Paul Carroll

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

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

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

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

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

Bob Marshall

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

We wanted to go faster—not just for business reasons but because we're trying to have an impact on homes, families, communities, and society. If we can't get Ting into tens of millions of homes, then there will be a lot of electrical fires and damage and loss and fatalities. Still, we’re probably approaching 1.4 million homes. 

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

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

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

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

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

Paul Carroll

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

Bob Marshall

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

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

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

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

Paul Carroll

When did you start Whisker Labs?

Bob Marshall

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

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

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

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

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

Paul Carroll 

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

Bob Marshall

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

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

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

Paul Carroll

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

Bob Marshall

Insurance companies are obviously very conservative by nature, and actuaries want 100,000 home years of data before they're comfortable with anything. So how do you get there? We were fortunate to have partnerships in place with people who believed in what we were trying to do and the mission. They were very much committed to the mindset of moving toward Predict & Prevent, and they were willing to make the investments to make that happen.

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

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

The way we structure our partnerships, we don't make any money on hardware. Literally, none. Sometimes we lose money. Our model is only about the service. We only get paid if the devices are plugged in and providing the service. So we literally lose money if they're not used. It's on us and the partnership to make sure customers are plugging in Ting, it's staying online, it's preventing fires and doing good. 

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

Paul Carroll

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

Bob Marshall

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

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

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

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

I think our carrier partners really want us to get to that next level of scale. If we're only in 5% of your book as a carrier, we're not affecting your bottom line. The 5% of your customers that have it absolutely love it and love you, but if 95% of your customers don't have it we're not making a meaningful impact on your loss ratio corporately. 

Paul Carroll

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

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

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

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

Risk is outrunning episodic decision cycles

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

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

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

The bottleneck is not the model

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

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

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

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

Decision making needs an operating layer

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

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

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

Governance makes speed deployable

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

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

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

The New Operating Discipline for Insurance

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

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

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

When allies of President Trump went on television over the weekend to say that a peace agreement between the U.S. and Iran was 95% complete, I was reminded of a truism among software developers in the 1990s: "The first 80% of a project takes 90% of the time, and the final 20% takes 90% of the time."

Comments on social media noted that even if negotiators had made it through 95% of the issues on their checklists, that progress meant that the U.S. and Iran still had to agree on what to do about Iran's stockpile of enriched uranium, about reopening the Strait of Hormuz and about U.S.-led economic sanctions against Iran. You know, the little stuff.

While there have been more reports today on potential progress, I'm ready to call it. Having covered an international mess or two in my time, I think the situation in Iran has all the markings of a long-term impasse. I'm convinced Iran will be an open wound for many years to come. The best we can hope for, I believe, is the sort of "ceasefire war" occurring in Gaza, where the fighting has officially been ended but the underlying conflict still festers.

Insurers need to prepare themselves. 

I'll still hold out hope that President Trump can achieve the kind of agreement with Iran he's been promising: reopening the Strait of Hormuz to any and all traffic, without tolls, while removing Iran's ability to ever build a nuclear weapon. But I no longer think any such resolution is likely. I don't even think a clean, lasting agreement is possible.

The clincher for me came this morning, when I read a column in the Washington Post by a former colleague of mine from the Wall Street Journal, Karen Elliott House, who is as plugged in to the Middle East as any journalist could possibly be. Karen won a Pulitzer Prize for her coverage of the Middle East in the 1980s and has stayed plugged in to the point that last year she published the definitive biography of Saudi Crown Prince Mohammed bin Salman. 

She wrote: "When the U.S. and Israel unleashed a blistering bombing campaign striking more than 10,000 Iranian targets, and Tehran’s response included attacks on Saudi oil installations and military bases, the Saudi air force initially struck back. But as President Donald Trump tolerated a ceasefire longer than the war itself, and repeatedly threatened to resume hostilities only to back off, the crown prince concluded that he must live with a hostile regime in Tehran. His focus now will be on placating Iran to protect Saudi."

If the Saudis have switched to appeasement — and I believe Karen implicitly — they leave Trump with almost no choice. Even if he felt he could ignore the need for congressional approval of a conflict lasting more than 60 days, under the War Powers Act, or could win approval from a Republican Congress increasingly nervous about an unpopular war as we head into the mid-term elections, Trump isn't going to resume hostilities without support from this major Middle Eastern ally. That means he's left having to negotiate with an extremist, theocratic regime that, by all accounts, thinks it has won the war.

Whatever deal ensues, Trump will surely claim a major victory, but Iran will remain volatile. Having shown the world that it can close the Strait of Hormuz, even while under attack by the world's greatest military power, Iran will keep shippers on edge, thus keep oil markets nervous. Iran will surely retain enough of a pathway to nuclear weapons that there will be the prospect of additional air strikes like the one the U.S. and Israel carried out on Iran last summer. If the U.S. eases economic sanctions, as Iran is demanding, Iran will surely funnel some of that money to its proxies throughout the Middle East as they try to destabilize Israel, Iraq, Lebanon, and Yemen. 

Because the Middle East is so unlikely to return to the conditions before the war, insurers should assume that conditions today will persist for at least many quarters and probably many years.

Shipping patterns will adjust to the higher risks in the Strait of Hormuz, and insurers will have to adjust to those new patterns. Global supply chains for all sorts of goods will change, keeping replacement parts for cars hard to get and limiting access to housing materials, so pressure on premiums will continue. 

Gasoline prices will drop somewhat but remain steep, keeping a lid on traffic and, thus, traffic accidents. People will fly less, in the face of increased air fares, reducing the demand for travel insurance. With gas prices driving inflation, interest rates are likely to stay elevated; the housing market is already a disaster, and sales will stay depressed, reducing opportunities for homeowners insurance companies to attract new customers. 

And so on. 

There will surely be secondary effects, too, though those are obviously harder to predict. The big question, for me, relates to the mid-term elections. With Trump's approval ratings already at record lows, and with Iran looking like a strategic error, the Republican party will almost certainly lose control of the House of Representatives and perhaps even the Senate. All those investigations that the Democrats have talked about wanting to launch into Trump administration actions could become reality next year. Democrats may be a bit cautious because some of the investigations they launched in the lead-up to the 2024 election backfired and let Trump generate support — or they may not. The federal government could pretty much shut down until the 2028 presidential election as Democrats and Republicans scream at each other. Meanwhile, issues that are important to the insurance industry, such as the fates of FEMA and the National Flood Insurance Plan, would be set aside.

My second biggest question relates to Taiwan. Might China decide that now is a good time to try to retake control of the island, with the U.S. looking weak and having used up so much of its weapons stockpile in Iran? What a catastrophe that would be for the whole global economy.

But now I'm getting really speculative. We'll have to wait and see how the secondary and tertiary effects unfold. For now, I really just wanted to note that I don't believe we'll have a clean resolution of the U.S.-Israel conflict with Iran and that we are likely going to be dealing with lingering effects for a long time.

Cheers,

Paul

Every few years, the insurance industry watches a new distribution channel open up and takes too long to walk through it.

It happened with auto telematics. It happened with embedded travel coverage. It happened, most painfully, with cyber — a line we hesitated on for a decade while insurtechs and specialist managing general agents built the playbook we now have to license back.

A similar window is opening right now. And almost no one in traditional insurance is talking about it.

OpenAI confirmed 50 million paying subscribers across all tiers in its April 2026 announcement. Anthropic confirmed that paid subscriptions to its Claude AI platform more than doubled in 2026. Google's Gemini is scaling through Android, Workspace, and Search. Microsoft Copilot is being purchased seat-by-seat across enterprises of every size. Add Perplexity, Grok, Mistral, and a long tail of specialist AI tools, and you have something the insurance industry has not seen in a generation: a brand-new category of paying users — most of them business users — being created at unprecedented speed.

The question I keep coming back to is simple. These users are taking on real, novel professional and digital exposure every time they use these tools. Who is going to insure them, and how?

I think the answer is embedded insurance — sold at the same moment they click "Subscribe to Pro."

Why This Moment Is Different

Embedded insurance is not a new idea. We've discussed it for years in the context of auto OEMs, travel platforms, and e-commerce. What's different about AI platforms is the intensity of the exposure being created relative to the price of the underlying product.

A small business owner who subscribes to ChatGPT Plus or Claude Pro and uses it to draft client deliverables, write production code, advise customers, or build autonomous agents is generating a brand-new risk surface every day — one that no existing policy was priced for.

The industry has already started reacting defensively, by introducing new AI-related exclusions.

But that standalone market is being built the traditional way — broker-led, application-heavy, aimed at mid-market and up. Meanwhile, the actual users of AI tools — millions of freelancers, consultants, small firms, and individual professionals — are buying their subscription in 30 seconds and getting straight to work. They will never call a broker. But they would absolutely tick a box for $10–$15 a month that protects them against the very tool they are using.

That is the embedded insurance opportunity.

What an Embedded AI Coverage Could Look Like

Imagine a world where:

• A user upgrading to a paid AI plan sees a single optional add-on: AI Use Protection.
• For an individual professional, the coverage bundles AI errors and omissions, cyber and privacy protection, deepfake and reputational harm response, and IP infringement defense.
• For a small business, the same product scales up by seat, with broader limits and incident response services.
• For an enterprise, the embedded layer feeds into an existing master policy with usage-based premium adjustments at renewal.
• Underwriting signals come directly from the platform: account type, industry, usage volume, integrations enabled, agent autonomy level, and governance controls.
• Pricing, binding, and endorsement happen instantly, through the same checkout flow as the subscription itself.

This is not a futuristic sketch. The pieces already exist. With AI-driven underwriting and instant pricing, carriers can now confidently offer coverage in context — at the point of need, and for the duration required. What is missing is the partnership — a carrier or insurtech sitting down with a foundation model company and building it.

Why Insurers Tend to Miss These Windows

There are three patterns that explain why insurance keeps arriving late to opportunities like this one, and they are worth naming honestly.

The first is that we wait for credible loss data before we move. Underwriters want triangles. Actuaries want credibility. By the time we have either, the insurtechs and specialist MGAs have already built the wordings, the distribution, and the brand recognition. Cyber between roughly 2010 and 2018 is the case study every carrier should re-read this year.

The second is that we instinctively treat new technology as a risk to exclude rather than a customer base to serve. Look at the carrier behavior above — exclusions, carve-outs, regulatory filings to remove coverage. These are all defensive moves. Very few carriers are asking the offensive question: if 50 million people are now generating new exposure every day, who is selling them an appropriate product?

The third is that we are not yet good at partnering with non-insurance platforms. Carriers know how to work with brokers, agents, and program administrators. Partnering with a foundation model company — meeting their API standards, their UX expectations, their speed of iteration — is a different operating muscle, and most carriers have not built it.

The Window Is Narrower Than It Looks

Embedded auto insurance took roughly a decade to mature. Embedded travel coverage, similar. But the AI subscription market is growing at a pace neither category ever saw. The platforms that will define the next decade of distribution are being chosen right now, in 2026.

The next great embedded insurance product is unlikely to come from an automaker or an airline. It is more likely to appear next to a "Subscribe to Pro" button, sold to a freelancer who never knew they needed it until the moment it was offered.

The risk is here. The exposure is here. The customers are here. The only real question is which insurers stop excluding the future and start underwriting it.

Every great distribution channel in insurance was obvious in hindsight and invisible in the moment. AI subscriptions are simply the next one.

The investment landscape is becoming ever more unpredictable, driven by economic uncertainty, geopolitical risks and evolving regulations putting a strain on traditional asset portfolio optimization techniques.

These techniques are becoming less effective in addressing the rapidly evolving financial environment, and insurers are facing the challenge of struggling to balance complex regulatory and financial objectives using tools and techniques that were designed for a simpler, more stable era.

Shortcomings of traditional portfolio optimization

For decades, investors have relied on techniques rooted in linear relationships such as mean-variance optimization, which seeks to balance expected return against risk. These closed form approaches offer clear frameworks for decision-making but require simplified approximations of insurer-specific objectives.

Insurance companies face objectives far more complex than simply maximizing return for a given level of risk. They must also account for objectives such as solvency capital requirements, regulatory compliance and liquidity management. Traditional optimization approaches struggle to accommodate these objectives, particularly when constraints are non-linear and when conflicting goals must be considered simultaneously.

To overcome this challenge, insurers had to resort to trial-and-error or brute-force methods, manually generating portfolios until one fits the desired criteria. While this approach can work, it is inefficient and offers no assurance of optimality. The time and resources expended in this process can be considerable and the resulting portfolios may still fall short of meeting the required objectives.

Scenario-based machine learning - a new approach

Scenario-based machine learning (SBML) represents a paradigm shift in portfolio optimization, enabling users to evaluate any combination of objectives within a stochastic scenario framework. Unlike traditional methods, SBML embraces the full complexity of the real world, allowing for non-linear objectives and the simultaneous optimization of multiple competing goals.

The key to SBML is its ability to learn from vast data sets of generated balance sheet projections driven by a stochastic real-world scenario generator. Machine learning algorithms train on these projections, identifying patterns and relationships between the complex objectives and constraints. This learning process identifies asset portfolios that best meet the objectives and constraints defined in the optimization exercise creating an efficient frontier of suitable portfolios.

Targeting balance sheet metrics

One of the defining features of using SBML tools for strategic asset allocation (SAA) optimization is the capacity to target the balance sheet metrics that matter most to insurers, leading to a targeted SAA approach.

Let's take solvency capital as an example. By and large, for all insurance regulatory frameworks globally, the amount of capital held is directly influenced by the risk profile of the investments held. Regulatory frameworks, such as Solvency II in Europe, impose strict standards on insurers, requiring them to maintain sufficient capital to cover the risks of running asset portfolios. SBML enables insurers to directly incorporate these considerations into the optimization process maximizing returns or surplus while minimizing solvency capital and imposing a constraint on the amount of capital required.

Insurers that embrace tools that use AI and machine learning for portfolio optimization will be best positioned to achieve their goals, adapt to new challenges, and secure their place in the evolving landscape of global finance.

Commercial auto rates have been climbing. Every market participant knows this. The standard explanation involves nuclear verdicts, social inflation, and litigation funding. Those factors are real.

What gets less discussion is the operational side of the loss equation. Not the litigation. Not the verdict environment. The claims management practices that run between first notice of loss and final settlement, and what those practices cost on a book-level basis when commercial trucking is handled like any other commercial auto line.

It's a different animal. The industry broadly acknowledges this. But acknowledgment hasn't produced widespread changes in how these claims get handled.

The Supplement Rate as a Performance Indicator

Supplement rates on commercial trucking and heavy equipment claims average between 20% and 25% industry-wide. A supplement is a revised repair estimate — the initial figure gets approved, disassembly begins, and the shop returns with a higher number.

A 20% to 25% rate tells you something specific. It tells you the first estimate was wrong at a high frequency. That frequency isn't random. It reflects a systematic gap between the complexity of the equipment being assessed and the expertise of the person writing the first estimate.

A general auto adjuster reassigned to a Class 8 truck or a piece of construction equipment doesn't know what to look for. A specialist does. The operations using appraisers with dedicated heavy equipment expertise consistently hold supplement rates between 10% and 14%. That 10-point gap on a large commercial trucking book represents a material dollars-and-cents difference in indemnity spending. It shows up directly in loss ratios.

Most program administrators and MGA executives can't tell you their supplement rate on trucking claims.

Towing and Storage as Indemnity Leakage

Towing and storage on commercial vehicles is a significant and largely unmanaged cost category on most trucking programs. Storage fees of $125 to $200 per day accrue from the moment a vehicle is taken to a yard. Claims that sit unworked for 30 to 60 days generate thousands in storage exposure before a single repair decision is made.

The towing invoice itself is a second problem. Inflated mileage, charges for equipment that was dispatched but not deployed, fees for services not rendered. These line items go on the invoice and, in most cases, get paid without challenge because the adjusting operation doesn't have the market knowledge to identify what a reasonable commercial tow should cost.

One carrier reviewing its annual towing spending found it had overpaid by more than $650,000 in a single year. That's not an outlier. That's what happens when commercial vehicle towing invoices go through a general claims operation that doesn't specialize in this exposure.

On a book of any meaningful size, towing and storage leakage is a line item that belongs in loss ratio conversations. It rarely appears there because nobody is measuring it separately.

Subrogation Recovery as Underpriced Leverage

Commercial trucking subrogation is a specialty within a specialty. The values are high, liability is typically contested, and the file has to be built correctly from day one of the incident. When it is, win rates above 80% are achievable on eligible files.

Most general TPA operations don't run dedicated commercial trucking subrogation programs. The case complexity is high relative to the volume they handle in that category. Recovery rates on trucking subrogation through general programs reflect that mismatch.

For MGAs and program administrators with meaningful trucking exposure, subrogation recovery represents a straightforward improvement to the economics of the book. It doesn't require renegotiating terms. It requires routing eligible files to a team that knows what it's doing with them.

What the 2026 Claims Conversation Is Missing

The industry's attention in 2026 is rightly focused on AI-assisted claims processing, faster FNOL response, and data-driven loss analytics. The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains. That framing is correct as far as it goes.

What it misses is that technology-assisted claims handling applied to a general adjusting model doesn't solve the expertise problem on specialized equipment. A faster general adjuster writing estimates on a crane or a loaded semi is still a general adjuster writing estimates on a crane or a loaded semi. Speed doesn't compensate for the knowledge gap that produces 22% supplement rates.

The gap between strategic intent and claims execution is where loss ratios on commercial trucking programs get made or broken. The intent to manage this exposure well is almost universal. The execution requires domain expertise that most general operations don't have and can't develop at a sufficient depth for an exposure this specialized.

The Program Design Question

For MGAs building or managing commercial trucking programs, the TPA selection question deserves the same analytical rigor as rate adequacy or reinsurance structure. The right question isn't which TPA can handle the claims. It's which TPA has the specific expertise to handle these claims at the supplement rates, towing spending, and subrogation recovery rates that a profitable book requires.

The specialty exists because general operations don't produce the outcomes this exposure demands.

The performance data from specialty operations — the supplement rates, towing savings, subrogation win rates — is publicly available for comparison. The loss ratio improvement potential is real and measurable. The question is whether program design conversations are treating claims expertise as a first-order variable or an afterthought.

For most trucking programs, it's still the latter.

Other Resources From Insurance Thought Leadership

1. "Insurance 2026: Progress Via Technology, Collaboration" (Jan. 8, 2026): "The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains."
2. "4 Key Trends Reshaping P&C Insurance" (Feb. 5, 2026): "The gap between strategic intent and claims execution"

Consider what 2025 demonstrated about the insurance industry's risk assumptions. No major hurricane made landfall in the United States. By the logic of traditional catastrophe modeling, which has always placed tropical cyclones at the center of loss scenarios, 2025 should have been a manageable year. Instead, global insured losses hit $107 billion. 

Secondary perils that catastrophe models have historically treated as background noise, including wildfires, severe convective storms and floods, accounted for a record 92% of that total, up from a 56% average over the prior decade. Severe convective storms alone delivered their third-costliest year on record.

The industry did not have the wrong year. It has the wrong product architecture.

The secondary perils mismatch hiding in plain sight

For decades, catastrophe reinsurance was built around a defensible logic: The events that would truly threaten the balance sheet were episodic, high-severity, well-modeled primaries, like a Category 5 hurricane or major earthquake. Secondary perils existed, but they were attritional, manageable, and amenable to the law of large numbers. That assumption is no longer valid. Secondary perils such as hailstorms, flash floods, wildfires, severe thunderstorms, and freezing events, produced $136 billion in total losses in 2024, well above their ten-year inflation-adjusted average of $110 billion.

The more important question is not why secondary perils are growing, but why, after a decade of this data, the market has not produced instruments adequate to transfer the risk. The answer is structural, and it is uncomfortable: The institutions with the capital and sophistication to absorb the frequency of secondary peril risk have rationally opted not to.

After 2022 and 2023 - years of punishing secondary peril losses - reinsurers raised attachment points sharply. Reinsurers redesigned their treaties to keep secondary peril frequency off their books. That was a rational response for their balance sheets, but it created a structural vacuum. Hailstorms, flash floods, wildfires, freeze events mark losses that aggregate across a portfolio but never reach a single-event treaty threshold. They now sit almost entirely on primary carriers, who lack the capital efficiency to hold them and are responding the only way their product architecture allows: raising premiums, tightening underwriting, and in some markets, leaving altogether.

What carriers' market exits actually signal

The consequences of this structural mismatch are accumulating in observable ways. In California, standard carriers have non-renewed more than 1 million wildfire-exposed policies since 2018. The California FAIR Plan, the state's insurer of last resort, grew from around 200,000 policies in 2020 to more than 450,000 by late 2024, a 123% increase driven almost entirely by wildfire-related withdrawals from the standard market. Nationally, approximately one in seven owner-occupied homes is now uninsured, a figure that jumped more than 6% between 2023 and 2024 alone as rising premiums priced households out of coverage. The E&S market has absorbed the spillover, reaching $86 billion in direct premiums in 2023, growing for a fifth consecutive year. But E&S is a pressure valve, not a solution. And 70% of residential flood losses go uninsured annually in the United States, representing roughly $17 billion in losses absorbed by households and taxpayers each year.

The instinct is to read this as a pricing problem: If the industry just charges enough, it will re-enter. But that logic misses the target. Premium increases are not restoring market access. They are accelerating the concentration of risk in residual markets that are structurally worse at absorbing it than the private market they replaced. Market exit is not a correction mechanism. It is the protection gap widening in real time, underwritten by public balance sheets that were never designed for the purpose.

Closing the gap between the trigger event and the realized loss

Traditional indemnity insurance requires an adjuster, a loss assessment, and a claims process calibrated to a world where individual events are large, distinct and infrequent. That workflow is expensive even when functioning correctly, and it was never designed to handle the accumulation of dozens of mid-severity events per year across a portfolio. Parametric structures remove that friction entirely. A defined trigger, such as hail accumulation exceeding a threshold, wildfire perimeter within a defined radius, flood depth at a gauge station, or freeze degree-days above a specified level, is met or not met. Settlement is rapid. There is nothing to negotiate.

There is a further irony that the insurance industry has been slow to absorb: Secondary perils are more parametrizable than primary ones, not less. Hurricane track and wind-field modeling involve genuine uncertainty that makes trigger design difficult. Hail accumulation, flood depth, wildfire proximity, and freeze intensity are all measurable in near-real-time from satellite and ground-based observation networks. The basis risk problem that has historically constrained weather derivatives - the gap between the trigger event and the realized loss - closes considerably when AI-driven models can calibrate triggers at the property level rather than the regional index level. The technical barriers to frequency-risk transfer are lower than they have ever been. The remaining barrier is product design inertia.

Where the unpriced accumulation is building

The geographies that have already experienced market disruption are not the only exposures deserving attention. The next unpriced accumulation is building in the Midwest and upper South, where severe convective storm frequency has been running at record levels for three consecutive years and reinsurance treaty structures still treat hail and tornado losses as below-threshold attritional items.

The carriers and risk managers who treat secondary peril accumulation as a known quantity that can be managed through pricing and underwriting tightening alone will find, in the next five years, that they have been solving the wrong problem. The cat model was built for the kind of disaster that makes the front page. The losses that will define the next decade are the ones that happen every season: individually unremarkable, collectively devastating, and structurally unhedged by the instruments the industry currently relies on.

In April, something significant happened in the auto industry: Tesla confirmed that production had begun on its Cybercab, a fully autonomous vehicle with no steering wheel, no pedals, and no human in the loop. Until now, the conversation has focused on what this means for Uber and Lyft and on whether robotaxis are going mainstream.

But perhaps there's an equally consequential question. What happens to the insurance industry once the driver has gone the way of the Edsel? Unfortunately, the industry has not seriously tried to answer it.

The Model Was Built Around the Human

Commercial auto insurance was designed around a single variable: the person behind the wheel. That is why insurance prices reflect driving behavior; liability follows whoever was driving, and policy language assumes a human making decisions on the road in real time. The full architecture of risk assessment, premium calculation, and claims resolution rests on the assumption that human judgment is what gets priced.

Open almost any commercial auto policy today, and the human driver as the unit of risk appears on nearly every page. But remove the driver, and pricing assumptions, liability triggers, and claims logic all rest on a human variable that no longer exists. So the language built for that world has to be rewritten.

Autonomous vehicles are no longer theoretical. From Level 3 consumer vehicles to more than 700,000 weekly robotaxi rides globally, deployment is moving faster than the regulatory frameworks meant to govern it. With that comes an even deeper anxiety the industry rarely discusses openly - autonomous vehicles are much safer than vehicles with human drivers. Research in Traffic Injury Prevention found Waymo cut injury-causing crashes by 79%, with intersection crashes down 96%. Tesla reports Full Self-Driving (Supervised) improves U.S. road safety by over 80%.

On its face, all of this is nothing but good news. But for an industry where roughly half of all premiums are tied to auto, those numbers describe an existential shift. Fewer claims are indeed good for society, but they also represent a fundamental challenge for a business model never redesigned to reflect it.

The Transition Is the Real Challenge

The most challenging chapter is perhaps underway, in the chaotic middle ground before full autonomy becomes the norm.

Waymo's current operating model shows how messy this can be. In Austin, it has partnered with Uber, while in San Francisco it competes directly against Uber and Lyft. In both markets, it works with maintenance fleets including Hertz, Avis, and new AV service companies. Each raises different insurance questions.

Once a Waymo comes off the road and a human driver takes it in for service, there is no settled answer for what is being insured. These vehicles can be worth hundreds of thousands of dollars due to their embedded sensors and software. If a maintenance technician damages a radar unit and that vehicle later causes an accident, is the resulting liability an auto insurance issue or product liability? Current policies do not offer a clean answer.

Mixed-fleet operations carry that ambiguity: overlapping liability, unclear ownership of risk, and policy language written for a world that no longer exists. The work ahead, therefore, is a fundamental redesign of how liability gets assigned in multi-party autonomous operations. When something goes wrong, the question of responsibility, whether the OEM, the platform, the maintenance fleet, or the software provider, has no clean answer.

Data is the starting point, and fleets like Waymo and Tesla are sitting on enormous amounts of operational data that could reshape how risk is understood and priced. But that means insurers need access to that data, and the frameworks to build products around how these vehicles actually operate.

Regulators have a significant role to play, too, because the state-by-state patchwork that just about worked for rideshare will not scale for autonomous vehicles. Federal coordination on liability standards and minimum insurance requirements for AVs would give the industry a target to build against.

The Window to Get Ahead Is Narrower Than It Looks

The rideshare era offers a partial template. When Uber arrived, insurance took years to catch up, but the industry muddled through. However, the trajectory this time looks faster. Nevertheless, unlike the rideshare era, the industry already knows how to build insurance products for markets without a rulebook.

But the scale is different, the liability questions more complex, and the next major AV incident will create enormous pressure to fix things quickly, in public, under scrutiny. Waiting for that moment is the wrong strategy.

Insurance has to shift from static to dynamic, using real-time data to map how risk is distributed across platforms, fleets, maintenance partners, and technology providers. Liability has to follow that data through every link in the chain.

Adapting will not be enough, because a model that priced human behavior for a century is finished. What replaces it will look almost nothing like today's commercial auto insurance. Carriers treating this as a rebuild will define the next era of mobility risk. Everyone else will be left writing policies for a road that no longer exists.

American household budgets are facing pressure from every direction. Grocery bills remain stubbornly high. Gas prices have shot up—and face further surges as politically volatile oil-producing regions continue to roil.

Meanwhile, layoffs across technology, retail, and financial services sectors have put millions on uncertain footing—many of them "white-collar" members of the homeownership class. In response, consumers are putting every line of their monthly budget under a microscope. As families cut out food delivery and forgo or downgrade streaming services and other niceties, a four-figure annual insurance premium is no longer the kind of expense people renew reflexively.

Together, pricing pressures and income instability combine to drastically change insurance shopping behavior. This puts carriers in a race to understand—and hopefully prevent or at least forestall—what looks like a retention crisis. (It's not the first time we've been here: the post-9/11 hard market of 2001-2003 triggered a similar wave of shopping and switching as carriers raised rates sharply across nearly every line, and the mid-1980s hard market produced comparable consumer flight before conditions softened.) The carriers that "crack the code" to curb inflation through efficiency will provide needed breathing room for their customers, while creating competitive advantages with a potentially long tail.

The Numbers Reflecting a Stressed Consumer

The percentage of U.S. consumers shopping around for a new auto insurance carrier reached a record 57% in 2025, up from 49% in 2024, and about 29% switched carriers outright, according to the J.D. Power 2025 U.S. Auto Insurance study survey. Progressive CEO Tricia Griffith assertively underscored what's driving this dynamic on a 2025 earnings call: "I think it's just easier to shop. And I think with all the other inflationary items out there, people are looking to figure out a way to save money."

This is not simply a market anomaly or part of a business cycle. It's evidence of a financially stressed customer base doing exactly what financially stressed people do: seek relief wherever they can find it.

For many households, reducing insurance costs is the rare large recurring expense that responds to user effort. When a family is already shopping in-house brands at the supermarket and delaying purchases, saving several hundred dollars on an auto renewal is a meaningful win.

Carriers that recognize the emotional and financial context behind that shopping behavior (hint: it's not a simple matter of competitive comparison shopping; it's born of necessity) will approach this moment via innovation and empathy.

Raising the Ceiling by Focusing on the High-Value Customer

Not all shopping activity carries equal risk. Many consumers most actively reconsidering their policies right now also happen to be the ones with the greatest profit potential. One-third of customers shopping in 2024 were seeking auto and home insurance bundles, according to the latest J.D. Power Insurance Shopping Study. These are multi-policy, long-tenured households, precisely the customers who anchor a carrier's book.

Winning one bundled household is worth multiples of a single-line acquisition. It's why insurance brands lean so hard into bundling offers and messaging. Carriers building strategies targeting this specific segment will see outsize returns. The opportunity lies not in chasing after new customers from a depleted pool, but from reaching the ideal existing customers at precisely the moment they are open to having constructive conversations about finding economies through scaling the relationship with their insurer.

Maximizing the Value of Every Touchpoint

To do this, your playbook doesn't need to be more complex, but your tactics need to be more intentional. Research consistently demonstrates that insurers who reach out to policyholders before renewal, with plain-language explanations tied to real cost drivers, see stronger results than those who respond only after a customer complains about a rate increase.

A customer who just paid more for ground beef, gas, and a car repair is not well-positioned to absorb a renewal increase without being told why. The same customer, reached proactively with a clear explanation and a conversation about coverage options, feels "seen" rather than squeezed. That distinction drives decisions more reliably than any pricing adjustment alone.

Reaching the customer before they open a comparison tool changes the entire dynamic. It signals that their relationship with you matters, which is exactly what a financially pressured household needs to hear.

Remaking Traditional Workflows

Seventy-six percent of carriers now deploy AI in at least one underwriting or pricing function, according to industry data. The carriers positioned to win are the ones who use it thoughtfully: "how will this AI-enabled workflow help us reach our [financial performance/customer service/NPS] targets consistently?" Surprisingly, this philosophy is not as common among insurers as one would hope. Carriers that get this right understand a critical distinction: the goal is rethinking how work gets done, not how they can reduce the number of people doing it. AI doesn't replace an underwriter's judgment or an agent's relationship with their client—it removes the friction that keeps both from doing their best work. McKinsey's research on AI in insurance further underscores this point, noting that the highest-performing carriers treat AI as a workflow redesign challenge, not a headcount equation.

Use AI to flag households where a proactive coverage conversation can strengthen relationships, rather than give competitors a foot in the door. AI deployment of this sort builds an advantage that compounds over time, making every renewal a trust-building touchpoint, rather than creating potential pricing negotiation standoffs.

The Open Window

Market disruption creates winners and losers—only now this happens at, well, the speed of AI. The carriers gaining the most ground in the next three years will not be those that waited for customers to leave before responding. They will be the ones who anticipate and respond to a record-size shopping market driven by "kitchen table" financial stresses as an opportunity to demonstrate why their policy is the one worth keeping.

The carriers who view this moment as an inflection point created by decades of shifting macroeconomic factors (wage stagnation, globalization, etc.), rather than a discrete trend to watch, will look back on 2026 as the year they separated themselves from a crowded field. The real choice is not whether to compete for customers who are shopping. It is acting with intent to keep your customers while giving consumers good reason to choose you over your less responsive competitors.

International insurance broking operates across multi-actor systems without a structured method for reading the connectivity between them. Complexity becomes concrete when renewals stall, when claims escalate without warning, when regulation forces last-minute adjustments. Pressure concentrates in certain places, travels along some pathways, and dissipates in others. 

The geometry of these movements is what I call adjacency: the measure of how tightly actors are bound to one another, and how their ties carry or absorb pressure. The concept draws on network theory's insight that structure shapes behavior, and on systems thinking's recognition that interdependence produces non-linear effects. What adjacency mapping adds is an operational instrument calibrated to the specific architecture of international insurance programs, one that translates structural insight into practitioner decisions.

An international program is not a set of bilateral relationships. It is a system in which master clients, local clients, brokers, and insurers connect continuously, and in which a shift in one part alters conditions across the rest. A disputed claim at the local level can reverberate upward until it unsettles the master layer. A regulatory delay in one jurisdiction will delay the entire renewal cycle. When negotiations falter between a master broker and a local insurer, expectations unsettle across several markets simultaneously. The system propagates pressure because its ties differ in weight, consequence, and resilience.

The structure begins with the system's elements. Six actors form the state vector of any program:

Here, Smc denotes the master client, Slc the local clients, Smb the master broker, Slb the local brokers, Smi the master insurer, and Sli the local insurers. The notation names the nodes that matter. The model captures structural connectivity. It measures the presence, intensity, and resilience of operational ties, not the informal influence, cultural distance, or reputational history that also shape relationships. Understanding how the system functions requires capturing how strongly these actors are tied to one another.

The adjacency matrix A fulfils this function. It represents the interaction weights between stakeholders: each element wij indicates the presence and intensity of the relationship between stakeholder i and stakeholder j. The matrix is first constructed in abstract form, mapping the position of each interaction within the system:

The abstract form locates each relationship within the system. The subscripts identify the two stakeholders involved; the element wij denotes the weight of their tie. The purpose of this construction is to formalize the network so that the system can be analyzed as a structure rather than through accumulated observation. Once defined, weights are assigned on a 0 to 1 scale. On this scale, 0 denotes the absence of adjacency; 0.3 indicates a weak tie with limited interactivity; 0.6 represents strong adjacency with effective coordination; and 1 signals optimal alignment. High adjacency is a marker of capability: two stakeholders are tightly coupled, mutually responsive, and able to sustain efficient workflows. Low adjacency signals fragmentation and the structural risk of disconnection. The weights are practitioner judgements. Their value lies in making an assessment explicit that experience tends to leave implicit. A broker who has managed the same program for a decade carries a mental map of its connectivity. The adjacency matrix makes that map visible, comparable, and open to revision.

Construction begins with a structured assessment across all active relationships in the program. The broker assigns an initial weight to each tie by asking three questions: how often do these actors interact operationally, how reliably does information move between them, and how quickly does the tie transmit pressure when the program is under strain. These criteria are observable without measurement instruments. They are the qualities experienced brokers already assess informally. The matrix makes that assessment formal, consistent, and transferable across programs and teams.

A populated matrix takes the following form:

The matrix is a map of the system's connective capacity. A weight of 0.6 between master and local clients reflects strong alignment: headquarters and subsidiaries adjust to one another with speed. A 0.3 between master clients and master brokers indicates a weaker tie, where coordination exists but is less intensive and more susceptible to friction. A 0.2 between master clients and master insurers signals low adjacency: limited interactivity risks disconnection unless brokers actively mediate. A 0.6 between master brokers and local insurers, by contrast, marks a high-value link, one where workflow is active and system coordination is at its strongest. High adjacency marks the ties through which decisions travel, alignment is secured, and operations proceed without friction. Low adjacency marks the fracture lines where interactivity is minimal, silos form, and misalignment compounds.

Adjacency mapping derives its analytical value from the fact that connectivity is never static. Strong ties allow programs to move with speed and coherence. When master and local brokers hold a 0.6 adjacency, coordination is tight and workflow advances without resistance. When a claim escalates across a 0.6 link between local and master insurers, the system responds rapidly. Weak ties do the opposite: they isolate segments of the program, delay decisions, and erode effectiveness.

The architect's objective is to sustain ties at 0.6, the threshold at which alignment holds, coordination costs nothing, and the program moves with structural coherence.

Three patterns govern how pressure moves through the system. Concentration forms where multiple strong ties converge, typically around master brokers holding 0.6+ adjacencies with both local brokers and master insurers. These nodes become coordination hubs, capable of synchronizing decisions across jurisdictional boundaries. Propagation measures the efficiency with which decisions travel. The difference between a 0.6 and a 0.3 tie is the difference between transmission and friction. A 0.6 link between master and local insurers ensures a claim escalates without delay; a 0.3 tie ensures it stalls, and the broker must compensate manually for what the tie fails to carry. Absorption occurs at weak adjacencies of 0.3 or below, where pressure dissipates rather than transmits. Occasionally this buffers noise; more often it marks a structural disconnection that prevents system-wide coordination. These patterns do not operate independently. A weak tie between master broker and local insurer becomes more consequential when the master client to master broker tie is also degraded. Compound weakness across adjacent nodes accelerates fragmentation in ways that no single tie, read in isolation, would predict.

Because ties shift, the map must be kept current. A static diagram decays. A weak link can be reinforced into a strong adjacency by deliberate effort; a strong tie will weaken if neglected. Four events should prompt a reassessment. First, personnel change at any node, meaning the tie shifts with the person. Second, a regulatory change in any jurisdiction covered by the program. Third, a claims event that escalated beyond its expected path. Fourth, the approach of renewal, which is always a structural stress test. Each signals that the weight of at least one tie may have moved without the broker noticing. Adjacency maps are instruments that require periodic review and active maintenance. Brokers who update them see the system. Those who rely on experience alone see only what the system once was.

During renewals, adjacency maps identify which ties sustain workflow and which must be reinforced before they become bottlenecks. In claims, they reveal which relationships enable rapid escalation and which will stall it. Consider a master broker to local insurer tie that registers 0.6 in stable conditions but drops to 0.3 during renewal following personnel turnover at the local level. The map makes this degradation visible in advance. The broker can then rebuild the tie through intensified communication, workflow realignment, or deliberate relationship investment before claims season converts a weak link into a coordination failure. The same logic applies during a major claims event. A local insurer holding a 0.6 adjacency with the master insurer will escalate rapidly and with precision. One holding a 0.3 will delay, misframe, or absorb the claim at the local level, forcing the master broker to intervene manually at precisely the moment when speed matters most. The map identifies this vulnerability before the claim arrives. In regulatory matters, the map shows where connectivity must be strengthened to secure compliance. In each case, the broker acts before disruption, reinforcing the ties the system depends on rather than repairing them under pressure.

The broker who monitors adjacency, reassesses ties under pressure, and rebuilds degraded links before they become failures is sustaining program coherence. That is what rigorous servicing looks like in practice.

The central proposition of adjacency mapping is that program performance correlates with the aggregate strength of ties between its actors. The broker whose counterpart is responsive, informed, and quick to act is not simply lucky in his relationships. He is operating across a tie with high adjacency. When that tie degrades, the program follows, regardless of how well the individuals involved know each other. This is a testable claim. Brokers who map their programs over time will find that degradation in tie strength precedes operational failure, and that deliberate investment in adjacency produces measurable improvements in renewal speed, claims resolution, and regulatory compliance. Together they provide foresight into where the system is strong, where it is fragile, and where investment in interactivity will deliver the greatest return. The program, read this way, becomes a structure with legible geometry.

International insurance broking will always be exposed to uncertainty. Renewals will clash with shifting regulation, claims will appear at awkward times, and timelines will compress under pressure. But complexity is not chaos. By treating programs as systems and adjacency maps as diagnostic instruments, brokers can anticipate rather than endure, and reinforce rather than repair. Pressure still moves through the system. Adjacency maps tell you in advance where it will concentrate, where it will stall, and where it will dissipate unnoticed. In a system this complex, that is the only form of control that holds.