Requests for proposals (RFPs) have long been the foundation of major insurance technology investments. Carriers spend months evaluating functionality, comparing integrations, reviewing timelines, and scoring vendors against detailed technical requirements. But even the most rigorous RFP process is often missing one of the most important evaluation criteria of all: will the vendor actually be a good partner?

When it comes to large and long-term technology investments, such as core system transformations, the solution is only part of the equation. The continuing relationship with the vendor is just as important. A platform may check every box, but if the carrier and vendor are not aligned, collaborative, and committed to solving problems together, even the strongest technology investment can struggle to deliver meaningful results.

Many Big Technology Projects Fail

Technology alone cannot overcome misaligned expectations, a lack of communication, or a lack of shared accountability between the carrier and vendor. The challenges that derail implementations often emerge only after contracts are signed. Organizations may discover there's no agreement about success metrics, that timelines are unrealistic or not aggressive enough, or that commitments made during the sales process aren't delivered. In some cases, carriers underestimate the internal resources and executive support required to drive transformation. Others focus too heavily on recreating legacy workflows instead of improving them.

Over time, these issues compound. Delays increase, frustration grows, and both sides can become more focused on protecting positions than solving problems together. That is when transformation efforts begin to break down.

The best vendors are more than just transactional providers. The right partner should function as an extension of the carrier's team, helping challenge outdated processes, guide decision-making, and identify where modernization can create meaningful business value. Successful implementations are driven by collaborative partnerships built on trust, alignment, and shared accountability.

Rethinking the Traditional RFP

For decades, the insurance software RFP process has centered primarily on evaluating functionality, integrations, timelines, and pricing. Those factors remain important, but as technology transformations become larger and more complex, carriers may need to rethink what an RFP is designed to measure in the first place.

Selecting core technologies is no longer just about choosing the platform with the longest feature list. It is about choosing a long-term strategic partner to guide the organization through operational change, process redesign, and future growth. That means carriers should consider expanding their evaluation criteria beyond technical capabilities to include the relationship factors that often determine whether a project ultimately succeeds.

Elements such as cultural alignment, communication style, transparency, adaptability, and executive alignment may not traditionally appear in an RFP scoring matrix, yet they frequently have just as much impact on implementation outcomes as the technology itself.

As carriers evolve their evaluation processes, they should look beyond solution capabilities and place greater emphasis on how the vendor approaches partnership. Things like how transparent the vendor is about implementation complexity and tradeoffs, whether sales, product, and implementation teams appear aligned, and how willing the vendor is to challenge outdated processes and recommend better ways of operating can take priority. Carriers should also assess the vendor's ability to have difficult conversations and whether the organization demonstrates flexibility and accountability when unexpected challenges emerge.

As carriers evaluate vendors, they should broaden their criteria beyond functionality and pricing. Important questions include:

• Does the vendor understand our business goals and operational realities?
• Are they honest about implementation complexity and organizational change requirements?
• Will they help us improve processes rather than simply replicate old ones?
• How aligned are their sales, product, and delivery teams?
• How do they handle conflict and shifting priorities?
• Can we realistically see ourselves working with this organization long-term?

Reference checks should also go deeper than curated success stories. Carriers should seek candid feedback about how vendors handled difficult situations, implementation challenges, and evolving customer needs. The goal is not to find a perfect implementation. The goal is to find a partner capable of navigating complexity alongside the carrier.

The insurance industry will continue evolving. AI, automation, APIs, cloud platforms, and data-driven underwriting models will reshape operations and customer expectations. But regardless of how technology changes, one reality remains constant: transformation succeeds when carriers and vendors work together as partners. Technology can enable modernization. Partnership is what sustains it.

The carriers that gain the most value from their technology investments will be the ones that look beyond the RFP checklist and prioritize trust, alignment, communication, and shared commitment to long-term success.

The commercial insurance industry is moving decisively toward prevention. Insurers are building AI-driven risk detection, deploying IoT sensors, expanding telematics, and investing heavily in predictive models. The shift from reactive claims management to proactive loss mitigation is real and accelerating.

But every insurer building this infrastructure has a critical blind spot. They can predict what's about to happen. They cannot see what's already true about the physical assets being protected. And more importantly, they lack the systematic tools to translate what they see into consistent operational action.

Consider what an AI risk model does. It ingests historical claims, property details, location risk, and weather patterns. It learns from what happened before and projects forward. Yet the current condition of the physical assets being protected remains invisible to most insurance partners. This gap creates operational risk that detection tools cannot address.

This is not a technology problem. It is a data architecture problem. And proactive loss mitigation cannot reach its full potential without resolving the problem.

The critical question is not whether insurers should invest in prevention. The question is whether their prevention infrastructure can see the complete picture of what they are trying to prevent, and more importantly, whether they have the systems in place to act on what they see. For most organizations, the answer is no on both counts. And that gap represents genuine competitive and operational risk.

What the Ecosystem Actually Sees

Over the past 18 months, insurers have deployed detection systems across their risk infrastructure. AI flags suspicious claims patterns in real time. IoT sensors predict equipment failures. Fleet telematics capture driving behavior and collision risk. Cyber risk platforms assess vulnerability across supply chains. Risk models increasingly incorporate climate data and weather prediction.

These investments reflect the industry's conviction that early detection reduces claims. Carriers combining AI modeling with proactive policyholder engagement demonstrate measurable improvement in both frequency and severity.

But one layer sits beneath all these systems and determines their effectiveness. That layer is the baseline operational condition of the physical assets being protected.

The evidence is immediate. More than half of U.S. commercial properties are over 40 years old. Deferred maintenance on aging infrastructure increases the likelihood that localized damage escalates into broader systemic losses. A foundation in poor condition amplifies structural risk during weather events. A parking lot with deferred maintenance creates slip-and-fall liability. Plumbing systems past their service life increase mold exposure. Fire alarm systems lacking routine testing create coverage gaps. This is the data that most powerfully predicts loss. Yet it remains fragmented, often tracked manually in spreadsheets or sticky notes at the property level if tracked at all, and crucially, not connected to the operational decisions that actually reduce risk.

The result is fragmented visibility with no systematic action. Insurers see behavioral risk through telematics and claims analysis. They see environmental risk through weather modeling. They do not see operational risk systematically, which is the baseline condition determining how much damage those other factors will cause. And even when property-level operational data exists, it is not connected to the loss control decisions, underwriting actions, and policyholder engagement that actually improve conditions.

The Research Confirms the Gap

Detection tools are most effective against acute risks that develop in real time. Weather events, for example. But many damaging claims develop across months or years as deferred maintenance converts latent conditions into active exposures. An insurer with sophisticated cyber detection still has exposure if firewall hardware is past its service life. A carrier with excellent telematics still has exposure if vehicles lack routine servicing. An insurer with perfect weather prediction still has exposure if the foundation being protected is already compromised.

Research quantifies this exposure. The American Society of Civil Engineers estimates that $9.1 trillion in investment is required across all infrastructure categories to reach a state of good repair, with a current funding gap of $3.7 trillion. This includes substantial deferred maintenance backlogs across federal buildings, municipal infrastructure, public schools, state universities, and public housing.

Verisk found that properties with poor roof condition sustain 50% more damage during severe weather. Public schools alone are facing an estimated $270 billion in needed infrastructure repairs, with the average school building nearly 50 years old and only 10% of education spending directed toward facility upkeep. Commercial auto claims severity increased 94% between 2015 and 2024, driven partly by advanced vehicle technology requiring specialized maintenance.

In commercial real estate, condition visibility directly affects underwriting outcomes. Two buildings in the same ZIP code can receive very different insurance terms depending on how seriously owners maintain critical systems like roofs, HVAC, parking surfaces, and electrical infrastructure. In each case, the gap between detection capability and actual loss is determined by operational condition. Without visibility into that condition, insurers cannot fully predict or prevent losses, regardless of how sophisticated their detection tools are.

From Visibility to Action to Measurement

The complete loss prevention infrastructure has three related dimensions.

First, the visibility layer:

Maintenance work order history such as what has been done, when, and by whom across every property. Equipment and asset condition scores like compressors running beyond service intervals, roofs past their rated lifespan, HVAC systems out of compliance. Compliance and inspection records like safety certifications, code inspections, regulatory documentation.

Together, this data answers the foundational question for underwriters and risk managers: What is the current operational state of the assets being insured?

Second, and critically, the action layer:

Visibility without action is static data. The solution requires systematic tools to translate operational insights into decision-making at three critical points. Loss control teams must be able to deliver risk-specific recommendations directly into maintenance workflows, not as a report reviewed quarterly, but as prioritized guidance integrated into daily operations. An asset owner needs to know not just that their roof is past rated lifespan, but that specific roof replacement is the highest-priority item to prevent weather damage and in what timeframe it should occur. Underwriters and pricing teams must integrate condition data into underwriting decisions and pricing models, adjusting rates based on observable maintenance behavior and current asset condition. Claims teams must establish feedback loops to measure whether maintenance interventions actually prevented losses or reduced severity.

Without this action layer, visibility becomes information without impact. With it, visibility becomes operational intelligence.

Third, the measurement layer:

The complete solution requires insurers to measure whether their loss prevention interventions actually worked. Which properties took recommended maintenance action? Did claims frequency or severity actually decline in those properties? What was the ROI? This feedback loop is what distinguishes insurers managing portfolios with data-driven insight from those managing individual properties without systematic measurement.

When an insurer combines condition data, IoT signals, behavioral data, environmental modeling, claims data, and the systematic tools to act on it all at scale, the result is genuinely predictive and preventive infrastructure. They see not just statistical risk but operational risk. The insurer identifies the specific properties where aging infrastructure, deferred work, and emerging environmental factors intersect. They deliver specific guidance into maintenance operations and measure results.

Building for Regulatory and Competitive Advantage

Insurance is shifting in one clear direction. From managing claims to managing risk. From indemnifying losses to preventing them. From annual renewals based on history to continuous engagement based on predictive modeling.

Condition intelligence is not a new strategic direction. It is the missing operational layer in the direction the industry is already committed to.

This distinction matters in 2026 for a specific reason. As regulatory focus on AI governance intensifies, insurers relying solely on opaque algorithmic predictions face increasing scrutiny. State regulators through the NAIC have adopted AI governance standards and are piloting evaluation tools to assess how insurers use and manage algorithmic systems. Insurers with transparent, explainable underwriting models backed by observable condition data will be better positioned to demonstrate governance maturity and operational capability at scale.

Beyond regulatory scrutiny, this operational discipline directly affects financial standing. Credit rating agencies increasingly evaluate deferred maintenance backlogs as a component of municipal and school district credit risk assessment. A large, undocumented, or growing deferred maintenance backlog signals fiscal management weakness and represents an unfunded liability. The insurer that has built longitudinal condition data and the operational partnerships required to act on it will have moved beyond competitive advantage into operational necessity. When loss margins compress and premium growth decelerates, managing loss at scale becomes essential to defending profitability. Insurers with visible, measurable infrastructure for operational condition, systematic action, and verified outcomes will have the advantage of scale. Those without it will struggle to keep pace.

Those who build this infrastructure will have both defensible competitive advantage and the operational discipline required to survive margin compression. The differentiation is not about speed to market or technology adoption. It is about building the observable, systematic, measurable infrastructure for loss prevention. That foundation matters now, and the gap between insurers who have it and those who do not will only widen.

Sources

• American Society of Civil Engineers. 2025 Infrastructure Report Card. March 2025. https://infrastructurereportcard.org/making-the-grade/
• Facilities Management Advisor. The Deferred Maintenance Crisis: How K-12 Districts Can Overcome a Growing Backlog. August 20, 2025. https://facilitiesmanagementadvisor.com/maintenance-and-operations/the-deferred-maintenance-crisis-how-k-12-districts-can-overcome-a-growing-backlog/
• Minico. 2026 Commercial Property Insurance Outlook for Agents. December 4, 2025. https://www.minico.com/2026-commercial-property-insurance-outlook-what-agents-need-to-know/
• Visual Capitalist. 6 Trends Reshaping U.S. Property Insurance. March 11, 2026. https://www.visualcapitalist.com/sp/ig01-6-trends-reshaping-u-s-property-insurance/
• The Pew Charitable Trusts. New Reports Provide Resources for States to Tackle Deferred Maintenance of Public Infrastructure. December 18, 2025. https://www.pew.org/en/research-and-analysis/articles/2025/12/17/new-reports-provide-resources-for-states-to-tackle-deferred-maintenance-of-public-infrastructure
• Verisk. U.S. Roof Claims Costs Reached Over $30 Billion In 2024, Underscoring Evolving Risks. April 8, 2025. https://www.verisk.com/company/newsroom/u.s.-roof-claims-costs-reached-over-$30-billion-in-2024-underscoring-evolving-risks/
• Swiss Re. U.S. Property-Casualty Outlook, July 2025. Institute Research. https://www.swissre.com/institute/research/sigma-research/Insurance-Monitoring/us-property-casualty-outlook-july-2025.html

Insurers are moving quickly to bring artificial intelligence into underwriting, compliance and risk workflows. And it makes sense. Insurance work is document-heavy, time-sensitive and filled with routine tasks that slow people down. AI can help teams read submissions, summarize documents, check required fields and route information faster.

But faster is not the same as safer.

When AI is added to a workflow that already has weak spots, those weak spots do not disappear. The work moves faster, the dashboard may look cleaner and the answers may arrive sooner, but the underlying risk picture may still be incomplete.

This creates an AI false positive: a team feels more protected because it has added AI, while the information feeding the process remains scattered, outdated, inconsistent or missing. Coverage gaps can still sit within the workflow: missing endorsements can go unnoticed, expired policies can create exposure and risk-transfer issues can remain unresolved until the wrong moment.

AI did not create those problems. But when AI is layered on top of old workflows without changing how information is collected, connected, reviewed and acted on, it can make organizations feel more confident than they should.

The danger is not that AI gets the work wrong. The danger is that it makes an incomplete process look finished.

Why Insurers Add AI to Underwriting First

Insurance teams usually do not start with big changes. They start where the work already lives. In underwriting, that is understandable. The stakes are high, the rules matter and no one wants to disrupt a process that already carries real business risk.

So AI often shows up first in familiar places: reading submissions, summarizing documents, checking fields and helping teams move through routine reviews faster. Those improvements can help. But they do not answer the bigger question: Does the workflow give people the full picture of risk when they need it?

You might have the certificate of insurance. You might even have the endorsement. But the real question is whether those documents actually satisfy the contract requirements and whether your team knows what to do when they do not.

A lot of insurance compliance work still depends on information that is hard to evaluate in context. A certificate may confirm that coverage exists, and an endorsement may appear to provide the required protection, but those documents only reduce risk if they match the specific limits, terms, exclusions and obligations in the contract. Too often, once the documents are collected and reviewed, they are filed away without a clear answer on whether they meet the requirement, what gaps remain or what action should happen next.

Those documents were never designed to give teams a living view of risk. AI may make the old process easier to navigate, but it does not make it capable of seeing more than it was built to see.

What AI Can and Cannot Do

AI can read, summarize and flag information faster than any manual process, but it can only work from what the workflow provides. Missing or stale data does not become reliable simply because it moves through a faster system, and a process that feels more complete is not always one that produces a stronger view of risk. Insurance leaders cannot afford to overlook this distinction: a faster review is not a better risk assessment, and bad inputs do not become good judgment just because they move faster.

The deeper problem is that teams already spend hours chasing, checking and filing certificates, endorsements and policy documents without those records ever being used as the risk intelligence they actually contain. Those documents are signals of whether risk-transfer requirements are being met, where coverage may fall short and where financial exposure may be building. Placing AI atop that old process and calling it modernization misses the point entirely.

The real value is not a faster file review. It is an earlier warning and the ability to rethink how critical information flows through the business so that gaps surface sooner, questions get sharper and a small administrative issue is caught before it becomes a material exposure.

Why Trust, Context and Accountability Matter

Underwriters, claims teams, compliance teams and advisors need to believe the information in front of them. They need to know where it came from, whether it is current and whether it supports the decision being made.

When AI produces an answer without sufficient context, or when the underlying data is incomplete, skepticism is not resistance. It is good judgment.

Insurance professionals are right to ask hard questions. What document did this answer come from? Is the policy current? Does the certificate match the requirement? Has the endorsement been reviewed? Who owns the next step? What happens if the system flags a gap and no one acts on it?

Those are not just technical questions. They are accountability questions.

It's why meaningful AI adoption in insurance cannot be treated as a software deployment alone. It requires better process design, clear accountability, practical guardrails and education that helps people use the information with confidence.

AI can surface a signal. It cannot decide who owns the next step. Without accountability, even the right alert can become another unresolved item in the workflow.

How Insurance Advisors Can Help Clients Prepare

As AI becomes more common in underwriting and insurance operations, advisors have an opportunity to become more valuable to clients. Client information will matter more, not simply because documents need to exist, but because those documents need to be complete, current, consistent and useful enough to support a real view of risk.

Advisors can help clients move away from last-minute, document-by-document compliance and toward a clearer understanding of what is covered, what is missing and where exposure may be building.

Clients do not need AI hype. They need plain-language guidance on how AI may affect underwriting speed, documentation expectations, renewal conversations, coverage questions and the way their risk profile is evaluated.

The most valuable advisors will not be the ones who talk most confidently about AI. They will be the ones who help clients show up with cleaner information, fewer surprises and a risk profile the market can actually evaluate.

The Future of AI in Insurance

The real promise of AI in insurance is not faster paperwork for its own sake. It is not automation that hides weak workflows. It is not a false positive that makes teams feel protected while risk remains unresolved.

The real promise is not acceleration. It is visibility.

AI can help insurance organizations get there. But only if leaders look beyond surface-level speed and address the underlying workflow.

The winners will be the ones who finally make risk visible before it becomes a problem.

Driver fatigue is one of the most consequential risks in commercial auto, and one of the least measurable. When dashcam footage is used as evidence in litigation, SambaSafety's 2026 Driver Risk Report highlights that fatigue is named in 9% of crashes, nearly eight times higher than what is reported in police reports.

With no agreed-upon definition, no field test to confirm it, and no reliable way to attribute it after a crash, driver fatigue has remained on the periphery of underwriting conversations—even as the claims it contributes to quietly shape loss ratios across the industry.

SambaSafety's report indicates that the industry is beginning to shift. As telematics and camera technology mature, insurers are using new tools to detect behavioral signals associated with fatigue. However, to interpret these signals and act on them, insurers need to understand why fatigue has been so difficult to measure in the first place.

Why Fatigue Is So Hard to Pin Down

Kyla Hagan-Haynes, principal researcher at SambaSafety, spent years researching this challenge.

"Historically, researchers have had to rely on self-reports and proxy measures—lack of skid marks, unexpected lane departures," she explained. "There's no breathalyzer equivalent. The variability in how prevalence is estimated is a direct result of that."

What's captured in loss data understates not only how often fatigue contributes to crashes but also how severe those crashes tend to be. More than 17% of fatal crashes involve fatigue, and one in three crashes involving a drowsy driver result in injury, according to SambaSafety's 2026 Driver Risk Report. For insurers, that severity gap has direct implications for loss ratios, litigation exposure, and how fatigue risk is, or isn't, priced.

Fatigued drivers rarely brake before impact, making fatigue-related crashes disproportionately severe, leading to higher rates of bodily injury claims and more total losses—events that push verdicts into nuclear territory. As Matteo Carbone, founder of the IoT Insurance Observatory, noted in the report, "Sleep is not a wellness topic. It is a road risk variable." With thermonuclear verdicts exceeding $100 million, up 81% year over year in 2024, the litigation landscape has become an amplifier of poorly documented risks.

Hagan-Haynes highlighted a dimension of this risk that doesn't get enough attention, noting that hours-of-service compliance doesn't ensure a driver is rested. Plaintiff attorneys use ELD logs not just to examine the day of a crash, but also to reconstruct weeks of scheduling patterns, building a picture of the pressure placed on a driver to consistently work up to the maximum limits allowed by regulation.

A fleet that's technically compliant can still face a compelling fatigue argument in litigation if its schedule practices don't reflect genuine fatigue risk management.

"When I talk to customers, I also see so many fleets that are not regulated drivers—they don't have CDLs, they have no hours-of-service requirement," Hagan-Haynes said. "There are no rules around how long a non-CDL driver can drive. And companies can be held liable even for a commute. I've seen it happen, where a driver worked two weeks straight, got in a vehicle to drive home, and fell asleep [at the wheel]."

It's a blind spot most commercial auto insurers aren't accounting for.

What the Data Is Starting to Reveal

A Discovery Vitality study referenced in SambaSafety's report found that sleep regularity is associated with a 36% lower crash risk, sleep duration with a 32% reduction, and total REM sleep with a 14% reduction. For underwriters, that data suggests that off-duty hour patterns in telematics aren't just operational—they're a risk signal worth accounting for in underwriting.

For Hagan-Haynes, the most important underwriting variable is almost straightforward. "The most significant factor when it comes to driver fatigue is whether a driver is getting sufficient sleep—for most people, seven to eight hours," she said. "Telematics data on working hours and off-duty windows is going to be an important piece of data to consider."

Whether a fleet has a fatigue management policy at all is another signal. Not just the existence of one, but whether it's enforced, communicated to drivers and managers, and addresses scheduling, medical examinations, and rest opportunity—not just regulatory minimums. "If they don't have anything to share with insurers other than their ELD logs, that's informative," noted Hagan-Haynes.

In-cab cameras that detect eye closure or lane deviation are valuable, and their adoption among commercial fleets is growing. But Hagan-Haynes was direct about where they sit in the risk management chain. "That's far downstream. By the time a camera is flagging eyelid closures, you're really late in the game."

Real fatigue management happens in scheduling practices, sleep education, medical screening, and a culture that empowers drivers to take rest when they need it. For insurers, these factors matter in most jury trials, not only in responding to what happened at the moment of a crash, but also to the whole pattern of what didn't happen—the interventions that could have occurred but didn't.

When telematics data shows fatigue signals despite a fleet's stated policy, Hagan-Haynes framed it as a root cause question, not just a pricing adjustment. Is the risk concentrated in a single driver, suggesting an individual issue with a medical component? Or spread across many drivers, pointing to something systemic in scheduling or culture? The answer shapes what comes next and what a productive conversation between insurer and fleet looks like.

The Role Insurers Play in Closing the Fatigue Risk Gap

Renewal conversations are among the most effective moments insurers have to influence how policyholders think about risk. A fleet with a genuine fatigue risk management program that goes beyond regulatory compliance represents a meaningfully different risk—one that should be reflected in how a book is evaluated throughout the policy term, not just at renewal.

The industry has spent years absorbing losses it couldn't fully explain. Fatigue is part of that story. The insurers that move first to build it into their risk frameworks won't just improve their own outcomes; they'll help define what responsible commercial auto underwriting looks like going forward.

To explore the full findings from SambaSafety's 2026 Driver Risk Report, visit http://sambasafety.com "

Conning maintains a stable outlook for U.S. states in 2026, reflecting continued balance sheet strength and generally prudent fiscal management across, even as operating conditions become more challenging.

South Dakota rose 17 places to first overall, while Utah maintained its second place position. Tennessee took third place over North Carolina this year, while Texas moved up six places to fifth overall. These states benefit from sustained in-migration, competitive cost of living profiles, and comparatively strong balance sheets on the liability side, supporting greater fiscal flexibility. South Dakota additionally benefited from strong personal income, GDP, and house price index (HPI) growth.

Fundamentals continue to favor the Plains, Mountain West, and parts of the South, where our rankings benefit from population inflows, diversified economic growth, comparatively low cost structures, and strong balance sheets. Top states are outperforming due to a combination of disciplined fiscal management, resilient labor markets, and favorable demographic trends.

By contrast, performance is more mixed in the Northeast and parts of the West Coast, where higher costs, slower population growth, and other structural pressures weigh on relative rankings. The lowest‑ranked states are similarly clustered among those facing persistent demographic, economic, and fiscal constraints, including weak population trends, elevated cost burdens, and limited fiscal flexibility.

While year-to-year movement occurs, the bottom tier continues to reflect longer-term structural pressures or cyclical weakness. West Virginia fell three spots to 50th overall. Maryland (49th), California (48th), and Rhode Island (47th) each experienced declines of 27, 17, and 21 places, respectively. Louisiana improved modestly to 46th place, up four spots in this year's report.

Ranking movements were more pronounced in 2026 than in our 2025 report. A total of 23 states moved 10 or more positions year‑over‑year (YoY), compared with 21 last year, and 11 states saw a shift of more than 20 positions versus just six in the prior period, despite no changes to our methodology.

Our framework, updated last year, incorporates cost of living and catastrophe losses per capita to better capture affordability pressures and climate-related risks. The wider dispersion observed in 2026 reflects the fading of the post‑pandemic period of broadly strong performance, as revenue growth moderates, migration slows, and widening differences in cost structures and balance sheet positioning translate more directly into relative ranking outcomes.

For the full report from Conning, click here.

With major companies such as Meta and Amazon announcing huge layoffs that they tie to the efficiencies AI is making possible, you might be thinking that you, too, should be looking to cut head count. 

Maybe belay that thought, at least for the time being.

Many of the layoffs actually stem from a whole series of factors, even if companies choose to use AI as the blanket explanation. Meanwhile, economic data suggests that AI has not led to widespread job cuts, and companies are increasingly reporting great successes by using AI to make employees' jobs easier and to enhance customer service.

Insurance, as the ultimate digital industry, stands to benefit from generative AI's efficiencies as much as any, but let's look at what's realistic and what isn't.

Elon Musk said earlier this year that AI will take on so much work that in 10 to 20 years jobs will be optional. If you don't want to work, you can just sit at home and let the AI and the money it generates take care of you. But Musk says a lot of things, including that he would land humans on Mars by 2025 and have them start building a colony for 1 million people and that he would have 1 million fully autonomous robotaxis on the road in 2020. (The total currently stands at two or three dozen.) 

Meanwhile, OpenAI CEO Sam Altman has retracted his claim that generative AI will massively reduce the number of entry-level jobs, and Anthropic CEO Dario Amodei has withdrawn his prediction that AI would eliminate 50% of white-collar jobs. Amodei now says, “If you automate 90% of the job, then everyone does the 10% of the job. And the 10% kind of expands to be 100% of what people do and kind of 10-times their productivity.”

In "A Reality Check on the AI Jobs Hysteria," the MIT Technology Review reports that "there’s scant evidence that AI has yet had any large-scale impact on the US labor market. Analysis of the data gathered for the US Bureau of Labor Statistics (BLS) shows that the unemployment rate for the jobs potentially most affected by AI is actually lower than that for occupations less exposed to the technology. And, critically in the mind of economists, there are no signs that large numbers of people are shifting from jobs threatened by AI to supposedly safer ones, such as those involving mostly manual labor."

Why, then, are so many companies announcing job cuts related to efficiencies they're achieving with AI? In my experience, companies will always glom on to a convenient excuse when announcing bad news. Even a flimsy excuse is better than saying, "We screwed up." 

But many companies did screw up. Lots hired too freely as the economy rebounded post-COVID. Some are just poorly managed, because there are always companies that are poorly managed. 

An article in the New York Times, "Is AI Replacing Tech Workers or Providing an Excuse for Job Cuts?", provides any number of examples of companies using AI as a cover story for layoffs. Meta is my favorite. As a columnist in the Times put it, "From 2021 to 2026, [CEO Mark Zuckerberg] poured $80 billion into the Metaverse in the firm belief that we would all want to don headsets and hang out in a virtual world populated by legless avatars." That flop [predicted here, in 2021, I note immodestly] is why he's having to cut 10% of his work force — but he hopes to save a little face by citing AI.

None of this is to say generative AI isn't have a massive impact. It is. Legendary venture capitalist John Doerr recently told the Wall Street Journal that AI is "underhyped." Doerr, 74 years old, said AI is the biggest of all the tech tsunamis he's seen in his long career.

The gains are, at least as of now, showing up in improvements in efficiency and service — and even in new types of jobs. 

Box, which makes software for storing and managing data, created 13 new kinds of roles, with titles such as AI architect, AI solutions manager and AI platform leader. The New York Times reports: "With the proliferation of these positions, Box expects to have more than 3,000 employees by early next year, up from 2,900 at the start of this year."

Schneider Electric made its call centers and manufacturing facilities more productive, without eliminating workers. Costco, Delta and IBM did much the same.

Insurers are already seeing major opportunities for efficiencies in gathering documents and triaging cases for claims representatives and underwriters, and companies are moving toward AIs that provide recommendations and can act as agents, or at least produce drafts of communications and reports for humans to review. Insurers are seeing great opportunities for AI to handle routine inquiries from customers and to provide at least rudimentary service during off hours. And much more.

Insurers should continue to pursue all available opportunities and think big about what breakthroughs might be out there via AI but, at least for now, should focus on removing the burdens on employees and enhancing customer service and not on reducing head count.

Cheers,

Paul

P.S. After writing this commentary last night, I woke this morning to find that the New York Times had done a long analysis of all the promises Musk has made over the years and failed to meet, including the ones I mentioned about colonizing Mars and filling our streets with his robotaxis. They counted 602 goals he set and found he achieved them less than a fifth of the time.

P.P.S. Finally, here's a wild AI story from the You Can't Make This Stuff Up Department: "Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I."

FROM THE EDITOR

Telematics and Trust: The UBI Revolution by Henry Kowal, Matteo Carbone Adoption of auto telematics has increased 28% a year in the U.S. since 2018. Usage-based insurance is no longer a niche. It is a mainstream strategy. Read More Key IoT Trends in 2026 by Marina Zudina From AI-based IoT to digital twins, five transformative technologies are making IoT deployments smarter, faster, and more secure in 2026. Read More

Why Prevention Is the New Protection by Daniel Grimwood-Bird Rather than inferring exposure solely from historical outcomes, commercial auto underwriters can now access leading indicators of attentiveness, distraction, and behavioral discipline. Read More Can Insurtech Fix Homeowners Insurance? by Matteo Carbone Even a 20% gain in operational efficiency doesn't move the needle enough. The real opportunity is in "connect and protect. Read More

Telematics Drives Shift in Commercial Insurance by Shammi Thakur Commercial insurance is evolving from reactive risk transfer to continuous prevention through real-time telematics and behavioral data. Read More Insurance Risks Being Left Behind by Dominique Roudaut Widening protection gaps demand insurers transform from risk transfer to resilience providers or face existential decline. Read More

Paul Carroll

I’m often intrigued by how far back the roots of innovation go. For instance, we know that automobiles trace back to at least the 1770s, because the first auto accident was recorded in 1771; a Frenchman hooked a steam engine to a cart and crashed into a wall. 

As for the Internet of Things, our ITL Focus topic for this month: Some students at Carnegie Mellon rigged a vending machine in 1982 so they could monitor whether it had sodas available and whether they were cold. In 1993, students at the University of Cambridge pointed a camera at a coffee pot and got it to send a live feed to their computers so they could see if coffee was available without having to walk over to the coffee station.

Those IoT examples say something about how clever students can be about being lazy, but I’m more interested in what they say about adoption curves. The IoT is avant garde these days, yet its roots trace back more than four decades.

You’ve become the poster child for the Predict & Prevent model for insurers, based on your innovations with the IoT that led to the Ting sensor that plugs into a wall outlet and detects electrical problems. You’ve done the research and shown that you can prevent so many fires that dozens of major carriers are giving Ting to customers at no cost. But you still face headwinds.

What can you tell us about the headwinds that IoT innovation faces and about how you’re overcoming them?

Bob Marshall

I would probably qualify the notion of headwinds or challenges. We're distributing roughly 50,000 Ting sensors a month, and by many measures that would be considered extraordinarily successful.

We want to go faster—not just for business reasons but because we're trying to have an impact on homes, families, communities, and society. If we can get Ting into tens of millions of homes, we will be able to help prevent electrical fires, protect families and prevent loss at an even greater scale. Still, we’re approaching 1.4 million homes with Ting today. 

What's driven the success, I would say, is the clarity of the mission. Customers worry about fire. They don't want their house and their family to ever have to deal with a fire. When we can communicate Ting’s value proposition to the homeowner, they adopt it enthusiastically.

The challenge when it comes to Ting is that it's a new category. It's a device and a service that nobody's ever had before. 

People recognize that you need multiple smoke detectors in your house, you've got to replace the batteries, and every 10 years you're supposed to replace the smoke detectors. But when we first describe Ting to a customer, it’s not something they know they need. So we have to build that awareness.

When we do a brand awareness survey of the U.S. population and ask if they're familiar with Ting, this thing that can prevent electrical fires in your home, less than 2% of people are going to say they're aware of it.

This year, we're going to make a pretty substantial investment to build brand awareness of Ting. When one of our carrier partners sends an email campaign to their homeowners asking them to get Ting, we want people to be more likely to say, "Oh yeah, I've heard of that, and I want it."  

Paul Carroll

Simplicity is a significant advantage for Whisker Labs. I’ve seen lots of innovations fail because companies assumed that the benefits they delivered were so great that consumers would be willing to jump through a few hoops. They almost never will. But when I got my Ting, I just plugged it into the wall and scanned a QR code to download the Ting app and enable notifications if you ever detect problems. Took me maybe a minute… and I’m technologically challenged.

Bob Marshall

100%. We've been focused on that from day one. The connected home and IoT became very significant about 15 years ago, but a lot of the early IoT sensors required too many devices, too many batteries, and the apps and UX were clunky. Anything that is not simple for the homeowner is not going to scale. It just won't.

If you go back 10 years or so, even if somebody signed up for an IoT device, only maybe 30% of people installed it. The economics were broken. Somebody paid for all that hardware to get shipped, and 70% of devices never got plugged in, so there's little loss prevention.

Batteries are an issue, too. If you power a device with a battery, you have to replace it after a few years. Are people going to remember to replace the battery? If they don't, the economics get all wrecked. We paid for getting hardware in, and the service has stopped.

By contrast, 85% to 90% of Ting sensors get installed, and there's no battery. The sensor will last 15 years or longer without needing anything to be replaced.

Paul Carroll

Where did you get the idea for Whisker Labs?

Bob Marshall

The impetus for the idea came from Earth Networks, where I was a cofounder. We were an IoT sensor company deploying weather and climate sensors, connecting them to the internet, and collecting massive amounts of data. We provided that data to NASA, NOAA, utilities, and insurance companies. We had a lightning detection network.

Then my sister-in-law's house burned down in 2015. They lost their entire house and a pet to an electrical fire. I didn't know anything about electrical fires at the time, but when you research them you learn they start from loose connections and damaged wires that arc and spark.

Lightning is essentially a big spark in the sky, and we had really sophisticated sensors that could measure every lightning strike on the planet. So I challenged our chief scientist, chief technology officer, and lead engineer: Why can't we take that global lightning detection technology, miniaturize it, and detect these tiny sparks that cause home fires?

We started work in 2016. It took us the better part of two years, incubated as a skunkworks project inside the old company. We incorporated Whisker Labs in September 2017 after we'd figured out a technical solution.

This is a life safety product, so it had to be completely proven. We did substantial testing in thousands and thousands of homes before we made Ting available to the public in early 2020.

Paul Carroll 

There’s a seminal book, “Crossing the Chasm,” by Geoffrey Moore, that is still much-read in Silicon Valley even though it came out in the early 1990s. It notes that lots of companies attract early adopters who are fans, maybe even fanatics, but never cross the chasm to a mass market. When you thought about crossing the chasm, did you target insurance companies, go direct to consumers, or do both?

Bob Marshall

Insurance only. There have been a couple of successes that took the direct-to-consumer approach—Nest and Ring—but then you've probably got 100 companies that failed. It takes too much money to market a new product to consumers and get the widespread adoption needed to make the business work.

So we elected to go insurance first, particularly given that fire is an important issue for carriers. It's a big loss category.

Obviously, carriers wouldn't deploy the Ting technology until they had seen full testing of it. So we deployed with a number of insurance companies. We went to employees. We went to agents. We deployed to their labs, and we did a couple years of extensive testing before our insurance carriers would offer it to their actual customers.

Paul Carroll

You've now commissioned research that demonstrates that the savings from fire prevention are significantly greater than the cost of deploying Ting. But in the early days, how did you move beyond test projects with insurance companies?

Bob Marshall

Insurance companies are obviously very conservative by nature, and actuaries understandably want comprehensive historical data before they're comfortable endorsing new technologies. So how do you get there? We were fortunate to have partnerships in place with people who believed in what we were trying to do and the mission. They were very much committed to the mindset of moving toward Predict & Prevent, and they were willing to make the investments to make that happen.

Now we've got ample data to document the performance of the technology. We know that customers love it. We know that customers stay with their insurance carriers longer when they're provided Ting. We cobrand the experience, and we know engagement is high. So we check all the boxes.

And the way we've structured our partnerships—I think this is super important—is something that wasn't done in the earliest days of IoT in insurance. Early on, the business models weren’t aligned. Companies selling prevention technology to insurance carriers were satisfied with selling the hardware. If you got an insurance carrier to buy 100,000 water sensors, that was a big win. The sellers didn't necessarily care whether customers plugged them in or not or prevented any loss.

Our partnership model is solely focused on the service, as opposed to the hardware. We only get paid if the sensors are plugged in and providing the service. It's on us and the partnership to make sure customers are plugging in Ting, it's staying online, it's preventing fires and doing good. 

It's critical to make sure the interests of all parties in our partnership are aligned.

Paul Carroll

How do you get from 1.3 million or 1.4 million homes to the tens of millions you want to reach?

Bob Marshall

We launched the product commercially in March 2020, which was terrible timing, as COVID shut everything down for a year or two, but progress is now pretty steady.

Nationwide is a good example. They were already at 80,000 homes and recently committed to putting Ting in 500,000. That's a sizable percentage of their home book. And that's because they know it works, they know it prevents losses, and their customers love it.

We’re working with almost 40 carriers today, and they probably cover 60 million to 70 million homes in the U.S. If we got every one of our insurance carriers to put Ting in just 25% of their book, that’s at least 15 million homes.

That's why we're investing in marketing to build brand awareness and make it easier for carriers to get their customers to adopt Ting. We're working on incentive programs to engage agents so it's not just the corporate part of the carrier motivated to get it out.

Paul Carroll

Thanks, Bob. I always feel better after we talk.

About Bob Marshall

Robert Marshall is the founder and CEO of Whisker Labs. Whisker Labs, a spinout of Earth Networks, delivers next-generation home energy intelligence technology to realize the full potential of the connected home.

In 1992, Marshall co-founded AWS Convergence Technologies, the company that would become Earth Networks, by pioneering the networking of weather sensors and cameras using the internet. By developing groundbreaking technology to find "signals" — valuable, meaningful intelligence — in big-data "noise," Marshall improves people's lives and protects their livelihoods. 

He has appeared on CNN, BBC World News and ABC Nightly News and has been quoted in major news outlets that include the New York Times, the Washington Post, Nature and Scientific American.

"AI will not replace humans, but humans who use AI will replace those who don't."

Whether or not you agree with that sentiment from Sam Altman, the implication is undeniable: Yet as businesses embrace AI, a precarious gap has opened between its capabilities and the insurance frameworks designed to protect organizations in light of this technology.

The Breakdown of Legacy Tech E&O

The P&C industry has long relied on tech E&O for risk mitigation when it comes to digital services. But these legacy policy forms were largely built for a software era where human error was the main culprit. There was a relatively clear trail of accountability, and a failure typically meant a system crash or a coding bug.

Today, we see autonomous agents making decisions that result in financial loss. Dynamic, self-learning algorithms are making questions of liability much more complex. New rules are being written seemingly in real time.

Take the Air Canada example – perhaps one of the highest-profile instances of AI E&O. Back in 2024, the airline's chatbot hallucinated a policy offering retroactive bereavement refunds. When the passenger tried to claim the refund, Air Canada refused, arguing that the chatbot was a separate legal entity, responsible for its own actions.

A Canadian tribunal rejected this defense, saying that a company is responsible for all information on its website – whether it is delivered by a web page, or an automated agent. "The AI said it" was not a legal defense.

There has been a distinct rise in grey-zone liabilities like this – risks that don't fit neatly into the buckets of a standard data breach or a traditional professional error. Consider issues such as algorithmic bias, data poisoning, and technology-driven discrimination. These risks often fall in the cracks between cyber exclusions and professional liability triggers.

For brokers and insured businesses, this gap creates dangerous exposure to a new class of litigation where policy language simply hasn't kept pace with technology.

Same Regulations, New Litigation

The idea of "cyber risk" itself is evolving thanks to the impact of AI. Where it was largely about data privacy in the past, businesses today also need to think about algorithmic accountability.

One of the most striking examples is how the Americans with Disabilities Act (ADA) is being used as a tool for technology litigation. For example, if an AI-driven hiring tool or a financial services algorithm inadvertently discriminates against a protected group, the resulting legal challenge can be seen as a violation of professional standards and statutory law.

Workday came up against this in 2025, facing a massive class-action lawsuit (Mobley v. Workday) that alleged its AI-based screening tools discriminated against applicants based on race, age, and disability.

This case showed that blaming a technical glitch isn't legally defensible. When technology begins to make decisions that affect human rights and equity, an error is ultimately a failure of governance – it's people that are ultimately liable.

Traditional E&O policies often focus on language such as "failure of technology to perform." This wording doesn't handle socially-driven technical failures.

From Risk Transfer to Integrated Resilience

For the P&C market to remain sustainable and relevant, we can't just be reactive. Digital innovation now happens so fast that by the time a claim is filed, the underlying technology has likely already iterated several times over. New technological uses for AI are emerging every day.

To meet this moment, the insurance industry must pivot to an integrated resilience framework – a ground-up re-engineering of policy language that addresses the reality of modern autonomous systems.

This requires a shift from simple risk transfer to a "predict, prevent, and insure" model. In this new framework, insurance can't be a static document sitting in a folder somewhere. It must include:

• A complete digital risk package that integrates cyber coverage, threat protection, and 24/7 incident response directly into the Tech E&O form.
• Insurer bundles that feature real-time threat intelligence and proactive monitoring.
• Explicit language to avoid exclusions and bridge the gap left by grey-zone liabilities.

By implementing these changes, we can provide incentives for early incident reporting (through motivators like retention waivers for fast action) rather than penalizing it – which ultimately leads to incidents that spiral into larger liabilities. We can create an environment where insurers and businesses are working together to strengthen resilience; rebuilding trust and collaboration.

Restoring Confidence Through Insurance

The ultimate goal of insurance should be to provide businesses with the confidence to innovate. In the early days of digital transformation, that meant protecting against hardware and human failures. Today, it means giving businesses of all sizes – from startups to enterprises – the self-confidence to deploy AI and SaaS solutions without the fear that an unforeseen algorithmic bias or a sophisticated social engineering attack could derail operations.

Simplicity is key here. Both cybersecurity and insurance have a reputation for being unnecessarily opaque. As we face sophisticated AI-related risks, our industry's response shouldn't be to add more jargon and complex exclusions. Instead, we should strive for unambiguous coverage that recognizes how professional services and digital delivery are connected.

Protecting Innovation for the Future

The path forward requires our industry to embrace a more proactive stance. We must move beyond the data breach and embrace a model where insurance is an active participant in a company's security posture. This means not only helping them respond to and recover from incidents faster, but perhaps more importantly, helping them predict and prevent incidents in the first place.

Validation for this approach is growing. We're beginning to see the market move toward all-in-one protection models that combine insurance with active risk management platforms. These platforms provide the tools and training necessary to strengthen controls before an incident occurs.

But the gap between legacy and modern Tech E&O is still growing. We need insurers and coverage models to keep pace with the ever-changing AI landscape.

The AI-fueled gap in Tech E&O is a challenge, but it is also an opportunity to build a more sustainable P&C market. By evolving our products to match the sophistication of the tools our clients use, we can ensure that the digital economy remains a safe space for growth and innovation for everyone.

Insurance has never lacked ambition when it comes to modernization. Most carriers recognize the pressures reshaping the industry. Risk is becoming more volatile, customers expect faster and more personalized experiences, and legacy operating models are making it harder to respond at the pace the market now demands. AI gives insurers an opportunity to close that gap by improving how decisions are made across the insurance journey. The challenge is to turn that intelligence into governed action fast enough to make a difference.

Across climate-exposed regions, carriers are reassessing where they write business, how they renew policies, and what levels of catastrophe exposure they can responsibly carry. In cyber insurance, threat vectors evolve faster than historical loss experience can reliably inform pricing and underwriting. Litigation trends, inflation, geopolitical disruption, supply chain instability, and specialty market complexity are all changing portfolio dynamics in ways that affect pricing adequacy, underwriting appetite, claims severity, capital allocation, and customer behavior at the same time.

In my opinion, the issue is no longer whether insurers recognize the need to adapt. The real challenge is whether their operating models can convert signals, models, rules, and human judgment into production decisions quickly enough to keep up with these changing conditions.

Risk is outrunning episodic decision cycles

Insurance operating models were largely built around periodic adjustment. Rate changes, underwriting rule updates, product modifications, compliance reviews, and distribution decisions often move through sequential processes. Those processes were rational in a market where risk signals developed more slowly and decision cycles could afford to be measured in months, but that environment is fading.

When market conditions shift faster than execution cycles, the consequences become real. Delayed rate action can weaken pricing discipline and expose margin before carriers fully see adverse selection building inside the book. Slow underwriting appetite changes create another form of exposure, especially when business continues to be written against assumptions that no longer reflect the carrier's strategy. Even customer signals lose value when they remain disconnected from pricing, product, and retention logic, leaving profitable relationships exposed.

Legacy systems are part of this tension, although they are not the villain. Policy administration systems, claims platforms, billing systems, and rating infrastructure remain essential systems of record. The problem is that many are being asked to support adaptive decision making work they were never designed to handle. Systems built to store, administer, and transact are now being pushed to sense, decide, govern, and adapt continuously.

The bottleneck is not the model

Boards and executive teams are investing in AI for good reasons. AI can accelerate analysis, automate repetitive tasks, improve modeling precision, and help teams process more complex data than traditional workflows allow. Working with customers, I see why that investment makes sense. The industry needs more speed, more precision, and better use of scarce expertise.

Yet many AI initiatives lose momentum once they move beyond experimentation. A pricing model can sharpen analytical precision without making the enterprise more adaptive if underwriting still moves through disconnected workflows, claims signals never reach product and portfolio decisions, and customer engagement tools improve outreach without connecting to the logic that determines risk, profitability, and retention.

The issue is not just model performance, but the ability to connect data, models, business rules, workflows, governance, and human oversight so AI can support real underwriting, pricing, claims, and customer decisions in production.

Insurance decisions carry financial, regulatory, and social consequences. They must be explainable, auditable, repeatable, and aligned with underwriting discipline and capital management. Horizontal AI tools can improve productivity, but insurance-grade decision making requires domain depth, governance, and operational context from the start.

Decision making needs an operating layer

Many insurers have made real progress inside individual functions, especially in pricing, underwriting, and claims. The problem is that local improvement does not automatically create enterprise agility. A stronger pricing model has limited strategic value if underwriting cannot act on the same intelligence, claims signals do not inform portfolio decisions, and customer engagement remains disconnected from risk and profitability. The deeper issue is not whether intelligence exists inside the business, but whether it can move across the business in time to change the outcome.

Insurers need governed decision making to work above and across existing systems. That layer should allow carriers to preserve operational stability while enabling intelligence to move across pricing, underwriting, claims, compliance, distribution, and customer engagement.

The aim is to reduce the distance between insight and action, giving carriers a more consistent way to test changes, understand likely impacts, govern approvals, deploy updates, and monitor outcomes as AI moves from experimentation to operational capability.

Governance makes speed deployable

Speed only strengthens resilience when it is matched by control. In insurance, faster decisions only create value when they remain explainable, auditable, and aligned with regulatory and business discipline.

This is where governance becomes a deployment advantage. Carriers that cannot explain how decisions are made will struggle to scale AI into production. Teams may trust a model in a pilot environment, but production use requires traceability, bias monitoring, approval workflows, performance monitoring, and clear human accountability.

That does not mean slowing the business down. It means building guardrails into the way intelligence operates. Pricing optimization, underwriting evaluation, portfolio steering, compliance validation, claims triage, and customer retention each require the right form of AI, the right level of automation, and the right degree of human involvement.

The New Operating Discipline for Insurance

Insurers need a new operating standard: one that connects intelligence across the policy lifecycle and gives carriers the speed, adaptability, and control to respond as conditions change.

The next phase of insurance transformation is as much about operating design as it is about AI. AI creates value when it is embedded deeply enough into the business to support faster, more disciplined, and more accountable decisions. That gives carriers a better way to recalibrate pricing, refine underwriting appetite, identify portfolio drift, support compliance, and respond to customer signals before opportunities or exposures have already moved.

AI capability alone will not close the insurance execution gap. The real advantage will belong to carriers that can make intelligence operational, connecting models, data, workflows, rules, and governance into decisions that keep protection available, profitable, and resilient.