Paul Carroll

Keeping in mind that conditions are changing rapidly, I’ll go ahead and ask, What's happening with the economy right now, and why are we seeing such significant disruption? 

Michel Léonard

We have basically been rethinking 50 years of American foreign policy and domestic policy, but mostly foreign policy. The State Department's mission, U.S. foreign policy’s mission, is to protect American citizens abroad and America’s economic interests abroad. It’s not surprising that, as we're rethinking our foreign policy or alliances, it's also disrupting the economy.

Institutions such as NATO [North Atlantic Treaty Organization], the IMF [International Monetary Fund], and the World Bank have been significant contributors to supporting American hegemony. For instance, the NATO charter says its supreme commander must be American. What does that mean in practice? It means that all NATO members' armed forces are, arguably, directly under the direction of the U.S. general in charge of NATO. And it’s been a practice that NATO members buy American weapons, American planes, and so forth.

Traditionally, the World Bank has an American as its head and the IMF a European. The World Bank was created in part to facilitate decolonization and the IMF to facilitate transition to market economics. In the second half of the 20th century, both organizations contributed to expanding U.S. influence and to reducing, among others, France's and the U.K.’s influence in their pre-war spheres of influence.

It's somewhat challenging to see the lack of understanding that these institutions contribute to U.S. strength, not U.S. weakness. We're transforming these institutional tools, part of a system that served us very well, that put us and kept us at the pinnacle of growth, wealth, consumption, and quality of life. And I don't think folks, whether on the left or the right, Democrats or Republicans, completely understand what’s happening and what that means for U.S. economic strength.

I’ll stop there so we don’t head into politics. 

Paul Carroll

Some of the projections about the impact of tariffs haven’t shown up in the numbers yet, but consumer confidence is way down. What impacts might we see in the coming months? 

Michel Léonard

The polarization in the U.S. has led to significantly different expectations of the economy and, as you're pointing out, the numbers (such as GDP and CPI showing tariffs’ impacts to date) haven't turned out to be what the consensus among economists was when tariffs were announced. Indeed, five months into the year, the U.S. economy remains more resilient than originally expected. There are some reasons for that: Inventories, for example, have been cushioning companies and softening or delaying increases in prices. Tariffs themselves have been implemented, suspended, reimplemented, and so forth. 

But we are indeed starting to see an effect with prices and CPI. And remember, price and inflation data are survey-based, and that's month to month. So, the data we are seeing now in May is already a month or a month and a half behind. On GDP, it's basically half a year. I think when the data is revised, it will show that consumers have it right, that the low confidence we’re seeing now will ultimately be justified by a higher CPI – they are living this inflation on a daily basis. Prices have increased significantly after several years of consecutive increases. 

And this time, unlike during COVID, people don’t have the option to stay home instead of commuting, which absorbed some of the inflation. Commuting is very expensive, as is eating out in downtown areas while working. I believe the economic numbers are actually worse than what we're currently seeing in the growth data, and inventories are being depleted. We're approaching that critical point now. 

The job market has tightened. Jobs are still growing, but at a much slower pace. This means the competition for employment is about to change, with job seekers losing bargaining power.

As you're pointing out, consumer confidence is at a low. What really matters most here is not the specific number. I always say it's better to be generally correct than precisely wrong. What matters here is that the current confidence numbers are comparable to during the financial crisis, the pandemic and the oil crisis.

There are significant differences between the University of Michigan and the Conference Board surveys, but they both point to consumers expecting prices to increase significantly as we go into Q3. And that has started: for example, the announcements from Amazon and Walmart and others, because inventories are being depleted. Both surveys also state that consumers expect further weakening in the job market, especially as inflation picks up.

Paul Carroll

How does tariff-related inflation differ from traditional inflation, and why don't the Federal Reserve's typical tools work as effectively against it?

Michel Léonard

I'm going to reference [former Fed Chairman Alan] Greenspan here. He used to say when it came to interest rates and monetary policy, "The medication must fit the disease." What he meant is that interest rate increases are the right medication for demand-driven inflation. When people are buying more and you want them to buy less, you raise interest rates. You're effectively making it more expensive for everyone to purchase, especially those who do so with credit cards or when buying homes and other big-ticket items like cars. 

What we're experiencing now is fundamentally different. We don't have more people or money competing for a stable supply of goods. Instead, we have decreasing supply, while consumer demand is also decreasing due to weakening sentiment and confidence. The issue is that supply is decreasing faster than demand, and that’s driving prices up. This inflation is supply-side driven. Therefore, increasing interest rates won't have the intended impact to decrease inflation. Consumer demand is already contracting, and rate hikes will have little effect on the supply constraints. We need to increase supply, and the answer there lies in trade policy. That's a political decision. The administration has stated its goals, though some aspects remain uncertain. 

Going back to Greenspan's principle, increasing interest rates would be the wrong medication for supply-driven inflation caused by tariffs. Technically, we would still decrease consumption if we increased interest rates. But to achieve meaningful impact, the Federal Reserve would be unlikely to succeed with modest 25-basis-point increases. The Federal Reserve would need bigger increases and a faster pace over several consecutive hikes. The Fed would need to telegraph to markets, companies, capital, banks, mortgage lenders, and auto financing companies that they'll keep raising rates. 

This approach would likely bring the economy to a complete standstill. For the property and casualty insurance industry, the goods traditionally most affected by interest rates increases are big-ticket items: homes, home improvements, cars, and major appliances – especially those typically purchased with financing. The P&C industry is fundamentally about replacing, rebuilding, and repairing. 

When looking at our forecast for 2025 and 2026, we expect P&C underlying growth, which is still above U.S. GDP growth in May 2025, to reverse over the next four or five quarters and start growing more slowly than overall U.S. GDP. We've always anticipated this shift. But what we're seeing now is that tariffs' impact, which would be worsened by interest rates increases, is shortening that period. If we previously expected four months of performance where P&C growth exceeded overall GDP, we now see perhaps three to four months. If conditions worsen further, that might shrink to just two to three months. 

Paul Carroll

What is your assessment of where the economy and markets are headed given the current political climate and protectionist policies? 

Michel Léonard

The way I've been thinking about this is through the lens of our international institutions. These multilateral organizations were designed first to create and then maintain a world with the U.S. at the center—militarily, economically, and diplomatically. We effectively had the world as our playground. Right now, we are pursuing decoupling. We haven't fully decoupled yet, but that's the goal.

If we continue decoupling, we would essentially reframe our economy from global to national— our market from 7 billion to 350 million people. I was speaking yesterday with financial professionals in Canada who noted that they are not seeing the same tariffs inflation as the U.S., and that while bilateral trade uncertainty with the U.S. is damaging GDP growth, the Canadian economy is seeing increases in investments from other countries that are now reluctant to invest in the U.S. Governments within the European Union and Canada have been eager to create other free trade zones and facilitate trade among themselves.

The implications are significant. For equity markets, U.S. companies are currently at the top of the global system with significant market access and the benefits of the dollar as the reserve currency. Equity valuations for the likes of Apple, Microsoft, our energy companies, and our banks are based on those companies being global. But if U.S. protectionism is met with retaliatory protectionism, American companies may suddenly lose access to a significant share of that global market. At the extreme, they may be left only with the U.S. market – say 75% fewer consumers and opportunities. When people ask me where's the floor for equity, I think of that 75% market loss scenario. I'm not saying it will happen tomorrow, but certainly in the longer term, there's no floor. The floor is being moved downward. 

In terms of fixed income, the curve is steepening, getting closer to what, I would argue, it should be. One should receive more money for committing funds for longer periods—at least enough to cover inflation. Otherwise, you're losing money. We haven't had a significant steepening curve for a long time, so I think that can be construed as positive, especially because bonds and CDs are the backbone of how middle-class households build wealth. 

In terms of the deficit, I don't think the deficit will significantly affect this dynamic. Economists have been warning for 50 years that we'll have a problem and the dollar will weaken because of growing deficits, but it hasn't happened. There are other intermediary variables at play. I'm less concerned about the deficit and a weaker dollar, but fixed income yields in the short term will probably increase. 

The concern [among rating agencies on the quality of U.S. debt] isn't really about debt but about policy uncertainty. This uncertainty could drastically affect the cost of money, potentially contributing to a recession. Ironically, protectionism would also act to depress prices, creating conflicting dynamics. 

For employment, the same thinking applies. Many American jobs at companies like Microsoft are supported by international operations. Similarly, in the insurance industry, while many carriers are domestic, we also have brokerage firms that operate internationally, as do many banks. 

Have we fully decoupled? No. Is it done? No. Is there a way back? I don't think we've gone that far in decoupling yet. But if the rest of the world sees this as a fundamental shift in what Americans expect rather than just a temporary change in administration, then we'll start seeing more permanent changes. Such a transformation would take 10 to 20 years, but in that scenario (a full decoupling, as unlikely as this can be at this point), our equity markets could potentially shrink by 75%. 

Paul Carroll

Wow.

What lessons can we draw from economic confidence crises like the one I witnessed while running the Wall Street Journal bureau in Mexico City in the mid-‘90s, and how might they apply to our current economic situation? 

Michel Léonard

This is a great example. Mexico, like Argentina, demonstrates economic resilience despite pessimistic predictions. After each emerging markets bust, industry and finance people often claim, "It's over, investors won't return,” but they always do. Bondholders keep going back to Argentina. We remain a huge market, and that's not going to change.

Free trade advocates have always acknowledged that open trade borders lead to jobs redistribution—lower-skilled jobs move to countries with cheaper labor. Under U.S. leadership, the world economy has grown incredibly. The Chinese couldn't have lifted hundreds of millions out of poverty and created a middle class without global trade. The same applies to India and Pakistan. This historic transformation deserves applause. 

The flip side is that substantial wealth transferred from the U.S. abroad, disrupting communities throughout America. Those good union and public sector jobs—many are gone. Unionization rates have declined. Pro-free trade economists and policymakers always said we need to retrain our people. Montreal, where I'm originally from, did this exceptionally well. It was like the Rust Belt—losing its harbor and entering a structural recession. But all levels of government there invested heavily in retraining, for example creating thriving IT, aerospace, and hospitality sectors.

When a significant portion of society feels disenfranchised and unrepresented—this is Political Economics 101, not politics—they disconnect. They turn to third parties or, in emerging markets, fundamentalism. For our democracy to survive, we need an economic system where everyone feels their prospects can improve. We're not aiming for France's economic model — I don't think Americans want that—but we need a system where everyone believes their situation can get better. That's a laudable goal this administration is trying to achieve, and I hope there's a middle ground. All of that said, we can contribute to people understanding the impact of policy choices regarding prices and inflation.

Paul Carroll

Thanks, Michel. I feel smarter than I was 20 minutes ago. I hope our readers and listeners feel the same way.

David Richardson, the acting head of the Federal Emergency Management Agency (FEMA), told staffers two weeks ago he was surprised to learn there is an annual hurricane season. The Trump administration put out a statement saying Richardson was joking, but Reuters quoted staffers as saying he seemed serious. And the Wall Street Journal separately reported that Richardson, who had no experience in disaster management when he was named to the job in May, has been surprised to learn of the breadth of FEMA's responsibilities.

Richardson's confusion comes on top of a whole lot of other confusion at the agency — an announcement promising a new disaster management plan, then an announcement that there will be no new plan this year; disaster recovery grants delayed, then provided, but only on an ad hoc basis; and a whole lot of mutating policy statements about scaling back or even eliminating the agency.

How do we make sense of all that?

I've been waiting and watching to try to understand what's going on and how it might affect insurers that provide coverage for disasters, and I think two things have finally become clear. 

One is good for insurers. One is bad for them. Both are bad for homeowners and other policyholders.

Richardson was named acting administrator at FEMA after his predecessor was fired, apparently, for telling Congress FEMA should continue to exist. That suggests strongly that the Trump administration's sometimes conflicting statements do reflect a plan to drastically scale back or even eliminate FEMA and the assistance it provides following natural disasters. 

Richardson said back in May that states would have to bear 50% of the cost of disaster recovery, up from the previous 25%. More recently, Trump has said he will mostly wind down FEMA, although not until after hurricane season.

Under the Constitution, only Congress can abolish an agency such as FEMA, and the executive branch is required by law to spend the funds that Congress allocates, but the Republican-controlled Congress has shown no inclination to push back against Trump's assertions of authority. Even if Congress suddenly reclaimed its authority, Trump has considerable executive power to deny or at least delay grants to states and to fire FEMA officials who stand in his way.

So he is making the federal government an unreliable backstop for people and communities facing calamities. That uncertainty will hang over FEMA even if Democrats retake control of one or both houses of Congress in the mid-term elections or if the next president takes a more traditional view of FEMA's role in disaster recovery. (This article in Slate does a great job of showing what the FEMA chaos already means on the ground in areas hit by disasters and how individuals and states are having to scramble.)

The step backward by the federal government will hurt property holders — and my heart is always with those suffering from natural disasters — but will, in fact, help insurers. Property holders now carry more risk than they did pre-Trump, and they're going to want to lay off some — maybe even a lot — of that risk with insurers. 

Even state governments, strapped for funds, may turn to private insurers for help with the risk the federal government is handing to them. (The Trump administration line is that states are being "empowered" to do more about disaster recovery, but states don't seem to see things quite the same way.)

The second thing that has become clear about Trump's FEMA is rough both for policyholders and for insurers. It is that Trump has little or no interest in a program called Building Resilient Infrastructure and Communities (BRIC). 

The program has long been used to help areas hit by disasters make themselves less vulnerable to future catastrophes, and it meshes — or meshed — with the Predict & Prevent movement in the insurance industry. 

Groups such as the Insurance Institute for Business & Home Safety have been promoting standards such as FORTIFIED roofs, and insurers have been working with communities to help prepare them for wildfires, hurricanes and other natural disasters. The expectation has been that the federal government would at least be some sort of partner, providing expertise and a fair amount of funding. Not any more. 

This post by the Triple-I quotes one expert as saying, “Eliminating [BRIC] entirely — mid-award cycle, no less — defies common sense,” and details how the decision pushes responsibility to state and local governments and to private interests, including policyholders and their insurers.

Here's hoping this year is kinder than recent ones in terms of hurricanes, tornados, severe convective storms and wildfires, but I'm not counting on any relief. And even FEMA says it is "not ready."

Cheers,

Paul

Mary Meeker, a high-profile analyst known as "the Queen of the Internet" because of her early, bullish calls on the prospects for Amazon, Google and Apple and then for the massive yearly reports she issued on the state of the internet, has turned her attention to AI. 

In her first major report in five years, Meeker makes the case that AI has been improving far faster even than we realize and that progress is accelerating, with no end in sight. 

Her forecast — and she's generally right — should be very good news for insurers.

Meeker's report, all 339 data-and-graphics-packed pages of it, uses the word "unprecedented" dozens and dozens of times. I'll spare you the detail, but here are a few nuggets you might want to include in any presentation arguing for investment in AI:

• ChatGPT hit one billion searches per day in less than two years. Google needed 11 years to reach that mark.
• While developing and training generative AI models is wildly expensive, the cost of using AI has declined 99% just in the past two years.
• The “Big Six” U.S. tech giants (Apple, NVIDIA, Microsoft, Alphabet, Amazon and Meta) are going to keep spending unfathomable sums to improve capabilities and drive costs down. They spent $212 billion on capital expenditures in 2024, up 63% from 2023, largely on AI chips, data centers, and cloud infrastructure. They are investing 13% of revenue on R&D, up from 9% a decade ago, and they have the resources to keep going: Their annual free cash flow is nearly $400 billion.
• While there is a lot of concern about the energy consumption of AI, Nvidia's latest 2024 Blackwell GPU achieves 105,000 times greater energy-efficiency than its 2014 predecessor. 

Meeker also offers many useful examples of how generative AI is, and will, find its way into the real world: 

• More than 10,000 doctors at Kaiser Permanente use an AI assistant to automatically document patient visits, freeing three hours a week for 25,000 clinicians.
• Stripe pushed one important fraud-pattern catch rate from 57% to 97% overnight.
• 27% of ride-hailing trips in San Francisco are handled by autonomous vehicles. (That's much higher than I would have guessed.)
• By early 2025,, evaluators thought 73% of the output from a GPT was written by humans.

Meeker's projections for five and 10 years out may be even more startling. By 2030, she expects that AI will:

• Generate human-level text, code and logic, in any number of languages.
• Run autonomous customer service and sales.
• Collaborate like a creative partner.

By 2035, she says AI will:

• Conduct scientific research
• Design advanced technologies
• Simulate human-like minds
• Operate autonomous companies
• Perform complex physical tasks in real-world environments

Any projections for technology that reach out a decade often verge on science fiction or at least fuzzy optimism -- a lot of projections about AI, for instance, were "coming in 10 years" for decades. But Meeker paints a picture of intriguing possibilities that we should all explore.

A lengthy analysis of her report on Substack offers an even rosier outlook for insurers. It says that, while many companies and jobs will be overtaken by the growing power of AI, it won't threaten businesses that have these three levers:

• "Data gravity – proprietary or regulated corpuses (medical imaging, trade documents, tele-metrics) that outsiders cannot legally pull into pre-training.
• "Reward ambiguity – industries where you can underwrite the outcome (fraud risk, quality-of-care scores, turbine uptime) and price on financial exposure.... Risk pays!
• "Compliance bottlenecks – any workflow where passing the audit is the moat."

That sure sounds like insurance to me: proprietary data, underwriting of risk, and compliance bottlenecks. So insurers can take advantage of the huge amounts of horsepower that the gen AI model companies are providing, while secure in the knowledge of the health of the underlying business.

Insurers can now start to raise their sights. At the Instech conference in New York City last week, where I had the pleasure of speaking, I heard about remarkable improvements in data intake from Concirrus, Cytora, and Federato, based on AI engines. By next year, I'd expect to hear about similar progress in the assistants that companies are building for underwriters, claims representatives, and agents and brokers so they can process more information faster and make better, more consistent decisions. The year after that, I'll bet we're hearing about whole streams of work being automated through AI.

In time, I suspect we'll stop even talking about chatbots because the capabilities will be built into everything, making AI essentially the user interface for companies. We'll just go to a website or make a call and ask a question. AI will then provide a summary answer, much as Google and other search engines are now doing, and offer next steps. 

Eventually, the arms race by gen AI companies may slow, as losers drop out of the competition. At that point, prices for us users could rise, or at least stop plummeting, following much the same dynamic that saw Uber and Lyft raise prices after years of subsidizing rides to lock in interest among riders and drivers.

But Meeker makes clear that any slowing won't come any time soon. For the next few years, at least, it's full speed ahead.

Cheers,

Paul

When insurance personnel speak about underwriting capacity, they usually are referring to underwriting compliance, risk-based capital (RBC) models, or reinsurance. Less commonly, carriers think about operational underwriting capacity. In this context, operational underwriting capacity refers to a carrier's ability to balance speed, risk, and resources to meet the demands of sales and distribution. Key considerations in evaluating operational underwriting capacity include:

• Agent and Customer Expectations – Agents and customers expect policies that can be issued quickly and accurately. For agents, this means strong quoting capabilities and fast cycle times. Policyholders are increasingly seeking instant decisions, wishing to avoid more invasive measures to underwrite policies (e.g., medical exams).
• Talent Considerations – Carrier struggles for underwriting talent are pervasive within the industry, fostering underwriting "hubs" within the U.S. to ensure the ability to attract talent (e.g., Charlotte, N.C.). But access to underwriters is only one part of the equation. Complicated risk also requires specialized skillsets, and all carriers are competing for the best underwriters.
• Risk Assessment Framework – Underwriting has often relied on guidelines and rules-based processing in risk assessment. But much of that framework relies on historical data that leads to inefficient pricing – either overpricing, harming the customer, or underpricing, putting the carrier at increased risk.

The need to address underwriting capacity is not new. Carriers have already pursued rigorous investment in underwriting. In 2024, property & casualty carriers reported a $22.9 billion underwriting gain and industry combined ratio of 96.6%, per AMBest. For life insurers, 2024 saw 3% growth in premium but flat growth in policies. Increased sales in indexed universal life and variable life policies drove premium growth - both products requiring more sophisticated underwriting skillsets.

For carriers, this means pressure to manage expenses, as well as innovative underwriting capabilities, to compete in the market.

One avenue for innovation to create greater operational capacity? Agentic AI. With agentic AI, carriers have the capability to tackle several underwriting challenges.

Leveraging Data to Create More Dynamic Underwriting

Agentic AI can be used to conduct real-time data analysis across a myriad of data sources to better underwrite risk. Behavioral data (e.g., telematics) and IoT sensor insights (e.g., home sensory equipment) have fundamentally changed how carriers can price risk in a dynamic way. For instance, Nationwide has reported that customers enrolled in its usage-based insurance programs tend to pay 20% less than those enrolled in traditional policies. Hippo Insurance has used sensors to detect smoke, carbon, and water leaks, resulting in discounted and customizable products for customers. Agentic AI performs this data analysis to create much more tailored customer segments for pricing purposes.

Although property & casualty is leading the way in this space, expect life and health insurers to follow suit. The opportunity to promote healthier living and improve longevity risk for carriers, using behavioral data and sensors, will improve underwriting. John Hancock is using data from connected devices like FitBit and Apple Watch to provide customers with the opportunity to reduce premium payments while derisking their life insurance business.

Improved Fraud Detection

Agentic AI is capable of identifying fraud before the policy is ever issued. Specifically, it can be trained initially on known fraud practices, freeing existing personnel to focus on more nuanced cases. Over time, carriers can train agentic AI to recognized more sophisticated fraud scenarios. As carriers seek to increase sales, both through premium and policies sold, well-developed agentic AI will be critical to scalability. For example, agentic AI can recognize fraudulent or digitally altered data to either automatically flag or reject an application. This can be particularly valuable in situations where AI can identify fraud more accurately than its human counterpart.

One property & casualty insurer developed an agentic AI PoC focused on identifying policies written by "ghost brokers," individuals who were not authorized to sell policies. In addition, the carrier improved their model's capability to detect misrepresentation, particularly during the "free look" period, to further attack fraud in the underwriting process.

Underwriting Copilot and Training Opportunities

Underwriting triage is a foundation of risk management. This is especially pronounced in complex claims situations, where more experienced underwriters are needed, creating process bottlenecks.

Carriers should consider using agentic AI as both a copilot and triaging tool. As a copilot, agentic AI can accelerate the training process for underwriting trainees, providing real world scenarios and the opportunity to "grade" the underwriter in real time for accuracy. But as a triage tool, an agent can bypass inefficient workflow processes and better manage capacity within the organization. Many underwriter teams are regional – for example, an underwriting team for a captive life insurer may be based in the Southeast to directly support agents within the region. Or there may be a property & casualty commercial insurer that is responsible for a given territory. While that model may continue to be necessary, agents can be used to prioritize and redistribute cases based on need. For example, a commercial property & casualty carrier can use an agent to identify the complexity of a given renewal, assign it to the next capable underwriter, and prioritize them based on the urgency and estimated time to complete them.

Where Do Carriers Go From Here?

While the exact results will be carrier-dependent, a commitment to agentic AI within underwriting will position carriers to be better prepared to meet both financial obligations and consumer sentiment.

As carriers design their underwriting strategy, they should consider if they have the requirements to execute an AI strategy in the space:

1. Data Quality and Data Sources – Without the right data, agentic AI is bound to fail. Carriers need to consider what internal and external data sources they want to use, how to remediate their internal data, and how to integrate external data sources into their underwriting platforms.
2. AI Governance Structure – At least 30% of AI use cases are abandoned after proof of concept, per Gartner. This is due to companies rushing to do something with AI without any plan. A proper governance structure not only provides a method for evaluating AI use cases at an enterprise level but also provides clear metrics and considerations that will be necessary to address regulatory scrutiny as AI regulation continues to develop.
3. Rethinking the Underwriter of Tomorrow – At the core of operational underwriting capacity are underwriters. Carriers need to rethink the entire underwriting function and decide what an underwriter will need to do in the not-too-distant future. For example, will underwriters still need to perform data entry functions or play a more collaborative role with agents or brokers in sales? This exercise typically highlights a key challenge – that there is significant upskilling required within the existing workforce to address underwriting change.
4. End-User Experience – As insurance carriers consider the future of underwriting, there must be a recognition that this is not happening in a vacuum. Competitors are also reevaluating their own underwriting processes. As carriers rethink underwriting, they should reconsider the experience with three lenses: agent, customer, and employee. A winning strategy will optimize the experience for all three stakeholders as a means of capturing and retaining all three.
5. IT and Process Transformation – Fundamentally, carriers need to reassess their underwriting function and engage in a potential core system modernization. Many carriers have not made investments into their underwriting platforms or modernized processes. For example, a lack of application programming interface (API) connectivity with underwriting platforms may limit the ability to integrate data necessary for agentic AI use cases.

The ability (or inability) of carriers to supplement their underwriting capabilities with agentic AI will affect their profitability and sustainability long-term. Customers, agents, underwriters, and financial investors will demand agentic AI. This cannot be achieved overnight and will require forward-thinking leaders.

Despite the buzz around digital transformation, a staggering 74% of insurance companies still use legacy systems to carry out their daily operations.

Hindsight has been the guiding light of risk management. Underwriters have used backward-looking data to evaluate risks, and loss events have been the driver of policy adjustments.

However, this method is now rapidly losing ground, thanks to the revolution of AI and IoT.

The current climate of volatility, looming cyber threats and supply chain fragility have created a world of escalating risks requiring more than a basic reactive model. Insurer needs something smarter, a more forward-looking approach that deeply involves tech in risk strategy.

That's where the Internet of Things and artificial intelligence are driving change. Forming the heart of the future of insurance risk management, their combined powers transform static risk profiles into dynamic systems that are capable of predicting, detecting and even preventing losses in real time. Powered by machine-learning algorithms, these systems don't just flag risks -- they consistently learn and adapt so insurers can be one step ahead of the risk lifecycle.

In this article, we will explore how AI and IoT are redefining the insurance landscape with advanced technologies like real-time risk scoring and hyper-personalized coverage.

We'll also discuss practical uses, hurdles to implementation and what insurers like you need to stay ahead of the curve.

The Evolution of Risk Management in the Insurance Industry

Once upon a time, risk management was a manual process, heavily dependent on spreadsheets, static questionnaires, and actuarial tables. But there's been a dynamic shift since then, ushering in brand new processes of real-time data analysis and algorithmic decision-making.

On the surface, it might seem like an optional shift. It is anything but.

As regulatory bodies demand greater transparency and faster reporting, customers are seeking more personalized support and responsive coverage. This massive shift makes the proverbial one-size-fits-all policies obsolete.

Armed with data-driven risk models instead, insurers can now easily leverage the insights that structured and unstructured data provide to make swift and accurate underwriting decisions. This data can come from anywhere -- be it financials and claims history or telematics and weather feed.

With predictive analysis, you can now keep an eagle eye on trends before they escalate. You can also adjust policies on the basis of real-time exposure and behavior with dynamic underwriting.

Calling these innovations revolutionary will be no understatement. The convergence of AI and IoT has transformed risk from something to price and transfer to a process that involves anticipation, monitoring and management.

The era of the retrospective stance is over as the age of forward-leaning approach takes over with AI and IoT in the driver's seat.

Role of IoT in Real-Time Risk Detection and Prevention

The Internet of Things or IoT might be best-known for connecting devices. But its not-so-glamorous role of helping insurers detect, assess and mitigate risks in real time is just as critical.

Devices such as telematics in vehicles, wearables, smart home sensors and industrial IoT create a continuous loop of feedback between insurers and insured assets.

How do these devices and their always-on data stream change the game? Take smart thermostats. These can spot a frozen pipe before it bursts. Meanwhile, telematics can identify high-risk patterns from a person's driving behavior even before an accident takes place. Industrial sensors can prevent workplace accidents by flagging faulty machines. Each data point can prevent critical loss.

That's why insurers now rely on IoT for multiple tasks, including sharing more alerts and building nuanced risk profiles so premiums can be adjusted in a dynamic fashion. In fact, IoT has also been instrumental in lowering costs associated with insurance claims processing by up to 30%, per Mordor Intelligence.

However, there's a technical issue, and that involves figuring out how to leverage large datasets. Sensor data can be unstructured - not to mention high-volume.

This is where custom-built software platforms can help. These solutions are capable of ingesting large amounts of data from diverse sources -- both processing and integrating them with legacy systems in real time to save you a ton of time, money and hassle.

With custom software in place, you can tap into the full potential of IoT, thus turning reactive claims into proactive risk management.

AI-Powered Risk Scoring and Underwriting

There's no doubt that IoT has revolutionized risk management -- but so has AI. With AI, you can make sense of the massive, fragmented data streams that keep pouring in from internal systems, connected devices and third-party sources. Plus, fast and smart underwriting is possible with AI.

While underwriting traditionally depended on backward-looking data, AI shifts it to the present by processing real-time data -- contextual, environmental and behavioral -- to generate dynamic risk scores unique to each profile.

As a result, pricing is now not probability-based, relying on historical cohorts. Instead, it reflects real exposure.

Take the recent insights released by McKinsey which show that insurers that use AI in underwriting have witnessed loss ratio improvements of up to 5%. That's not all. They have also seen expense reductions of 10%–15%. And this is just the beginning.

Personalization is another major advantage when it comes to AI insurance models. You can use AI to gather lifestyle factors and wearable data of your customers to craft personalized plans for them with dynamic premiums that adjust to their real-time behavior.

Property insurers can use AI to determine occupancy trends and environmental risks when drafting plans -- a granularity level that was impossible in the days of manual underwriting.

Consequently, with the advent of AI, insurance policies are now not only highly customizable but also very adaptive to changes. Moreover, AI can trigger early interventions, adjust coverages and flag anomalies before the commencement of a renewal cycle.

The presence of AI in insurance might seem futuristic, but incumbents and startups are already leveraging AI-based underwriting engines to prevent fraud and improve accuracy while keeping personalization as the basis for all liaison.

Lemonade uses AI bots and behavioral data to assess risk in real time, settling some claims instantly while reducing loss ratios and operational costs.

Addressing Ethical and Operational Risks in AI Integration

As with anything new, challenges abound with the integration of AI and IoT into the mechanisms of the insurance sector. But none of the threats arise from policyholders. Rather, it's the system itself that poses risks, ranging from algorithmic bias to data privacy and regulatory scrutiny.

You see, IoT devices are responsible for collecting data -- location, behavior, even biometric information -- that can be classified as strictly personal. Use of such information without clear boundaries can be considered a breach of trust and a liability. Thus, for insurers, it is critical to protect the data and have stringent rules for using that data in underwriting, pricing, and claims decisions.

However, that's not the only AI hurdle.

Most AI models can be likened to black boxes -- which means they often make decisions that cannot easily be backed by an explanation. This can put the fairness and accountability of such decisions into question, especially when it comes to sensitive tasks like claims automation, where transparency and equity are a must.

As for regulation, jurisdictions around AI are getting tougher. Auditability and model governance are now standard practices. As an insurer, you must guarantee your system can be monitored, tested and documented for any inherent biases.

The message is clear: Without the ethical use of AI, insurers and agencies can land in hot water.

While having a well-governed AI system can boost compliance, it can also serve as a competitive differentiator -- helping insurers build trust in a world where speed with fairness are paramount.

Building a Future-Ready Risk Management Infrastructure

By the year 2027, the global insurance market is expected to reach $9.8 trillion. That's a CAGR of 12% between 2022 and 2027.

With such rapid growth in store, retrofitted tools or patchwork systems for risk management just won't do. The shift from a reactive strategy to a proactive one requires a solid infrastructure that is equally agile, intelligent and purpose-built.

The first step is to rethink your existing tech stack. Your legacy system can likely process only batches of data instead of the continuous loops that emanate from AI engines or IoT devices.

The result?

Data silos, stalled workflows and zero opportunities for intervention. Either you need to modernize these systems or build custom integrations around them to stay viable. It's the only way.

Going down the custom software solution route will offer you the flexibility to centralize disparate data sources without ripping out your core system. As a consequence, you can automate decision-making and enable modular upgrades that help your company with underwriting, claims, and compliance workflows.

That said, infrastructure isn't simply about redefining your tech stack. It's also about ensuring seamless collaboration.

As a forward-looking insurer, your aim should be to track specialized vendors you can partner with to co-develop tools that suit the operational model of your company. Such strategic alliances can benefit your business by bringing domain expertise, speed and long-term innovation to the table.

To be ready for the future, you must choose wisely. You want your risk strategy to lead in the years ahead, not lag.

Final Thoughts and Strategic Recommendations

Don't treat AI and IoT as just another tech tool that comes and goes. Instead, think of them as the switch that lets you alter your entire risk management strategy from the ground up. Both are key to accurate forecasting, faster response times and loss prevention.

With AI and IoT working together for you, you get an insurance model that benefits carriers and policyholders alike.

However, if you want a competitive advantage, being an early adopter is the only way to go. You need to be willing to address ethical risks that arise as you modernize your infrastructure by investing in the right partnerships.

The path ahead for senior insurance executives is dotted with specific tasks:

• A thorough assessment of where and how your reactive models are falling short
• Prioritization of AI and IoT use cases that offer long-term scalability and near-term wins
• Modernization of legacy systems with custom platforms that enable real-time integration in a seamless fashion
• Formation of strict governance frameworks that foster a culture of fairness, auditability, and compliance

The future of risk management is here.

But the real question for CXOs and underwriting leaders like you is: Are you ready to evolve your risk infrastructure, or are you willing to lose your competitive edge?

The choice is yours.

Change is inevitable; managing it effectively is where the challenge lies. Many transformation initiatives fail, not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in Send's INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest problems is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

• Clear Planning and Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear road map, businesses risk implementing solutions that fail to deliver tangible benefits.
• Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.
• Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.

Biggest Barrier to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark.

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

• Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.
• View Technology as an Ecosystem - No solution exists in isolation; successful adoption depends on integration with existing processes.
• Avoid the "Silver Bullet" Mindset - No single piece of technology will resolve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will follow naturally.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."

Change is inevitable, but managing it effectively is where the challenge lies. Technological advancements are moving at an incredible pace, creating numerous opportunities. Many transformation initiatives fail - not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in the INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest challenges in implementing change is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

• Clear Planning & Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear roadmap, businesses risk implementing solutions that fail to deliver tangible benefits.

• Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.

• Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.

As Matt Carter, practice director at Altus Consulting, put it during the webinar:

"You have to keep an abstracted view of the prize you're going after. Programs evolve quickly, and people lose sight of the bigger picture. Keeping them focused on where they're headed ensures success."

Biggest Barriers to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of spreading efforts too thin, organizations should focus on one or two key areas where they can create meaningful effect.

Emma Cullum, head of operational strategy change and excellence at QBE, emphasized this during the discussion:

"One of the biggest challenges I've seen is organizations trying to do too much at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark."

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

• Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.

• View Technology as a Connected Ecosystem - No solution exists in isolation, successful adoption depends on integration with existing processes.

• Avoid the 'Silver Bullet' Mindset - No single piece of technology will solve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will naturally follow.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."

At Send's INFUSE April 2025 webinar, we explored a question many in our industry have been asking for years, but perhaps never more seriously than right now:

Is this the year AI goes mainstream in insurance?

From my vantage point at Sixfold, working directly with underwriting and operations teams to implement AI into core workflows, the answer feels clearer than ever:

Yes, if we focus on execution over experimentation.

The hype around AI is not new. But what's changing in 2025 is that AI is no longer confined to innovation teams or isolated proof-of-concepts. It's showing up in daily underwriting, claims triage, and delegated authority oversight, and doing so in a way that improves business results.

Here are five takeaways from the INFUSE discussion and what I'm seeing in the market right now.

1. We've Moved Past the Chatbot Phase

There was a time when AI in insurance meant a chatbot or a clever email assistant. But that phase is behind us. Today, carriers and MGAs are deploying AI to help triage submissions, extract unstructured data from highly variable documents and emails, and flag risks that fall outside appetite.

In short, AI is no longer theoretical. It's operational.

And insurers are realizing that they've long been ahead of the curve in key areas like data science and predictive modeling. What's new is embedding that intelligence directly into workflows.

2. Practical Uses Are Driving Momentum

One of the reasons AI is sticking this time is because it's solving real problems. Manual document processing. Risk triage. Data normalization. Appetite checks. These are not innovation buzzwords, they're the day-to-day blockers underwriters face, and we now have the tools to tackle them.

AI isn't being dropped into the business from above. It's being built around specific uses that create efficiency and unlock underwriting capacity.

3. AI Should Augment, Not Automate Away

At Sixfold, we're strong believers that AI should support underwriters, not replace them.

During the panel, I mentioned this specifically, and it's something we frequently hear from our users: AI gives underwriters back the time to think, to strategize, and to focus on what matters. It takes on the repetitive, facts-based work, so underwriters can focus on judgment, negotiation, and client relationships.

That distinction is critical. The industry doesn't want AI to take over. It wants AI that empowers its experts and amplifies the impact of every underwriter.

4. You Don't Need to Rip and Replace

One of the most common barriers I hear is, "We want to modernize, but we're still working with legacy systems." The good news is: you don't need a greenfield environment to get started.

At INFUSE, Paul Armstrong from AWS put it perfectly: You can start small. You can integrate modular tools into your underwriting process, things like ingestion, enrichment, or renewal comparison, and test the value before scaling.

The key is to be surgical, not sweeping. Solve a specific problem. Show the result. Then move to the next.

5. Trust Is What Makes It Stick

While we didn't dwell on the term "explainability" during the session, the importance of trust came through loud and clear.

Underwriters want to understand how recommendations are made. They want to know that what the AI is surfacing is based on real logic, not a black box.

If AI is going to become a true partner in underwriting, it has to earn that trust. That means surfacing insights clearly, showing the source of decisions, and giving users the ability to validate what's under the hood.

Final Thought: Execution Wins

So, will 2025 be the year AI finally goes mainstream in insurance?

I believe it can be. But only if we shift from strategy to execution. The technology is ready. The uses are known. What matters now is enabling teams, aligning business owners, and embedding AI where it can drive measurable outcomes.

This isn't about adopting AI for its own sake. It's about solving real problems with tools that work.

And in that sense, AI isn't a futuristic idea anymore, it's just smart business.

Eighty percent of people love personalized solutions, and that includes for insurance.

For decades, insurers have been relying on generalized risk models and broad demographic assumptions to design their policies. But consumers today want more than just general policies. They want plans that suit their unique lifestyle, habits, and needs. Traditional one-size-fits-all policies are no longer relevant to them.

And now, with the entrance of AI, insurers can uncover huge amounts of data and read patterns.

Let's dive deep into this.

What Is Personalization in Insurance?

Personalization in policy means tailoring every aspect of a policy. It starts from coverages and premiums and runs to improving services and communication. Instead of giving the same plan to everyone, you give them something that they need. Completely flexible and relevant policies.

Imagine two people, Alex and Jordan. Alex is a 30-year-old city dweller who cycles to work and has a clean history. Jordan is a 45-year-old suburban resident who drives daily and has a family history of hypertension and prefers telehealth options.

If we personalize a policy for both of them, Alex might get a low premium, while Jordan would get a policy that includes regular health check-ups, diet consultations and more.

Role of AI In Improving Personalization

Let's understand how AI will help the insurance sector.

1. Machine Learning 

Imagine getting a compilation of a policy's data within seconds that lets you study history, buying behavior, and wearable device data. For example, in car insurance, machine learning can access the driving data of someone using telematics and determine whether he is a cautious driver. In health insurance, machine learning can track a user's behavior, including how frequently he is going to the gym, through healthy biometric data. The knowledge can reduce premiums.

2. Predictive Analysis

With predictive analytics and AI, you can assess future risks by reading historical data. This will help mitigate risk overall. If data shows a customer entering a high-risk age group, for instance, he might face certain health problems. So the policy could be amended and preventive health services prescribed. In property insurance, geographic and weather data can be used to predict risk levels and offer personalized coverage.

Four Benefits of AI-Powered Personalization

1. Increasing Customer Satisfaction and Loyalty

When policies and services are completely personalized according to users' needs, they feel valued.

Think about it: A health insurance plan that adjusts to someone's lifestyle goals or a car insurance policy that rewards safe driving builds trust. The more you personalize, the better you can build strong relationships and improve customer satisfaction scores.

2. Reducing Churn Rate Through Relevant Offerings

You can sell generic policies to people, but customers will be disengaged. AI solves the issue. Continuously analyzing customers' data to make better decisions improves offerings. Giving timely recommendations, reminders, or added suggestions feels helpful.

3. Better Risk Management and Profitability

With AI, insurers can assess risk in a very detailed way. Instead of reading the broad-level data, you can now check based on behavior and lifestyle. AI can identify high-risk behavior that informs appropriate pricing and preventive measures.

4. Increased Operational Efficiency and Reduced Errors

With the involvement of AI, insurers can automate tasks like data analysis, policy customization, and enhanced customer interactions. You can start using chatbots and virtual assistants to handle common queries that reduce human intervention. This saves time and money. It also reduces human errors, ensuring faster response to queries.

Challenges and Ethical Considerations

1. Data Privacy and Consent

Providing personalized insurance services requires huge amounts of customer data – starting from wearable device metrics to online behavior. The challenge lies in collecting data and managing it properly. Proper consent must be obtained from customers so their data can be used for product and service improvement. Otherwise, the insurer might face compliance issues with HIPAA or GDPR.

2. Avoiding Bias In Algorithms

The data processed by AI is based on historical information. If there's some societal bias in the past data, this might be reflected in the solutions, as well. AI might unintentionally amplify biased data related to race, gender, or economic disparities.

Conclusion

AI is changing the insurance landscape fast. Insurance planning is becoming more dynamic, with data-driven personalization. Now, insurers can use real-time behavior to predict risks with precision.

The Costly Strategy Hidden in Plain Sight

Every year, property and casualty carriers leave billions on the table—not because of nuclear verdicts, runaway juries, or third-party litigation funding, but because of something far more subtle and entirely under our control: the way we negotiate.

In an era where 99% of litigated claims settle, the cultural instinct on the defense side to “hold back” our strongest arguments has become a billion-dollar blind spot. We ration key negotiating points, fearing we’ll run out of ammo. We save key arguments to “surprise them at trial.” We frame less, anchor less, and persuade less. Meanwhile, the plaintiff bar is doing the opposite—and it’s working.

This isn’t a legal problem. It’s a strategy problem.

And it gets worse.

Not only do we hold back the arguments that matter—we rely on formats that make persuasion nearly impossible. While plaintiff attorneys lead with structured, written advocacy in the form of demand packages, defense teams default to brief, reactive phone calls that suppress advocacy and concede control.

We’re not just saying less—we’re saying it in the least effective way possible.

Defense Negotiation Is the Real Battleground

We are seeing more and more claim organizations taking fewer than 2% of their litigated cases to trial. Many are under 1%. This means 99 out of 100 cases are resolved through negotiation.

Negotiation isn’t a placeholder—it’s the battlefield. The weapons are advocacy, storytelling, anchoring, and framing. And the defense is losing that ground war.

Plaintiff attorneys are presenting persuasive, data-rich demand packages early. They’re setting narratives. They’re leveraging AI tools like EvenUp Law to benchmark value, build urgency, and preempt our defenses. 

There’s a reason EvenUp raised $235 million in 18 months and reached unicorn status. Good persuasion works, and if personal injury attorneys like the results, they’ll come back for more. They are. Because this approach of not concealing their case works. Compelling packages work. Anchoring works—even when the recipient knows they’re being anchored, according to multiple studies!

Meanwhile, defense teams are often confined to hurried phone calls, where the plaintiff attorney dominates the airtime and the adjuster is expected to recall and deploy key defenses from memory. This dynamic favors narrative over nuance, emotion over evidence. It makes storytelling nearly impossible.

We almost never prepare comprehensive offer letters in a manner similar to plaintiff demand packages. When we do, it’s usually just a number, or a number with some boiler plate denials. We don’t sell our offers they way they try to sell their demands. 

In short, we’re losing the negotiation battle by not showing up with our best weapons—or using them at all.

What the Plaintiff Bar Understands That We Ignore

We’ve been programmed not to show our hand. The question is, by whom? Examples of what we hear every day:

#1 - “I’m Saving It for Trial”

This had logic in the 1980s, when trial was common (or in movies, where a surprise reveal causes the jury to gasp). Today, trials are rare, and saving your best arguments for the courtroom is like saving your best sales pitch for a client who’s already walked out. When we don’t use our strongest defenses in the 99% of cases that settle before trial, we’re leaving value on the table. Worse, we’re letting plaintiffs drive up expectations with no rebuttal narrative in place. We’re not framing, we’re not anchoring, we’re not controlling the narrative. Given the legal system environment, we should be using our strong points to avoid trial (not to win at trial in the one out of 100 times a case may find its way there).

#2 - “I Don’t Want to Show My Hand”

This assumes that showing strength is a liability. In negotiation theory, it’s the opposite. Revealing credible, well-supported defenses early can shift expectations, reshape the perceived case value, and create decision-pressure. It’s not about tipping your hand—it’s about owning the story and framing the narrative. Hiding defenses cedes narrative control.

#3 - “I Need Ammo for Later Offers”

This is backwards. If your strongest arguments can drive resolution now, why wait? 

This is like saying, “I don’t want to use my strongest points to persuade the other side to settle now, because I might need them later if they don’t settle.” The plaintiff bar doesn’t hold back information with this goal in mind; we don’t need to, either. Sometimes, the absurdity of holding back becomes clear through analogy. Think about salary negotiation. Imagine asking for $150,000 but saying, “I’ll explain why I deserve it in a few weeks.”  

#4 – “Plaintiff counsel won’t engage early”

This is an argument commonly cited on both sides of the fence. Imagine what plaintiff counsel says after they’ve submitted an extensive demand package, only to get a non-response or simply a counter-number in return. Both sides feel this way. Yet, plaintiff counsel are rational actors. Whether fully engaged or not, persuasion affects their perception of the case. 

#5 – “I’d prefer to negotiate orally rather than in writing”

There is a reason plaintiff attorneys produce written demand packages, rather than just calling the claim professional to run through elements of the demand orally. Put simply, written persuasion in this context is more effective. Precision and documentation matter. Substantive evidence builds credibility. Written persuasion has reference value. And, powerfully, a written offer letter (in most jurisdictions) might just make it to the underlying claimant.

Distracted by Threats We Can’t Control While Overlooking Those we Can

The defense community spends enormous energy discussing external threats: nuclear verdicts, litigation financing, venue shopping, social inflation. These are real—but they’re also out of our control. They don’t require us to change. They allow us to feel victimized.

By contrast, how we choose to advocate is entirely within our control. And right now we’re choosing to hold back key arguments. We’re also choosing to not to write things down, believing it won’t influence the plaintiff attorney or their client—which is exactly how they want us to think.  

Our Call to Action

Today’s plaintiff attorneys are no longer winging it. They’re investing early. They build narratives and leverage data. They’re using AI to strengthen their demand packages, augment them with verdict data and aggregated settlement value intelligence. They target every relevant stakeholder: the adjuster, defense counsel, and even the insured (via hammer letters) And they apply pressure with time-limited demands, designed to trigger urgency and fear of bad faith exposure. 

We must do the same! 

• Develop structured offer packages that counter the persuasive impact of demand packages.
• Don’t hold back key arguments—use them early, when they have a chance to shift the case trajectory.
• Leverage written formats to clarify and reinforce the defense position—don’t rely on bits and pieces raised in phone conversations.
• Stop waiting for mediation to present a persuasive case for settlement—get out in front of it.

Not doing these things is costing our industry billions. We can win this battle. We have the smarts, the tools and the experience. We can be powerful advocates, persuasive negotiators, and we can do better to anchor, frame, and own the narratives of our cases.