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.

For years, the mantra in financial services was simple: Control the front door so you influence the purchasing decision.

That thinking is now being challenged.

Decision-making is now moving into AI-mediated environments. Consumers can ask AI agents to evaluate products, compare policies, and recommend the best options. In some cases, agents authorize transactions directly. Recent research from Adobe shows rapid growth in generative AI-driven traffic to retail and financial sites, underscoring how quickly behavior is evolving.

This evolution marks the emergence of agentic commerce that is not just restricted to the retail industry and is poised to disrupt the financial services and insurance industry. In this model, AI acts as an intermediary in the purchasing journey. Comparison and evaluation extend beyond an institution's website and occur wherever people rely on AI.

It introduces a new distribution layer for financial services. Institutions are now competing for algorithmic visibility alongside human attention. Rather than simply attracting prospects, products and data must surface meaningfully within AI-driven marketplaces. For financial institutions, this raises urgent strategic questions.

The Changing Rules of Engagement

Financial services have always been comparison driven. Consumers routinely weigh options between insurance policies, loan terms, credit card offers, and savings rates before committing. The friction involved in that process has historically worked in favor of incumbent organizations. Consumer switching takes time. Research requires effort.

AI reduces both.

Consider insurance. A consumer looking for auto coverage no longer needs to navigate multiple carrier websites. An AI agent can assess requirements and compare pricing structures within seconds. As this capability improves, the effort required to evaluate alternatives declines.

When evaluation becomes continuous and low effort, loyalty becomes more performance based. Renewal periods may feel less automatic and more like fresh buying decisions. Pricing transparency becomes more consequential. In this world, product clarity becomes a competitive advantage.

This does not mean financial institutions lose control. But it does change the rules of engagement. If AI agents continue shaping how options are presented and prioritized, institutions must consider how their products are interpreted by machines, not just by human buyers.

Questions for Leaders

If AI agents become the primary venue for evaluation, how will your products be accurately and competitively surfaced? Just as search engines reshaped digital marketing, AI-driven discovery will require structured data and transparent product logic that machines can interpret and rank.

The second question concerns product design. AI agents excel at normalizing complexity. They compare features, pricing, and policy terms quickly. Institutions that rely on opaque language or intricate structures may see those advantages fade. Clear, straightforward products may stand out when machines evaluate them at scale.

There is also a broader distribution consideration. Insurance and lending have long relied on brokers, agents, and referral networks to guide purchasing decisions. Those roles may shift. Advisory expertise may matter more than control over the transaction. Institutions should consider how their distribution strategies hold up if the first conversation takes place with an AI agent.

Finally, transactional authority. It is one thing for an AI agent to recommend a policy or a loan. It is another for a consumer to authorize that agent to complete the transaction. As this capability develops, governance becomes more important. Institutions will need to define how consent is captured and how credentials are managed.

How to React

Organizations that take early, deliberate steps will be better positioned for this new reality. Here's where they should start.

Make Product and Policy Data Machine-Consumable

Digital optimization is largely centered on user experience and conversion rates. That still matters. But if AI agents are evaluating financial products, they need clear, structured data to work with.

Look at how pricing, eligibility rules, policy terms, and disclosures are stored across your systems. If that information sits in disconnected platforms or dense documents, AI will struggle to interpret it consistently. The clearer and more structured your product data is, the more accurately it can be compared.

Rethink Transaction Governance for Delegated Decisions

Allowing AI agents to research products is a modest shift. Allowing them to initiate transactions on behalf of consumers is a huge one.

Leaders should begin by defining frameworks for how consent is captured and verified. What controls govern the use of payment credentials and account access? How are transactions audited and monitored for anomalies?

Security and compliance teams need to be closely involved. Fraud detection models may need to account for transactions that originate through AI agents rather than traditional user interfaces.

Prioritize Orchestration Strategy Over Channel Strategy

For many institutions, customer experience modernization has centered on optimizing individual channels. Voice, mobile, chat, and branch interactions have each been refined over time. But agentic commerce deprioritizes the channel and prioritizes the continuity of the journey.

If a customer begins the journey with an AI agent and then transitions into an organization's system for origination or servicing, that movement must feel seamless. Data should flow consistently, and context should be preserved. The experience should not break down when the point of entry changes.

This requires architectural coordination across systems of record and servicing platforms. Treating AI-mediated interactions as just another inbound channel risks fragmenting the customer experience.

The goal is not to control where the conversation starts. It is to ensure that wherever it begins, the institution can deliver a cohesive experience from evaluation through fulfillment and beyond.

A Distribution Shift That Demands Attention

Financial institutions have navigated major inflection points before. Search engines reshaped acquisition strategies. Mobile transformed engagement expectations. Each transition required institutions to rethink where decisions were made and how influence was established.

Agentic commerce is yet another change. Institutions must remain visible, interpretable, and trustworthy in the context of AI-driven product discovery. If transactions can be initiated through those platforms, governance and orchestration frameworks must be ready.

This is a big opportunity. Those who prepare early can expand their reach and remain relevant at key decision moments. Those who wait risk losing position in AI-driven marketplaces.

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.

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• Performance:  Higson executes rules with an average latency of 0.23 ms and supports up to 9,000 requests per second in a standard configuration. Thanks to the built-in ONNX runtime, trained ML models can run directly inside decision tables without requiring a separate inference layer.
• Scalability:  Our CPU-based licensing model means you pay for actual production usage rather than user counts. A proof of concept can run on AWS at approximately $0.63 per hour, with infrastructure costs scaling predictably alongside transaction volume.
• Business-Owned Decision Logic:  Pricing analysts, underwriters, and compliance teams can create and deploy rule changes directly using decision tables, Groovy and Python functions, as well as visual flows without relying on IT support or opening development tickets. The platform includes full version control, auditability, and simulation capabilities before deployment to production.

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Most P&C insurers manage pricing, underwriting, claims, and compliance rules across separate systems - each governed by different teams and operating on different release cycles. When regulatory changes occur, organisations often end up running multiple parallel workstreams while hoping all outputs remain aligned.

Higson consolidates decision logic within a single rules engine, giving business users direct control over processes that are traditionally dependent on IT delivery queues.

Pricing analysts, underwriters, and compliance teams can author and deploy changes directly using decision tables, visual flows, and embedded scripting capabilities with full version control and auditability built in.

From a technical perspective, Higson executes rules with an average latency of 0.23 ms and supports up to 9,000 requests per second. A proof of concept can run on AWS at approximately $0.63 per hour, while CPU-based licensing ensures infrastructure costs scale with actual usage rather than user counts.

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As long as everyone has been telling their Ted Turner stories in the wake of his recent death, I thought I'd tell mine, before getting on to this week's business: what I see as a growing backlash among younger generations toward AI that business leaders need to contend with.

My story comes from my friend Marc (a former managing director at Marsh McLennan, as it happens). He was at the helm in a multi-day sailboat race around Long Island in the 1980s and timed the start almost perfectly. In the chaotic way that these races start, you don't know when the horn will blow, so you circle as you try to be at full speed with a clear path to the starting line when the horn sounds. Marc had succeeded — but Ted Turner was bearing down on him, aiming for the same spot on the line that Marc was going to cross. 

Marc had the right of way, but this was Ted Turner, recent winner of the America's Cup, in a much bigger, faster boat, with a world class, steely glare as he steered his boat on a collision course with Marc. 

Marc never wavered, and at the last possible moment Turner bore off, did a 360, and crossed the starting line a minute or so later. Turner won the class among the biggest boats, while Marc and his crew just did well in his class of smaller ones. Finishing late at night, he and his crewmates headed to a bar to decompress. At 1am, they were getting ready to call it a night, when the bartender set a round of drinks in front of them and said they were sent with the compliments of the gentleman at the door. The gentleman was Ted Turner. He nodded respectfully in their direction. Then he gave them the finger with both hands and stormed out.

The bartender told Marc that Turner said he'd been scouring every bar on the waterfront in search of Marc and his friends. Whatever else you want to say about Turner, the man had style.

Now on to the backlash against AI that we all should be watching. 

I use my daughters, aged 32 and 29, as my antennae about attitudes among Millennials and Gen Z, and they started bristling about AI months ago. Initially, they complained about the huge amounts of water required for cooling. If I ever mentioned using an AI for something, one of them might make a snide remark — they're given to snide remarks with their father — like, “I guess the real prompt is: ‘Hey ChatGPT, could you please drain another reservoir for me?’”

Hyperscalers' wild need for electricity for their gen AI data centers led to concerns about what AI was doing to the environment. That my daughters' electric bills were climbing didn't help matters.

More recently, they've resonated with the concerns of those facing the prospect of having data centers built near them, each spanning perhaps tens of thousands of acres. To top it all off, my older daughter lost her writing job to an AI, as I mentioned last week. The girls have told me to turn off the AI summary that Google Search now offers.

A recent New York Times article reports on a Gallup survey that found Gen Z's attitude toward AI souring, and for reasons that go well beyond the sorts of environmental concerns that initially triggered my daughters. 

"Many respondents did acknowledge that A.I. might make them more efficient in school and the workplace," the article said. "But they were concerned about how the technology would affect their creativity and critical thinking skills.

"Young adults in the work force were especially skeptical. Close to half of those surveyed said the risks of artificial intelligence outweighed its potential benefits in the workplace, an 11-point jump from the previous year. Only 15 percent said they saw A.I. as a net benefit."

The Times also reported on a viral video (that my daughters had already made sure I saw) of a woman giving a commencement speech in which she declared that "the rise of artificial intelligence is the next Industrial Revolution" — only to be roundly booed by the students. 

“'What happened?' [she] stammered, looking over her shoulder, as if searching for an escape hatch," the Times reported.

She continued: 

"'Only a few years ago, A.I. was not a factor in our lives.

"The crowd erupted in cheers.

“'And now, A.I. capabilities are in the palm of our hands.' Boooooooooo.

"One might call it a 'read the room' moment."

Eric Schmidt, former CEO of Google, got booed even harder when talking about AI in his commencement address at the University of Arizona on Friday.

I'm not saying dissatisfaction among younger generations will stop the adoption of generative AI, any more than concerns by earlier generations could stop the internet or the smartphone. I'm also not saying Millennials and Gen Z are Luddites; they're extremely sophisticated about technology. 

What I'm saying is that younger generations seem to be taking a warier approach than those of us of a certain age, who've not only been through a few technology revolutions and have accepted their inevitability but whose views are perhaps softened by what all the AI investments are doing for our retirement accounts. 

And those younger generations get a vote. The discussions among business leaders may be about use cases for AI, about how to implement AI most effectively, about how to demonstrate ROI to shareholders, and so on, but your employees are going to be doing that implementing. If a big chunk of your work force dislikes or distrusts AI, they can provide a lot of silent resistance that may surprise you if you haven't made the effort to understand their concerns and to work with your employees to address them.

Cheers,

Paul

P.S. After writing this commentary last night, I wake up today to find that I'm not the only one thinking about the AI backlash. A New York Times columnist wrote: Why College Grads Are Booing Their Commencement Speakers. The Wall Street Journal led its website with: The American Rebellion Against AI Is Gaining Steam. Their reporting/reasoning differs a bit from mine, but my conclusion remains the same: Proceed with caution. 

P.P.S. It is with great sadness that I note the passing of Stephen Applebaum at age 81. Stephen was one of the earliest and dearest friends of ITL and was generous not just with me but with everyone he met in his decades of work in the insurance industry. I looked back through the 80-some articles Stephen wrote or co-wrote for us over the years to see if I might single out a few, but there are just too many sharp insights. I will point to one, which he wrote a year ago with his business partner, Alan Demers, because it's not only very smart but because Stephen always struck me as an empathetic man: "Re(Defining Empathy in Insurance." 

Here is a link to a brief obituary, to the funeral arrangements and to a way to donate to the Dragonfly Foundation, a favorite of Stephen's that focuses on pediatric cancer care.

May his memory be a blessing.

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