Selling insurance, whether automotive, home, life, or other types, has traditionally been a relationship-based experience. Many agents work with clients for years, and knowing their customers' evolving needs is key to upselling and building a book of business. As artificial intelligence (AI) seeps into every industry and consumers are more cost-conscious than relationship-driven, insurance companies and agents are taking a critical look at the technology to determine how it fits into the insurance business model.

On the surface, AI can lessen the administrative burden for agents who answer frequent and simple inquiries, while also helping to process claims, identify potential risks, and deliver personalized plans based on historical data. Up to 20% of claims filed are fraudulent, and AI is analyzing patterns to help insurers identify which cases are legitimate.

Yet, pushing too much of the relationship to self-service, automation, and bots can hurt brand loyalty.

While there are many potential benefits to AI, how it is implemented within the structure of the business is key to using it effectively and supporting a better customer experience.

Barriers Between Insurance Companies and Customers

Industry data has identified a generational divide between younger and more technologically savvy customers, who prefer digital solutions, and older generations, who prefer traditional phone-based services where they can speak directly to a human.

Across generations, some people are open to using self-service chatbots, automated SMS messaging, or AI agents, while others are less interested in this style of communication. In fact, up to 40% of people feel "unfavorable" toward chatbots due to past negative experiences or a lack of trust in the technology.

To protect the customer's experience when researching or purchasing insurance, filing a claim, or requesting support, insurance companies should consider an approach that integrates the human touch with elements of AI. This approach will ultimately optimize efficiencies and cost savings without sacrificing the quality of the member experience connection.

Taking a Human-First Approach to Filing Insurance Claims

There are several key downsides when human oversight is left out of the customer experience journey. Many interactions with insurance companies follow stressful experiences like a car accident or home damage caused by a natural disaster. While efficient, automation does not have the capacity to deal with these situations using empathy.

Customer needs are too complex for AI. Allowing a human agent to be the first touchpoint in the journey ensures that the customer is receiving personal and empathetic support, lessening their stress and anxiety and helping them through difficult scenarios.

For example, if someone experiences hail damage and is looking to file a claim, they may contact their insurance agent via the app, phone, or online. From here, a human agent can evaluate the customer's needs and communicate the best route for effectively filing a claim while helping to put the customer at ease. Then, it is up to the agent to decide if and when AI should be used.

In this case, AI could deliver basic information such as next steps in the claim process, common safety measures homeowners should take after a hailstorm, such as securing broken windows, and how to photograph damage. AI can also gather data from the customer, such as date and time of incident, address, and property details. AI can help schedule an inspection with an adjuster and automatically input all data into the customer record.

During this part of the transaction, it is important that the customer has the ability to reconnect with a human agent if they have questions or concerns to ensure they are not stuck in a frustrating loop with a chatbot or AI agent. Balancing this combination of human and AI interaction creates a sense of personalization, supports empathy, and frees agent time by offloading common or administrative tasks. It also supports brand loyalty because the customer feels supported by their insurance agent and always has a path back to a human.

Continued Optimizations for AI Advancements

Insurance companies should continue to monitor technology advancements and be open to adapting customer service models as AI evolves. There is not a one-size-fits-all approach when it comes to AI and automation. As roles and responsibilities of human agents continue to shift due to AI, it is important to document where humans and AI each add their own value to new and existing processes. Finding a strategy that effectively balances human support and AI will lead to increases in productivity and efficiency while still ensuring that customers are highly satisfied with their experience.

Technology plays a pivotal role in transforming the insurance industry, but it's not always an easy relationship. Many insurers struggle with outdated work methods as well as legacy systems.

While most insurance companies view technical debt as a major hurdle for innovation, it's easy for them to overlook the other two legs of the stool: process debt and organizational debt. These three legs work together to form a complex system, and neglecting any one of them can lead to stagnation.

Tackling technical debt

Technology is the backbone of the modern insurance industry, yet many companies still grapple with how to replace, integrate or phase out their older technologies.

Approximately 70% of IT budgets is consumed by legacy system maintenance, according to Forrester. Meanwhile, insurers struggle with complex integrations that are costly and hard to implement. As a result, they are constrained by outdated, siloed ways of operating.

For example, an outdated policy management system used by a life insurance company can result in long claim settlement times and difficulty complying with new privacy regulations.

The good news is that cloud, AI and other solutions can help insurers modernize their technology infrastructure and work more efficiently. For example, AI tools are available to help with migrating, consolidating and even converting a company's multiple legacy policy administration systems into a more modern, future-proofed solution.

Overcoming the weight of process debt

Insurers are under pressure to accelerate growth and innovation, streamline operations and provide faster, more reliable services to policyholders. However, they are often constrained by complex and highly manual, outdated processes and workflows. This leads to wasted time, money and productivity.

HFS Research estimates the insurance industry is burdened by $66 billion in process debt, which is the buildup of outdated, overly complex, or inefficient workflows and practices that made sense at one point but no longer align with goals or realities.

The key to overcoming process debt is to identify and address its root causes. This requires a thorough assessment of current workflows and practices, followed by targeted interventions to streamline and simplify processes. Then, businesses can recapture lost productivity, reduce waste, and ultimately achieve their goals more effectively.

For example, new technologies like smart workflow systems and persona-based portals help connect different front-, middle- and back-end tasks (like customer service, policy administration and billing/collections) so they can be completed automatically. This allows a company's external users (e.g., customers and producers) to do front-office work on a self-service basis, freeing internal staff to focus on more complex middle- and back-office tasks. As a result, insurance companies can offer more modern, efficient and personalized experiences for their customers.

Unburdening organizational debt

When insurance companies tackle technical and process debt, they often overlook the accumulation of inefficiencies, outdated practices, and structural impediments that hinder their ability to adapt and evolve with the times. In addition, organizational debt accumulates when the knowledge of these products, processes and procedures is not documented effectively and is only available from an aging workforce.

Think of organizational debt as the "interest" an organization pays for not addressing these problems. Overcoming it requires a fundamental shift in how teams collaborate, how culture manifests and influences decisions and overall team dynamics, and how customer needs are met at every turn.

For insurance companies, this means understanding the individual experiences customers crave — from preferred channels to accurate recommendations. It means using data intelligence to identify specific touchpoints that meet customer needs. It also means making sure that all the different departments in the organization (claims, finance, legal, underwriting, etc.) are aligned to the same goals.

From debt to innovation

Paying down each of these three types of debt isn't easy, but it is a worthwhile goal to pursue.

It requires a holistic approach that involves upgrading technology infrastructure, streamlining workflows, and aligning organizational culture with modern practices. New tools and solutions can help by automating manual processes, improving data visibility and reducing overall risk.

By shifting the focus from maintenance to innovation, organizations can explore new ways of working, create a culture that is flexible, adaptable and forward-thinking and be free to focus on what truly matters: delivering value to their customers.

The International Insurance Society's 2025 Global Priorities Survey found that two-thirds of the senior insurance executives surveyed listed artificial intelligence as a top priority for their technology and innovation agendas. That is up from 55% last year and represents a huge increase from 17% in 2021. 

But it also means that one-third of the executives DON'T think AI is a top priority for innovation. Hmmm.

The survey also found that "concerns over the speed of technological advancement have eased." Really? 

I just published an article that predicted that an AI available to insurers a year from now will be 10 times as powerful as today's, at 1/100th the cost. Whether that's precisely right, it's certainly directionally correct. So I, at least, am thoroughly discomfited by the speed of change and can't imagine why others aren't, too.

The IIS survey provides a great baseline every year for understanding the state of play in the insurance industry... and I have thoughts.

The survey — which I encourage you to preview here — includes a number of responses that suggest executives are alive to the possibilities of AI. For instance, among internal priorities, operational efficiency is a top issue for 50% of respondents, making it the highest priority for the second year in a row. I suspect that emphasis doesn't just reflect a need in a highly competitive environment but also shows an understanding of the huge number of relatively straightforward opportunities that generative AI presents for automating processes. 

I think the possibilities of AI also show up in the near doubling of respondents who said the aging workforce is a top priority (from 11% last year to 20% in 2025). Again, there is a huge need, given that hundreds of thousands of insurance company employees are expected to retire over the next few years. But AI also presents great opportunities, both to preserve the knowledge of those walking out the door and to provide data and tools to new recruits that can bring them up to speed much faster than in the past. 

The responses on cyber seem to incorporate some AI optimism, too. The percentage of those identifying cyber security as a top three priority in the political and legal category dropped to 57% in 2025 from 75% in 2024. While AI certainly makes hackers more effective, the good guys seem to be using advances in technology, including AI, to improve defenses at least as fast as the attacks are intensifying.

It's certainly encouraging to see a huge increase in the number of respondents saying they are focused on addressing technological advancements — 41% in 2025, up 16 percentage points from 2024.

But I worry that too many executives are still too complacent about all the change that AI will effect. Yes, we're almost 2 1/2 years into the generative AI era, and the sky hasn't fallen. But Amara's Law is undefeated. It says we overestimate the effects of a major technology change in the short run but underestimate its effects in the long run, and we're starting to move into the long run. 

I think insurers are getting a pretty good handle on the operational efficiencies available to them, but they should be acutely aware of the larger possibilities. Someone may figure out how to reinvent processes for claims or underwriting or to radically improve agents' and brokers' productivity. There's also a huge amount of effort going into producing AI agents that can operate as, essentially, employees, with considerable autonomy. Imagine a world where you can give every employee 10 or 20 or 30 AIs that work for them at essentially zero cost.

So I'm delighted to see the emphasis on AI and innovation in this year's IIS survey. I just want to be sure we don't get comfortable.

Cheers,

Paul 

In today's insurance market, policyholders have more choices than ever. Switching providers is quick, easy, and often encouraged by comparison tools and challenger brands. And while price has traditionally been the battleground, it's now only one part of a much bigger picture. According to new research from Nuvei, nearly half of policyholders who switch insurers do so for reasons unrelated to cost.

At the center of this shift lies the claims experience, and more specifically, the speed and flexibility of payouts to customers.

Why faster payouts are the future of customer loyalty in insurance

For many policyholders, filing a claim comes during moments of stress or financial uncertainty. They expect insurance to provide reassurance, yet many are met with delays, outdated processes, and inflexible options. Long wait times, lack of transparency, and rigid payout options erode confidence—often permanently. In fact:

• The average claim lifecycle exceeds 100 days, while most policyholders expect significantly faster resolutions.
• 48% of policyholders say they would pay more for a faster payout, proving that speed isn't just convenient—it's valued.
• Only 35% of claimants receive direct deposit payouts, despite 58% saying it's their preferred method.

These delays can seriously affect customer satisfaction. A slow payout undermines confidence in an insurer's ability to deliver when it matters most. With 40% of policyholders switching providers annually, that perception can be costly.

How flexible payouts can give insurers an edge in a highly competitive market

Flexibility is increasingly essential. Policyholders want to choose how they receive their funds, whether that's a real-time bank transfer, digital wallet, or scheduled installments for larger claims. Meanwhile, 18% were still paid by check, introducing additional wait times and banking steps.

When insurers fail to provide this flexibility, frustration builds. The result? 19% of claimants report struggling to access their payout, reinforcing the belief that claiming is more hassle than help.

Meanwhile, digital-first insurers are raising the bar. With streamlined onboarding, transparent communication, and instant payouts built into their platforms, they're capturing market share from traditional providers who haven't kept up.

To stay competitive, insurers must stop viewing payouts as a back-office function and start seeing them as a core part of customer experience and retention.

The bottom line?

Faster, more flexible payouts build trust. They increase satisfaction. Ultimately, they give insurers a lasting edge in a market where loyalty is harder than ever to earn.

Discover more insights, data, and strategies in Nuvei’s latest whitepaper: “Mind the Claims Gap – Why UK Policyholders Are Losing Faith in Insurance Products, and How Payments Can Fix It.”

You can't be everywhere at once, and your training and processes can only be so good. Adjusters, especially junior adjusters, can miss things in claims files that make a big impact on losses. These might be big misses, like with deadlines, or might be smaller misses that have a big impact, like whether contributory negligence was present. Both these kinds of errors can have a significant impact on your bottom line.

Claim leakage can be avoided with better tools, and AI is the ideal solution. AI can handle most aspects of file review with accuracy and consistency. However, you need a trained AI model to yield true value.

If you already use a product like OpenAI's GPT, you know that it can run into issues with deep or complex issues. Even many of the newest models that help with deed research still run into problems with lengthy and detailed output at speed. However, you should not compare your experience with the publicly available models on the internet against the quality that AI-specific insurtech companies can provide. AI is amazingly accurate when properly directed and trained.

Well-trained AI can handle virtually all aspects of a file review. After a file is closed, AI can also supplement the audit process to ensure your carrier's best practices were followed. AI has the ability to review tens of thousands of pages and compare any checklist against the claim's ultimate outcome and payout. AI can be incredibly proficient at this kind of outcome.

Using AI does not mean you (or your team) abdicate control over claim files, or the review of files. You should still validate the data. However, because AI has the ability to cite to specific pages relevant to its analysis, this process can be significantly sped up. This is especially true with mountains of medical files that are not relevant to the claim, or significant witness statements or communication logs, where only small bits of information are helpful or relevant.

This kind of review catches mistakes quickly and can be a terrific learning tool for your team. AI can speed up training time for new adjusters, who can immediately see areas of files that they may have not considered. Even when adjusters are manually trained, AI can be implemented to validate results and facilitate faster understanding of the key job functions and key performance indicators.

AI is not perfect, but neither are humans. The good thing about programming AI is that it follows your instructions every time the same way. Even if some of its output needs adjusting here and there, AI can be given a checklist of 10, 20, or even 100 things to review for every single file. It can effectively replicate your best and brightest over and over again.

Replicating your "best" is a key point as you consider software options. You want an AI that will replicate the best practices in your organization that are followed by the top 1-5% of all adjusters (or whichever team you are considering the use of AI with). Good AI software will start with your output in mind and work backward to determine what data, and ultimately what type of AI focusing, is necessary to produce that best output.

This process is different from a technology company that wants reams of data to "find" and tell you about your company's best practices. This generally produces average results (at best) and requires substantial internal training and focusing time to get you something very useful. The process takes a very long time, does not succeed, and costs substantial amounts of money. The reason why this can fail is in the inherent nature of how AI learns.

If you give any AI system 100,000 documents and you ask it to provide you with key concepts from all those documents, it will do a good job at summarizing them. It may even produce parts of a usable document as a template. This is because AI works by looking for correlations in the content of the documents. It is going to look for the things that most commonly appear. If you think about all the sections within the 100,000 documents as appearing on a bell curve, AI is going to go for the meatiest middle part of that bell curve. It will give you the results that closely match the middle because it is looking for correlation among the documents.

The issue with AI giving you the meatiest middle part of the bell curve is that the middle is the average. Nobody wants the average. Mitigating risk and reducing losses isn't about catching the average issues within a file - it's about catching the absolute largest number of issues no matter who is reviewing the claim. Average seems helpful in theory but is a failure in practice.

You do not want AI to produce average results, so you do not want it to evaluate the middle section of the bell curve. You want it to give you the very best, which means you want the results from the right-most area of that bell curve that represents the top of the top results. Conversely, you want AI to stay away from the very worst examples that reside at the left-most area of the bell curve - the place where the majority of leakage resides.

To get the best from AI, you must instruct it on your best practices. "Best practices" can mean either the best process/checklist you use, or the best example of a report that contains all the data you expect to see from your best people. Once you instruct AI on the best practices, then you can move backward into the reams of data to fill in the content. With the right application layer that directs AI, the results can be truly remarkable. This does not require creating a large language model just for your company's use, but rather harnessing smart applications built on top of the existing models.

Remarkable results can help reduce risk through better and more consistent file analysis, whether by an adjuster, outside counsel, or as part of a file audit. It can also reduce staff time by removing much of the labor-intense review of files that can take hours or days. Because AI doesn't get hungry, stressed, or tired, the time savings also means higher quality.

AI can offer greater benefits beyond time and file management. For example, AI can identify red flags in files, like excessive treatment, pre-existing conditions, or missing documents. It can provide an adjuster with a clearer understanding of property damage or bodily injury to better assess the claimant's demand. Using AI can even reduce the likelihood of a claimant getting counsel because an offer can be made within days versus weeks of the first notice of loss. The faster an offer is made, the less likely the claimant is to hire a lawyer.

Addressing demand letters is a new and powerful use of AI that smart carriers are implementing immediately. The plaintiffs' bar is already using AI to produce those demand letters, and the companies creating them brag about how much more money their AI-generated demands yield. One demand-generating company that recently raised funds on a billion-dollar valuation advertises that its users are 69% more likely to max out policy limits.

AI can effectively be used to counter these demands by recognizing holes in the file and presenting those to claimant's counsel. This includes identifying holes in coverage, such as endorsements or intentional conduct that might reduce or eliminate exposure. AI can do an initial review of liability by comparing police reports and witness statements to determine causation, and even flag contributory negligence and the lack of mitigation of damages.

As part of a file review, AI can also analyze damages and whether those appear excessive in light of the injury or economic information in the file. These kinds of robust demand responses point out all the ways a claim's value is not as high as the other side believes. This can yield higher leverage and lower payouts through appropriate risk analysis. This kind of analysis and response would be ideal for every file, but it takes a lot of time to do manually. AI offers the ideal solution, with the ability to produce a comprehensive response in less than five minutes.

AI can also reduce employee and customer churn. Using AI can lead to greater job satisfaction for adjusters and for customers. Your employees all of a sudden get to focus most of their time on the things that bring purpose and meaning to their jobs. They get to think more about strategy, talking to stakeholders, and analyzing files versus simply sifting through piles of documents that AI can do faster and more accurately anyway.

Customers are less likely to churn as claims are resolved faster and at fairer, more consistent valuations. When AI follows the same standards in every file, then variation in claim payouts stabilize, leading customers to appreciate the transparency and speed in which their claim is resolved.

The benefits of using AI are many, but it is not perfect. However, when AI reduces the time and energy it takes to review one file from 20 hours to two, that still equals a savings of 18 hours. And that is just one file. As you consider using AI for your organization, focus first on the best results that your best people produce. Then work backward. Also remember that perfect should not be the enemy of the good.

Early forms of artificial intelligence (AI) have played a role in shaping our technological landscape since the mid-20th century, from Grace Hopper's early programming breakthroughs during World War II to the codebreaking efforts involving the Enigma machine. Innovations like ELIZA—an early natural language processing program in the late 1960s designed to simulate human conversation—paved the way for today's AI-powered tools. Over the decades, AI has been quietly integrated into everyday life, from generating entertainment content and powering virtual assistant chatbots in banking apps, to recommending shows based on our streaming habits. That quiet presence changed dramatically in 2023, when generative AI tools, like OpenAI's ChatGPT, disrupted the market and brought AI to the mainstream.

Alongside these advances comes a troubling counterpart: deepfakes, which are capable of creating hyper-realistic videos, audio, and images that can be weaponized to impersonate executives, manipulate markets, and erode public trust.

This article explores the cybersecurity and reputational risks posed by AI—particularly deepfakes—and considers whether existing insurance products are equipped to handle them. How will the response to generative AI incidents differ from those traditional cyber-related incidents? As generative AI technologies continue to advance and become more sophisticated—and adopted on a wide scale—insurance providers face the challenge of determining how AI risk should be treated within the scope of existing insurance products or if they warrant their own distinct insurance product.

The Threat of Deepfakes to Businesses

Deepfake threats can take many forms. While the types of threats discussed in this article are demonstrative, they are just a small sample of the possibilities AI opens to cybercriminals. Like "traditional" cybersecurity security threats, AI threats evolve hand-in-hand with the underlying technology.

Blackmail & Extortion: Threat actors could use deepfake videos to manipulate or blackmail a company. By creating fake footage of executives or key employees in compromising situations, cybercriminals can pressure organizations to comply with demands or face reputational damage.

Social Engineering: Imagine a deepfake impersonating a C-suite executive, authorizing fraudulent wire transfers, or gaining access to sensitive information. This scenario is no longer hypothetical. A notable case saw a finance worker at a multinational company tricked into paying out $25 million to fraudsters who used deepfake technology to pose as the company's CFO. The ability of deepfakes to mimic the voices, likeness, and even the mannerisms of company leaders make them a powerful tool for cybercriminals.

Market Manipulation: Competitors or even nation-states could deploy deepfakes to damage a company's reputation, manipulate stock prices, or disrupt public trust. Fake announcements, altered earnings reports, or fabricated speeches from top executives could quickly erode investor confidence, causing significant financial losses. And once information is out, even if false, it is hard to contain. For example, on April 7, 2025, a misleading tweet on X regarding President Donald Trump's tariff policy caused turmoil in the U.S. stock market.

Reputational Damage & Liability Exposure: While reputational harm was once a major concern in the early days of cybersecurity, evolving public perception has made such risks feel more commonplace—though that may change as sophisticated AI-driven deepfake attacks push the boundaries of what's believable and trustworthy. Deepfake attacks can cause significant reputational harm —especially for high-profile leaders of publicly traded organizations. A CEO's image and trustworthiness are critical for stock performance and investor confidence. Deepfake technology has the potential to erode that trust almost instantly. Even if the content is later proven to be fake, the damage to a company's public image can linger, and the financial impact can be substantial.

Beyond public image, these incidents may lead to allegations that company directors and officers violated fiduciary duties, such as inadequate financial reporting, or failure to implement prudent AI policies or safeguards. Professional liability exposure may arise if AI adversely affects the rendering or performance of professional services.

The creation of fake content—such as a deepfake video of an executive making damaging statements—could also lead to immediate loss of consumer trust, stock price volatility, and lasting damage to the brand. This kind of damage is not only hard to quantify but also harder to recover from in a traditional sense, as rebuilding reputation takes much longer than addressing technical fixes or financial losses.

How Should AI Risk Be Covered by Insurers?

AI-driven incidents present unique challenges that may not be fully addressed or appreciated by traditional insurance policies.

From a policy language perspective, defining what constitutes an "AI incident" could be difficult. While deepfakes are a clear example, AI is also being used in various other ways, such as in decision-making processes, automation, and data analysis. Will all AI-driven incidents fall under this coverage, or will they need to be explicitly defined?

Furthermore, the complexity of claims associated with AI incidents, such as fraud or misinformation, may require new expertise and claims handling processes. For example, it could be difficult to identify liability in a deepfake scenario—will the board of a publicly traded company be found at fault for failure to implement adequate AI safeguards if a deepfake impersonates a CEO and causes stock price drops thus negatively impacting investors?

These challenges have created a debate over whether AI-driven incidents are sufficiently covered under existing insurance products or whether an AI-specific insurance product should be created to address these risks.

There are two schools of thought on how to approach coverage:

1. Traditional Coverage Perspective: Some argue that AI risk does not inherently change the covered risk, but rather changes the magnitude of the risk. For instance, traditional cyber insurance generally covers the financial losses incurred by an insured arising out of a cybersecurity incident; be it business interruption, crisis management costs, reputational harm, or damages arising out of third-party liability claims or regulatory investigations. If a threat actor group uses AI to infiltrate an insured's system, and then deploys a ransomware attack, the use of AI does not change the covered risk (loss due to a network intrusion), but rather makes it easier for the network intrusion to take place. The same can be said about other lines of insurance whose insureds interact with AI. Therefore, AI risk should not be covered under a standalone insurance product, as it is sufficiently covered under existing products. Notwithstanding, carriers should actively consider AI risk in the underwriting process and amend pricing and modeling operations accordingly.

2. Standalone AI Coverage Perspective: Given the unique nature of AI-driven incidents, some argue that this risk should warrant its own stand-alone product. Traditional insurance products were not designed with AI in mind. This could lead to gaps in coverage for losses involving AI. There is also a rising trend of specific AI exclusions in existing products. Without a dedicated product, businesses may find themselves unprotected from AI risks.

While this is far from a settled matter, it will be interesting to see how the industry reacts and adapts to AI risk in the near future.

Final Reflections

The rise of AI-driven risks poses a significant challenge for businesses and insurers alike. Whether AI-driven risks are adequately covered under existing insurance products or whether they should have their own distinct coverage category is a nuanced debate that requires careful consideration of the risks involved.

On one hand, AI-specific coverage could offer more tailored protection for financial, reputational, and operational risks. On the other hand, integrating AI-related incidents into traditional coverages might offer businesses more streamlined protection.

Ultimately, insurers must stay ahead of the curve by adapting their policies, training claims teams, and rethinking risk modeling. Businesses, too, must reevaluate their coverage and internal controls to ensure they are not caught off guard by AI-driven incidents.

Employee Retirement Income Security Act (ERISA) lawsuits have experienced a fever pitch, with 136 new cases coming to light in 2024, a shocking 183% increase from the previous year. This trend seems likely to continue into 2025, as new ERISA-related lawsuits filed against Southwest Airlines and Charter Communications were brought forward. While these legal actions are becoming prominent, the ERISA legislation experienced a milestone, recently celebrating its 50th anniversary, further indicating the endurance of this law and its strong framework in protecting employee benefits and emphasizing the need for clear guidelines of fiduciary duties for those managing retirement plans.

2025 and beyond will be high stakes for employers and companies that are maintaining retirement plans for employees, otherwise known as plan sponsors. Risk mitigation and contingency planning for protection of individuals and companies are essential.

A new consideration for plan sponsors?

ERISA, major federal legislation that took effect Jan. 1, 1975, governs employee benefit plans of almost all types, and holds fiduciaries – as broadly defined – personally liable for plan administration and management. It has evolved over the years to ensure it is up to date with market changes and retirement planning requisites to better support employees. Inherently, it is a long and complex legislation with numerous nuances that contribute to plan sponsors' challenges in maintaining compliance. Recent class action litigation indicates that there are standout fiduciary areas where plan sponsors are struggling – including 401(k) plan forfeitures in defined benefit plans, pension risk transfers (PRTs) and health plans, as well as who bears administrative costs for these plans.

Most current lawsuits challenge how forfeiture in 401(k) plans is handled, and the outcome could have significant repercussions for the sponsor community. Typically, forfeiture funds are those contributions associated with employees leaving their jobs before fully vested in their employer's contributions to their 401(k) plans. Plan sponsors can use these forfeited funds to offset their contributions. However, many lawsuits argue that ERISA requires these funds to be used solely for plan expenses or redistributed to plan participants. While the verdict is still out on the court ruling, one thing is for sure – should the plaintiffs come out victorious, there could be a massive shift in forfeiture policies.

In the same vein, PRT-related cases are under the spotlight. PRTs have long been leveraged as a favorable strategy by employers to eliminate their pension obligations and associated risks. Plan sponsors have typically conducted transfers to an insurance company through an annuity purchase or a lump-sum buyout. Yet recent court cases indicate that the tides may be turning as plaintiffs have filed a handful of cases alleging that these annuities are too risky and thus fail to meet ERISA fiduciary requirements. This is another area that plan sponsors would be wise to watch, as the outcome could result in higher standards for PRTs.

Health plan litigation is another area of concern for plan sponsors. In 2024, class actions against health plans were all over the spectrum, from actions on health-based wellness programs to how plans choose to provide pharmacy benefits to their employees, particularly their choice of pharmacy benefit managers. Plan sponsors should be keen on keeping up with the effects as they can broadly affect their programs and require significant adjustments.

The impact of an ERISA-based lawsuit

Legal issues are never on the agenda for businesses, as they bring forward an onslaught of consequences, but ERISA-related lawsuits can play a particularly malignant role in an organization's continued growth and success.

Firstly, the short- and long-term financial strain can be debilitating. ERISA lawsuits incur mountains of legal costs – from attorney fees, settlements, and more – potentially reaching well into the seven-figure range. Additionally, under ERISA, plan sponsors may face personal liability for confirmed fiduciary breaches, potentially leading to civil penalties, removal of fiduciary status, or criminal prosecution.

Companies should also be wary of the reputational damage an ERISA lawsuit can cause. Stakeholder trust can be eroded following a lawsuit, making it challenging to hold onto and attract new investors. Similarly, talent attraction and retention are heavily affected. Employee benefits and retirement planning support are now expected by employees. If marked by an ERISA-related lawsuit, top talent may look for other organizations that meet their long-term financial wellness needs. By losing top talent, businesses will struggle to maintain and grow their business performance.

These examples of potential impacts underscore the importance of companies and plan sponsors effectively managing ERISA compliance and fiduciary responsibilities. The best way to mitigate these issues and their impact is to avoid falling victim to alleged breaches and staying alert about legal rulings. However, given the complexities and nuances of ERISA, it can be challenging to keep pace. Realistically, plan sponsors and businesses must be prepared to address potential issues from all angles.

The need for fiduciary liability insurance

Plan sponsors may be aware that bonds are required by ERISA; however, their protection is limited to fraud and dishonesty. For comprehensive risk management and to better navigate the growing trend of litigation, fiduciary liability insurance should be at the top of the list for fiduciaries and their organizations. Typically sold in increments of $1 million, this insurance offers valuable protection against allegations of improper judgment related to employee benefit plans, including, most importantly, covering legal defense and even settlements.

While it is understandable that concerns about costs exist, neither the mandatory ERISA bonding nor the optional fiduciary liability insurance should be seen as expensive. Considering the backdrop of regulatory fines and penalties from the Department of Labor for non-compliance and the increasing cost associated with defending against litigation, the cost of insurance is quite reasonable.

The protection from this coverage extends to the sponsoring organization, officers and directors, and plan fiduciaries. As ERISA holds individuals with discretionary authority over retirement plans personally liable for decisions that harm employee beneficiaries, fiduciary liability insurance provides essential protection.

Additionally, firms should enhance their compliance processes by leaning on technology-driven solutions to stay current with new ERISA provisions and automate wherever possible. Leveraging tools and platforms that can support tracking vesting schedules and contributions reduces human error and oversight, often the drivers of fiduciary breaches. Furthermore, digital-first solutions can support generating audit-ready reports if needed to demonstrate ERISA fiduciary duties are being met.

Navigating the future of fiduciary risk

The long-term success of ERISA demonstrates the effectiveness of its framework in protecting American workers' retirement plans. As new retirement trends emerge, market volatility increases, and regulations evolve, there will be a continued emphasis on risk mitigation and compliance. It is crucial for plan sponsors to stay updated and not fall behind in these areas. They must remain vigilant in managing funds in accordance with ERISA, especially as legal scrutiny intensifies.

However, due to the complexity of ERISA, it is not uncommon for gaps to arise. A significant aspect of risk mitigation involves preparing for the worst-case scenario, particularly in facing potential allegations of fiduciary breach. Robust defense plans should include solid fiduciary liability insurance, monitoring evolving regulatory frameworks, and updating/automating compliance practices. Only then can organizations and plan sponsors have the peace of mind to run excellent plans for the sole interest of participants and beneficiaries, which in turn benefits themselves and the organization.

In the complex world of insurance, underwriters and brokers play crucial roles — but they don't always see eye to eye. Brokers act as trusted advisors, helping clients find the right policies, while underwriters evaluate risks to keep coverage financially sound. Even though brokers and underwriters share an end goal, miscommunication and disconnected data often create friction between them.

What's the fix? Better data sharing and governance. 

Here's why the two factors are key to improving collaboration, building trust, and driving efficiency across the insurance industry.

The Role of Data Sharing

Underwriters and brokers can benefit from seamless, secure data sharing with enhanced risk assessment. For example, brokers can send underwriters detailed client data about applicable risks, such as operational metrics or a history of claims, enabling the underwriter to evaluate the risk with much greater precision. Underwriters can also share dynamic insights with brokers, so they can create custom policies that might better suit client needs. In general, better data sharing could reduce redundant communications and ad hoc, manual entry, streamlining the process of issuing policies and processing claims.

Data Governance

While secure data sharing is essential, the data itself must also be governed, not only to enable compliance and security, but also to improve the confidence of both parties in the integrity and authority of the data to be shared. In terms of compliance, mandates such as the California Consumer Privacy Act (CCPA) in the U.S. and the General Data Protection Regulation (GDPR) and Solvency II in Europe require especially strong data governance capabilities to align data with these regulatory requirements. Additionally, data governance can establish rules about which users can access which data, to protect sensitive client and business information from breaches and misuse. Finally, data governance maintains accuracy across shared platforms, reducing errors and improving decision-making. 

The Limitations of Current Technology

One would think that in this age of powerful data lake houses and other cloud platforms, data access, governance, and sharing would not be an issue. Although this is largely true, the gray areas of data lake houses — that is, the situations in which data lake houses alone cannot enable seamless collaboration — are becoming larger and larger.

Although data lake houses are considered capable of storing all data to support every need, they cannot store all data, and they will never be able to do so. During mergers and acquisitions (M&A), for example, the data resources of entire companies will be temporarily unavailable. And in the case of multi-cloud infrastructures, in which companies leverage the capabilities of different cloud providers, certain datasets or workloads will never be stored in the main, central lake house. Data-export restrictions, to comply with data privacy and other laws, are yet another reason why some data will always remain distributed.

From a collaboration perspective, when data is distributed, it is simply not immediately accessible, and therefore not governable, especially if it changes rapidly.

Even if a company did manage to keep all of its data in a data lake house, data lake houses have a few limitations with regard to collaboration. They lack universal-semantic-layer functionality, which means that some data within the lake house will not be immediately usable. Universal semantic layers automatically transform data from myriad applications and departmental silos into the form required by the end user. Similarly, data lake houses do not provide extensive search and discovery features with comprehensive access controls, presenting another obstacle to seamless underwriter-broker collaboration.

Logical Data Management: The Enabler

It is evident that underwriters and brokers need a solution — one that works either standalone or alongside data lake houses and other cloud platforms — and one that can connect disparate data sources and create a semantic layer above all of them, to enable seamless, secure, and governed data sharing, in real time.

One such solution is logical data management. This is a data management approach that operates differently from traditional, physically oriented data management approaches that rely on extract, transform, and load (ETL) processes. In contrast, logical data management platforms enable data management, including real-time access and governance, without first having to physically replicate data into a central repository. Organizations with data lake houses can easily implement logical data management platforms to include other cloud and on-premises data sources, even though the data may be geographically separated or otherwise in a functional silo.

Logical data management platforms enable insurers to create a unified view of all related data for brokers and underwriters. Leveraging APIs and open insurance standards, brokers and underwriters can use logical data management platforms to engage in seamless collaboration. AI-powered analytics can further enhance the potential of underwriter-broker collaboration, helping them to gain predictive insights in the realms of risk assessment and policy personalization.

The Missing Link

The entire insurance ecosystem benefits when underwriters and brokers can collaborate effectively and securely through the seamless sharing of well-governed data. For starters, policy issuance becomes faster, leading to shorter turnaround times, which helps improve client satisfaction and gives companies a competitive edge. With more accurate data, risk pricing becomes more precise, ensuring better profitability and fewer disputes. Plus, this collaboration enables the creation of client-focused solutions, offering policies tailored to specific needs and strengthening the relationship between brokers and their clients. On top of that, clear communication fosters trust and transparency and paves the way for long-term partnerships built on mutual respect.

As the industry continues to evolve, the need for seamless collaboration between brokers and underwriters is only going to grow. Embracing advanced data-sharing practices and strong governance helps bridge gaps and sets the groundwork for innovation, agility, and resilience in what's becoming an increasingly complex market.

Navigating the complex considerations affecting organizations and their third parties presents challenges for insurance professionals advising businesses on compliance matters. As external experts, insurance professionals can often provide key guidance on verification requirements that protect organizations, but this takes time. What begins as a simple call or email can often cascade into a series of lengthy exchanges — turning a straightforward inquiry into a time-consuming back-and-forth.

AI solutions are emerging as supportive resources that strengthen insurance knowledge, expertise and efficiency. What makes AI a game-changer isn't automation alone, nor is it replacing human expertise — it's how it equips those with insurance expertise, and those without, with intelligent insights to better understand what items are needed to achieve insurance compliance.

According to a recent survey, 90% of small business owners are unsure about the adequacy of their coverage. AI serves as an intelligent assistant, quickly surfacing important information and providing context when needed. This allows both insurance experts and non-insurance professionals alike to understand what's needed and why it matters, fostering alignment and transparency.

The impact includes faster verification, fewer coverage and requirement gaps left unaddressed, and faster time to compliance. As Gartner predicts a doubling in risk and compliance technology spending by 2027, companies recognize that AI solutions that enhance collaboration deliver the greatest returns.

In insurance compliance, AI provides benefits in three ways: quickly identifying emerging risks, providing deeper insights and analysis, and enabling informed decisions — all while reducing manual effort and enhancing accuracy.

AI provides intelligent risk insights

Insurance verification has long operated as a black-and-white checkbox: compliant or non-compliant. This binary approach frequently disrupts insurance professionals, who must answer repeated basic questions from clients and their third parties, taking time away from key advisory work.

AI can enhance the process by offering intelligent, real-time insights within existing workflows. The technology automatically screens uploaded certificates, instantly identifying non-compliant documentation and generating precise, tailored communications to insurance professionals in their preferred language and professional context.

By acting as both an intelligent flagging system and a nuanced translator, AI eliminates the time-consuming back-and-forth that typically delays compliance processes. Insurance professionals can now focus on strategic risk assessment, while the AI handles routine verification, communication, and alignment across different stakeholders.

For instance, the system can identify specific documentation needs. Instead of a simple status notification, an AI-powered platform can share what's needed in clear language, why it matters, and how to obtain it.

This clarity fosters an environment where insurance knowledge is seamlessly integrated into the process, creating alignment across all parties involved. With everyone operating from the same information, AI tools can streamline communication and reduce confusion.

The result is a more collaborative, transparent, and simplified process in which AI can handle routine inquiries. This allows professionals to trust that compliance is properly managed without the administrative headaches.

Meanwhile, third parties and their agents benefit from improved transparency, easy communications and automated notifications that demonstrate their compliance status, establish them as reliable vendors, and facilitate timely payments — all of which strengthen their business relationships.

AI centralizes compliance and enhances visibility

Compliance verification often involves multiple parties with different priorities and levels of insurance knowledge, which can create communication challenges and process inefficiencies.

For insurance professionals, AI transforms client advisory services through three key capabilities: providing real-time visibility into compliance status, identifying and clearly communicating specific documentation needs, and enabling automated, precise notifications to address emerging compliance gaps.

The transparency provided by these systems allows third parties to see precisely where they stand on compliance at any moment, enabling them and their insurance agents to take steps toward resolution. Organizations gain comprehensive visibility into compliance trends across their network, identifying patterns and opportunities for process improvement that might otherwise remain hidden in dispersed data.

AI's ability to analyze large volumes of compliance data also provides risk insights tailored to the appropriate industry context, flagging potential gaps in compliance that can be addressed by humans before they escalate into business disruptions. While AI can't yet fully interpret complex or conflicting information, these automated alerts help identify areas needing expert attention.

AI systems can provide instant responses to routine questions, highlight complex insurance industry terminology, and offer contextual guidance as end users navigate through the system, thereby enabling insurance professionals to dedicate their expertise to more nuanced and strategic case analyses. This creates efficiency while equipping end users with insurance knowledge, ensuring specialized expertise is applied where it adds the most value and creating a more streamlined experience for everyone involved. Requirements remain firmly in place, but the path to meeting them becomes clearer and more transparent.

AI delivers business impact

For insurance professionals, the business case for AI extends beyond helping clients achieve processing efficiency — it can enhance their own service delivery and advisory capabilities. AI creates value through three strategic dimensions: efficiency gains, speed to compliance, and relationship enhancement.

Time savings represent one of the most immediate benefits, as AI automates routine verification tasks and provides instant feedback. This acceleration removes bottlenecks that delay project starts, contract finalizations, and service initiations.

Coverage verification quality also improves. AI doesn't get distracted, tired, or rushed during busy renewal periods. Organizations typically see significant improvements in compliance rates when implementing AI-powered solutions. It's always helpful, ready, and insightful. It also flags renewals well in advance, giving everyone ample time to meet deadlines — eliminating those last-minute rush requests that disrupt workflows. This improvement represents real risk reduction through faster time to compliance and potential cost avoidance from unexpected claims that could arise from non-compliance.

Perhaps most valuable for insurance professionals is how AI can transform their client communications by automating timely, precise notifications across all parties. These systems ensure instantaneous, compliant updates that eliminate missed deadlines, reduce administrative stress, and keep insurance agents, businesses, and third parties seamlessly aligned — transforming potential communication chaos into a streamlined, proactive workflow.

Companies that adopt AI compliance tools can gain competitive advantages through faster onboarding, stronger protection, and more collaborative relationships. These advantages translate directly to bottom-line results through reduced administrative costs, lower risk exposure, and improved operational efficiency.

The future of insurance compliance

AI is transforming how risk insights are distributed and leveraged across the insurance compliance ecosystem. By providing relevant information exactly when it's needed, AI helps organizations, their third parties, and insurance professionals work together more effectively.

As these technologies continue to evolve, increasingly sophisticated applications will empower everyone involved in the compliance process — whether they have years of insurance expertise or are new to these requirements. These systems will enhance visibility across the compliance ecosystem, automate review and renewal workflows, and facilitate more transparent communication channels between all stakeholders.

With AI-powered compliance tools, insurance compliance can become even more efficient, accurate, and collaborative. Insurance compliance is evolving from a necessary process into a strategic advantage that strengthens business relationships while enhancing protection. The future belongs to companies that recognize compliance is about creating value, not just checking boxes.

Recent improvements in the dexterity of humanoid robots and of the AIs that control them have led to a surge of publicity about their prospects, not just for industrial uses but as possible helpers in the home. Elon Musk, never one to shy away from a bold prediction, says there will be more humanoid robots than people on Earth by 2040. 

So my ears perked up when I heard that a half-marathon in Beijing over the weekend would include a classification for two-legged robots, as a way of showing off all the progress Chinese scientists have made. The robots didn't do so well.

Of the 21 robots that were entered, one veered into a barrier right at the start and shattered, while throwing its human handler to the ground. Another's head fell off. Still another had smoke pour from its head, while one ran in the wrong direction at times, then sat down and declined to get up. 

All the robots took large amounts of human attention: changing batteries, spraying water on the robots to reduce overheating, etc. Many had to be tethered to controls held by a human, who ran (or, more often, walked) behind the robots. 

Only four of the robots finished in the allotted time of less than four hours, and the fastest took more than 2 1/2 times as long as the human winner, who clocked in at an hour and two minutes. 

None of that is to say that humanoid robots have no future. Enthusiasts liken the race to the Grand Challenge for autonomous vehicles held in 2001 that also produced embarrassing results but, 24 years later, has Waymo providing 200,000 fully driverless, paid rides each week in its robotaxis. 

But the race does suggest the need for a sober look at the hype about robots, to set expectations for the insurance industry over the next five to 10 years.

If you want to see for yourself what the race looked like, here is a video summary. (The broadcast cuts away after 50 seconds.)

For me, the upshot of the race, in keeping with other progress reports on AI and robotics, is that, no, humanoid robots won't outnumber humans in 15 years. Not even close. 

They will be especially scarce in homes, where they will accomplish little while costing as much as a car. (Musk says his Optimus robots will cost $20,000 to $30,000 when they become available next year — and he has a long history of overpromising.) I dislike doing dishes and laundry, vacuuming and dusting as much as the next person, but I'm not going to pay tens of thousands of dollars to avoid minor chores, especially when my Oura ring keeps telling me to get up and stretch my legs. And you want me to maintain the thing? The extent of my trouble-shooting consists of turning a device off and then turning it on again. 

Robots have much better prospects in manufacturing, where they are already a force and are helping workers' compensation carriers and employers keep reducing injuries and, thus, premiums. The robots don't look at all human, but they have automated an awful lot of the assembly in electronics factories and others. Amazon and others use robots to handle much of the grinding work in warehouses. 

Progress in manufacturing will continue, likely rapidly, because robots can benefit from improvements in AI while operating in a controlled environment, not having to worry about maneuvering in a small kitchen full of  little kids and a puppy.

Even in manufacturing, though, there are limitations. The Wall Street Journal reports, for instance, on how hard it's been for shoemakers like Nike to move work to automated factories in the U.S. and out of Vietnam and China. It turns out that the soft materials in the upper parts of shoes change consistency based on heat and humidity. Skilled human workers can adapt, but robots have trouble. Robots also struggle with the fact that no sole of a shoe is quite the same as any other. They have trouble, too, with the constant changes in shoe design; robots function best when they can finetune their handling of a task and then do it over and over and over and over. 

Those of us of a certain age long for Rosie, the maid in "The Jetsons." My daughters tell me the updated version is "Smart House," a Disney movie in which a boy tries to keep his father from dating by programming a house to be a surrogate mother. Or there's "Cassandra," a recent series about a family that moves into a decades-old smart home and reactivates its dormant AI assistant, who was once a human and who was transferred into an AI system. 

Whatever your hopes are for robotics in the long term, as you think about the prospects for the next five or 10 years, especially in the home, it's worth keeping in mind this image of the robot that crashed, shattered and threw its handler to the ground only a few feet past the starting line of the Beijing half-marathon:

Cheers,

Paul