The World Cup always produces breakout stars. Think of 2018, when the teenaged Kylian Mbappe announced himself to the world by scoring four goals as his French team won the title. So far this year, you might lean toward Folarin Balogun as the possible breakout; he scored twice for the U.S. and looked brilliant as it dominated Paraguay in the opening round. For a team? Perhaps you're partial to Cabo Verde, a country of 500,000 that I confess I did not know existed but that tied mighty Spain, 0-0, on Monday.

For me, the clear breakout star is Freddy. 

The young German has taken social media by storm, growing his follower count on Twitter to 635,000 from the 11,000 he had when he arrived in the U.S. with some friends in early June for a six-week road trip to experience the World Cup. His earnest observations about the U.S. have made him so popular that when he posted that the group was headed to Houston, he arrived to find that former Houston Texas J.J. Watt had paid for a huge room for the group at a posh hotel, and that local businesses had stocked the room with gifts. When Freddy expressed admiration for the music of country music star Ella Langley, she invited the group to meet her backstage after a concert in Oklahoma City. A resort offered to send its plane to pick the group up in Oklahoma City and fly them to Las Vegas for a watch party for a game involving the U.S. men's team. 

There's a reason Freddy has become a sensation, and it suggests something that all businesses, including those in insurance, should do periodically.

An adage attributed to Marshall McLuhan (though with earlier roots) says, "We don't know who discovered water, but it wasn't a fish" — the notion being that anyone immersed in an environment can't understand it the way someone outside that environment can. And Freddy (@FreddyLA7 on Twitter/X) is an outsider providing an unvarnished, unbiased view of America to those of us immersed in it.  

He has shared video of his drive through Alabama and Mississippi and marveled at how beautiful the landscape is — something I certainly missed when I drove through the states on my way from Georgia to Louisiana. Freddy posted a picture of a pile of food at a Taco Bell and called it "the holy land." He wrote: "We were about to walk an hour to the stadium in the rain to save on an Uber, and the receptionist at the hotel we were parked in front of decided to drive us there." Freddy discovered that a Bass Pro Shop had a shooting range inside.

My favorite is a post with two pictures. On the left is a building so big and lit it up it looks like it could be the entrance to an amusement park. On the right is a line of gas pumps stretching way out into the distance. Freddy wrote: "DUDE LMAO THIS IS A GAS STATION." (Someone else said there are 120 pumps and wondered if the travel stop was designed to refuel the U.S. Air Force.)

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Freddy then added a photo of the mountain of barbeque he bought inside the Buc-ee's in Texas.

He has surely resonated partly because he's so positive about what he's experiencing in the U.S. Everybody likes to be told they're great. But he still demonstrates the power of outside, objective observation, which is something every company and every individual should solicit as a regular exercise. 

A BCG study I've cited before and will surely cite again found in the early 2000s that 80% of senior executives thought their product was superior to competitors' — and that 8% of customers agreed. Businesses unintentionally erect filters that distort what insiders see, so they have to try extra hard to either remove those filters for themselves or solicit feedback from Freddies who never faced those filters to begin with.

I once interviewed Colin Powell, between his time as the chairman of the Joint Chiefs of Staff and his term as Secretary of State, and he described what I thought was an insightful way to get around the filters. He set up half a dozen phones in his office and gave the number of each phone to a single person whom he trusted to provide a smart, non-DC perspective and reliable, unfiltered information. He told his assistant to never answer any of those phones, to hide the identity of the callers. (I note that the interview was before his time as Secretary of State under President George W. Bush because, after initially resisting the plan to attack Iraq, Powell let himself be sold a bill of goods and made a speech at the United Nations that relied on distorted intelligence to sell the world on the disastrous invasion.)

As I've written before, I think the best way to get unfiltered insight is to experience your company without identifying yourself or to sit with randomly chosen customers as they interact with your company. Make up a persona and call your call center or text it, so you can see what your chatbot actually does. Sit with a relative as they try to decipher the language in the policy you've issued them, without helping. Call people after they've had a claim processed to see just how smoothly your theoretically seamless handoffs from call center and app to adjuster to collision repair shops and rental car companies actually went. And so on.

You surely won't get the sort of joyful feedback Freddy is giving to the U.S., but you'll be able to improve faster than the companies you're up against — and business, like soccer, is a harsh competition.

Cheers, 

Paul

P.S. For those of you who, like me, have been immersed in America so long that the environment feels completely natural, here are a few other observations from visitors for the World Cup:

To start with the negatives, Americans are loud, the U.S. is expensive, and the distances are inconvenient. Traffic is awful. The culture of tipping is baffling. And why are so many items, such as toiletries, locked up in stores?

That said, Ranch dressing seems to be quite a hit. One woman marveled at being able to order a chicken waffle with Ranch dressing and ice cream in an iHOP. Another wrote: "Ranch dressing should be a human right." He added: "The portion sizes are hilarious."

Our grocery store culture has struck a chord, too — the enormity of Walmarts and Costcos, the extraordinary variety of foods offered, and the quality in some of the upscale stores. A Frenchman posted a hilarious screed about how he arrived in the U.S. intending to be a snob but has to admit that the bathrooms in Buc-ee's are nicer than in his apartment. He says, "You could eat the brisket off the floor. It's cleaner than a hospital."

One woman wrote: "I can’t lie… the food in America is ridiculous. Everyone talks about portion sizes, but nobody talks enough about how GOOD everything tastes. Even the ‘quick’ food feels elite compared to what I’m used to in the U.K."

Portion size does come up a lot. One man wrote: "Nobody warned me that American portion sizes are actually a threat to your health. I ordered a medium coffee and received what my country would classify as a bucket."

My favorite, non-Freddy post is a lengthy, almost poetic, one from a Japanese tourist about the biscuits and gravy that a waitress recommended to him at a breakfast counter:

"When the plate arrived, I thought something had gone wrong in the kitchen. I say this with shame. The dish looked like a construction site after rain. Pale mounds. Gray ladle-fall. Speckles I could not identify. In my land, the eye eats first. A meal is arranged like a garden. This meal was arranged like weather.

"I must now formally apologize to the biscuits, the gravy, the waitress, the kitchen and the entire breakfast tradition of the American South. 

"It was magnificent. Warm. Peppered. The biscuit drank the gravy the way a field drinks rain — THAT is why it is shaped like that, you fool — and every mound I had insulted was a soft fold of comfort that my homeland, in 800 years, never once thought to invent."

I'll never look at biscuits and gravy the same way again.

Surging demand for artificial intelligence is reshaping the data center landscape at a remarkable pace. McKinsey projects that companies will invest nearly $7 trillion in data center infrastructure globally by 2030, with more than 40% of that spending concentrated in the U.S.

Much of the near-term opportunity is on the development side, with new construction ranging from ground-up hyperscale builds to core-and-shell projects that will eventually be converted into data centers. Beyond new construction, market activity is also being driven by owners and operators of existing facilities, ranging from colocation providers to large enterprises managing their own infrastructure.

As a class, data centers present property insurance exposures that differ significantly from other commercial risks. Understanding the full scope of exposures and the coverage required to address them is critical to building an insurance program that responds as expected when a loss occurs.

TOP EXPOSURES AND RISKS

While data centers are designed for resiliency, meaningful exposure still exists across every stage — from construction and commissioning to continuing operations — and extends beyond the physical asset itself.

Business interruption and downtime are the primary concerns. These facilities are built for continuous uptime that developers and owners depend on, meaning even a brief outage can generate a significant claim. Location compounds that exposure. Data centers are increasingly being built in areas where severe weather is common, considerably raising the risk for catastrophic losses. Even if a major weather event takes a facility offline temporarily, it can produce revenue losses that far exceed the physical damage. As such, carriers are focusing attention on loss control, engineering standards, business continuity and disaster recovery planning. They want confidence that construction can resume at a development site or that a facility can return to operations quickly after a disruption.

Secondary exposures also complicate the risk picture. These include:

• Power and grid reliability. Power disruptions stemming from grid instability, utility constraints, or insufficient local infrastructure are increasingly common. When operational disruptions occur that don't involve physical damage to the facility, a standard property policy may not respond to losses.
• Community opposition and project approval risk. Public pushback has become a defining obstacle to data center development. Last year saw 25 project cancellations — more than quadruple from 2024 — largely driven by intensifying community opposition across the country. Access to the power grid and water supply are among the most common sticking points, with the potential for municipal resistance to derail projects persisting well into the development process.
• Equipment procurement delays. Waitlists are already common for critical, high-value components that are in short supply. If equipment is damaged, stolen or lost in transit, sourcing replacements can further extend a project.

SPECIALIZED COVERAGE TYPES TO CONSIDER

A standard property policy covering physical damage and business interruption is foundational, but data centers frequently require additional layers to address the range of exposures that fall outside traditional coverage triggers.

Builder's risk coverage addresses the construction phase — a critical window of exposure for data center projects. With contractors, lenders, and third-party operators all at the table, each with their own coverage requirements, structuring a program that satisfies every stakeholder is critical.

Parametric and alternative risk transfer products are increasingly relevant for data centers, particularly when the cause of a financial loss doesn't stem from physical damage. Parametric coverage, captive structures, and self-insurance components can be structured to fill gaps where a standard policy doesn't respond. They can also serve as deductible buy-downs on programs with large retentions as well as supplemental capacity where needed.

Transit, cargo, or stock throughput policies are essential given the value of equipment moving through the supply chain. Millions of dollars in power equipment, servers and GPUs may be in transit, held in interim storage, or staged on-site before a facility is operational. If something goes wrong at any point in that chain, both replacement costs and project delays can escalate quickly.

Environmental insurance is worth considering for new construction, given the scope of ground-up development activity and its potential effect on the surrounding site and community. Contamination, pollution, and construction-related environmental liability are exposures that a standard property policy won't address.

Political risk insurance is particularly relevant for international projects. Geopolitical instability, strikes, riots, and government actions can affect data center operations in ways that generate both physical and financial loss.

WHAT THE CURRENT INSURANCE MARKET MEANS FOR OWNERS AND OPERATORS

The property insurance market is in a soft cycle, with data centers largely seen as a desirable class of business. Some insurers are deploying staggering single-line limits on data center risks, which translates to more options and more competitive terms for owners and operators. This is a welcome stance as many lenders and other sources of capital currently require full value limits of insurance vs. limits set by modeled losses based on probabilities and site attributes.

But questions remain as to whether that dynamic will last. Data centers remain a relatively untested class, and how the market responds when significant losses arrive is still an open question. Owners and operators who use the current environment to structure comprehensive, well-designed programs will be better positioned as conditions evolve.

The FBI’s Internet Crime Complaint Center (IC3) just gave the world a $21 billion wake-up call. According to its 2025 report, cybercrime costs reached a record $20.8 billion in losses last year, sounding the alarm bells on the critical need for personal cyber insurance policies that offer proactive risk management.

The FBI IC3’s annual report states that business email compromise (BEC) and financial fraud are the two leading methods of cybercrime. Long considered a corporate network issue, BEC is now the primary way cybercriminals infiltrate enterprises by targeting the personal vulnerabilities of key leaders. At the same time, high-net-worth individuals (HNWIs) are increasingly victims of cyber-enabled fraud, with AI playing a central role in enabling cybercriminals to create deepfake impersonations and realistic phishing emails and texts, thereby lowering the barrier to entry for malicious hackers.

As the adage goes, “an ounce of prevention is worth a pound of cure.” For cyber insurance underwriters and brokers, offering a policy that only covers remuneration for damages after an event is no longer enough. To manage risk effectively for HNWIs and business leaders, insurance must pivot toward a preventive cybersecurity model that stops incidents – and the long-lasting financial and reputational consequences that follow – before they can happen.

Modern Executives and the Expanding Attack Surface

The FBI IC3’s 2025 report findings highlight growing dangers and underscore the rapid expansion of the personal attack surface, fueled in large part by the rise of AI:

• Business email compromise: Scams that compromise business and individual email accounts to conduct unauthorized transfer of funds accounted for 15% of all 2025 losses, totaling slightly more than $3 billion.
• Cyber-enabled fraud: 85% of all losses reported in 2025 were due to cyber-enabled fraudulent activities, including theft of money, data, or identity, or the creation of counterfeit goods or services, totaling $17.7 billion in losses.
• Tech/customer support fraud: In 2025, nearly 48,000 complaints were filed by individuals about cybercriminals posing as technical or customer support/service representatives, resulting in losses totaling $2.1 billion.
• AI-fueled threats: For the first time, the report includes a section on AI-enabled cybercrime and scams, reporting that IC3 received 22,364 complaints in this category, which accounted for over $893 million in losses.

High-profile enterprise executives and high-net-worth individuals are caught in the crosshairs of these attacks. Because they have broad digital footprints, typically own multiple homes with numerous smart devices, and maintain significant public profiles, they are now prime targets of cybercrime. Additionally, it’s a tremendous challenge to maintain privacy in the digital era, with social media and the instant, broad dissemination of information. Events we prefer to keep private can instantly become public knowledge, creating a risk profile that corporate cybersecurity policies typically fail to address.

Personal cyber insurance brokers are ideally suited to help their clients consider how a single personal indiscretion or data leak can have long-term career and reputational impacts. Beyond that, however, they have a professional and ethical responsibility to protect their clients – not just provide reactive cyber coverage.

Making the Case for a Digital Bodyguard

In the modern cyber landscape, fraught with potential exposures and points of vulnerability at every turn, highly vulnerable individuals can benefit from personal cybersecurity protection that fills the gaps left unaddressed by enterprise cybersecurity. By shifting from a reactive cyber policy to a proactive, preventive risk-management offering, insurance brokers and underwriters can provide clients with a “digital bodyguard” to protect them while safeguarding their bottom line. I’m not talking about security software; I’m talking about a preventive capability that protects people and stops incidents from ever causing harm.

The core elements of comprehensive, proactive protection include:

• Minimizing digital footprints: Actively reducing an individual’s "attackable" surface area by minimizing how much information they share online.
• Data broker removal: Scrubbing personal info from the sites that feed cybercriminals.
• Hardening accounts and devices: Moving beyond basic passwords to elite-level security to ensure personal accounts and devices are not vulnerable to malicious access.
• Home network scanning: Continuous monitoring of home networks – the "soft underbelly" of executive security – to detect suspicious activity before it causes harm.
• Training & hygiene: Empowering individuals and their families on appropriate online behaviors, how to spot scams and threats, and continuing ways to minimize their attack surface.
• Incident response: Around-the-clock expert support to respond rapidly when a significant threat is detected.

The reasons for offering this type of personal cybersecurity protection and risk mitigation to corporate and individual clients are a no-brainer: insurance brokers can foster greater trust, improve client retention, and safeguard their clients’ financial posture. Preventing a $1 million breach and the resulting reputational and financial repercussions is far more advantageous than paying out on a claim.

Demand for this type of specialized protection is rapidly growing, as organizations and individuals gain a better understanding of what’s at stake. Insurance underwriters and brokers have a window of opportunity to capitalize on this growth before other trusted advisors step in and bolster their own service offerings.

Don Poster, vice president and senior director - national family office leader of Aon Private Risk Management, states: "Our clients rely on us to preserve both their legacy and their lifestyle. In 2026, you simply cannot protect a client’s wealth, assets, family, and privacy without also protecting their digital identity. We view digital executive protection not as a tech add on, but as a fundamental component of holistic risk protection.”

Diane Delaney, executive director of the Private Risk Management Association (PRMA), agrees. “The PRMA recognizes that the most successful brokers are those who offer more than just an insurance policy safety net. By taking proactive risk management approaches such as integrating digital executive protection, brokers can provide a holistic layer of protection that helps mitigate the reputational and financial fallout that traditional cyber insurance policies are meant to cover,” she said.

Locking Down the Future

Traditional insurance reacts to and repairs damage – in simple terms, it’s like getting a broken arm treated at the hospital. Proactive personal protection prevents the break from happening in the first place. In the modern digital realm, AI-powered threats targeting high-profile, high-net-worth individuals are rampant, and the personal attack surface continues to expand. The unfortunate reality is that corporate defenses often fall short of protecting individuals.

The solution is a proactive "digital bodyguard" approach – the best and most sustainable way to manage and mitigate executive risk, and protect people and corporations from significant, costly attacks and breaches. It’s time for insurance brokers and underwriters to stop viewing personal cyber as a standalone policy and begin offering a comprehensive service to lock down an individual’s digital footprint and safeguard their future.

Why are insurance company boards suddenly treating CRM investments as strategic priorities rather than IT decisions? Because CRM has evolved from a contact management tool into a competitive differentiator, determining which insurers win and lose in digital markets. 

Modern insurance customers expect personalized experiences, instant responses, and seamless interactions across channels that only sophisticated CRM enables.

Board members also recognize that CRM capabilities directly affect revenue growth, customer retention, and market positioning. A recent survey showed that a CRM can improve customer satisfaction by 47%, leading to a 47% increase in customer retention, 45% increase in revenue, and 39% increased chance of upselling or cross-selling. What once seemed like an operational technology investment now represents a strategic business decision requiring board oversight and approval, given the implications for competitive advantage and shareholder value.

The Key Forces Driving the Importance of CRM

The conversation around CRM is shifting because of:

Policy Renewal Chaos

Agents lose sleep over missed renewals. When you’re managing hundreds of auto, home, or life policies manually, something always falls through the cracks. A solid CRM for insurance agents flags every coming renewal date automatically. That means no more angry calls from clients who lost coverage. The board needs to see that renewal retention directly ties to revenue, and that manual work is killing it.

The second part is the ripple effect. One missed renewal leads to a complaint, followed by a bad review, and finally a lost household account. Agents can’t afford that domino effect any more. When you bring a CRM into the boardroom discussion, you’re really talking about protecting the renewal base. That’s the safest money the agency earns. Without it, you’re leaking cash slowly and painfully.

Cross-Selling Blind Spots

An agent knows a client just had a baby but forgets to mention life insurance. Or someone buys a car but doesn’t get gap coverage. These are easy misses. A modern CRM for insurance agencies spots those gaps for you. It looks at what a client already has and suggests what they’re missing. That turns a casual conversation into an extra sale without feeling pushy.

The board should care because cross-selling costs almost nothing to deliver and adds pure profit. When agents aren’t reminded, those opportunities vanish. Leadership needs to hear that the CRM acts like a silent partner sitting next to every agent. It doesn’t replace their gut feeling; it just catches what tired eyes miss after the 10th call of the day.

Carrier Relationship Pressure

Insurance carriers are getting picky. They want clean, fast data from agencies before they give good commissions or favorable terms. If your agency sends messy client info, carriers push you down the priority list. A CRM for insurance companies cleans that data automatically. It makes sure every policy number, effective date, and claims history is where it should be.

On the flip side, strong carrier relationships mean competitive pricing for clients. And attractive pricing means happier clients who stick around. The board needs to understand this isn’t back-office fluff. It’s leverage. When an agent walks into a boardroom discussion about CRM, they’re really asking for better bargaining power with every carrier they work with. That’s a competitive edge no one should ignore.

Service Speed Expectations

Clients today want answers in minutes, not days. They’ll text an agent at 7 p.m. about a small collision. If the agent fumbles to find their policy, trust erodes. A mobile-friendly CRM puts every client file in the agent’s pocket. They pull up coverage, claims history, and carrier phone numbers in seconds. That speed turns a stressful moment into a heroic one.

From a board perspective, speed drives referrals. A client who gets help fast tells friends. An agent stuck shuffling papers gets dropped. When you frame CRM as a speed tool, not a reporting toy, leaders lean in. Nobody wants to be the agency known for “we’ll call you back tomorrow.” That reputation dies hard.

Commission Tracking Mess

Agents fight for every earned commission. But when policies change mid-term, or clients adjust deductibles, commission math gets tricky. A robust CRM ties each policy change to the correct agent and payout. No more spreadsheet fights. No more “you owe me $47 from last May.” It’s all right there, calculated automatically.

The board should care because commission disputes kill morale. An agent who feels underpaid stops prospecting. They get quiet and leave silently. Replacing an agent costs a fortune in the lost book of business. So, when an agent raises CRM in a boardroom discussion, they’re not being picky. They’re asking for basic fairness in how their paycheck gets calculated. That’s a people problem with a software solution.

CRM As Critical Infrastructure

Insurance boards now see CRM systems as critical business infrastructure because of:

Digital-First Customer Expectations

Today's insurance buyers expect instant quotes, online policy management, and immediate responses as they get from Amazon or Netflix. CRM for insurance brokers enables these digital experiences that customers now demand as standard service. Companies without modern CRM lose customers to competitors offering convenient digital interactions.

• Provides instant online quote generation capabilities
• Enables 24/7 policy access through portals
• Delivers immediate responses to customer inquiries
• Matches consumer experiences from other industries
• Prevents customer defection to digital competitors

Aging Agent Demographics

Many experienced insurance agents are retiring and taking decades of client relationships and industry knowledge with them. CRM systems capture relationship details, communication history, and customer preferences that would otherwise disappear when agents leave. Documented knowledge ensures smooth client transitions to new agents without losing business.

• Captures client relationship details before retirements
• Documents customer preferences and communication history
• Enables smooth handoffs to younger agents
• Preserves institutional knowledge securely
• Prevents revenue loss from departing agents

Competitive Pressure From Insurtechs

Startups using technology to sell insurance directly threaten traditional agencies with lower prices and faster service. CRM for the insurance industry levels the playing field by giving established companies similar technology advantages. Boards realize technology investments are survival requirements, not optional upgrades anymore.

• Matches insurtech speed and convenience levels
• Automates processes to reduce operational costs significantly
• Enables competitive pricing through efficiency gains
• Provides customer experience matching digital startups
• Protects market share from technology disruptors

Regulatory Compliance Issues

Insurance regulations require detailed records of customer interactions, disclosures, and consent tracking that manual systems can't reliably maintain. CRM automatically documents all communications to create audit trails that regulators demand during examinations. Compliance failures result in massive fines, making proper documentation a board-level risk management issue.

• Creates automatic audit trails for regulators
• Documents all required customer disclosures systematically
• Tracks consent and authorization properly
• Proves compliance during regulatory examinations
• Reduces fine risks from documentation failures

Cross-Sell and Retention Revenue Opportunities

Most insurance customers buy only one policy type when they could benefit from multiple coverage options, increasing lifetime value significantly. CRM for insurance agents identifies cross-sell opportunities, showing which customers need auto, home, life, or business insurance they don't currently have. Systematic cross-selling generates revenue growth without expensive new customer acquisition.

• Identifies customers with coverage gaps
• Suggests appropriate additional policy offerings automatically
• Tracks household members requiring separate policies
• Calculates lifetime customer value comprehensively
• Generates revenue from existing customer relationships

Data-Driven Commission and Performance Management

Boards need visibility into which agents, products, and markets generate profitability versus losses to make strategic resource allocation decisions. CRM provides real-time dashboards showing commission costs, retention rates, and profitability by agent and product line. Data transparency enables informed decisions about expansion, training, or territory changes.

• Tracks commission costs by agent accurately
• Measures retention rates across different segments
• Calculates profitability per product line clearly
• Identifies top and bottom-performing agents
• Guides strategic resource allocation decisions effectively

Lifetime Value Maximization

Acquiring new insurance customers costs five times more than retaining existing ones. This makes retention a critical profitability driver boards care about deeply. CRM for insurance agencies tracks satisfaction, identifies at-risk customers, and triggers retention campaigns before cancellations happen. Proactive retention directly affects bottom-line profitability and company valuation.

• Identifies customers likely to cancel soon
• Triggers retention campaigns before policy lapses
• Tracks satisfaction scores predicting retention likelihood
• Reduces costly customer acquisition spending needs
• Improves profitability through better retention rates

Omnichannel Customer Communication Coordination

Insurance customers contact agencies through phone, email, text, web chat, and social media expect consistent experiences across all channels. CRM for insurance companies unifies communication tracking, preventing customers from repeating information across different touchpoints. Omnichannel coordination improves satisfaction and operational efficiency simultaneously.

• Tracks conversations across all communication channels
• Prevents customers from constantly repeating information
• Maintains context when channels switch mid-conversation
• Enables consistent service regardless of contact
• Improves satisfaction through seamless omnichannel experiences

Predictive Analytics for Risk Assessment

Modern CRM systems use AI to predict which prospects will buy, which customers might cancel, and which risks to avoid. These insights help agents prioritize efforts and boards allocate resources toward the highest-return opportunities. Predictive capabilities provide competitive advantages impossible with traditional systems or intuition alone.

• Predicts which prospects will likely purchase
• Identifies policies at high cancellation risk
• Forecasts renewal likelihood for planning purposes
• Scores lead quality for prioritization decisions
• Guides resource allocation toward the best opportunities

M&A Integration and Scalability

Insurance companies grow through acquisitions, requiring the integration of different agencies and systems into unified operations quickly. CRM provides common platforms consolidating customer data and standardizing processes across acquired entities. Scalable systems support growth strategies without creating operational chaos or data silos across organizations.

• Consolidates customer data from acquired agencies
• Standardizes processes across merged organizations quickly
• Enables the rapid integration of post-acquisition timelines efficiently
• Supports growth without proportional cost increases
• Creates a unified view across multiple entities

Shareholder and Investor Expectations

Private equity investors and public market shareholders expect insurance companies to demonstrate digital transformation progress and technology investments. CRM implementation signals to investors that management understands market trends and invests in competitive positioning. Technology adoption directly influences company valuations and investor confidence in leadership.

• Demonstrates digital transformation commitment to investors
• Signals competitive positioning awareness to shareholders
• Influences company valuation in funding rounds
• Shows management understands market evolution trends
• Builds investor confidence in an enduring growth strategy

Key Issues for Board Members

Board members' key challenges are:

Lack of Clear Business Outcomes

Many CRM investments fail because the goals are unclear. Boards should define what success looks like before investing. This ensures that the system is measured on real outcomes beyond just usage and activity.

Compliance and Record-Keeping Risks

In insurance, every customer interaction matters from a compliance point of view. Missing records or unclear communication history can lead to serious issues. A CRM must capture conversations, updates, and changes properly. Board members should confirm that the system supports proper record keeping without making work harder for brokers.

Low Adoption by Brokers

One of the biggest risks is that brokers simply do not use the system. If it adds extra steps, they return to emails, spreadsheets, and personal notes. This is why ease of use matters more than features. The success of any CRM for insurance brokers depends on whether brokers find it helpful in their daily work without needing extra effort.

Hidden Costs Beyond the Initial Investment

The cost of CRM is not limited to buying the system. There are continuing costs such as setup changes, support, upgrades, and training. Many investments look affordable at first, but grow over time. Boards need a clear view of total costs over several years in addition to the initial investment.

Old Data That Never Gets Cleaned

Most carriers have client files full of typos, wrong phone numbers, and policies that ended years ago. Establishing a CRM for insurance brokers on top of that mess doesn’t fix it. It just organizes your garbage into neat folders. You pay for speed but get faster chaos. Furthermore, the cleanup takes up real work. Someone has to call clients, verify addresses, and merge duplicate records. That’s not fancy software work; it’s boring and tedious work. If your board isn’t ready to pay for that manual cleanup first, the CRM will turn into a failure. You can’t automate what you haven’t fixed manually.

Summing Up

CRM has become part of how insurance sales teams work every day. It affects how deals move, how customers stay, and how risks are managed, making it a board-level concern. Good decisions come from looking at real use cases and real outcomes. When boards stay involved, CRM turns into a support system for sales growth, not a cost burden.

Following a period of historic profitability and record capital of $785 billion at the end of 2025, the global reinsurance market is entering a pivotal transition. As rate momentum stabilizes, relying solely on broad hard-market pricing corrections to drive returns is no longer a viable long-term strategy. To defend technical underwriting margins and achieve sustainable growth, reinsurers must now shift their focus from riding market cycles to generating true operational alpha. 

For business and data leaders, the mandate is clear: The experimental phase of AI is over. The next competitive frontier demands a seamless alliance between deep underwriting expertise and enterprise-grade technological capabilities, transforming data assets into the ultimate strategic moat. This article outlines the blueprint for that transformation.

Chapter 1: The New Reality of Risk (“Why”)

For decades, reinsurers have mastered 'cycle management,' thriving in both hard and soft markets by intelligently deploying capital. However, today's connected risks are making historical cycles dangerously unpredictable. We are facing a perfect storm: climate change is intensifying natural catastrophes, state-sponsored cyber-attacks threaten global infrastructure, and geopolitical tensions are fracturing supply chains.

The new masters of the cycle will not be those who simply manage capital but those who leverage data and AI to anticipate, price, and mitigate risks before they materialize. These are no longer just "emerging risks"; they are immediate, systemic threats to underwriting profitability and operational resilience. Addressing them requires a paradigm shift in how we perceive, quantify, and aggregate exposure across the globe.

Chapter 2: The Strategic Response (“What”)

To survive and thrive in this volatile new reality, reinsurers must elevate their strategic response. This is not about making incremental operational improvements; it is about establishing robust business pillars necessary to navigate an unpredictable world.

• Underwriting Discipline: Reinsurers need to prioritize technical underwriting excellence and disciplined risk selection to ensure sustainable profitability after years of volatility. This means aligning underwriting with better data and enforcing pricing adequacy over volume. It requires a forward-looking discipline that prices in the cascading effects of modern perils. For instance, Swiss Re’s strategy emphasizes being “performance-driven, bottom-line focused.”
• “Value Added Services” for cedants: Reinsurers are increasingly focusing on client-centricity – going beyond transactional risk transfer to offer solutions and services that add value for cedants. This involves leveraging reinsurers’ data and expertise to help clients manage risks such as portfolio optimization and assessing exposure to climate risks.
• Operational Agility & New products: The ability to rapidly ingest new data streams, model novel products, and execute complex claims efficiently is now the baseline requirement for maintaining a competitive advantage. In an environment where reinsurance pricing is on the rise, parametric reinsurance for events such as severe convective storms (SCS) of a certain intensity may be an alternative for cedants for pre-determined payout. Similarly, Munich Re’s aiSure™ provides performance warranties and indemnifies clients of providers for their financial losses or legal liabilities directly related to AI errors.

Chapter 3: The Engine of Transformation (“Data & AI”)

While the strategic pillars define what must be done, data and AI determine how it will happen. They should no longer be treated as isolated IT efforts or experimental pilots; they are the core engine of the modern reinsurance enterprise.

To execute dynamic portfolio optimization and maintain underwriting discipline, reinsurers must transition from fragmented, siloed systems to an intelligent, interconnected ecosystem. Advanced predictive analytics and generative AI offer the unprecedented capability to synthesize vast amounts of structured and unstructured data—from dense legal contracts, submission in-take, and geospatial data to risk models - turning ambiguity into actionable, quantifiable foresight.

Chapter 4: “The Way Forward”

The underpinning fabric to realize the above priorities lies with data, analytics, and AI. Hence, leading reinsurers need to refresh their strategy to deliver a trusted, intelligent, and perpetually adaptable data and AI ecosystem (“cycle management”) for the enterprise. This involves building foundational capabilities rooted in principles such as domain-driven design, data product thinking, privacy by design, decision-grade data, and explainability to build trust.

• Decision-Grade Data (beyond data governance): Stop treating data governance as a compliance exercise or a cost center. The goal is to ensure that every underwriting and capital allocation decision is based on trusted, transparent, and auditable information.
• The Enterprise Context Fabric (The Digital Twin): To scale AI in an enterprise, frontier models today lack the capability to understand the business context. Hence, most AI implementations to date were limited to efficiency plays (such as contact center, Q&A, summarization) and hence have not delivered major business value in proportion to their investment. This is where ECF comes to play, a flexible semantic layer that serves as a glue to unify process and data context, and understand the complex relationships between policies, clients, risks, and capital. This "Digital Twin of the Business” enables the sophisticated, cross-portfolio analysis required to spot hidden risk accumulations.
• Agentic AI: Key processes such as underwriting, claims, and risk assessment need to be reimagined in entirety (with human-in-the-loop) using a systems-thinking approach to realize the value. For example: How might we augment an underwriter with a team of AI agents (i.e., multi-agents) that can instantly analyze a submission, research the client's risk profile, model the impact on the portfolio, and draft a set of recommended terms - for the underwriter’s review and decision making.

Final Chapter: What would you build first?

The time for isolated, disjointed pilots is over. If you were to start tomorrow, the critical step is not to build another predictive model or deploy a Q&A chatbot, but to establish an enterprise context fabric.

Why? Because without a unified understanding of your business, any AI initiative will remain a siloed, tactical solution. By first creating this semantic layer, you build the foundation to reimagine core processes like underwriting and claims from the ground up, transforming them from linear, manual workflows into dynamic, AI-augmented decision engines.

This is how you do not just adapt to the future of risk - you build it.

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.

For 30 years, the insurance industry has treated First Notice of Loss as an intake event. A form to fill out. A call to log. A queue to manage. That framing made sense when fraud was something you investigated months after a payout, on a claim file that had already cooled. It does not make sense in 2026.

Today, FNOL is the single highest-leverage minute in the entire claims funnel. It is also the moment when the industry is most exposed. Roughly 10% of property and casualty claims carry some element of fraud or exaggeration, and the Coalition Against Insurance Fraud now estimates total annual U.S. insurance fraud at more than $300 billion across all lines (CAIF 2024; Insurance Research Council 2023). The overwhelming majority of that exposure is decided in the first claimant interaction, not in post-payment forensics. Yet the average carrier still treats those opening 60 seconds as a workflow problem rather than a decision problem.

Three structural shifts have changed the ground under FNOL in the last 24 months, and most of the industry has not caught up.

Shift one. FNOL stopped being a call.

The first thing to acknowledge is that FNOL is no longer one channel. It is at least five.

A modern claim opens across phone, chat, web form, direct message, and email, often in parallel, with photos and documents arriving asynchronously through whichever channel the claimant finds most convenient. A claimant who calls in might also upload damage photos through the carrier's app and submit a supplemental statement through a web form within the same hour. The single "call recording" that the industry's QA and SIU practices were built around is now one of five surfaces, none of which by themselves contain the full claim.

That fragmentation has a cost. The contradictions that used to surface naturally inside one conversation with one adjuster now scatter across surfaces that no single human reviews end to end. A claimant can say "no injury" on the FNOL call, upload medical imagery inconsistent with that statement to the photo portal, and submit a supplemental narrative that quietly raises a soft-tissue claim, with each of those three artifacts living in a different system. The fraud signal is the gap between channels. Most carriers cannot see it.

The line items underneath this add up quickly. Bodily injury buildup, the classic profile of the minor collision turned into the multi-thousand-dollar demand letter, costs the U.S. industry an estimated $13 to $18 billion annually, at industry detection rates of only 12% to 18% (IRC 2023; NICB 2024). Staged accidents add another $7 to $10 billion, with detection rates as low as 4% in some carrier books (NICB 2024). Personal injury protection mill activity in no-fault states accounts for another $10 to $15 billion (NICB 2024). Almost all of these morphologies begin in the first claimant interaction. Almost none of them are caught at that interaction today.

Shift two. The bad actors got AI before the carriers did.

The second thing to acknowledge is that the offensive side of the fraud equation reached operational AI faster than the defensive side did.

Synthetic documents, AI-generated damage photos, voice-cloned callers, and coordinated multi-channel fraud scripts moved from research curiosities to commodity tools in the last 18 months. Pindrop's 2024 Voice Intelligence Report tracked a 475% year-over-year jump in synthetic voice attacks against contact centers, with insurance among the top three targeted sectors. The FBI and FinCEN issued separate 2024 advisories on AI-enabled financial fraud, including specific guidance on voice cloning and synthetic identity. None of this is hypothetical anymore.

The implication for the claims function is uncomfortable but straightforward. The defensive stack the industry built between roughly 2010 and 2020, which consists primarily of post-payment forensics, structured-data anomaly detection, and rule-based scoring, was designed for a world where evidence presented to the carrier was, at a minimum, real. That world no longer exists. A carrier facing a deepfaked recorded statement, an AI-generated damage photo set, and a synthetic supporting document does not have a fraud problem that legacy SIU tools were built to solve. They have a verification problem at the front door, in real time, while the claimant is still on the channel.

Shift three. Compliance and customer experience are squeezed in the same minute.

The third shift is regulatory and operational. Carriers are being asked to do two things in the same FNOL window that used to be addressed sequentially.

The NAIC's December 2023 Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, the NYDFS Circular Letter No. 7 of 2024, Colorado SB21-169, and 3 CCR 702-10, and the EU AI Act's Annex III provisions for insurance decision making all push in the same direction. They expect documented, explainable, auditable decision support at every point where an algorithm influences a claim outcome. They do not accept "the model said so" as a defense.

At the same time, the customer experience side of the carrier organization is being asked to deliver frictionless digital intake, low effort scores, and same-day or instant decision making for low-complexity claims. The two pressures do not contradict each other in principle. In practice, they compress into the same minute of work, and the workforce most carriers staff at FNOL is not built to hold both at once.

Manual call QA helps less than it used to. Industry benchmarking suggests that most carriers audit under 5% of claimant interactions, sampled after the fact (Verisk 2024; LIMRA 2024). Rule-based bots cannot read intent, hesitation, coercion, or contradiction. The gap between what regulation now expects, what customers now expect, and what the existing tooling can actually deliver is the gap where loss ratio is leaking.

The category gap

There is a useful way to read the existing AI-in-insurance vendor landscape, which is to ask what each category of tool is actually telling you.

Detection tools tell you what has already happened. They scan claim files after the fact and surface anomalies.

Prediction tools tell you what might happen. They score claims, prioritize SIU queues, and forecast severity.

Customer experience automation handles workflow. It routes, summarizes, and responds.

The category that does not yet exist at scale, and the one the FNOL problem actually demands, is the layer that tells the rep, the system, and the SIU lead what to do right now, while the claimant is still on the line. Real-time, in the interaction, explainable, omnichannel. Not a dashboard for tomorrow morning. Not a score on a closed file. A decision-support layer that sits inside the conversation as it is happening.

That category has been the missing piece of the claims AI stack for the entire post-2015 era. It is what the next decade of FNOL has to deliver.

What the new FNOL operating model looks like

The carriers that figure this out will have four properties in common.

First, they will treat the claim as a single multi-channel entity, not as a call plus a form plus a photo. Contradictions and red flags will be surfaced across channels, not within them.

Second, they will operate in real time. The decision to fast-track, to probe further, to escalate to SIU, or to request additional evidence will be made while the claimant is still in the interaction, not three weeks later.

Third, they will be defensible. Every alert, every recommendation, every score will carry its reasoning, its source evidence, and its audit trail. Regulators are not asking for this politely anymore.

Fourth, they will close the loop with the human. The rep on the phone, the supervisor in the QA chair, the investigator in SIU, and the executive watching loss ratio will all see the same signal, in the same explainable form, at the same moment. The system's job is to give them the next move. The human's job is still to make the call.

A one-point loss-ratio improvement on a mid-sized property and casualty book translates to tens of millions of dollars on the bottom line (Verisk 2024). That is the economics that makes the new FNOL operating model a CFO conversation, a CCO conversation, and a compliance conversation, not only a fraud conversation.

Closing

FNOL is not a workflow problem. It has not been one for a long time. It is the highest-leverage minute the insurance industry has, and right now it is also the most exposed. The industry's defensive posture was built for a world that no longer exists. The tools we use to meet today's claimant interactions, fraudulent or legitimate, need to be designed for what the front door of the claim has actually become.

The carriers that move first on this will not save a few basis points on loss ratio. They will redefine where claims operations sit in the carrier's value chain. The carriers that move last will keep paying the bill, in larger and larger checks, for a problem that was always solvable at the very first minute.

Sources cited in the article

• Coalition Against Insurance Fraud (CAIF), 2024 industry fraud estimate.
• Insurance Research Council (IRC), 2023, claim fraud and buildup rates in personal auto.
• National Insurance Crime Bureau (NICB), 2024, staged accident, BI buildup, and PIP industry detection ranges.
• Pindrop Voice Intelligence Report, 2024, +475 percent YoY synthetic voice attacks against contact centers.
• FBI and FinCEN 2024 advisories on AI-enabled financial fraud and synthetic identity.
• LIMRA, 2024, Life carrier fraud and contact center benchmarking.
• Verisk, 2024, contact-center QA coverage benchmarking and loss-ratio sensitivity.
• NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, December 2023.
• New York Department of Financial Services (NYDFS), Circular Letter No. 7 of 2024.
• Colorado SB21-169 and 3 CCR 702-10, life insurance algorithm and predictive model governance.
• EU AI Act, Regulation (EU) 2024/1689, Annex III provisions relevant to insurance decision making.