims systems, so claims handlers have greater real-time insight when making decisions.

What's next for AI and claims innovation?

The integration of AI into the claims process offers insurers numerous benefits. By leveraging AI, insurers can transform their operations, delivering better outcomes for both their business and their customers. But insurers that are tempted to lag might well be targeted by fraudsters or see their customers turn away due to relatively slow processing and decisioning.

The reality is that the scope and breadth of claims AI applications are vast, and part of the secret to successful AI deployment is to identify and prioritize effort.

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.

For generations, the fundamental promise of insurance brokers and insurance companies has been reactive: a financial safety net designed to catch clients after they fall. We repair, and we replace.

However, as we navigate an increasingly volatile risk landscape marked by climate change, supply chain complexity, and inflationary pressures, this traditional "utility" model is under increasing strain, particularly in sectors where loss frequency and severity are on the rise.

The industry is approaching an inflection point where the frequency and severity of losses in certain sectors are threatening the viability of traditional indemnity coverage.

The most critical strategic insight for leadership today is that sustainable profitability and continued relevance will no longer come solely from sophisticated pricing of risk. It will come from reducing the risk itself.

We are witnessing a necessary paradigm shift from a reactive "repair and replace" model to a Predict & Prevent partnership.

The Burning Platform: Why the Shift Is Necessary

The traditional insurance model is under growing strain. Rising losses from weather-related catastrophes and so-called secondary perils have increased earnings volatility and placed pressure on the affordability and availability of coverage in certain regions.

Swiss Re's research highlights that global protection gaps — the difference between economic losses and insured losses — remain large, despite recent improvements in industry profitability and capital strength.

While favorable macroeconomic conditions may support insurers' ability to absorb risk, significant portions of global exposure remain uninsured, underscoring structural limitations of risk financing alone.

If insurers continue to operate primarily as financial utilities that engage only at the point of loss, two strategic risks emerge:

• Commoditization: Clients increasingly perceive transportation insurance premiums as a necessary cost rather than a source of real value.
• Adverse selection: As pricing hardens, lower-risk clients may retain more risk through captives or higher deductibles, leaving carriers with deteriorating risk pools.

These dynamics reinforce the need for insurers to move beyond indemnification toward models that improve client resilience.

The New Value Proposition: Active Risk Partnership

The future winners in commercial lines will be those that become active risk partners. The goal is to move from merely financing the loss to mitigating the circumstances that cause it.

McKinsey says the future of insurance lies in evolving from "detect and repair" to "predict and prevent," estimating that this shift could fundamentally reshape the industry's role in the global economy.

This shift requires converging three key capabilities to change the client relationship:

1. IoT: Moving From Observation to Intervention

For years, telematics has been used primarily for pricing segmentation. The strategic pivot involves moving from passive monitoring to active intervention.

In commercial property, the focus is shifting toward sensor technologies that can physically intercede to prevent losses. Water damage—a primary driver of non-catastrophe commercial property losses—can be significantly mitigated through IoT-enabled automatic shut-off valves.

Deloitte has highlighted how this technology is transforming commercial real estate risk management, allowing insurers to eradicate high-frequency attritional losses and preserve capital for true catastrophes.

2. AI and Data: Democratizing Risk Engineering

Historically, bespoke risk engineering advice was reserved for the largest corporate clients. Today, advanced analytics and AI allow carriers to scale this advisory capability across the mid-market portfolio.

Analysis by Accenture indicates that generative AI is moving beyond back-office efficiency and toward core business functions, including underwriting and risk advisory. By ingesting vast amounts of data regarding location and assets, insurers can create near-instant, personalized risk assessments, enhancing the underwriting process and improving portfolio quality.

3. Parametric Structures: Closing the Resilience Gap

Traditional indemnity remains vital, but its claims adjustment process is often too slow for modern business continuity needs.

We are seeing increased interest in parametric solutions used not as replacements for traditional covers, but as complements. These solutions, triggered by objective data parameters (such as wind speed or flood depth), provide rapid liquidity. Marsh McLennan notes that parametric structures are increasingly vital for covering non-damage business interruption (NDBI) and providing immediate cash flow while traditional claims are processed.

The Executive Takeaway

The transition to Predict & Prevent is not merely a technology upgrade; it is a fundamental business model evolution.

It changes the carrier's role from a distant payer of claims to an always-on partner in business resilience. For the C-suite, prioritizing this shift is essential not only for improving long-term underwriting ratios but for ensuring the continued relevance of the insurance industry in a riskier world.