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.

These 2026 cybersecurity predictions offer insights into the trends that will be front and center in the coming year, particularly around AI and data protection.

There is a great deal at stake. The business world is on the verge of making significant AI breakthroughs, but the technology has some serious security gaps that must first be addressed. At the same time, regulatory environments around the globe are increasing pressure on companies and AI providers to establish provable, trustworthy practices. And while practical quantum attacks may be several years away, the "harvest-now, decrypt-later" threat is creating urgency for organizations to act today.

All of this is driving a critical need for organizations to ensure end-to-end protection of data at rest, in transit, and, most importantly, in use – without which, companies expose themselves and their customers to potentially irreparable harm.

The predictions below are intended to help guide organizations in embracing AI-powered innovation while maintaining persistent protection of sensitive data and ensuring regulatory compliance across regions worldwide.

1. Confidential AI: A Business Imperative

In 2026, enterprises will integrate AI more deeply into core operations, moving beyond experimentation toward scaled, business-critical deployments. This expansion will expose the limits of today's security measures and accelerate demand for "Confidential AI" — systems designed with built-in privacy, encryption, and trust guarantees.

Much like the early days of the Web, when open protocols gave way to HTTPS and SSL, organizations will shift from simply using AI to securing the full AI lifecycle – from data ingestion to model training and inference. As breaches targeting AI models and systems increase, companies will adopt proactive protection strategies by embedding privacy, encryption, and integrity controls directly into their AI architectures.

As enterprises advance their AI capabilities, Confidential AI will emerge as the new standard – embedding privacy and protection into every layer of the AI lifecycle. Through continuous, end-to-end encryption and confidential computing, organizations can train and run models securely, even on sensitive data. In the year ahead, growing demand for zero-trust AI ecosystems will redefine the landscape, making security the hallmark of enterprise AI rather than an afterthought.

Predictions 2026:

• Organizations will move beyond protecting perimeters and threat detection to securing models, data, and inference chains.
• Confidential AI, powered by continuous encryption and secure enclaves, will define the next phase of AI security.
• AI security will evolve toward "Confidential AI," where encryption and privacy-preserving computation become essential for trusted enterprise deployment.

2. Compliance and the Rise of Sovereign AI

In 2026, as AI compliance takes center stage in the enterprise, we'll also see the rise of Sovereign AI – nationally governed AI ecosystems that affect global data flows. As nations tighten restrictions on how models are trained, hosted, and shared, companies will face growing pressure to demonstrate compliance across multiple jurisdictions simultaneously. This will require organizations not only to meet their country's privacy and data security standards but also to comply with foreign laws governing AI transparency, data residency, and model integrity.

The next wave of regulation will focus on "trustworthy" models – AI systems that can prove, through cryptographic means, that data remains secure and private throughout the entire lifecycle. Governments, telecom companies, and cloud providers will need to go beyond contractual promises to offer verifiable assurances that they cannot see or misuse a customer's data or model. Model theft, data exfiltration, and misuse of AI-as-a-Service will rise sharply, forcing providers to deliver cryptographic evidence of confidentiality to satisfy regulators and clients alike. In 2026, enterprises operating in multiple regions will recognize that compliance and security are inseparable, and that continuous encryption will become the cornerstone of regulatory trust in AI.

Predictions 2026:

• Sovereign AI will push multinational compliance beyond borders, demanding alignment with overlapping global standards.
• Model-as-a-service providers will face rising risks of theft, exfiltration, and regulatory scrutiny.
• "Trusted models" will require cryptographic proof of data confidentiality and model integrity.
• Technologies such as FHE and secure inferencing will underpin compliance verification frameworks.
• Cryptographic assurance will become the foundation of AI trust, compliance, and cross-border collaboration.

3. Quantum Resiliency and Latency-Optimized FHE

In 2026, quantum computing – both a looming technological breakthrough and cybersecurity threat – will remain a key concern among enterprise CISOs. The "harvest-now, decrypt-later" tactic, where adversaries stockpile encrypted data to decrypt once quantum hardware matures, will accelerate demand for latency-optimized, quantum-safe encryption. When the day comes, traditional standards like RSA and elliptic-curve cryptography will be rendered obsolete by quantum algorithms, leaving unprotected financial data, health records, and AI models vulnerable.

The next wave of security innovation will focus on quantum-resilient encryption at scale, capable of protecting data without slowing real-time AI workloads. Latency-optimized, production-grade fully homomorphic encryption (FHE) enables computations on encrypted data, safeguarding information throughout the AI lifecycle.

Predictions 2026:

• The "harvest-now, decrypt-later" threat makes post-quantum migration urgent today.
• Latency-optimized FHE enables quantum-resilient AI inference without compromising performance.
• Quantum-safe, latency-optimized FHE will be recognized as essential infrastructure for securing AI systems worldwide.

In the coming year, the benefits of AI will only be accessible when the technology is built upon a tapestry of privacy and security. The collective, urgent need for proactive protection, data sovereignty, and quantum resilience is driving Confidential AI to become a prerequisite for enterprise competition and trustworthiness. By building security into the very fabric of their AI, organizations can confidently protect their most valuable assets, innovate across borders, and gain the competitive trust necessary to thrive in the global digital economy.

As we take our first steps into 2026, many enterprise leaders are focused on a familiar concern: how to keep employees' digital skills current. Strategies center on reskilling programs, digital academies, and AI literacy initiatives. These efforts are well-intentioned, but they target the wrong problem. The issue is no longer that employees lack digital skills. It is that the usable life of those skills has collapsed.

Technology is evolving faster than organizations can formalize roles, update training curricula, or redefine career paths. By the time a skill is taught, validated, and embedded, it is often already outdated. In this environment, treating digital fluency as something employees can "catch up on" is a losing strategy. Heading deeper into this year, enterprises must stop thinking about digital fluency as a destination and start treating it as a continuous operational capability.

The skill gap is widening despite investment

There is no shortage of data underscoring the urgency. McKinsey research shows that companies with leading digital and AI capabilities outperform competitors by two to six times in total shareholder returns. Yet McKinsey also reports that nearly six in 10 workers will require significant retraining before 2030, and fewer than half of job candidates possess the high-demand digital skills employers list in job postings. These gaps persist even as learning budgets expand.

The disconnect is structural. Traditional upskilling assumes relative stability, predictable tools, and slowly evolving roles. That assumption no longer holds. AI, automation, and workflow technologies are reshaping jobs continuously, often faster than organizations can document the change. No centralized training function can keep pace with that level of volatility.

Digital fluency is no longer an IT issue

Another constraint holding organizations back is the belief that digital fluency primarily belongs inside IT or data teams. That distinction has eroded. Business leaders now oversee AI-driven decision systems, automated workflows, and technology-enabled products. According to McKinsey's "We're all techies now" analysis, executives increasingly need foundational understanding of cloud architecture, data governance, cybersecurity risk, and AI trade-offs simply to perform their roles effectively.

In insurance, this shift is unavoidable. Underwriting, claims, fraud detection, regulatory reporting, and customer engagement are all shaped by technology decisions. When only technical teams understand how these systems function, the organization becomes slower, more brittle, and more exposed to operational and regulatory risk. Digital fluency must extend across underwriting, claims, compliance, operations, and leadership if insurers expect to adapt at speed.

Training cannot keep up. Adaptability can.

Faced with accelerating change, many organizations respond by expanding course catalogs, launching academies, or mandating certifications. These efforts provide value, but they do not solve the core challenge. Skills decay faster than training cycles can replenish them. What matters more is whether employees know how to adapt as tools, workflows, and assumptions change.

This is where the conversation needs to shift. Continuous learning is not about more content. It is about redesigning work so learning happens inside execution. McKinsey has found that while 80% of tech leaders view upskilling as the most effective way to close skills gaps, only 28% plan to meaningfully increase investment in the next few years, in part because traditional programs struggle to show return. Learning that sits outside real work rarely scales. Learning embedded into workflows compounds.

The insurance workforce will be judged differently

As digital skills become more transient, performance expectations must change. Employees cannot be evaluated solely on mastery of specific tools. They must be assessed on how effectively they adapt, challenge outputs, collaborate across functions, and apply technology responsibly.

For insurers, this means valuing underwriters who understand how models behave rather than simply how to operate them. It means claims professionals who can work alongside automation while exercising judgment in ambiguous cases. It means leaders who can interrogate AI-driven outcomes, governance structures, and risk exposure without relying exclusively on technical intermediaries.

The real challenge for 2026

The organizations that succeed in 2026 will not be the ones with the longest skills lists or the most certifications. They will be the ones that redesign work so adaptation is unavoidable and learning is constant. Managers will be expected to coach in real time. Workflows will be designed to expose employees to change rather than shield them from it. Technology will teach through use, not through periodic retraining.

The uncomfortable reality is this: no enterprise can train its way out of rapid technological change. Skills will continue to expire. That is not a failure of people or programs. It is a structural condition of modern work.

The real question for leaders heading into 2026 is not how to preserve digital skills, but whether their organizations are built to function when those skills inevitably expire.

Insurance fraud in property and casualty insurance is no longer dominated by isolated bad actors slipping through the cracks. Increasingly, the most damaging fraud comes from coordinated networks of claimants, service providers, and intermediaries who understand carrier processes and exploit their fragmentation. These schemes rarely trigger obvious red flags at the individual claim level, which is precisely why they are so costly and so difficult to stop.

The Scope and Impact of Fraud in P&C Insurance

The cost of fraud in insurance is staggering. According to the Coalition Against Insurance Fraud (CAIF), total insurance fraud (across all lines) is estimated at $308.6 billion per year in the U.S. Within the P&C sector, fraud represents a significant portion of losses. Industry analyses consistently estimate that 10% of P&C claims are fraudulent or involve fraud-related padding.

A recent report on fraud detection solutions estimates that roughly $50 billion in P&C paid claims may be fraudulent each year. Meanwhile, the FBI (via NAIC-supported research) has historically pegged total non-health insurance fraud — including P&C — at $40 billion annually. Another survey found that many insurers believe fraud costs represent 5-10% of their claims volume; some even reported that up to 20% of their claims were subject to suspicious activity.

These losses hit insurers' bottom lines directly through inflated payouts, but the effects ripple outward:

•  Premium increases: Fraud raises loss costs, which then contribute to higher premiums for honest policyholders.
• Operational drag: Investigating suspected fraud takes time, manpower and specialized expertise.
• Reputational risk: Organized fraud rings damage trust in the industry and can create regulatory scrutiny.

Why Network Fraud Evades Traditional Detection

Network fraud (also called collusive or organized fraud) refers to schemes where multiple actors cooperate to submit fraudulent claims or inflate losses. Rather than isolated "opportunistic" fraud, these are coordinated efforts that may involve:

• Claimants staging accidents or exaggerating damage.
• Repair shops or body shops submitting inflated or fake invoices.
• Brokers or agents steering business toward fraud-friendly networks.
• Collusion between claimants and medical providers, attorneys or investigators.

Because these networks are interconnected, detection is especially challenging. Traditional rule-based fraud detection — checking individual red flags on a claim — may not catch the systemic patterns. That's why more insurers are turning to network analytics, which analyzes relationships across participants to spot suspicious clusters.

In academic research, for instance, social network models have been applied to fraud detection. One study built networks linking claims, brokers, garages, policyholders and other participants, then applied specialized graph analytics to identify highly suspicious claims. These network-based approaches outperform traditional models, because they reveal hidden collusive structures that might evade conventional detection.

How Fraud Strategy Has to Change

Fraud Can't Be Evaluated One Claim at a Time Anymore: Most fraud controls were built to spot problems inside a single claim file. That approach works for opportunistic fraud, but it breaks down when activity is coordinated across people, vendors and claims over time. In network fraud, no individual claim looks especially suspicious. The risk only becomes clear when patterns emerge across claims, repair shops, providers or intermediaries. That reality is forcing insurers to connect signals across systems and teams rather than relying on isolated reviews.

Better Detection Still Requires Human Judgment: Advanced analytics and AI have made it possible to surface patterns that would never be visible through manual review alone. Network and relationship analysis can highlight repeated interactions and unusual clustering across participants, while machine learning can flag claims that deviate from expected norms. But technology does not replace investigation. These tools are most effective when they help experienced SIU teams focus their time on the cases that truly warrant deeper scrutiny.

Organized Fraud Rarely Stops at One Carrier: Fraud rings are adaptive and mobile. They move across insurers, jurisdictions and lines of business, learning quickly which controls are enforced and which are not. That makes fraud difficult to contain when each carrier operates in isolation. Industry collaboration, shared intelligence, and participation in fraud bureaus play an increasingly important role in disrupting organized activity. When carriers connect what they are seeing, repeat actors and emerging schemes become much easier to identify.

Prevention Starts Earlier Than Most Carriers Expect: As fraud becomes more organized, prevention matters as much as detection. Strong identity verification, consistent documentation requirements early in the claim, and training for frontline claims staff all reduce opportunities for coordinated fraud to gain traction. Clear communication around fraud consequences also acts as a deterrent. Over time, these measures lower investigative workload and help ensure that legitimate claims move through the system without unnecessary friction.

Network fraud is not a future concern for property and casualty insurers. It is already reshaping loss costs, investigative workloads, and customer trust. As fraud becomes more coordinated and less visible at the individual claim level, carriers that rely on traditional controls will continue to react after losses are locked in. Those that treat fraud as a networked risk can disrupt organized schemes earlier, protect margins more effectively, and reduce the burden on honest policyholders. The difference is not whether fraud exists. It is whether insurers can see it clearly enough and early enough to act.

A cyber incident hits a small business in a way that feels personal. The owner watches familiar tools fail, customers get locked out and schedules fall apart. They turn to their insurer looking for clarity, yet their expectations rarely match the process ahead. That disconnect shapes the entire claim and exposes where small businesses still misread cyber risk.

Many small operators still think that cybercriminals are carefully selecting their next target, but the reality is that automation has made it easier than ever for attackers to run broad scans across the Internet and strike wherever a shared account or outdated plugin gives them an opening.

It really doesn't take much. A rushed login, a forgotten update or a credential reused for convenience gives attackers the foothold they need. Those details rarely seem dangerous until the incident interrupts revenue, and the business owner realizes the breach reached everyday tools they rely on to run their business.

Misunderstandings around what triggers coverage

Confusion around coverage starts before a claim reaches the adjuster. Many small companies expect a cyber policy to function like a technical repair plan and anticipate quick fixes while assuming the carrier will take over the issue. Business owners also underestimate how fast costs build when dealing with investigations, data restoration and income loss that pop up in just the first hours of an incident.

The same pattern appears when incidents look minor from the outside. I've seen something as simple as a corrupted device wiping payment records for a food truck, and a damaged workstation forcing a photographer to cancel paid work. These events lacked the drama of national ransomware stories, but they still created lengthy downtime.

Tight staffing makes the fallout worse, because a single compromised login or failed device slows customer communication and sales. With no spare hands to absorb the disruption, small issues turn into long setbacks that strain the entire operation.

Where expectations diverge from what coverage provides

Cyber liability policies support recovery on two fronts.

• First-party coverage helps with investigation teams, data recovery, income loss and negotiation support during ransom talks.
• Third-party coverage addresses the fallout customers experience when their information becomes exposed.

Many small operators focus only on the technical failure and overlook how much involvement they will have once the claim starts. They may need to grant investigators access to certain devices or review which customer touchpoints were affected, and those responsibilities hit while they are already trying to steady the business.

On the flip side, executives hear from owners all the time who assume the carrier will fix the technical problem outright and feel blindsided when they learn how many steps sit between the first alert and a stable recovery. They do not anticipate the coordination needed to rebuild systems or manage customer notifications. That misunderstanding adds pressure to an already tense situation.

Security habits that stall during underwriting

Underwriting often uncovers a different kind of confusion. Many owners treat basic safeguards as add-ons rather than the foundation that keeps incidents contained, and controls like MFA or scheduled backups usually receive attention only when the application requires them, not when the business starts taking on digital risk. Once owners finally put those safeguards in place, they tend to keep them because the checks and alerts make daily operations steadier.

An office can cut risk by using a password manager and running brief phishing reminders with staff. A restaurant benefits when its reservation system restores from a recent backup instead of staying offline after a plugin failure. These steps rely on consistency more than technical skill, yet many owners delay them until an attack forces the lesson.

A clearer path forward

Cyber incidents reveal more about a business than the breach itself. They expose how prepared the team feels, how they handle uncertainty and how they respond when everyday tools fail. Insurance leaders watch this play out across industries, and those moments show the real gap between perception and reality for small companies.

Carriers cannot remove the stress that comes with a breach, but they can steady the path forward. Helping owners understand their role and their responsibilities changes how they navigate the experience. Cyber liability coverage gives them a path to continue operating during their hardest moments. Helping small companies understand that earlier reduces friction and sharpens the support they receive when an incident tests their systems.