No matter which side of the argument you land on over AI job creation or destruction, an AI image crisis looms. Phrases like, "AI job apocalypse" say it all. Growing negative sentiments about data centers have found their way into political campaigns with concerted efforts to halt or divert construction. To the surprise of many, the mere raising of the AI topic drew jeers by young graduates at several recent commencement ceremonies. According to Pew research, just 10% of Americans say they are more excited than concerned about AI, down from 37% when first asked in 2021. 

Of late, however, the tenor of the AI job destruction conversation is softening to creation of capacity. In other words, using new capacity for people to do other work instead of merely cutting jobs. 

Capacity Creation

Capacity creation happens when AI, especially agentic AI, unlocks productivity by performing all sorts of tasks around the clock with no days-off.  More than just AI productivity gains, repurposing people work so they can do more. For instance, both underwriting and claim handling include large portions of routine, manual work. Gathering, validating, summarizing and sharing information for decision making are prime areas for AI. Once AI does all of this heavy lifting, employees will be freed to shift to new and higher-grade work – at least in concept.

Instead of mass layoffs, companies must contemplate what to do with new capacity by redirecting employees to focus on more meaningful tasks. “More meaningful,” higher-value work is loosely defined, but, either way, the precept of shifting resources to higher importance is well-suited to fit the P&C insurance industry, which runs on people and prides itself on doing business through people and relationships.

Aside from the constant chatter about huge AI productivity gains reducing insurance workforces, reality shows little evidence of overall job loss so far. However, even with the emerging mindset to repurpose work, there is expected to be considerable job disruption. This is important to distinguish from net job losses considering negative AI sentiment comes from real people, whether based on perception or reality. Job disruption should not be taken lightly even if the net amounts remain modest. It is also worth contrasting industries because some job types outside of insurance, such as coding, factory work, taxi driving and administrative tasks, are already being hit.

The insurance industry also takes great pride in resilience which has proven helpful in attracting and retaining talent offering “job security” in good times and bad.  At the same time carriers are eager to automate a wide-range of manual tasks while already outsourcing others. So, what should the insurance industry do with all of this expected future capacity?

Where to Deploy New Capacity

Nearly all functions of insurance could make a case for greater resources – essentially having more hours in a day. Some of the sentiments expressed include:

• CEO’s are certainly eyeing how to reduce both expense and loss ratios to boost profitability with AI, trying to gain first-mover advantages to take market share and outpace competitors across the value chain
• Stakeholders are considering how fraud may be reduced and better contained
• Insurance insiders are enthusiastic about avoiding or mitigating losses to accelerate Predict & Prevent initiatives
• Customers are wondering how AI efficiencies translate to lowering the cost of insurance

Here are some of the top contenders for more people resources:

Customer Service

True customer service has become a rare commodity despite digital self-service adoption and better communication tools. Because of inherent insurance complexities, customers still demand human touch and often have more conversational needs. Whether point-of-sale, renewal, billing or claims, there are elements of consumer distrust and lacking confidence to make the right decisions without talking with an expert. Shortcomings in service often revolve around communication breakdowns and difficulty in reaching the right person. Meanwhile digital tools and work habits have distanced human interaction. Customers vent about repeating the same information and navigating the onerous insurance process and just want help.  Improved customer service and touch would be a top contender for any new capacity. 

Lower Expenses

For every dollar of premium, about 25 cents is spent on expenses. While this amount is generally accepted in today’s environment, new capacity to absorb growth-related work, gap filling for the retiring insurance workforce and enhanced management of expenses are prime areas for focus. AI can also play a direct role to advance underwriting and claim automation and vendor management and, in more specific ways, such as litigation expense control. Simply having deeper insights to control and better manage expense is also on top of this new capacity list.

Loss Avoidance and Mitigation

A Predict & Prevent mantra has gained in popularity with the advent of sensor technology and obvious demand for resilience from evolving climate exposures. Loss control has long served the upper insurance markets well, where resources, experts and actions invested can support effective ROI expectations. Such efforts have made some inroads in personal lines through telematics, water and fire detection. Yet, adoption remains a struggle, as does customer engagement. Similarly, loss mitigation efforts are inconsistent and limited, with some bright spots during CAT events to emulate and expand. However, prevent and mitigating losses is widely underserved and screaming for more attention and resources.

New Insurance Products/Services

The core principle of insurance, commercial risk transfer, has been heavily tested over the last decade. Catastrophes, soaring premiums, restrictive policy language and higher deductibles are reshaping the degree of risk transfer; policyholders are absorbing more risk, particularly in homeowner lines. New requirements such as fire prevention, resilient roofs and new construction standards increase these burdens. In several scenarios, such costly measures are required just to be insurable. An older roof can be uninsurable altogether and most definitely will be on a predetermined actual cash value (ACV) schedule, paired with a huge wind/hail deductible. Translation, the homeowner bears all or most of the risk, which begs for new solutions.

New insurance and financial solutions must be in the forefront to address homeowners' resiliency and prevention investments. The healthcare industry addressed high deductible and out-of-pocket issues through Health Spending Accounts (HSA). Perhaps some sort of home spending account would be similarly beneficial. Because exploring and developing new products require time and resources, these also make the list for new capacity. 

Another way to prepare for capacity shift is to look at underserved areas in which there currently are not enough resources. Although insurers work hard on these areas, most are far from optimized. Interestingly, most are highly important. Here’s a partial list:

• Training and Development
• Upskilling for AI with attendant Change Management
• Auditing and Quality Control
• Legal and Regulatory Compliance
• Vendor Management
• Subro/Salvage Recovery
• Fraud investigations and deterrence
• Working with Communities on Resiliency
• IT Project backlogs
• System Integration waiting list

There are numerous and exciting possibilities for deploying new capacity, but it will take some significant alignment and rethinking. Visionaries see a future of abundance, with some extreme views that depict little to no time spent working and living lives of fulfillment in other ways. Such majestic predictions only fuel AI skepticism and outright rejection of what feels like turning society completely upside down. It is daunting enough for businesses to get started with AI and even more ambitious to prepare for capacity redeployment. 

At present state, there has been marginal readiness to retool roles, and perhaps timing is premature. Consider how claim adjusters and underwriters are anticipated to operate in the future when all or most of the administrative portions are solved, with AI accounting for 70% or greater of the work. It’s a stretch to suggest claim adjusters and underwriters will readily concentrate on “approving” AI decisions and naturally spend much more time interacting with customers and agents without significant change management. 

Any plans to deploy AI in ways that preserve human jobs by reallocating work must apply equal effort to thoughtfully address the many people and structural barriers. The scope is wide and will include new requirements around; hiring/selection, differing skill needs, role redefinition, rewards/incentives alignment, workload expectations, workflow and process reengineering, to cite just a few. As the use of AI expands and solves problems, there will be unintended byproducts that are likely to be as difficult if not harder to solve.

The good news is the continuing discussion to shift future people capacity upward – inspiring for all stakeholders, especially employees (and not to mention the whole value chain and economy built around them). 

Time will tell if this budding attitude sustains or is simply more AI washing. 

Insurance organizations are spending billions modernizing systems without fixing the operating model underneath them. 

For years, the industry has treated modernization as a technology initiative—replace the policy system, upgrade claims, improve workflow automation. But despite massive investment, most insurers still operate through fragmented architectures stitched together across policy, billing, claims, reinsurance, and finance. The result is inefficiency and operational drag embedded into the economics of the business. 

This is why so many organizations still rely on spreadsheets, manual reconciliation, delayed reporting, and disconnected financial visibility despite years of digital transformation. The issue is not that insurers lack technology. The issue is that most insurance operations were never designed to function as a unified operational system. And nowhere is that more visible than in reinsurance.

A recent field study, commissioned by INTX and grounded in independent research conducted by RSM, surveyed more than 250 property and casualty insurance professionals. The findings point to an industry operating under structural strain—where inefficiency is not episodic but systemic.

The financial impact of these challenges is significant and continues to grow. Across the industry, insurers are investing millions in implementing and maintaining multiple core systems, while also absorbing continuing costs tied to support, downtime, and manual work. These expenses extend well beyond initial implementation and compound over time, creating sustained multimillion-dollar pressure on operating budgets. 

As these costs scale across systems and business units, they limit the ability to invest in innovation, slow responsiveness to market demands, and weaken overall business performance. These cost pressures are reflected in how insurers actually operate on a day-to-day basis. 72% of respondents reported using Excel or homegrown tools to manage critical workflows. Further, most organizations operate multiple core systems at once, supported by spreadsheets and manual processes. This fragmented environment creates complexity, reduces visibility, and slows execution across the business.

The study identified four persistent pain points that continue to shape performance across the industry. These are symptoms of a broader issue: operating model debt.

Cost Distortion

Implementation remains a major barrier to modernization. Organizations report spending an average of up to $1 million to deploy a single system. With most insurers operating two to three systems on average, total implementation costs can reach $3 million or more.

These costs are driven in part by reliance on third-party system integrators. More than half of system users depend on integrators for training, and 40% rely on them for project management and implementation. This dependency introduces additional expense and complexity. Core systems often require external support to deliver functionality that should be standard.

Every dollar spent on implementation limits the ability to invest in innovation. As costs rise, organizations navigate these difficult trade-offs that affect their long-term growth and sustainability.

Time Distortion

Legacy systems limit the ability to adapt. 45% of organizations report implementation cycles of 18 months or longer. Even targeted initiatives, such as adding a new product line, can take more than six months.

These delays represent missed opportunities. Organizations are unable to respond quickly to market shifts or regulatory changes. Competitiveness declines as faster-moving peers gain ground.

Even after long timelines, outcomes often fall short. In fact, average satisfaction with implementations remains below 71.8%. Many projects fail to deliver expected value, reinforcing frustration and limiting confidence in future investments.

These operational challenges can be reflected in industry performance. Over the past 15 years, U.S. property and casualty insurers have operated at an underwriting loss when measured without investment income. A combined ratio of 102.1% shows that claims and expenses exceed premium revenue. This pattern highlights the structural inefficiencies within core operations.

Visibility Failure

Support costs extend far beyond licensing and maintenance fees. Insurers report spending from $100,000 to nearly $5 million annually on recurring system costs. These expenses are only part of the picture.

Nearly half of organizations report significant additional costs tied to internal IT support. Teams spend valuable time maintaining outdated systems, resolving issues, and supporting users. Organizations report up to 888 hours of lost productivity each year due to system issues, with financial impact reaching as high as $450,000 annually. Delays in resolving tickets disrupt operations and slow critical workflows.

These costs are often hidden, but they have a direct effect on profitability and planning. Over time, they create operational fragility and limit the ability to scale.

Financial Leakage

Manual processes remain deeply embedded in core system workflows. 52% of policy administration tasks require human intervention. Many insurers rely on spreadsheets alongside their core systems, often working across multiple vendors and tools.

This reliance introduces risk and slows operations. Employees must move between systems, reenter data, and reconcile information. Data latency increases, and errors become more likely.

The financial impact is significant. Organizations spend between $475,000 and $1,125,000 each year on manual work. 36% of respondents identify quoting, policy issuance, and claims processing as the areas most affected.

Manual workarounds reduce efficiency and limit scalability. Time and talent are diverted away from strategic priorities. These inefficiencies weaken performance and make it harder to respond to change.

The Missing Layer: Reinsurance Outside the System

Nowhere is this fragmentation more visible, or more consequential, than in reinsurance.

In most organizations, reinsurance is still managed as a downstream process. Risk is written first. Reinsurance is applied later. Recoverables are calculated separately. Financial impact is understood only after multiple systems are reconciled.

This creates a structural disconnect between underwriting, claims, and capital.

The result is delayed recoverables, incomplete exposure visibility, and inefficiencies in capital deployment. What should function as a strategic lever for growth instead operates as an administrative process.

A Shift Toward Modern Systems

Addressing these challenges requires replacing legacy platforms and rethinking how insurance operations are structured. Modern systems are beginning to reflect this shift by improving and unifying individual functions. Policy, claims, billing, reinsurance, and financial reporting operate within a single system, with a shared data model and real-time processing. In this model, reinsurance is embedded at the moment of transaction. Financial impact is visible immediately. Recoverables are tracked continuously, not reconstructed after the fact. New platforms reduce reliance on multiple systems and eliminate the need for extensive third-party integration. By providing direct support and more efficient implementation models, they lower costs and accelerate time to value.

Transparent pricing improves cost predictability and reduces hidden expenses. These improvements help organizations operate with greater stability and confidence.

Automation is central to modern platforms. Advanced workflows streamline quoting, policy issuance, and claims processing. Real-time data validation improves accuracy and removes the need for many manual workarounds. Integrated functionality reduces duplication and improves consistency.

Speed is a defining advantage. Implementation timelines that once extended beyond a year can now be reduced to months. New product lines can be introduced within three to six months, and expansion into new states can occur in days. This agility allows organizations to respond quickly to changing conditions.

Moving Forward

The insurance industry does not have a technology problem alone, but also an operating model problem that has been compounded over decades of system layering and process workarounds. Modernization, therefore, is about eliminating fragmentation. The future winners in insurance will be the organizations with the fewest operational gaps—not the most systems.

Recent headlines around the hiring landscape have been daunting. Amazon, Meta and Oracle announced significant layoffs in recent months, and many other firms appear to be following. The more telling story is what happened next: thousands of capable, experienced people entered the job market at once, and many of them are still looking. The people losing jobs aren't struggling to apply. They're struggling to be seen.

Much of this reflects how far AI has shifted the workplace, raising what an individual can produce while leaving the way that capability gets recognized largely unchanged. And the investment pouring into the space is not new: last year investors put $4.93 billion into HR technology, a 20% year-over-year increase, according to HR Executive. Yet for all that capital, many would argue the industry has only become more complicated. Employers have the tools to hire faster, but something has been lost: connection, individuality, and a clear path for capable candidates to secure meaningful careers.

The candidate experience bears the weight of it: endless application portals, automated rejection emails, AI screening systems, and interviews that feel transactional. People spend hours on the perfect resume and cover letter only to receive an impersonal response, or nothing at all. Meanwhile, employers struggle with their own inefficiency, from overwhelmed hiring teams to high turnover to a flood of applications, and the same persistent question of how to identify the right people.

The hiring paradox

It's worth understanding where the discrepancy lies. Hiring has become faster than ever, so why is finding the right people more difficult?

The instinct in the market has been to add another layer of software to the employer's side of the equation, whether that means more sourcing tools, more screening tools, or more AI-assisted outreach. But the imbalance the funding is trying to solve doesn't sit on the employer side. It sits on the candidate side. Companies have always had infrastructure: applicant tracking systems, recruiters, sourcing teams, agencies, and the entire HR tech stack. The candidate has had a resume and a job board login.

That gap is what makes the current moment different. As AI compresses the cost and time required to do knowledge work, the distance between what a worker can produce and what their resume can communicate has widened sharply. A two-page document submitted to a portal was never a great representation of capability, and it is a worse one now. The result is a market where the people most able to do the work are often the least visible inside the systems built to find them. That is exactly why a wave of skilled professionals can hit the market after a layoff and still go unseen. So while optimizing for efficiency, hiring has lost the very qualities that make recruitment work: trust, timing, and human understanding.

There's a useful parallel in how other industries solved a version of this problem. Professional athletes don't apply for teams; agents place them. Actors don't apply for films; agencies represent them. In finance and law, the senior end of the talent market has run on introductions and trusted intermediaries for decades. Each of these industries reached a point where the value of an individual's work was high enough, and the cost of a bad match was high enough, that a representation layer became standard. In the knowledge economy, however, that infrastructure simply does not exist.

The result is a labor market where qualified candidates disengage from traditional application funnels altogether. Many people are not applying to jobs anymore, not because they aren’t ambitious, but because the process itself feels exhausting, repetitive, and deeply inhuman. They are not motivated enough to tailor resumes, rewrite hundreds of cover letters, or coordinate multiple rounds of screenings for opportunities that may never result in a real conversation.

At the same time, on the employer side, businesses are running on thin margins as more workers leave on a consistent basis. According to a recent report from LinkedIn Talent Solutions, hiring teams are prioritizing quality-of-hire and retention over sheer recruiting volume, indicating a deeper shift in how companies evaluate talent.

The paradox is crucially clear: the actual experience of hiring is more detached than it has ever been. This is where a new kind of recruitment tool must fall into place.

A new kind of AI bridges the gap

The companies that endure will be the ones future-proofing their strategies. Instead of automating tasks, the most promising AI tools are turning toward relationship-building, personalization, and long-term career alignment, away from processing applications at scale and toward intentional connections between employers and candidates.

In many ways, this transformation reflects how hiring has always worked at its best. Historically, the strongest career opportunities have always come through genuine introduction, referrals, and direct conversations. By putting people back into the mix, it creates a much more connected dynamic: technology to surface opportunities and remove administrative friction, people to weigh leadership potential, skill, and personality.

An AI agent that works

The idea of an introductory economy is where HR funding has a significant effect, and it’s an approach being directly accomplished through platforms that run on a simple premise: recruiting cannot run on automation alone, but requires direct introductions that put each candidate into the hiring conversations they deserve. The agent meets candidates on the messaging apps they already use, including WhatsApp and iMessage, helping qualified talent express their goals, find the right opportunities, and connect directly with hiring managers. The goal is making high-context relationships that would otherwise take years to build.

That is the difference this model makes for modern-day recruitment. It advocates for the candidate so they can get careers that actually last. In a market where most tools are designed to serve the employer side, this rebalancing creates a more equitable and ultimately more effective hiring process.

The future as we know it

As AI continues to reshape the workforce, and as more funding is prioritized in this space, recruitment is quickly becoming one of the most urgent challenges of the next decade.

If jobs keep disappearing, how can people access the next roles that matter? These are the questions hiring managers and candidates still cope with every day.

Hiring can no longer afford to be a standardized solution. It is due for change, to get individuals into the worthwhile roles they have worked long and hard for. The companies shaping the future of hiring are the ones putting their money in the right kinds of tools. It is the companies emphasizing a candidate-first model like Clera, where no machine can say where a person lands a job next.

The auto insurance industry today is facing many new challenges, including elevated repair costs, evolving fraud risk, and policyholders still expecting fast, almost immediate answers when a vehicle claim disrupts their lives.

In fact, the CCC Intelligent Solutions reported that total-loss claim share reached a record, with vehicles seven years or older accounting for more than 72% of total-loss valuations as aging vehicles and rising repair costs continue putting additional pressure on claims operations. Average total repair costs were above $4,730 in 2024, a 3.8% increase year-over-year, with costs rising a further 1.4% during the first half of 2025.

That said, the real problem extends beyond just claims volume or repair inflation. Many teams still rely on fragmented data across multiple sources when verifying basic claim information. And every delay in that process causes additional expenses and friction. This means the claims process can only modernize if adjusters can access verified data early enough to make better decisions before those costs escalate.

Digital Claims Tools Need Stronger Data Beneath Them

Forrester expects U.S. insurance technology budgets to reach $173 billion in 2026. So, it’s safe to assume that carriers have spent millions, if not more, investing in front-end claims technology. FNOL automation to mobile photo uploads, AI-assisted triage, and digital communications have all undoubtedly improved speed and customer access. But, like many other technological advancements, those tools only perform as well as the data that feeds them.

Many bottlenecks appear after intake, when adjusters need to confirm whether a claimant has clear ownership or whether title activity creates settlement risk. A claim can move through digital intake quickly and still stall once a team needs verified vehicle data from these disconnected systems.

In total-loss workflows, a missing lien record can hold up payment, a title discrepancy can force late-stage review, and a VIN inconsistency can trigger additional investigation after the carrier has already invested time in valuation and settlement coordination. Digital claims systems create speed at the front of the process, but it’s verified data that protects that speed through resolution.

Claims Leakage Often Starts With Small Data Failures

It’s very rare that claims leakage happens due to just one dramatic error. It usually builds through repeated friction across thousands of files. For example, a delayed lienholder confirmation adds handling time, and a late title issue creates settlement rework. Each of these issues may look manageable individually, but across the total claims book, those small failures add up to real cost.

Claims leaders already track macro severity drivers such as repair inflation and litigation exposure. Operational leakage deserves the same attention because it sits closer to the daily work of claims teams. It affects cycle time, adjuster capacity, policyholder satisfaction, and payment accuracy.

The cost environment makes those small breakdowns harder to absorb. But it’s better data that gives carriers a direct way to reduce friction inside the claim, rather than only reacting to severity after it shows up in the file.

Total-Loss Claims Need Earlier Verification

Total-loss claims place a heavier burden on data quality because they require coordination across multiple parties. The carrier may need to confirm ownership, communicate with a lienholder, validate title status, process documentation, and resolve payment expectations within a very compressed timeline.

When adjusters can access verified title, lien and ownership information in real time rather than relying on fragmented lookups across disconnected systems, they can identify title issues before any valuation discussions advance. Earlier visibility helps claims teams spend less time handling administrative issues late in the process and more time focused on claim resolution, policyholder communication, and overall exposure management.

That can help improve control over claim outcomes, because adjusters spend less time resolving administrative issues late in the file and more time managing exposure, documentation quality, and policyholder communication.

Stronger Data Also Strengthens Fraud Detection

Fraud risk has also increased the importance of connected claims intelligence. Modern fraud schemes often exploit gaps in vehicle records, ownership data, title activity, and identity verification.

NICB projected a 49% rise in insurance crime involving identity theft by the end of 2025. Its analysis also found that nearly one quarter of identity-theft referrals involved synthetic identity activity. And with insurers in the U.S. losing roughly $300 billion to fraud per year, nearly 25% of the industry’s total value, it’s a costly issue to have.

Auto claims teams need to see these risks earlier in their workflow to stop schemes in their tracks. Title manipulation, VIN inconsistencies, suspicious transfer activity, irregular lien documentation, and undisclosed prior vehicle events can all indicate exposure. When adjusters or SIU teams see those indicators late, it’s the carriers that face higher investigative costs and weaker recovery options.

Connected verification data helps claims organizations identify suspicious patterns before payments even move forward. It also helps SIU teams prioritize the files with the highest risk, rather than forcing adjusters to chase disconnected data across every claim.

Data Security Has Become Part of Claims Performance

Claims data carries high security value because it often combines personally identifiable information, vehicle identifiers, ownership records, payment information, and lienholder details. As claims operations become more digital and increasingly dependent on outside data providers, carriers are placing greater scrutiny on how sensitive information moves across third-party systems and whether those systems meet modern security expectations.

That makes claims operations an attractive target for fraud actors and cybercriminals, and it is also why claims leaders need strong data governance and clearer visibility into the vendors supporting critical claims workflows. Teams need to know who accessed sensitive claim data, how systems use it, and whether third-party workflows protect it with the same discipline expected inside the carrier’s environment. As more carriers rely on outside data partners to support total-loss, fraud, and settlement workflows, security can no longer sit apart from claims performance. For data partners operating in this environment, SOC 2 compliance is not optional. It is the baseline signal that security controls have been independently verified, not just self-reported.

Better Claims Data Improves Adjuster Productivity

Claims organizations also continue to face staffing pressure and heavier file complexity. Experienced adjusters should spend their time evaluating exposure and guiding claim outcomes. Many still spend too much time searching for records, confirming basic facts, and resolving data inconsistencies that technology should surface earlier. Earlier verification can help reduce that burden.

When claims teams can trust core vehicle and ownership data, adjusters can move files with greater confidence. They can reduce manual follow-up, improve documentation quality, and focus attention on claims that require judgment rather than administrative tracking.

This also improves consistency across claims teams. Fragmented workflows create uneven outcomes because different adjusters may use different sources, ask different questions, or catch problems at different points in the file. Connected operational data provides teams with a shared foundation for decision making.

Why the Next Phase of Claims Modernization Should be Operational

Carriers need infrastructure that enhances data integrity across verification-intensive workflows, especially in total-loss processing and settlement coordination. Stronger claims data helps reduce leakage, improve cycle time, strengthen fraud detection, and protect adjuster capacity. Security also needs to sit at the center of that infrastructure. Claims data has become too valuable, too sensitive, and too operationally important for carriers to treat governance as a secondary concern.

The insurance industry has already improved customer-facing claims technology in abundance. Therefore, the next phase of modernization should naturally focus on the quality of the underlying data, especially the verified title, lien, and ownership layer that total-loss and fraud workflows rely on most.

Johnny Carson famously joked that California's four seasons are Fire, Flood, Mud, and Drought. Well, Fire season is here, and hardly just in California. Wildfires have already burned 2.5 million acres in the U.S. this year, including more than 1 million in Kansas, Nebraska and Oklahoma.

And we're on the verge of not only an El Niño, which makes the world much hotter and drier, but of what is classified as a "super" El Niño — in fact, one that may be the strongest in recorded history.

With summer upon us, there isn't a lot that can still be done to prepare, but there are still some to-dos for carriers, agents and others in the insurance industry. 

Let's have a look.

The 2.5 million acres already burned in the U.S. this year are more than twice the average over the past decade for this time of year, and conditions should become even more dangerous as the year progresses. The developing El Niño is already strong enough that the Washington Post reports that people are going to the beach near Lima, Peru, where temperatures have been in the 80s — even though it's winter in the Southern Hemisphere. Ocean water temperatures have been measured at more than 14 degrees above normal.

There is some good news here: An El Niño typically makes it harder for hurricanes to form in the Atlantic, so forecasts are for less damage than normal. (That is hardly guaranteed, though. El Niño typically heats up the ocean waters that fuel hurricanes, and a single, exceptionally powerful storm making landfall in the U.S. can be devastating.)

But, as the Post notes, an El Niño also typically leads to "unusually high summer temperatures in the Pacific Northwest, Midwest, Mid-Atlantic and Southeast, as well as across Europe [and] unusual downpours and humidity for parts of the West, including the drought-stricken Intermountain West, which could increase the risk for flooding."

In the short term, the key is opening lines of communications with customers who are the most likely to be hit, and making sure those lines operate flawlessly. Eileen Potter, vice president of marketing, insurance, at Smart Communications, makes four key points in Carrier Management:

"Don’t wait for a disaster to explain the policy.

"One of the most important things insurers can do right now is make sure policyholders know exactly what their coverage includes—and excludes—before they need to use it.... Insurers that deliver clear, easy-to-understand policy summaries and coverage explanations as part of their overall customer communications strategy stand out and earn lasting trust.

"Fill the information gap before someone else does.

"When disaster is approaching, policyholders also need to know how to reach their insurer or agent, what the claims process looks like, and where to find reliable guidance on protecting their home and family. This is where insurers have an underused opportunity. With 41% of U.S. households relying on social media for preparedness tips, the demand for reliable guidance is clearly there—and largely unmet. 

"Build integrated digital communication systems.

"When systems are disconnected, policyholders may receive conflicting information depending on the channel they use. Consider a customer who has had a significant homeowners policy claim. They call the contact center and are told their claim is under review, then check the app an hour later to find it still shows as not yet received. In a moment already defined by stress and uncertainty, that contradiction can cause the policyholder to question whether their insurer is on top of the situation at all.... Smooth integration across every touchpoint... is even more important during a weather event when people can be displaced and don’t have access to mail or phone services.

"Maintain communications during and after a claim.

"Providing consistent updates, whenever possible, makes a meaningful difference in the customer journey. Confirming the receipt of documentation, providing status updates, outlining expected timelines and clearly communicating next steps all reassure policyholders that their claims are progressing and they are being well cared for."

Underscoring what Eileen writes, I'd suggest you test the lines of communication yourself, rather than just rely on data from those in charge of communications with customers. In theory, communications are always perfect, but in practice....

If you have any sort of responsibility for communicating with customers, maybe pick 10 recent claims and call the policyholder to ask how the process went. You'll surely find that many didn't understand their policy, that they had trouble getting responses at various times in the claims process, that they didn't have a good sense of where they stood in the process, that they had to provide the same information repeatedly, that information they provided to someone via email or on the phone didn't show up in your app, or vice versa, and so on. You'll unearth all kinds of frustration that tends to be glossed over by the time statistics show up in a PowerPoint slide. 

Other insurance entities, beyond carriers, agencies, call centers and TPAs, should do similar testing to make sure they're ready for the disasters likely to come this summer and fall. I'm thinking, in particular, of anyone involved in payments. Those have to be handled quickly and flawlessly in an event such as a wildfire. 

If you test now, you might even have time to fix some of the problems before your customers suffer catastrophic losses — when your lines of communication and other systems will be under far more stress than they are now. 

Cheers,

Paul

P.S. Dealing with wildfires in the long term isn't the subject of today's note, but I'll include links to two very smart papers that tackle the enormously complex problem of how to make homes and communities more resilient in the face of wildfire threats. One, by Veronika Torarp and five colleagues at PwC, is here. The other, by Nancy Watkins of Milliman and two colleagues, is here. 

P.P.S. If you find Six Things valuable, please forward to any colleagues whom you think might also benefit. They can sign up for the free weekly newsletter here. 

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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.

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."