Paul Carroll

Based on what I’m seeing at ITL, customer experience has become a truly hot issue in the insurance industry, especially as customers are more willing to shop around. Is that what you’re seeing, too?

Sean Eldridge

Absolutely. With the advent of many of the GenAI and agentic AI solutions that can be customer-facing—such as agentic voice for inbound and outbound calls—we're definitely seeing more interest.

Just to step back, I think "customer" is often too narrowly defined. Companies are just solving for the claimant, or just solving for the policyholder, or just solving for the client in a TPA-type experience. We've always looked at it as an ecosystem—your claimants, your policyholders, your adjusters, your supervisors, your agents, your brokers. How do you not just optimize for one group but look at them more holistically to make sure any CX solutions don't help one group but potentially hurt another.

The industry should think more broadly about how to help both our people behind the scenes, as well as that end user on the other side.

Emily Cameron

If the user has a positive experience, they understand what's going on and they feel good about what's going on, that will lead to fewer phone calls over to the adjuster, less litigation, etc. There are just so many interdependencies throughout the process.

Thinking in terms of an ecosystem and focusing on that claimant experience—even if maybe efficiency is your primary goal for the year—people are starting to understand how everything is interrelated and focus on how we can simplify things to have a more successful process and experience for everyone involved. This has been our mission from the start.

Paul Carroll

Over time, every industry becomes a technology industry. The computer industry used to be simple—if you had a problem, you called IBM. They sold the mainframe, the software, the peripherals—everything. But with personal computers came an ecosystem based on different software pieces, and solving problems became difficult. Vendors pointed fingers at each other. Insurance is even more complicated because there are two levels of customers: the broker or agent as intermediary and the end customer. How do you think about developing a plan that maps out the customer experience in a complicated industry involving lots of pieces of technology?

Sean Eldridge

When we got started six years ago, we made a deliberate decision to start with the problem, not the technology. Before writing a line of code, we spent thousands of hours with carriers, third-party administrators, self-insureds, claims teams and claimants to understand the friction points across the claims process, the service process, the underwriting process—you name it.

For us, that meant building an underlying architecture first that allowed for a wildly extensible level of configurability and interoperability. We see a lot of fantastic technology coming to bear with the advent of GenAI and agentic AI, but those are still point solutions for specific use cases that are just scratching the surface of what's possible. Without great configurability and interoperability, you can’t do a lot.

When you talk about reducing phone tag, clarifying expectations, and getting the right information to the right person in the right system, really special things can happen in our industry. And again, that starts with the problem, not the technology.

Emily Cameron

I like to think of us as a meat-and-potatoes company. It's easy to see a cool feature or solution and say, "I need that," adopt it, and then find it's just not used. So we go on-site, sit next to the adjuster, and see how their current workflow works to make sure that we're actually improving it and not adding work for them. We even go to grocery stores or hardware stores and talk to those employees to get their experience.

To your point about companies becoming tech companies: I think that's really interesting because oftentimes our main competitor is just the traditional phone call and snail mail. There is definitely a technology adoption curve. People resist. They’ve been doing this a long time. They’re used to their flow.

So we started slow, focusing on their first priority: It's difficult to communicate, so let's improve the messaging capabilities. Once people get comfortable with that, they're upset that things are taking so long. So how can we get information back faster? Well, here’s our electronic document solution to exchange forms faster. Or they say, "There are so many systems I have to deal with." So here’s our ETL solution to deeply integrate and pull in information. Once they get their foot in the door, they start seeing the value.

Paul Carroll

What are some specific examples of problems you've identified through this kind of field research?

Emily Cameron

Oftentimes the biggest struggle we've heard directly from claimants is, "I just don't know what's going on." So we get some resources and information over to them right away. We also built an automated intake solution, to make it super easy to report a claim quickly. Then we can immediately send a text or email while we're still processing their claim and figuring out who their adjuster is. This process can take 24 to 48 hours sometimes, and we don’t want claimants to just be kind of twiddling their thumbs and not quite sure what to do.

We've done virtual interviews with adjusters, and they say, "Hey, I'm fine. I'm just doing my job. No issues." Then you sit down next to them and realize they're spending hours a week just looking through files manually, and they didn't even think to bring that up.

Once we build a relationship, they’re more likely to say, "Hey, I'm having this issue. Is this something you guys could take a look at?" But at first, people just don't know what they don't know, especially if they're not used to technology.

Sean Eldridge

I can think of countless examples where we've been in the trenches side by side with claimants, policyholders, and insurance professionals to really understand their challenges. Someone says, "Oh, I have no problem keeping track of all these things." Then you see their desk, and it's got 74 Post-It notes on it. You're thinking, okay, there might be a better way here.

The devil is in the details on how those solutions come to life. There’s been an explosion of AI document processing tools over the last 18 months, but how do you think about interoperability, whether with the document management system, the core systems or whatever? How do you let claims professional configure that document processor but still within guardrails set by the organization? There are all the fine details you don't know until you're in the weeds with those individuals and teams.

Paul Carroll

Configurability has always been a bugbear. I vividly remember the early days of enterprise resource planning (ERP) systems, led by SAP. They were great, but even the biggest customers pretty quickly found that they had to redo accounting, requisitioning and other processes to fit SAP’s way of doing things, when it should have been the other way around.

Emily Cameron

Every company we've worked with likes things a different way. We call them the "special snowflake." So we try to make sure that almost everything is configurable.

It even gets down to the user level. One adjuster told us, "You know, I just don't want text or email. I like the old days when someone called me." So, we built a system that calls that person and uses what sounds like an authentic voice to let them know they have a new claim or an update.

Paul Carroll

I mostly see customers wanting claims to move faster, with regular updates, and you’ve talked about those issues. What are other key touchpoints that define a good customer experience in insurance?

Sean Eldridge

At a high level, it's reduction of uncertainty. Whether you're talking about a claimant versus a policyholder versus even the adjuster or other insurance professional—a client service rep who might be touching that claim or client in some way, shape, or form—everyone just wants to be able to set expectations. When can I expect to hear something? When I have a scratch-my-head type moment, how do I get an answer for it really quickly?

I think the industry is still just scratching the surface of what's possible on reducing uncertainty. Personal lines have probably done better than commercial lines historically, but there's a sea change coming in terms of what that's going to look like.

And I think the research-based approach—problem first, technology next—will get us there. One of our cofounders is a highly published researcher and has a focus on behavioral science. We've got the former lead out of IDEO's behavioral design division who's helped us think about, from a human factors standpoint, how do you ask the right questions at the right time to unlock insights? And I think our approach is part of a rising tide that will lift all boats in the industry.

That's very exciting in terms of improving customer experience for the long run.

Paul Carroll

Here’s hoping. Thanks, Sean and Emily.

 

About Sean Eldridge

Sean G. Eldridge is the Co-founder and CEO of Crosstie, a venture-backed insurance technology company that helps P&C carriers, TPAs, and self-insured organizations modernize claims and service workflows through configurable AI and automation. Outside of Crosstie, Sean led a private equity-backed roll-up in the disaster restoration industry, giving him firsthand exposure to the realities of claims operations, and previously held leadership roles at Johnson & Johnson, Procter & Gamble, and Weight Watchers focused on building and scaling technology-enabled services. He earned a B.S. in Management Information Systems from Rochester Institute of Technology and an MBA from Harvard Business School. He resides in Cambridge, MA with his family.

About Emily Cameron

Emily Cameron is the Head of Product and Customer Success at Crosstie, where she leads the development and adoption of technology that improves claims and service outcomes for P&C carriers and TPAs. By unifying product strategy with customer success, Emily ensures the platform delivers measurable operational efficiency, clearer communication, and better experiences for both insurance professionals and the people they serve. Emily began her career at Epic, one of the world's largest healthcare software companies, where she held escalating technical and customer-facing leadership roles supporting complex, mission-critical implementations for large organizations. She later joined Crosstie to build the product and customer success function as the company scaled its platform across P&C insurance. Emily holds a B.S. in Bioengineering, cum laude, from the University of Washington.

The IoT industry is growing fast, changing cities, factories, healthcare facilities, and industrial sites. To remain competitive, businesses are employing the most advanced technologies that make IoT systems smarter, faster, and more secure. This article explores the key IoT trends expected to lead the market in 2026, helping businesses understand where the industry is headed.

1. Sentient AIoT

According to the research published by Markets and Markets, the AI-based IoT (AIoT) market was valued at $25.44 billion in 2025 and is estimated to hit $81 billion by 2030, growing at a CAGR of 26% during the forecast period. Embedding AI into IoT solutions reduces human error through automated daily operations and facilitates real-time data analysis. AI-based IoT systems monitor operational data, environmental conditions, and equipment status, using this information to continuously optimize operations.

AIoT is valuable for various operations and workflows:

• Real-time threat recognition: Detecting abandoned objects or unusual activity, such as crowd formation, movement during off-hours, or any other predefined suspicious behavior, and triggering instant alerts for security teams by using AI-powered cameras.
• Quality control: AI-driven vision systems for production lines can identify defects in manufactured parts, preventing the release of faulty goods.
• Predictive maintenance: Analyzing sensor readings and equipment configuration history, AIoT systems help forecast equipment failures before they occur, reducing downtime and repair costs.

2. Cloud-edge hybrid architecture

Hybrid architectures combining cloud and edge computing continue to increase in popularity for IoT deployments as they address the limitations of cloud-based architectures in terms of bandwidth, scalability, and security.

Processing data close to its source on edge devices significantly reduces latency and enables real-time responsiveness and immediate decision-making. At the same time, cloud servers provide scalable resources for data analytics and storage, data aggregation across multiple devices and locations, and AI model training. In hybrid environments, security can be reinforced through zero-trust architecture, a modern cybersecurity framework, because data is spread across multiple edge and cloud environments, requiring continuous verification for secure communication between endpoints.

Advanced technologies, such as 5G for high-speed connectivity, containerization for flexible deployment, and AI-powered resource management optimization help maximize performance in cloud-edge architectures.

The hybrid architecture is valuable for various applications in smart cities, medical facilities, industrial automation, and autonomous vehicles, providing the base for low-latency IoT ecosystems.

3. Sustainability-driven IoT

The goal of green IoT is to reduce environmental impact through power-conserving device design and robust power management software, employing low-power processors and wireless connectivity to guarantee reliable performance with minimal energy use. Having remained prominent for some time, green technology usage shows no signs of declining. Grand View Research forecasts that the green technology and sustainability market size will reach $80 billion by 2030, growing at a CAGR of 23% from 2025 to 2030.

By processing data locally, edge analytics software reduces transfers to the cloud, which allows for saving energy and cutting carbon emissions alongside low-heat hardware and micro data centers powered by renewable energy. Low-power chipsets and energy-efficient communication protocols (LoRa or BLE) minimize power consumption, extending device lifespans and reducing battery waste. Solar-powered and other energy-harvesting sensors enable battery-less operations, decreasing maintenance costs and ecological footprints.

IoT-based systems are also widely used to support sustainability initiatives across various industries and application areas, including precision agriculture to optimize water and fertilizer use, smart grids to enable demand-response for balanced energy distribution, refrigerators in supermarkets to optimize cooling cycles and transportation systems to reduce emissions through smart routing and fleet management.

Sustainability-driven IoT principles can also be applied in other IoT contexts, such as industrial automation, manufacturing, and healthcare.

4. IoT-based digital twins

According to Research and Markets, the digital twins market will reach $154 billion by 2030. Digital twins combine IoT data, edge analytics, and AI to optimize decisions in a virtual environment before executing them in the real world. Virtual models of physical objects can be used to predict equipment behavior as well as forecast failure and safety risks, while digital twins of an organization (DTO) help in planning enterprise-level operations and workflows.

5. Advanced connectivity

In 2026, companies are prioritizing next-generation connectivity technologies to enable uninterrupted data flow across IoT networks, which is critical for devices positioned across multiple locations.

5G

5G brings fast speeds and large network capacity, making it possible to process data instantly and power IoT systems at scale.

Wi-Fi 7

With its expanded bandwidth, Wi-Fi 7 permits 320 MHz channels, which are ideal for bandwidth-heavy data transfer, such as 4K video streaming.

LPWAN

Designed specifically for IoT, this technology enables long-range communication with minimal bandwidth and energy use, being cost-effective for managing large numbers of connected devices, such as in utility monitoring.

Satellite connectivity

Satellite networks enable global asset tracking and connectivity for devices located in isolated regions where terrestrial networks fail. GPS data is sent from the device to the central hub immediately, so in case of an emergency, the issue can be addressed in time.

In conclusion

For IoT solutions to support complex operational processes and provide data-driven insights, high-performance software and hardware are critical. Therefore, in 2026, the focus is on creating smarter, more resilient, reliable, and easier-to-manage IoT systems. New technologies are helpful for that by enhancing data analysis, establishing stronger connectivity, reducing operational failures, and improving the use of resources.

To build secure and sustainable IoT solutions, it is beneficial to follow the latest trends to keep pace with technology advancements while setting the standard for resilience and growth.

Even as I've tracked every twist and turn in the technology for autonomous vehicles for going on 15 years now, the key development I've been waiting for occurred, not in the lab or on the road, but in a recent op-ed in the New York Times. In it, a neurosurgeon made the case for AVs as a "public health breakthrough."

He said he was horrified by the more than 39,000 deaths from motor vehicles just in the U.S. last year, "more than homicide, plane crashes and natural disasters combined.... These crashes are also the leading cause of spinal cord injury. We surgeons see the aftermath of the 10,000 crash victims who come to emergency rooms every day. The combined economic and quality-of-life toll exceeds $1 trillion annually, more than the entire U.S. military or Medicare budget."

Then he made the sort of case technologists have been making for years about the potential for AVs to drastically reduce death, injuries and property damage... but this time it came from a doctor. 

And he framed his case as a public health issue, the sort of effort government gets behind and citizens appreciate, even if they may discount the claims they hear from those they see as techno-optimists and rapacious capitalists.

Based on other favorable press in recent weeks and on the relentless rollouts of robotaxis planned for this year, I think we're seeing a sea change. Arthur C. Clarke famously wrote that "any sufficiently advanced technology is indistinguishable from magic," but, over time, the magic wears off, and wildly advanced technologies begin to seem almost normal.

AVs are now being normalized to the point where I think the clock has started ticking on what will be a fundamental rewiring of the auto--and auto insurance--landscape. 

While I've long been an enthusiast about driverless technology, I've always been worried about winning over public sentiment. The techies initially argued that they just needed to be demonstrably better than human drivers and felt that was a fairly low bar, given that more than 100 people a day die in car crashes just in the U.S. But that's not how people look at technology. 

When a driver causes a crash, we may be understanding. We've all done careless things and had near-misses. But if software causes a crash, it wasn't making a mistake in the heat of the moment. Someone designed that software, and they screwed up. The huge company employing that coder somehow missed that mistake, too. 

Machines aren't supposed to make mistakes. Ever. So the bar for AVs' safety is actually higher than it is for human drivers.

Uber stopped its efforts to develop autonomous vehicles after a single fatal accident in 2018 caused a PR disaster. GM's Cruise halted its robotaxi development after one of its cars hit a pedestrian in 2023. The collision was a freak accident, in which another car hit a jaywalking pedestrian and flipped her in front of the robotaxi. But Cruise's AV, programmed to get out of the way after an accident, pulled off to the side of the road--not understanding that the pedestrian was caught underneath the car. The multibillion-dollar AV program couldn't survive the bad PR and scrutiny that followed.

The Cruise debacle left a bad taste. Subsequent press played up resentment of AVs, such as by people who learned they could paralyze a driverless car by putting an orange traffic cone on the hood.

But the press has gradually been shifting. Recently, for instance, Tesla got some nice publicity because one of its cars drove the famous Cannonball Run route between Los Angeles and New York entirely in self-driving mode. (CEO Elon Musk had promised one of his cars would do so by the end of 2017, but still....) Tesla got more attention when Lemonade said it would offer steep discounts to Tesla drivers for miles they traveled while in so-called Full Self-Driving mode, based on the belief that Tesla's AI is much safer than human drivers are, at least in certain circumstances. 

Public opinion has seemed to shift, too, both based on the press and on the growing familiarity with the cars. Yahoo! Finance reports that a survey "conducted in San Francisco this past July found that ​​67% of San Francisco residents now support the operation of driverless robotaxis, up from 44% in 2023, with 'net favorability' of robotaxis swinging from -7% in late 2023 to +38% in mid-2025."

The New York Times piece by the neurosurgeon pulls all those threads together for me and suggests that the public is ready to accept whatever the technologists can deliver. 

Yes, there is always a danger that a car will do something catastrophic. And we'll still see the occasional story about an embarrassing glitch, such as the recent one where a power outage in San Francisco knocked out all the traffic lights, and Waymo's cars just stopped, citywide, because they didn't know what to do.

But, barring a disaster, people are certainly going to see a lot more robotaxis on the road this year. Google's Waymo is already up to about 250,000 paid, fully autonomous rides a week and aims to quadruple that by the end of the year. Waymo already has fleets in San Francisco, Phoenix, Los Angeles, Austin, and Atlanta and plans to add 20 markets this year--including Miami, Dallas, Houston, San Antonio, Orlando, Las Vegas, San Diego, Detroit, Washington, D.C., Baltimore, Philadelphia, Pittsburgh, and St. Louis. Waymo is testing in New York and plans to test in London soon, too.

Waymo is doubling its production of AVs and expects to build more than 2,000 this year. 

And that's just Waymo. Tesla has big plans to expand this year--though any prediction from Musk must be viewed with skepticism, given that he's been consistently overpromising about AVs for more than a decade. Amazon says it will build 10,000 robotaxis a year starting in 2027. Some smaller companies say they're testing robotaxis in Tokyo and Southeast Asia. A host of Chinese companies have pursued autonomous driving aggressively, though generally at the driver-assist level, and the government recently became more cautious after a gruesome accident. Baidu says it will test in London this year, and Europe is shaping up as a battleground. Most of the players there figure to be Chinese and American companies, but Mercedes just announced an autonomous venture with Nvidia, and Nvidia hopes to provide the technology, including simulators, in similar ventures with other manufacturers. 

Insurance won't feel the effect right away, by any means. The tectonic shift won't happen until individuals start buying driverless cars, and when that happens is anybody's guess. But even robust adoption of robotaxis, which should happen over the next couple of years, could be material. If Waymo is really doing 1 million paid rides a week by the end of this year, that's maybe $1 billion of revenue, if annualized. That revenue would have gone to gig drivers and taxi drivers, who buy traditional insurance, but instead will go to a corporate behemoth that self-insures. 

That shift in revenue will mean maybe the loss of just $100 million of premium for auto insurers (based on my back-of-the-envelope calculation). That's a drop in the bucket in a U.S. market measured in the hundreds of billions of dollars of premiums. But exponentials are crazy things. If Waymo quadruples its size this year, what will it do next year? The year after that? And after that?  It's pretty easy to imagine Waymo having 20X its current presence within a few years. And if Tesla, Amazon, Baidu and other Chinese behemoths can deliver, too....

It's worth watching, especially if the public health argument really gains traction.

Cheers,

Paul

Out Front Ideas with Kimberly and Mark kicks off annually with the "20 Issues to Watch" webinar. While there are certainly more than 20 issues to discuss, the focus is on high-impact matters in risk management and employee benefits that require more attention. These are essential issues for every risk manager, HR manager, and insurance professional to monitor in 2026.

1. Connected Risks

Risk does not happen in a silo, requiring assessment across the business and agile planning. Risk managers and their partners must be more diligent than ever in evaluating overlapping risks, including environmental, technological, and human factors. Most organizations recognize their risks, but few are fully prepared to tackle them.

2. Fraud as a Systemic Risk

The Coalition Against Insurance Fraud estimates that fraud costs the insurance industry over $308 billion per year. It can take the form of unethical physicians performing unnecessary surgeries, medical providers billing for services never rendered, and plaintiffs fabricating or exaggerating injuries. Fraud must be met with meaningful consequences, and the industry must actively identify fraud, share intelligence, and demand prosecution.

3. AI Lessons Learned from Early Adoption

Many early AI adopters initially focused solely on platform deployment, only to learn that success hinges on a clear use case tied to measurable business outcomes and a return on investment. Goals for adoption must be clear and concise. Additional considerations include stakeholder engagement, alignment and execution, data readiness, and governance.

4. Industry Engagement

In-person industry events bring colleagues together to help solve problems, exchange ideas, and learn from one another. Conference attendance has not returned to pre-pandemic levels, and that shift has come at a cost to the collective learning and collaboration that strengthen our industry. Reconsider the value of active industry participation and, if given the opportunity, attend a conference.

5. Healthcare Trends

Access to care remains a critical concern, particularly for rural healthcare entities at risk of closure due to continuing physician shortages. As pandemic-era waivers expire, telehealth opportunities are also ending, as physicians can only treat patients within the states where they are licensed. However, AI continues to drive health technology innovation, offering early diagnostic testing and opportunities for self-guided care. Wearables continue to gain popularity, with more industries deploying them to enhance workplace safety.

6. Insurance Market Pressure Points

Legal system abuse continues to worsen the development of liability claims, keeping commercial auto unprofitable despite a decade of premium increases. In response, there is growing interest in quota-share liability towers and captives. The property market avoided hurricane impacts last year, but those benefits were offset by wildfires, with hail and severe convective storms now driving most global catastrophe losses. Workers' compensation remains competitive, yet deteriorating claims have pushed combined ratios above 100% in California and Nevada, signaling an end to the prolonged soft market and flatter rate expectations ahead.

7. Catastrophe Risk Becomes Baseline Planning

For risk managers, what was once an ad hoc emergency response has become a structured playbook to follow in the event of a catastrophe. Some are even shifting from a coordinated team to a business unit that oversees climate, business continuity, and catastrophes. This approach outlines clear roles, responsibilities, and consistent expectations in the event of an incident.

8. Claims Insights

Medical inflation in workers' compensation has historically lagged behind broader healthcare inflation due to fee schedules, but those pressures are now clearly emerging. The National Council on Compensation Insurance (NCCI) reported a 6% increase in both indemnity and medical claim severity in 2024, while the Workers' Compensation Insurance Rating Bureau (WCIRB) in California noted a 9% increase in medical costs. Additionally, expanded mental health claims, catastrophic injuries, and cancer presumptions for first responders are affecting long-term costs.

9. AI in Business Transformation

Fluency in AI is becoming essential for organizations as they adapt to challenges. When paired with user-centric design, AI can drive transformation by improving efficiency while still relying on employees' critical thinking. Organizations that hesitate to adopt automation risk falling behind, especially as time savings can be reinvested in innovation.

10. California Workers' Compensation

California remains one of the costliest workers' compensation states. Savings from the 2012 reforms have been eroded by rising litigation and medical inflation, driving a 127% combined ratio in 2024 and prompting renewed reform discussions. The primary cost driver is cumulative trauma (CT) claims, which broadly cover degenerative conditions and account for over 21% of claims and 38% of litigated cases. While meaningful reform would require addressing CT claims, political resistance makes significant change unlikely.

11. Employee Benefits

Employers are prioritizing engagement, retention, and culture through continuous employee listening and lifecycle surveys. At the same time, health plan costs continue to rise, with projected increases of 6.5–7.6% in 2026, according to Mercer, and growing concern over GLP-1 drug spending. While point solutions remain popular, complexity is increasing. Employees increasingly value purpose, belonging, well-being, and psychological safety as organizations brace for tighter budgets and slower pay growth.

12. Legal System Abuse and Tort Reform

Between 2023 and 2024, the number of verdicts exceeding $10 million increased by 50%, while verdicts over $100 million surged by 68%. These trends can be exceptionally difficult to reverse. However, there has been incremental success, with Florida and Georgia enacting reforms to curb litigation abuse, and several other states considering similar legislation. For the impact to truly resonate with the public, the focus must shift to how these verdicts affect everyday life, including lost jobs, higher prices, and reduced access to services.

13. Workplace Mental Health and Well-being

Supporting psychological well-being is a strategic imperative for engagement, productivity, and safety. Burnout and mental health directly affect business performance and recovery outcomes. Mental health claims now rank second only to pregnancy in leave and disability, surpassing musculoskeletal injuries, and are increasingly recognized as barriers to injured worker recovery. Early identification and targeted support, including behavioral health resources and virtual care options, can improve outcomes and shorten recovery timelines.

14. Cyber Risk

Cybersecurity remains a top concern as ransomware attacks scale through ransomware-as-a-service, expanded attack surfaces, and third-party vulnerabilities. Many breaches go undetected for months, and repeat attacks are common when weaknesses persist. AI-driven tactics, including deepfake executive scams, are increasing risk. Human error remains the weakest link, making continuous employee training, phishing simulations, and healthy skepticism essential to an effective cybersecurity strategy.

15. Workforce Considerations

Retaining today's workforce begins with understanding employee expectations around growth and flexibility. Employees increasingly value career mobility, skills that support current roles, and opportunities to build future capabilities. In hybrid environments, organizations are expanding virtual reality training and self-paced, high-impact "burst" learning programs. Clearly defined career paths are more important than ever, particularly as expectations for flexibility rise. PwC's 2025 research found that 58% of employees would rather quit than return to full-time office work, up from 35% in 2023.

16. Public Entity Challenges

Workers' compensation presumptions and heightened law enforcement liability exposures present unique risks for the public entity sector. At the same time, public entity risk managers face constrained budgets, limited staffing, and aging infrastructure. Pension liabilities remain a significant concern, with recent estimates placing nationwide unfunded public pension obligations at $1.2 trillion. As claim costs rise, higher taxes are likely to follow, and when revenues fall short, essential services are reduced. Because taxpayers ultimately bear these costs, these challenges should matter to private sector businesses and risk managers as well.

17. Reputational Risk in a Real-Time World

Reputational risk is a concern throughout organizations, whether a social media post misses the mark or an operational blunder occurs. Reputational events may affect business success, customer relationships, and growth opportunities. When a crisis occurs, preparation is critical. The response often determines the extent of reputational damage and risk exposure. Knowing how to frame that response for the intended audience is essential and requires understanding stakeholder, employee, and customer perceptions in advance.

18. Regulatory Overreach and Unintended Consequences

Overregulation continues to create significant challenges for businesses and risk managers. In-state physician licensing requirements temporarily waived during the pandemic improved access to care, but those requirements have since been rolled back. Similar regulatory friction exists for claims professionals, particularly in the handling of in-state workers' compensation claims. While well-intentioned, these regulations can fail to keep pace with technology, market realities, and evolving risks.

19. Operational Readiness in the Age of AI

As technology reshapes business models, organizations must ask whether their operations are ready for the future. Without adaptation, some business models risk becoming obsolete within the next decade.

20. Critical Digital Infrastructure: Data Centers as a System Risk

None of today's AI-driven innovations are possible without the massive data centers that power AI and cloud-based systems. These facilities have come under increased scrutiny as some communities court them, while others resist due to strain on critical infrastructure, particularly electrical grids and water supplies. Data centers also create substantial downstream risk due to their role as critical service providers. A single outage can disrupt operations for thousands of businesses.

Listen to the archive of our complete Issues to Watch webinar here. Follow Out Front Ideas with Kimberly and Mark on LinkedIn for more information about coming events and webinars.

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

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

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

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

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

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

--George Kesselman

Executive Summary

AI transformation is sweeping the insurance industry

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

Efficiency remains the primary driver of AI adoption 

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

Experimentation is widespread but deployment maturity is limited

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

Key challenges focus on governance, data, and talent

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

Innovation is balanced with risk in the era of AI agents 

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

To download the full report, click here.

Catastrophic events no longer feel rare, and the trend has moved far beyond what businesses once planned for. Over the last decade, the United States averaged nearly 19 billion-dollar disasters each year, but recent seasons have pushed well past that baseline. NOAA’s National Centers for Environmental Information recorded 28 such events in 2023 and 27 in 2024, a stark shift from the 1980s when the country saw only about three annually. The financial toll has climbed just as quickly, with losses from 2020 through 2024 averaging $149 billion per year, about 50% higher than the previous decade. These patterns are redefining what catastrophic risk looks like and challenging long-held assumptions about how often businesses can expect major disruptions.

As the ground shifts, companies that once planned around familiar cycles of storms, fires or floods are now confronting events that fall outside historical patterns. The frequency, intensity and unpredictability have changed, and the insurance system built on those past patterns is adjusting in real time. Carriers are reassessing their ability to absorb these losses, and many businesses are finding that the coverage they relied on a decade ago is no longer guaranteed.

A market adjusting in real time

The commercial market isn't simply "hardening." It's recalibrating. Carriers are confronting losses that strain decades of assumptions, and the adjustments are landing squarely on policyholders. Reinsurers increased rates after several consecutive years of heavy catastrophe losses, and carriers passed those costs down the line.

Models that once guided underwriting with confidence now struggle to predict what a season will look like. As a result, insurers have taken steps that affect companies of every size, even those with long relationships and disciplined risk management histories.

Those steps include:

• Cutting limits or declining coverage in regions prone to catastrophic weather
• Raising premiums for property and business interruption programs
• Adding exclusions tied to infrastructure failures, utility outages or wide-area events
• Requiring higher deductibles that shift more exposure back to the business

These changes reflect market realities, not a lack of commitment from carriers. But the outcome is the same: businesses face a widening gap between the risks that threaten their operations and the coverage that remains available.

Catastrophe looks different than it did even a decade ago

Today's catastrophic events aren't limited to the storms or wildfires that dominate headlines. Businesses are experiencing losses through disruptions that don't always produce physical damage but still create significant operational fallout.

A regional power grid failure can shut down production for a week. A port closure can stall shipments and freeze revenue. Smoke from distant fires can force evacuations or limit facility access. Even a minor storm can disrupt transportation networks enough to halt essential deliveries.

For some companies, especially mid-size operations, even a short disruption can derail revenue targets or strain cash flow. Many later discover those losses don't fit the triggers in their commercial policies.

When coverage no longer matches the risk

The shift in catastrophic risk has exposed a structural gap. Traditional policies were designed for events linked to clear physical damage. But the biggest financial pressures today often stem from indirect losses: supply chain interruptions, extended downtime or infrastructure outages that fall outside standard policy language.

Companies now face the possibility of:

• Uninsured business interruption when utilities fail
• Supply chain breakdowns that halt production but do not trigger property coverage
• Delays caused by transportation failures that fall outside conventional business interruption terms
• Vendor or contractor failures that create cascading operational consequences

The result is a landscape where businesses carry far more risk on their balance sheets than they did a decade ago, often without realizing how exposed they are until a disruption occurs.

How businesses are adjusting their approach

No single strategy solves this challenge, and companies are not walking away from the commercial market. They still rely on traditional policies for core protection. But many are broadening their risk management approach to address exposures the market can't or won't take on.

One of the clearest trends is that more companies, including mid-size businesses, are evaluating ways to finance retained risk. That includes expanding deductible layers, building internal reserves and, for many, exploring captive insurance structures that allow them to address operational exposures that are difficult to insure elsewhere.

A captive isn't a replacement for commercial insurance. It's a tool that helps companies take control of risks that fall through the cracks. When designed correctly, it supports recovery from disruptions that create financial pressure even without physical damage.

Why this shift matters

Businesses operate in an environment where catastrophic events have outpaced the insurance system built to cover them. The market is doing what it needs to do: adjust, correct and protect solvency. But that correction forces companies to rethink what resilience looks like.

Executives are asking new questions:

• What happens when a catastrophe affects operations but does not trigger a claim?
• How much financial exposure sits outside the commercial program?
• What mechanisms exist to fund losses that commercial policies exclude?
• How can the business recover quickly without waiting for external aid or slow claim processes?

These questions are driving strategic conversations that didn't exist a decade ago, especially among companies that cannot afford extended downtime.

Planning for volatility, not predictability

The path forward requires a more flexible approach to risk financing. Businesses are developing programs that combine traditional coverage with internal mechanisms designed to respond to catastrophic exposures the market excludes or restricts.

For many, that includes:

• Assessing where catastrophic risk exceeds available coverage
• Quantifying operational vulnerabilities that don't trigger standard policies
• Considering alternative financing tools, including captives, to bridge the protection gap
• Building long-term strategies that reduce reliance on unpredictable market cycles

Companies that take these steps move from hoping the market will respond to preparing for a reality where disruptions are more frequent, more complex and more costly.

The bottom line

Catastrophic events are no longer rare, and insurance structures built around predictable patterns can't always keep pace with today's volatility. Businesses that want to remain resilient must look beyond traditional coverage and consider additional strategies that help them withstand disruptions that threaten operations.

Captives play a role in that shift, not as a cure-all, but as a practical tool for financing the risks the commercial market can't absorb. The companies that adapt now will be better positioned to stay operational in a landscape where catastrophe is a continuing part of doing business.

Executives in every industry grapple with fragmented information streams that obscure the full picture of customers, competitors, vendors, and risks.

They want pictures of places with perils, proximity, price, sanctions, anti-corruption, regulation, crime, and compliance. Those map the playing field.

They need profiles of players on that field. Best, worst, and next customer, competitor, consortium, and criminals. Active, passive, and latent friction on the field of business and nature.

What are the risks they need to manage themselves? Which risks can be transferred with insurance? How do things change over time? When and why should they adopt new tactics?

Nobody builds or operates anything without risk management and insurance.

Some risks are well-enough understood that there are formulas on maps with data that explain:

• rate to risk – distance to coast, nearest fire line, closest body of water, feet to fire hydrant
• rules of risk – sovereign borders, zoning, taxation boundaries, legislative hellholes, politics
• range of risk – proximity to population, nearness of combustible materials, crime indexes

Some risks are still being grappled with:

• "invisible" risks – the internet is not a place, criminals lie, organized criminals lie better
• "known unknowns" risk – pandemic, war, supply chain, cyber, lawsuits, climate, tech
• "emerging" risks – aging infrastructure, connectivity, AI, casualty CATs, land use

Entity resolution and digital entity resolution are two key dimensions where massive progress is being made in transforming our understanding of the players on the field as well as navigating current and foreseeable changes in the field of play itself.

Adding entity data with geo-digital entity attributes is now a new avenue for putting risk on the map.

Are my neighbors my adversaries?

"See the battlefield, know your enemy" is a strategic imperative in conflict – some see that business competition, combating criminals, and complying with rules, regulations, and laws as necessary conflict, akin to war. Sun Tzu's "The Art of War" makes compelling sense in creating an awareness about your own situation and a diligence in understanding others with whom you interact or that are in your environment.

Tactically, assigning ultimate owner entity resolution to all the places and resources on your business battlefield makes a frustrating legacy of poor data a daily problem. Strategically, you can add more sustainability and resilience to your business by improving your knowledge of "brick and mortar, with click and order" data and clearly mapping your known trusted customers and partners as well as those you do not trust. Then everything else is open for business, with trust unknown, yet not unknowable.

Blending hyper-local with hyper-linkable on a map

The risks you can see and those you can only know as relatable can be illustrated visually on a map now.

Visible overlap of the world and the e-world can take a picture where two worlds interact - physical names and addresses and internet names and addresses pool into entities and relationships. Perils and problems in either or both can create business risk, but a peril on the internet might manifest at multiple physical locations. A duality of risk with asymmetric shapes.

You can play these visuals forensically and in a forward-looking fashion to understand risks, supply chains and single points of failure to improve your sustainability and resiliency. It's a new frontier of both understanding risk as well as a new entrée for Predict & Prevent initiatives.

Tracking relatedness on a map when water is rising, the wind is blowing, a freeze is coming, the earth is shaking, or when a fire is raging are traditional processes now. But today's risks now include more layers of risk that extend to digital assets and brand reputation as well as cyber exposures and regulatory and compliance requirements tied to knowing your business, your customers, your vendors, and your physical/digital/legal/cyber ecosystem.

When is a bunch of dots on a map really a single organization with legitimate purpose? If you don't tie them together appropriately, then you create aggregation and accumulation risk.

When are those dots nefarious sanctioned shells, all being operated in shadowy collusion? If you don't find these accurately, then you are dealing with the wrong customers.

Only entity resolution can help you sort it and keep it sorted.

A company in New York running on servers in North Korea owned by companies controlled by criminal cartels in sanctioned and unsanctioned countries is different than a legitimate NYC business entity. The same for Frankfurt, London, Quebec, Sao Paulo, Mexico City, or any hub.

Knowing what's behind the dots on a map matters.

Entity resolution and digital domain entity mapping emerge as pivotal technologies, bridging disparate data points to reveal actionable insights.

From unmasking fraudsters and untrustworthy entities, we can now blend data and view them in maps and graph analytics like never before. These connected and resolved entities can show what is otherwise hidden – how "click&order" meets "brick&mortar" – and then relates these to maps and graphs that bring entity resolution data and GIS tools together as new ways for reshaping how businesses operate. In some regards, a GIS coordinate or polygon is the same as a street address in creating a unique identifying reference. In other regards it may be even better.

Classic geospatial information systems are mashing up with federated streams of disparate identities getting resolved with industrial grade entity resolution engines on names and addresses from the real world and modernized digital entity names and addresses from the e-world.

Unraveling Entities: The Foundation of Clarity

Entity resolution, at its core, is the art and science of identifying when different records refer to the same real-world entity, despite variations in naming, addresses, or other attributes. Think of it as a digital detective work: matching "Acme Corp." in one database with "Acme Industries" in another, accounting for typos, abbreviations, or mergers. This process relies on advanced algorithms, machine learning, and sometimes geospatial data to link entities across sources like customer databases, transaction logs, and public records.

In the business world, poor entity resolution leads to potentially costly blind spots—duplicate customer entries inflating marketing budgets or missed connections in supply chains. But when done right, it creates a unified view, often called a "Customer 360," enabling personalized experiences and efficient operations. Financial institutions, for instance, use it to consolidate profiles from multiple accounts, spotting patterns that standalone data might overlook.

Business leaders face a perennial challenge: How do you connect the dots in a sea of disconnected data? Consider a scenario where a financial institution spots unusual web traffic patterns on its site. Is it a legitimate corporate inquiry or a sophisticated fraud attempt? Or imagine a real estate firm assessing a commercial property—does the tenant's online activity signal stability or hidden vulnerabilities? These questions underscore the power of entity resolution and digital domain mapping, two opportunistically intertwining techniques that transform raw data into strategic advantage.

Mapping the Digital Footprint: From IP to Insight

Companies are complementing entity resolution with digital domain mapping, particularly in the practice of tracing web traffic back to specific companies through reverse IP lookups. When a visitor lands on your site, their IP address can be cross-referenced against databases of corporate networks, revealing not just location of the domain server, but also the operating organizational identity - using B2B signals to understand transactional behavior.

Tools like reverse IP tracking turn anonymous visits into named prospects, enriching CRM systems with firmographic data such as company size, industry, and revenue. When integrated with entity resolution, it resolves ambiguities—ensuring that traffic links correctly to the parent corporation, even if subsidiaries are involved.

Key Use Cases: Where Resolution Meets Reality

The true value shines in practical applications. Here are a few ways businesses are leveraging these technologies to drive decisions and mitigate risks.

Fraud Detection: Spotting the Anomalies

In fraud prevention, entity resolution and digital domain mapping form a dynamic duo. Banks analyze transaction data alongside web traffic to detect mismatches—say, a login from an IP tied to a known risky entity, or duplicate profiles attempting wire transfers. For example, if multiple accounts share an email but originate from disparate company IP addresses, it could flag account opening fraud. Anti-money laundering (AML) teams use this to uncover hidden networks, reducing false positives and accelerating investigations. Real-time resolution cuts fraud losses by identifying suspicious patterns across channels more accurately and faster than other means.

Property Due Diligence: Assessing Digital Vitality

For real estate investors and developers, due diligence extends beyond physical inspections. Entity resolution helps verify tenant identities by linking lease records to corporate filings, while digital domain mapping evaluates a company's web traffic footprint. High traffic from reputable IP addresses might indicate a thriving business, boosting property value; conversely, erratic patterns and patterns with "bad actors" could signal instability. In M&A contexts, this combo accelerates reviews, slashing due diligence time from weeks to hours by automating entity matches and traffic analysis. OSINT techniques further enhance this, pulling in public web data for comprehensive risk profiles.

Marketing and Lead Generation: Targeting with Precision

B2B marketers thrive on digital domain mapping to identify anonymous site visitors as potential leads. By resolving these entities, teams may personalize content—serving tailored ads or emails to decision-makers at visiting companies. Account-based marketing (ABM) benefits immensely as well, prioritizing high-value prospects based on traffic intent.

Charting the Future: Integration and Innovation

As data volumes explode, entity resolution and domain mapping will evolve with AI, incorporating real-time geospatial layers for even richer insights—think mapping traffic to physical locations for everything from trusting a transaction to supply chain optimization. Executives invest in resolving uncertainties while positioning their organizations for what's next – the unknown to the knowable.

The global insurance market is used to discussing war risks in terms of coverage, limits, and pricing.

In Ukraine, war risk is no longer a theoretical construct or a niche extension of property insurance. It is a daily operational reality.

Over the past few years, Ukrainian insurers have gone through a learning curve that most markets only explore through stress tests or academic scenarios. This experience is not about heroism or communication. It is about how claims are actually handled when war becomes a physical risk environment.

When PVI stops being theoretical

In stable jurisdictions, political violence insurance is typically perceived as:

• an add-on to property coverage,
• a tool for large infrastructure or cross-border projects,
• a low-frequency, high-severity product.

In Ukraine, this logic no longer holds.

War risks here:

• materialize with high frequency
• take multiple forms — from direct hits to secondary damage,
• overlap with active production, energy, and logistics processes.

As a result, the key question is no longer whether war risks can be insured, but whether insurers are operationally capable of settling such claims in a controlled and professional manner.

Claims ≠ payment

One of the most common misconceptions outside Ukraine is the idea that war risk claims follow a linear process:

incident → report → payment.

In reality, war-related losses are rarely simple.

Assets affected by attacks — power plants, manufacturing facilities, logistics hubs — have multi-layered technical structures, including:

• core equipment,
• auxiliary systems,
• cable networks,
• control and monitoring systems,
• infrastructure elements with indirect or secondary damage.

Each component requires separate technical assessment, and standard claims-handling templates are largely ineffective.

In practice, war risk claims become engineering-driven analytical projects, not administrative exercises.

The limits issue: why "EUR 250,000" or "UAH 10 million" is not underinsurance

A frequent question from international partners is why war risk limits in Ukraine often appear modest.

The answer lies in reinsurance availability and affordability.

After every major attack, insurers receive a surge of requests from corporate clients. International reinsurers — including the Lloyd's market — are formally willing to quote. In practice:

• quotes are valid for hours or days,
• pricing can reach 10–15%,
• terms fluctuate significantly depending on the phase of the conflict.

Under such conditions, full risk transfer frequently becomes economically unviable for insureds.

As a result, Ukrainian insurers have developed an alternative model — providing war risk coverage backed by their own capital, within limits that are financially sustainable.

This is not a compromise.

It is pragmatic capital risk management.

Speed versus accuracy

Another underestimated dimension is claims settlement timing.

War risk claims require a delicate balance:

• excessive speed increases the risk of technical or legal errors,
• excessive delay jeopardizes business continuity for insureds.

In the Ukrainian context, 30–40 days from incident to payment is not slow. It reflects:

• comprehensive documentation,
• multi-level technical expertise,
• decision-making under non-standard operational conditions.

This balance is difficult to model theoretically but emerges through practice.

The human dimension of claims handling

An often-overlooked element of war risk claims is the human factor.

Claims teams operate:

• on physically damaged sites,
• in constant interaction with clients facing business disruption or loss of critical infrastructure,
• under intense responsibility for accuracy, timing, and capital impact.

In such conditions, policy wording alone is insufficient.

Effective claims handling requires the ability to combine technical expertise, expectation management, and professional restraint.

This dimension is largely absent from traditional claims-handling frameworks in peaceful markets.

What global markets still underestimate

The core lesson from Ukraine is uncomfortable but clear:

War risk is not a standalone insurance line.

It is a systemic stress test for underwriting, capital adequacy, claims handling, and human management.

Ukrainian insurers are currently accumulating experience that:

• cannot be fully replicated through simulations,
• is not captured in standard methodologies,
• will, unfortunately, become relevant for other markets sooner or later.

Ideally, such experience would never be needed.

But since it exists, it deserves to be discussed professionally and without illusion.

You can't scan a business headline, listen to an earnings call, or sit in a leadership meeting today without hearing the term: "elastic staffing."

Once considered a niche workforce strategy, elastic staffing has quickly become a defining operating model for organizations navigating sustained uncertainty. At its core, the concept is straightforward: maintain a strong internal team, then flex capacity up or down by tapping pre-qualified external talent as business needs change.

What's driving its rise isn't novelty—it's pressure. Persistent talent shortages, accelerating retirements, cost volatility, rapid digital transformation, and unpredictable demand have exposed the limitations of rigid, fixed-headcount staffing models across nearly every industry. Nowhere is this shift more apparent than in insurance.

Why Traditional Staffing No Longer Fits Insurance

The insurance workforce model was built for a very different era—one defined by predictable workloads, long employee tenures, and incremental change. For decades, insurance organizations could rely on steady volumes and institutional knowledge accumulated over entire careers. That reality no longer exists.

Today's carriers, MGAs, MGUs, and brokerages are operating in an environment marked by volatile claims activity, rapid technology adoption, tightening margins, heightened regulatory scrutiny, and shrinking talent pipelines. Demand now comes in peaks and valleys rather than steady, forecastable patterns. At the same time, the industry is grappling with a wave of retirements that is steadily draining deep institutional expertise.

Fixed staffing forces leadership into a constant balancing act. Overstaff to prepare for demand surges and organizations absorb unnecessary cost when volumes normalize. Understaff and they risk backlogs, service breakdowns, compliance exposure, and employee burnout when activity spikes. Neither option is sustainable, and both create long-term organizational drag.

As Carrier Management has observed, insurers need workforce models that can "expand, contract, reorganize and modernize quickly without compromising culture or compliance, or creating a disjointed customer experience." That requirement alone challenges the viability of traditional, static staffing approaches.

Elastic staffing addresses this gap by turning labor into a variable resource rather than a fixed constraint. It allows organizations to scale specialized expertise—claims, underwriting, accounting, compliance, account management—without committing to permanent headcount that may not align with future demand. Just as importantly, it provides a practical way to adapt as automation and AI reshape roles, without relying on layoffs as the primary lever.

But flexibility alone is not the answer.

Why Elastic Staffing Can Fail in Practice

Many insurance organizations struggle with elastic staffing because they approach it as a short-term fix or a substitute for temporary labor. When elasticity is treated as a transactional solution, it often becomes reactive rather than strategic—and can introduce new forms of risk.

To succeed at scale, elastic staffing must rest on three critical foundations:

• Access to experienced, insurance-specific talent
• Operational consistency and accountability
• Alignment with how insurance work is actually performed

Without these, organizations may gain short-term capacity but sacrifice quality, continuity, and control.

Insurance is not an industry where generalist labor can be dropped in without consequence. Regulatory requirements, system complexity, and client expectations demand professionals who understand the nuances of the work. Elastic staffing models that overlook this reality often struggle with long ramp-up times, rework, and execution errors.

The Role of Specialized Workforce Models

Elastic staffing works best when it is built around talent pools that are purpose-built for insurance, rather than broad labor marketplaces.

The most effective models share several defining characteristics.

• Immediate access to experienced professionals: Elastic staffing only delivers value if talent can contribute quickly. In insurance, experience is not optional—particularly in high-stakes functions such as underwriting, claims, finance, compliance, and client service. Deep domain knowledge shortens ramp-up time, reduces errors, and minimizes operational risk.
• Flexibility without constant turnover: Elastic does not have to mean short-lived engagements. In fact, longer-term, embedded professionals often deliver the greatest value. This approach allows organizations to flex capacity for peak workloads, remediation efforts, or transformation initiatives while maintaining continuity, institutional knowledge, and service quality.
• Reduced execution risk: Moving from a fixed headcount model to an elastic one introduces new challenges around onboarding, security, performance management, and accountability. Workforce models that already address these operational realities allow organizations to adopt flexibility without redesigning internal processes or overburdening HR and operations teams.
• Smarter cost control: Elastic staffing converts labor from a fixed expense into a variable one while preserving access to senior-level expertise. When recruiting costs, benefits, turnover, and downtime are considered, this approach often proves more cost-effective than traditional hiring—particularly for roles tied to fluctuating demand or specialized projects.
• Support for long-term workforce resilience: As retirements accelerate and talent pipelines thin, insurance organizations need solutions that balance immediate capacity needs with long-term continuity. Elastic staffing provides that balance, stabilizing operations today while creating space to rethink workforce design for the future.

Elasticity as a Strategic Operating Model

Organizations that succeed with elastic staffing do not treat it as a stopgap. They treat it as an operating model.

That means planning for flexibility, building repeatable processes, and aligning workforce strategy with business volatility and technological change. It also means recognizing that elasticity is not about replacing internal teams, but about augmenting them in ways that preserve culture, compliance, and customer experience.

In an industry where regulatory exposure and trust are always at stake, execution matters as much as intent. When implemented thoughtfully, elastic staffing enhances resilience. It enables organizations to absorb demand shocks, manage transformation initiatives, and adapt to AI-driven change without destabilizing their workforce or compromising service quality.

The industry is already moving in this direction. As uncertainty becomes the norm rather than the exception, workforce agility is no longer a competitive advantage—it is a requirement.

The elastic wave is here. How insurance organizations ride it will determine who keeps pace—and who falls behind.

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

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

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

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

The Gap Between Hype and Production

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

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

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

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

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

The Human Touch

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

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

The Failure of End-to-End Automation

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

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

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

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

The Real Innovation: Modular AI

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

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

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

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

Building Digital Transformation Through Integration

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

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

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