Personal care has long been part of insurance's promise. That's why Allstate has assured customers they're in good hands and why State Farm has claimed to be a good neighbor for so many years. And now "big data" and AI allow insurers to know so very much more about their policyholders, so they can tailor products and services to individual needs and tastes. 

But a TransUnion study found that customers didn't get the memo. 

While 70% of insurers told TransUnion they deliver a personalized experience, only 43% of customers agreed. Among members of Gen Z, which is becoming a more important market for insurers every day, only 32% said they received personalized care.

This should be a teachable moment. 

Part of the lesson from this study is simply about the importance of getting objective information, so you aren't breathing your own exhaust and exaggerating how well you're doing on important initiatives such as personalization. That point seems to have become a theme for me, based on the number of times I've hit it in these commentaries, including in a recent one about a young German soccer fan on a six-week trip with friends to watch World Cup matches. He has gone viral by providing an eye-opening look at American culture that has surprised those of us steeped in that culture. 

But I think there are two other points worth making about personalization, one about playing defense, the other about playing offense. I'll start with defense, because few insurance executives seem to be focused on it.

The TransUnion survey found that 46% of respondents invested in hyperpersonalization as a way to sell more products. That's great. Growth makes the world go 'round. But only 10% invested in personalization as part of adjusting to evolving customer expectations. That's not so great.

For three decades now, companies have had to adjust to being "Amazoned." As customers became accustomed to Amazon's one-click purchasing and other innovations, they began to demand similar simplicity from other companies, even ones in far more complex industries than book selling. The CEO of Deere complained to me in the late 1990s about being held to a standard set by FedEx. He said a customer noted that when he spent $10 with FedEx, it could tell him to within 30 minutes when an envelope would be delivered, but that when he spent $350,000 with Deere, it couldn't tell him to within three months when the equipment would arrive.

The insurance industry has done a lot to make life easier for customers, but Amazon, FedEx and others are a moving target. They keep innovating, so customers keep raising their expectations, including for insurers. They aren't going to feel like they're getting personal treatment if they end a call wondering, "Why did I have to dig up my policy number and member number? Don't they recognize my phone number by now?" Or, "Why do I have to keep checking on the status of my claim? Don't they care enough about me to keep me posted?" Or any of the innumerable other questions that arise when a customer doesn't feel valued as an individual.

The effect of disaffected customers is harder to quantify than the upticks in sales that investment in personalized selling can generate, but it's still clear that unhappy customers are more likely to jump to another broker or carrier. You also undercut the brand if you brag about personalized attention, then treat people like a number. So more defensive spending on personalization — to keep customers from becoming unhappy, as their expectations keep rising — is needed. 

The issues with playing offense are straightforward — but mind-numbingly hard. We all know the issue is about gathering more data, merging it with existing data streams and making the information available to whoever needs it, whenever they need it. But saying you need to break down the barriers between data silos is a lot easier than actually doing it, given the various ways data is defined and managed. And, by the by, how reliable are those external data sources?

Given that the issues are known and that money is being invested, I'll just add one thought, from "Beyond Digital," a 2022 book I helped write. The authors, PwC partners Paul Leinwand and Mahadeva Matt Mani, have a section on what they call "privileged insights" that was perhaps my favorite part of the book. The basic idea is that you construct a virtuous circle with customers. You do something useful for me, which makes me trust you enough to tell you a bit more about myself and my needs, which lets you serve me better, which.... 

The idea doesn't apply as well to insurance as it does to industries where interactions with customers are frequent, but the principle still applies. You warn me that hail is coming and that I'd better get my car under cover or send me a Ting sensor that spots an electrical problem in my wiring before it can cause a fire, and I'm going to become more open with you. You might find yourself creating that virtuous circle that gives you privileged insights about me that competitors can't get, no matter how much third-party data they purchase.

The drive toward personalization makes all the sense in the world, and we've published scores of articles on how to accomplish it — among my recent favorites are Reimagining Insurance Via AI and Personalization and How to Leverage the Personalization Boom. But we've all seen how theory doesn't always translate seamlessly into practice. 

The TransUnion study suggests that we should spend more money and effort on using personalization to treat customers as they want to be treated and, as always, must get outside our echo chambers and see the world as our customers see it.

Cheers,

Paul

A lot of that anxiety comes from so-called "vibe coding." Vibe coding means using AI to build software by merely describing requirements, without worrying much about structure, testing, or long-term maintenance. Vibe coding can feel reckless (because it is). As if "moving fast and breaking things" at human speed wasn't scary enough; now we can break things with lightspeed automation. Use vibe code in production? No way.

AI coding has clear potential for productivity gain, but only if insurers address two obstacles:

• Obstacle 1: AI-generated code needs to be safe enough for production use
• Obstacle 2: AI-generated code must work with existing systems

Without addressing these issues, AI-generated code is limited to standalone prototyping. That does indeed have value, but the real opportunity comes from moving past the two obstacles. 

Over time, we've developed practical ways to make AI-generated code production-ready.

Solving Obstacle 1: Making AI-Generated Code Safe for Production

An LLM can write a research report for me in seconds, but I would never publish the result as is. Code is no different. AI-generated code should be treated like code written by a very junior engineer. It should never go directly into production. Frankly, no code—whether written by a human or AI—should go directly into production!

Here are some best practices for making it safe.

• Engineer Code Review: Experienced engineers need to review AI-generated code, as they would review a junior engineer's code before putting it into production.
• Unit Tests: Unit tests are highly valuable, but it's dangerous to let AI generate these. If the AI misunderstands your requirements and generates a bug, it'll probably misunderstand your requirements for the test too. The AI is likely to write unit tests that actually pass only if the bug is there!
• Integration Tests: Integration tests validate the contracts between components. Most production failures occur here, not inside isolated functions. AI often models these complex interactions too simply: over-mocking dependencies, testing only happy paths, and ignoring real failure modes. Integration tests require judgment about how the systems are intended to collaborate. AI can generate integration tests, but you need to review the tests thoroughly and hand-write any test cases that AI missed.
• End-to-End Tests: I have some good news: here is a place where AI works great! Use AI to specify tests for overall user-level workflows, which will only pass if the entire system is working as expected.
• Static and Dynamic Code Analysis: All the tools that review engineers' code quality can also review AI-generated code. There are many popular tools such as SonarQube, CodeQL, and OWASP ZAP. Choose some good ones and use them.
• Code-Generation Tool Selection: The best AI tool to use is a matter of opinion and will likely change faster than your browser can refresh this page! At the moment, popular code-generation tools include Codex from ChatGPT, Claude Code from Anthropic, and Cursor. Keep watch on the latest developments and be prepared to change when something better comes. Most importantly, only use AI tools approved by your security team.
• Security Team Collaboration: Every insurer has a security team. All developers should work closely with their security teams to keep up with AI's rapidly evolving capabilities and vulnerabilities.

Solving Obstacle 2: Integrating AI-Generated Code with Other Systems

We've now covered best practices for high-velocity, production-ready AI-generated code. That's great if it's all you need, but in the enterprise, code doesn't operate in isolation. Most insurance software (certainly most insurance core software) wasn't engineered with AI in mind, and that can drastically reduce the benefits of AI for the enterprise.

Here are the things that make software compatible with AI-generated code. They are a must-have list when selecting enterprise software vendors.

• Look For Modular Design

  Remember when I said AI-generated code should be treated like code written by a very junior engineer? If you give a junior engineer the keys to your whole code base, you can expect an intractable amount of code reviewing before you can ship it. They need guardrails, and so does AI.

  This is why modularity (with well-defined contracts!) is highly important. If the architecture is divided into well-defined plugins, configurations, and integrations, and all the connection points use open standards that are well documented, you can give AI these small components and reasonably review and test each one.

• Look For Open Languages and Formats

  Code-generating AIs are trained on all mainstream programming languages and file formats. They all know Java, Python, and JavaScript. They also know JSON, CSV, and RESTful APIs. Unfortunately for insurers, a lot of insurance core platforms have invented proprietary languages and file formats, which no LLMs are trained on. Insurers have great difficulty trying to use AI-generated code around these systems.

• Look For Documentation

  Whether it be APIs, configuration syntax, or system architectures, engineers hate it when they have to ask vendors for information that should be provided in documentation. Whereas humans have the privilege of calling support or emailing other engineers, AI gets stuck.

  Insurers today need to look at their vendors' documentation with a very critical eye. In the past, poor documentation was acceptable when supplemented with weeks of training and continuing access to experts. This model is annoying with human developers, but it totally breaks with AI-generated code.

• Look For Data-Fluent Systems

  From report generation to business intelligence to data lake integrations, many use cases for AI-generated code deal with data. Your AI-generated software will be no better with data than the enterprise software it relies on.

  • Data fluency for an enterprise software means:
  • Strong APIs for accessing individual records
  • Included data lake for mass queries
  • Webhooks and delta file exports for keeping external systems updated in real time
  • High-speed server responses for all data retrieval operations
  • High uptime so data is always available
  • Real-time data consistency across the system

  If your enterprise platform doesn't have strong APIs, then your AI will struggle to write code to interact with it. If it can't support mass queries, then your AI can't generate reports for you. If the system is slow, the code your AI generates will be slow. If the system has frequent downtime…you get the idea.

  If the flow of data around your enterprise is too complicated and asynchronous that your own engineers struggle to add new capabilities, then your AI-generated code will struggle too.

• Look For MCP Servers

  Model Context Protocol (MCP) is currently the most popular standard for connecting AI with other software platforms. A modern enterprise software platform has user interfaces for human interaction, APIs for external software interaction, and MCP servers for external AI interaction.

  This is admittedly off-topic when evaluating enterprise software's ability to integrate well with AI-generated code, but any enterprise software must be considered AI-compatible. With an MCP server, the latest LLMs require no code at all to connect with enterprise platforms. This is why MCP is supported by such enterprise software giants as Salesforce, Snowflake, Atlassian, and HubSpot, among others.

Conclusion

AI-assisted development is here, and it's already too powerful for insurers to ignore. Like any powerful tool, it can be immensely valuable or immensely dangerous. Insurers today can safely realize 75% reductions in their development costs and a 2x increase in their IT velocity if they use best practices and work with AI-compatible platforms.

As AI continues its rapid pace of development, smart platform decisions today will amplify into massive future advantages.

If you've ever wondered who pays for those highway billboards promising life-changing payouts for your injury lawsuit, the answer is: We all do.

Those billboards are largely funded by plaintiff firms manipulating the legal system to prioritize profit over justice. And based on the return on investment, there's no reason to believe we won't continue to pay for those garish signs through increasing costs for everyday products and services.

Businesses are deemed by the plaintiffs' bar to have deep pockets of wealth and are able to afford to settle multimillion-dollar and multibillion-dollar lawsuits. However, those costs are passed on to consumers in the form of rising prices for goods and services. The American Tort Reform Association found that lawsuit abuse costs every American $1,424 annually—nearly $6,000 per year for a family of four.

Legal system abuse is not new. Terms like "social inflation" and "nuclear verdict" have been around for years, and both continue to play a role in the broader realm of legal system abuse.

In this article, we will take a deeper dive into what constitutes legal system abuse and explore the rise in nuclear verdicts and the effect of third-party litigation funding in civil suits. We'll explain how policymakers and the insurance industry are working to curb the abuse and, finally, what businesses can do to protect themselves and effect change.

The role of nuclear verdicts

Legal system abuse is defined as the misuse of courts and legal procedures to gain a strategic, financial, or tactical advantage. It has become a growing problem, driving up costs, delaying justice, and eroding public trust in the civil justice system.

One of the defining characteristics of legal system abuse is the rise in nuclear verdicts, which are defined as jury verdicts exceeding $10 million.

Nuclear verdicts often include punitive damages that far exceed a plaintiff's actual economic losses. In addition to the financial effect on defendants, they can raise settlement expectations and contribute to rising insurance premiums.

Nuclear verdicts can also have a significant effect on the public's perception of fair and proportional damages in civil litigation. These awards go beyond what most legal experts consider rational compensation for the harm suffered. They are driven by emotional appeals, aggressive litigation tactics and expanding theories of liability. Here are two examples of nuclear verdicts:

• Juries have issued massive punitive awards against a multinational pharmaceutical, biotechnology and medical technology company in cases that allege its talc-based products caused cancer. Some verdicts have exceeded $1 billion, and earlier cases produced punitive damages surpassing $4 billion before reductions on appeal.
• In 2019, an auto-liability case in Nassau County, N.Y., resulted in a jury award of $100 million to the victim's parents for pain and suffering and $900 million in punitive damages against the defendant, a trucking company.

The number of corporate nuclear verdicts rose to 135 in 2024, a 52% increase over 2023 numbers, according to a report, "Corporate Verdicts Go Thermonuclear 2025 Edition," by research firm Marathon Strategies. The total sum of these verdicts reached $31.3 billion, a 116% increase over 2023. "Thermonuclear verdicts" of $100 million or more increased to 49 in 2024, with five of those cases resulting in verdicts greater than $1 billion. A decade ago, these verdicts would have been considered extreme outliers.

Tactics of the plaintiffs' bar

Legal system abuse can occur when attorneys exploit procedural rules, court structures or litigation mechanisms to gain the upper hand in civil litigation.

Common tactics of legal system abuse demonstrate a shift from using the courts as a forum for justice to using them as leverage for financial gain. They include:

• Frivolous claims and lawsuits: Frivolous litigation is one of the most recognizable forms of legal system abuse. These cases burden courts, increase defense costs and pressure defendants—especially small businesses—to settle rather than endure prolonged litigation.
• Abuse of class-action procedures: Class actions are intended to efficiently resolve widespread harm, but they can be manipulated. Some filings prioritize attorney fees over meaningful relief for plaintiffs or rely on expansive class definitions to inflate potential damages. This can coerce defendants into large settlements regardless of the underlying merits.
• Predatory litigation funding: Third-party litigation funding has expanded rapidly, allowing outside investors to finance lawsuits in exchange for a share of the recovery. While sometimes beneficial, it can also encourage speculative or unnecessarily prolonged litigation.
• Manipulation of courtroom practices, discovery, and evidence: Discovery abuse—such as excessive document requests, refusal to produce information or strategic withholding—can dramatically increase litigation costs. Parties may use discovery not to uncover facts but to pressure opponents into settlement by making the process prohibitively expensive. Jury anchoring is another tactic allowed in some jurisdictions where an attorney introduces a higher numerical value to jurors to rely, or "anchor," on that figure as a reference point when assessing potential damages in a lawsuit. Numerous studies have shown the strong effect this practice has on jury award decisions.

Of these tactics, the recent emergence of third-party litigation funding potentially has the most effect. It has become one of the most influential—and controversial—forces shaping civil litigation. Its rapid growth raises concerns about transparency, fairness and escalating legal costs.

Litigation funding

Third-party litigation funding (TPLF) is the practice in which outside investors finance a lawsuit in exchange for a share of any settlement or judgment. These funders generally have no direct claim in the underlying dispute but use litigation as an investment vehicle. Analysts increasingly identify TPLF as a driver of social inflation, contributing to rising claims costs and larger verdicts because funders profit only when payouts increase.

TPLF operates through several models. In single-case funding, a financier backs one lawsuit, typically one with high potential damages. Portfolio funding involves financing multiple cases at once, spreading risk across a broader set of claims. The key parties include the funder, who supplies capital; the plaintiff, who receives financial support; and the attorneys, who may coordinate with funders on litigation strategy.

While proponents argue that TPLF expands access to justice, critics warn that it can provide incentives for frivolous or overly aggressive litigation. Because funders profit only from large recoveries, they may encourage plaintiffs and attorneys to pursue riskier strategies, prolong litigation or reject reasonable settlements. Reports also highlight that the industry operates with minimal transparency, often without disclosure to courts or opposing parties. This secrecy raises ethical concerns about who is influencing litigation decisions and whether funders exert control over case strategy.

Tort reform

Legislative and policy responses to legal system abuse have grown significantly in recent years, with states adopting new tort reform measures, transparency rules and procedural changes aimed at curbing excessive litigation and rising verdicts. These reforms, however, face political resistance and uneven implementation across jurisdictions.

Efforts to address legal system abuse often begin with limits on punitive damages, which several states have enacted to curb unpredictable and disproportionate jury awards.

A wave of state-level tort reform has also emerged. Georgia, for example, enacted sweeping reforms in 2025 through Senate Bills 68 and 69, introducing new procedural rules, damages limitations and updates to trial practices. Similar initiatives in other states aim to streamline litigation, reduce forum shopping (the strategic practice where a plaintiff chooses to file a lawsuit in a specific court or jurisdiction that is most likely to provide a favorable outcome) and promote fairness in civil proceedings.

Another major development is the push for transparency in third-party litigation funding, requiring greater disclosure of funding providers and financial interests in lawsuits. Between 2023 and 2025, eight state legislatures, including Georgia and Louisiana, have made the contents of TPLF contracts subject to automatic discovery or upon request.

These reforms face significant opposition from trial lawyers and advocacy groups, who argue that caps on damages and procedural restrictions limit access to justice for injured individuals.

Potential solutions to the problem of legal system abuse include federal legislation to create uniform standards for litigation funding disclosure, punitive damages and class-action procedures. Congress is currently examining legislation that would introduce transparency when TPLF agreements are present in a suit before a federal court or bar foreign actors from participating in federal TPLF arrangements. Nationwide rules could reduce inconsistencies and prevent forum shopping.

The role of insurance

The insurance industry has become one of the most active voices in confronting legal system abuse, responding with risk-management strategies, advocacy efforts and collaborative initiatives aimed at reducing inflated claims costs and restoring balance to the civil justice system.

Insurers have adopted risk-management practices to stem the rising costs associated with legal system abuse. These practices include enhanced claims monitoring, early case assessment and the use of analytics to identify patterns of excessive litigation. Industry research shows that legal system abuse has significantly inflated claims payouts across multiple lines of insurance, with commercial auto insurers alone paying $20 billion more than expected between 2010 and 2019 due to litigation.

Many insurers advocate for tort reform, supporting legislative efforts to curb abusive litigation tactics and reduce the frequency and severity of nuclear verdicts. Insurance industry groups such as the American Property Casualty Insurance Association and the Insurance Information Institute have led the charge.

What can you do?

Putting an end to legal system abuse will require coordinated efforts from individuals, businesses, policymakers and the broader public. With tort costs reaching hundreds of billions annually and driving up prices for consumers, every stakeholder has a role to play in promoting fairness and transparency. And consumers and businesses have the most influential voices.

Companies can reduce exposure to abusive litigation by strengthening compliance programs, improving documentation and using early dispute-resolution strategies. Businesses can also actively support state and national efforts to curb abusive tactics.

Working together, insurers, consumers, businesses and policymakers can help restore balance to the civil justice system, reduce unnecessary costs and ensure that the legal process works in the service of justice instead of profit.

A senior leader has been in their role for 15 years. They know how the steering committee reads bad news. They know that if they lead with the complication, the conversation shifts from "how do we solve this?" to "should we proceed at all?," and this initiative is too important, too close, to lose to a room that hasn't seen what they've seen.

So, they frame the complication as a minor implementation consideration. It's real. It's manageable. They're protecting the initiative. They're advocating for the people this change is meant to serve.

Six months later, that minor implementation consideration is the reason three functions can't use the system.

The leader didn't lie. They translated. And the translation left something out.

The Skill That Made You Good at This Job

Every effective leader in a complex organization learns, early and through experience, that the same initiative requires different conversations with different stakeholders. The CFO needs a business case. The front-line manager needs to know what changes about how they do their job on Monday. The steering committee needs enough confidence to approve. The actuary needs the model assumptions. The board needs strategic context, not operational detail. And each stakeholder comes to the table with different expertise, different risk tolerance, and different decision authority.

This is not manipulation. It is sophisticated communication. I call it calibrated disclosure: the practiced judgment of what to say, to whom, in what form, at what moment. In insurance and financial services, calibrated disclosure is more than a skill. It is a professional requirement.

Part of an initiative leader's role in this sector is building the confidence required to move change forward in an environment where every proposed modification will be examined under a microscope by internal functions, by regulators, and in some cases by rating agencies.

And then there is the problem.

The same discipline that makes the briefing cleaner makes any omission invisible. The skill that builds confidence can, without anyone deciding it should, begin to protect the initiative rather than serve it. This shift does not announce itself. It operates below the level of ethical self-examination. You don't decide to calibrate away a risk. You calibrate because you've always calibrated. Because it works.

Where the Coalition Compromise Lives

This year, I developed a framework for change leaders on the ethical traps that become available under pressure. The one most relevant to this industry is what I call the "coalition compromise": the trap that activates when building stakeholder support begins to require managing information differently across audiences, not just for clarity but for outcomes.

Three structural features of regulated industries make a coalition compromise more available, more normalized, and harder to catch.

Disclosure is a professional discipline. In insurance, calibration is not optional. It is governed. Actuarial standards specify how risk models are presented to different audiences. Regulatory filings require precise framing for specific agencies. Leaders who have navigated all of this for years develop a deep, practiced confidence in their own judgment about what belongs in which conversation. There is an additional psychological dimension worth naming: When the calibration feels like it is following the rules, when a leader genuinely believes they are operating within the standards set by compliance and the regulators, the ethical self-check rarely fires. The leader is doing the right thing. They have the documentation. They have the process. And they are wrong in a way the process cannot catch.

Stakeholder complexity is structurally high. A single AI initiative at a large carrier may require navigating underwriting, claims, actuarial, compliance, legal, IT, distribution, and the board, each with different languages, expertise, risk tolerances, and decision criteria. In markets with distributed regulatory oversight, the complexity multiplies: A product requiring filing across dozens of jurisdictions generates separate stakeholder conversations at each level. The temptation to frame the initiative differently for each audience is not laziness. In the short term, it works.

The initiative window is often compressed. Regulatory timelines, competitive pressure, and board cycles create genuine urgency. Urgency compresses the ethical review. The question — "Am I framing this differently because it's clearer, or because it speeds movement?" — doesn't get asked when the steering committee meets in four days and the filing deadline is the following week. The coalition compromise doesn't require a decision to compromise. It requires only the absence of time to examine what you're doing.

The Line That Moves

The coalition compromise doesn't arrive as a single choice. It moves through stages. What makes it difficult to catch in regulated environments is that the early stages are explicitly trained.

Stage 1 — Translation. You simplify a complex finding for an audience that doesn't need the technical detail. A valid actuarial concern becomes an implementation consideration.

Stage 2 — Emphasis. You lead with the upside and position the complication toward the end of the document. The information is present. The weighting is a choice.

Stage 3 — Selective inclusion. The complication is in the record, available to anyone who asks. Steering committees are time-compressed environments where significant decisions get made amid competing "day job" responsibilities. Key members may not be present. Questions don't always get asked. The meeting moves on.

Stage 4 — Omission. The finding doesn't appear in the materials for this audience. It may have been raised briefly in a prior meeting where it wasn't picked up. In the room where the decision is made, it isn't there.

What makes this particularly difficult in regulated environments: Stages 1 and 2 are competencies organizations develop deliberately. The line between Stage 2 and Stage 3 is invisible in the moment. Stage 4 is reached without a single decision that felt like a decision.

What No Compliance Framework Catches

Compliance frameworks, including the three-lines-of-defense model that governs risk oversight across most large carriers, are built for decisions: for specific acts that can be governed, audited, and reviewed against a documented standard. The coalition compromise operates below the level of decision. It lives in framing choices, in the weighting of a slide, in which objections are raised before the meeting, and which are handled in the hallway afterward. None of those frameworks are designed to catch it.

There is a second dimension that rarely gets discussed: organizational contagion. When a transformation leader calibrates information selectively, they do not do it in isolation. The briefing gets built with a team. Materials get shaped by people who observe what their leader includes and what they don't, what gets said in the room and what gets managed around the edges. Those observations form behavioral norms: what is acceptable here, how we handle complications when the stakes are high.

The coalition compromise, left unexamined, doesn't stay in the steering committee room. It permeates. Product requirements to IT get shaped by the same logic. Customer-facing materials, sales training, and claims handling documentation are each an opportunity for the same framing choices to compound. What started as one leader's judgment call becomes the organization's operating standard. 

I have seen this dynamic inside a large financial services organization where siloed functions, low inter-unit trust, and a prevailing culture of risk aversion made it easy for misunderstandings to surface at the point of delivery to the customer, and for finger-pointing to follow when issues finally became visible.

What would catch the problem isn't a framework. It's a question built as a habit before every steering committee, every board presentation, every stakeholder briefing:

What does this audience not know that they would want to know if they did?

That question is not a compliance requirement. It is a capability. And most organizations have never built it deliberately.

Building the Discipline Before You Need It

The coalition compromise is most likely to emerge precisely when the initiative is most important, the pressure is highest, and the leader's conviction is strongest. Those are not the conditions under which most people do their best ethical reasoning. Which is why the work must happen before you're in that room.

Three disciplines, developed deliberately:

Name your translation standard before the briefing. Before preparing materials for a specific audience, define in writing what "translation" means versus what "omission" means for this conversation. What is the threshold — whether the finding, the risk, the complication — below which simplification is legitimate and above which it must be present regardless of how it lands? The act of writing forces the distinction. Calibrated disclosure that has been examined is a professional skill. Calibrated disclosure that hasn't is a liability waiting to surface.

I have worked with leadership teams who approach this not as a compliance exercise but as a personal commitment, drafting explicit principles about what they will and will not do when the pressure is high, before the pressure arrives. The ones who find it most useful are not the ones who already have strong ethical instincts. They are the ones who understand that under sufficient pressure, strong instincts alone are not enough. You're not writing rules for other people. You're building your own code.

Apply the adversarial read before sending. Before any significant briefing goes out, ask: if someone who actively opposed this initiative reviewed this document, what would they say is missing? If the answer is something that would change the audience's decision if they knew it, it belongs in the document. This is not about playing defense. It is about the difference between advocacy and selective presentation.

Build one truth-telling relationship. The coalition compromise flourishes in the space between what a leader knows and what the room is allowed to see. One relationship, built specifically for this purpose, changes that structure: a peer, a trusted direct report, a board member whose explicit role is to tell you what you don't want to hear before you've told the room something incomplete. This relationship is worth more than any compliance protocol, because it operates at the level where the coalition compromise actually lives — in the judgment calls made before the materials are finished.

The Capability the Industry Has — and the One It Needs

Insurance has built deep institutional capability around disclosure. That capability is a genuine competitive advantage, and the precise environment in which the Coalition Compromise becomes structurally available, culturally normalized, and professionally indistinguishable from good judgment.

Until the moment it isn't.

The leaders who navigate this well over time are not more ethical. They are more deliberate. They have built the habit of asking, before the brief is finalized, before the room fills up, what this audience doesn't know that it would want to know.

If the answer is "nothing," then proceed.

If the answer takes more than a moment to arrive, that pause is worth honoring. It is the one place a framework can't go that a leader can.

For health plan officers and financial leaders, pharmacy benefits have crossed a threshold. U.S. prescription drug spending surged to $915 billion in 2025 and is projected to exceed $1 trillion in 2026 — one of the fastest growth rates in two decades. Pharmacy benefits is no longer a cost category — it has become a material driver of financial volatility.

The forces driving this exposure are converging. Specialty medications now account for roughly half of total drug spending, with many therapies exceeding $100,000 annually per patient, and some cell and gene therapies reaching into the millions. Overall healthcare spending is projected to grow 10% or more, with pharmacy costs among the primary drivers. At the same time, the passage of the Consolidated Appropriations Act of 2026 (CAA 2026) has expanded transparency, reporting and fiduciary requirements for health plans — adding regulatory and operational complexity on top of financial exposure.

In one-on-one conversations, I am hearing an acute reaction to mounting pressure being felt across the payer ecosystem. Plan sponsors are increasingly focused on how to maintain cost control while also meeting expanding fiduciary and transparency obligations. Health plans and TPAs are actively exploring more proactive approaches to medical and specialty pharmacy management as usage and cost volatility increase. At the same time, PBMs are working to redefine their role as traditional operating models face greater scrutiny and disruption.

The legacy PBM model was not designed to manage challenges at this scale. It was designed to aggregate volume. The result is a system that is increasingly difficult to measure, interpret, and control, precisely when greater visibility, accountability, and agility are most needed.

The Legacy PBM Model Concentrates Risk in the Wrong Places

The legacy PBM structure systematically limits visibility. Opaque pricing methodologies, hidden rebate structures, and aggregate-only reporting make it difficult for health plans to determine true net cost, validate savings claims, or identify the actual drivers of spending early enough to intervene effectively. Addressing this requires a more transparent operating framework built around integrated data visibility, component-level financial reporting, and greater accountability across pharmacy benefit stakeholders.

Flexibility is equally constrained. Bundled PBM arrangements limit a health plan's ability to adjust benefit design, introduce new strategies, or respond quickly when specialty usage shifts unexpectedly. Fragmented vendors and siloed data eliminate the possibility of a single, reliable source of truth, which is essential for effective financial and operational oversight. More adaptable, modular benefit structures allow plans to introduce targeted specialty management strategies, optimize site-of-care programs, and implement new financial controls without overhauling the entire pharmacy benefit model.

Market concentration compounds the challenge further. The top three PBMs control roughly 80% of U.S. prescription claims, limiting competitive alternatives and slowing adoption of models better suited to the current environment. The practical consequence is a widening gap between what health plans are accountable for and what their current structure actually allows them to manage.

To close these gaps, stakeholders are looking for new models that provide access to broader contracting scale, specialty networks, and integrated management capabilities to maintain greater control over benefit strategy, vendor selection, analytics, and member experience.

A Marketplace Model Approach to Pharmacy Benefits Strategy

Managing pharmacy benefit risk requires the same discipline applied to any significant financial exposure: visibility into the drivers, flexibility to respond, and the ability to make decisions based on empirical data rather than aggregated summaries.

A marketplace-based approach to pharmacy benefits addresses each of these directly. Rather than relying on a single bundled arrangement, health plans can evaluate pharmacy benefit components individually — assessing performance, replacing underperforming elements and aligning each component with specific organizational priorities and financial objectives. The result transforms a monolithic, difficult-to-audit arrangement into a set of discrete, measurable risk factors that can be actively managed and adjusted as market conditions evolve.

The implications are real:

Concentration risk is reduced. Dependence on a single PBM relationship — with its inherent opacity and limited leverage — is replaced by a diversified model in which no single vendor controls the full picture of pharmacy economics. A marketplace model broadens choice and competition, enabling organizations to manage risk based on the profile of their covered lives, not that of the masses.

Regulatory and fiduciary exposure are addressed directly. A marketplace model built on transparent, component-level reporting supports the disclosure, audit trail, and accountability requirements that CAA 2026 and ERISA-aligned fiduciary standards now demand. Plans can demonstrate not just what they spent, but why, and what oversight was applied. Furthermore, real-time access to integrated data enhances the power of predictive modeling and the potential for proactive risk mitigation.

Specialty drug volatility becomes more manageable. A small number of high-cost therapies now account for a disproportionate and increasingly unpredictable share of total spending. A marketplace model makes it possible to coordinate key cost savings components — site-of-care optimization, stronger clinical oversight, and more targeted financial management strategies — within a unified framework designed to intervene where costs are most concentrated. For example, plans may redirect eligible specialty infusions from high-cost hospital outpatient settings to lower-cost ambulatory infusion centers, implement enhanced monitoring protocols for emerging high-cost therapies, or better align pharmacy and medical benefit management strategies to more effectively identify, track and control specialty spending across the continuum of care.

Operational risk from rigidity is eliminated. Currently, when the prescription drug market changes — new therapies enter the market, GLP-1 usage accelerates, or a new cell and gene therapy creates an unanticipated claim event — payers can be slow to react, often having to renegotiate a bundled contract or wait for a vendor partner to build a new capability. A marketplace model enables organizations to act in real time, rather than reactively, as these dynamics unfold. This flexibility enables organizations to rapidly deploy targeted utilization management programs, introduce condition-specific clinical management strategies, expand specialty network access, or adjust financial controls in response to changing market conditions.

From Reactive to Proactive: Data as a Strategic Management Tool

Effective pharmacy benefit management is anticipatory, not retrospective. The legacy pharmacy benefits model is built around periodic, aggregated reporting — a structure designed for billing, not strategic decision-making. A marketplace approach changes the information architecture fundamentally.

With integrated, component-level data and real-time reporting, health plans gain the ability to identify cost drivers earlier, compare vendor performance against benchmarks, and make empirically grounded decisions rather than relying on assumptions embedded in aggregate summaries. This shifts pharmacy benefit management from a reactive posture — responding to spending that has already occurred — to a proactive one, where risk is identified and addressed before it becomes a financial event. Unified analytics also help align pharmacy and medical benefit insights, improving visibility into total specialty care costs and supporting more coordinated enterprise-wide decision-making.

This capability is especially critical as the pace of change in the drug market accelerates. Traditional tools — formularies, rebates, utilization controls — remain important, but they are no longer sufficient as standalone management mechanisms. Precise, real-time visibility into cost drivers and direct operational control over how those costs are managed have become baseline requirements for any health plan operating in today's pharmacy environment.

A Sustainable Framework Requires Structural Change

The structure of pharmacy benefits will continue to evolve as drug acquisition costs remain elevated, new high-cost therapies enter the market, and regulatory expectations increase. Health plans that continue to rely on the legacy model are operating within a framework that often limits visibility and flexibility at the exact moment both are most needed.

Organizations with greater expertise, visibility, and flexibility will be better positioned to adapt to ongoing market disruption, manage specialty cost pressures more effectively, and meet rising stakeholder expectations. Those that fail to evolve risk operating within a pharmacy benefit framework that is increasingly misaligned with the financial, regulatory, and operational realities of today's healthcare market.

An adaptable, marketplace-driven pharmacy benefit model enables health plans and payers to adapt to these realities without major structural disruption or service interruption. It replaces opacity with transparency, rigidity with modularity, and assumption-based management with data-driven oversight.

Executing this kind of transition requires more than a vendor swap — it requires a new paradigm and a strategic partner with the clinical, financial, and operational depth to navigate this environment. Collaborating with specialized pharmacy benefit partners — those with proven capabilities across specialty management, analytics, rebate optimization, and benefit design — can help plans build a more responsive and sustainable pharmacy benefit strategy.

When people think about a healthy culture in most insurance organizations, they usually say the same things: People follow the process, leaders avoid trouble, and long-tenured employees are promoted. Leaders track metrics like compliance, regrettable attrition and engagement scores to determine success.

But this definition is incomplete because it hides what's beneath the surface: behavioral norms fragmenting across business units and new hires leaving within 18 months because the operating environment is significantly different from what was described during recruitment. The people who stay do so because of inertia, not because the work feels meaningful.

Behavior design matters now more than ever. Cultures built on adherence and compliance are based on a set of rules that are assumed to stay static. But when disruptions occur, the organization is not prepared, and confusion and bad decisions are made.

Culture Redefined as Adherence

In high-compliance industries, culture and process compliance tend to conflate. When regulatory requirements, audit cycles, and procedural accuracy define how work gets evaluated, organizations start measuring culture through proxies like tenure, compliance rates, and survey scores. These operational measures are important, but they describe stability rather than resiliency.

The result is a culture that performs well on paper and fragments in times of instability. This distinction is becoming costly in ways that are hard to ignore. Gallup's 2026 State of the Global Workplace report found that only 20% of employees worldwide are engaged at work (the lowest scores ever), with disengagement costing the global economy an estimated $8.9 trillion annually in lost productivity.

AI Is Making the Problem Visible

When AI shifts what the work actually requires, carriers that have spent years measuring culture through stability metrics cannot answer basic questions: what does good behavior look like to support and sustain change in the organization and the industry overall? How do we sustain and manage our teams through all the changes? How do we ensure confusion isn't jeopardizing team performance?

We need to start moving away from dependence on easy-to-measure metrics, most of which are lagging, and tell us how people worked in the past, during a moment in time. What companies need to start thinking about is how to ensure their teams can learn and adapt to new technologies, new processes, and new requirements. How will their employees learn, develop, and grow in the new AI world? Answering these questions requires the much harder work of defining culture at the behavioral level, specifically enough that people can act on it even when circumstances shift.

Five Steps to Building a Culture That Is Specific Enough to Work

Behaviors don't just change by mandate. They require deep thought into understanding what outcomes the company is looking to create, and then designing the behaviors required to achieve those outcomes. Getting clear and actionable provides transparency and, finally, building recognition and accountability systems around those specific behaviors guides future alignment. Here is how to do it:

Step 1: Get Clear on the Problem and the Desired Outcome

Before naming behaviors, leaders need to name the gap. Is the issue that collaboration is breaking down at the team level? Does accountability diffuse when decisions cross business units? Are new hires not connecting to the real operating and cultural norms fast enough? The behavior work only holds if it is designed to close a specific gap that is clearly articulated. This is where value statements can't hold up.

Step 2: Get Clear on the Dos and Don'ts

Get team members into a room and via digital whiteboard (like Miro or FigJam) or sticky notes, ask them to independently write down:

• Green Behaviors (Dos): Actions you want to see more of (e.g., "turn on cameras during internal video calls," "respond to messages within 24 hours").
• Red Behaviors (Don'ts): Actions to avoid (e.g., "canceling 1-on-1s without 24 hours notice," "speaking over colleagues in meetings").

Step 3: Write a One-Sentence Definition

Condense the list into clear, actionable statements and avoid buzzwords as much as possible. If the definition requires interpretation, it is not finished yet. Build a simple test: read it to someone two levels below you and ask them what they would do Monday morning. If the answer matches what you had in mind, the definition works.

Step 4: Apply the Street Test

The street test asks: Would someone off the street be able to easily recognize the stated behaviors in play? Behaviors are exhibited and reflected in the day-to-day actions of employees. It should be in the feedback of customers or vendors. Behaviors that fail the street test are aspirational language that do not change anything.

Step 5: Model It and Reward It

Defined behaviors only become cultural norms when leaders model them visibly and when the recognition system tells the stories of the people doing them. In insurance, where performance management tends to be tightly tied to technical output metrics, this requires a deliberate decision to build behavioral recognition into performance conversations, team meetings, and promotion criteria. What gets recognized gets repeated.

The Real Competitive Risk

Culture defined as adherence will hold together as long as the rules stay stable. The insurance industry is entering a period where the rules, the work, the tools, and the talent expectations are all shifting simultaneously. Carriers that have not done the behavioral definition work have a structural gap that a survey will not capture.

The organizations that will retain the best people and navigate transformation most effectively will be the ones where culture is specific enough to guide behavior when the process is no longer clear. That requires doing the unglamorous work of naming what good actually looks like, one behavior at a time.

Engagement scores will tell you how people feel today. Behavioral culture design determines how they act tomorrow.

In the past two years, I have sat in on more board-level reviews of AI deployment proposals than in the previous 10 combined. The dollar amounts have gone up, as have the strategic stakes. What has not gone up enough is the quality of the questions board members ask before approving these proposals.

Most board reviews follow a predictable script. Management presents a use case, a vendor, a timeline, and an ROI model. Board members ask about the vendor's track record, the integration risk, and the projected savings. The proposal gets approved or sent back for revisions on the basis of those answers.

Those questions are not wrong. They are insufficient.

Across 20 years of building insurance software and watching deployments succeed or fail, I've seen three other questions matter more for the long-term success of an AI decision-making deployment. Most board members are not asking them yet. The ones who do are the ones whose organizations end up with AI in production rather than stuck in pilot.

Question 1: What is the data foundation we are building this on?

Every AI deployment runs on data the organization already has. The proposal will say so. What the proposal usually does not say is whether that data is in the shape the model needs.

In my experience working with carriers, the data foundation question surfaces three different problems depending on the organization. Sometimes the data exists but is fragmented across systems of record that cannot be reconciled to a single customer or transaction. Sometimes the data is reconciled but tagged inconsistently across business units. Sometimes the data is consistent but does not include the variables the model actually needs to make a defensible decision.

Each of these is a deployment-stopping problem. None of them is visible from the vendor demo. All of them surface in the first 60 days of integration when the model starts producing outputs that the business cannot use.

When my team published research from 20 US insurance leaders earlier this month, data quality and fragmented systems came in as the single biggest blocker to AI deployment. ROI uncertainty came second. Internal IT capacity came third. The order surprised some readers. It did not surprise me.

Board members who ask "what is our data foundation" are not slowing the project. They are giving it a chance to succeed.

Question 2: How do we document an adverse decision?

Most AI deployments at the decision-making layer touch outcomes that affect customers. In insurance, that means claim denials, payout reductions, and coverage decisions. In banking, that means lending denials and credit limit reductions.

For each of those, the regulatory environment in 2026 expects you to be able to document why the AI system reached the decision it did, what input features it considered, what model version was active at the time, and what human review was involved. The expectation is not theoretical. The NAIC Model Bulletin on AI in insurance, adopted in 24 U.S. states as of early 2026, makes this explicit. The EU AI Act makes this explicit. Federal regulators in banking and healthcare are moving the same direction.

In recent research, only three of 14 carriers with AI in production were confident they could produce a complete decision audit trail within five business days if a state regulator asked. The other 11 were less confident. Some far less.

The board question is not whether the AI deployment will be regulated. It will be. The board question is whether the deployment as proposed has the documentation discipline built in, or whether it will require an expensive retrofit when a regulator first asks.

Question 3: Does the AI touch the actual decision?

This is the question most likely to change the structure of the deployment itself.

In my experience, AI deployments succeed faster and create more measurable value when they sit on the edges of a decision-making pipeline rather than at the decision itself. The reasons are operational and regulatory. Capabilities that handle structured but tedious work, such as document interpretation, intake triage, and fraud signal generation, scale faster because the data they consume is more structured, the output they produce is easier to measure, and the regulatory scrutiny they attract is lower.

Capabilities that touch the decision itself, such as reserve recommendation in claims, credit decisioning in lending, and care recommendation in healthcare, scale more slowly. The data is messier. The measurement is harder. The regulatory exposure is higher.

This does not mean decision-making AI is wrong. It means decision-making AI should generally not be the first deployment. The board approving a first AI deployment that sits at the decision itself, before the organization has built the data foundation and the documentation discipline on lower-stakes capabilities, is approving the most complex deployment under the worst conditions.

In that same research, of the 20 U.S. insurance leaders surveyed, just three described their organizations as running AI-assisted decision making. Seventeen still relied on humans for every meaningful decision. The 13 with any AI in production had deployed it on the edges of the pipeline, not at the decision.

A different conversation

When boards ask these three questions, the conversation about an AI deployment changes. The vendor selection becomes secondary. The integration timeline becomes secondary. The ROI model becomes secondary, because all three depend on getting the data foundation, the documentation discipline, and the pipeline position right first.

The boards that ask these questions get more AI deployments into production. The boards that do not ,get more pilots that never move beyond pilot.

For years, insurance agents and brokerages have pursued growth using a familiar model: Add producers, expand carrier relationships, and absorb the operational load through incremental hiring or outsourced support. When pressure builds, they introduce technology to accelerate individual tasks.

That model is now breaking under the burden of the way agencies actually operate.

Across the industry, agencies are losing significant capacity to operational drag. Anywhere from 30%-40% of their time per week is lost to manual, repetitive work such as rekeying data or manually executing analyses – time that should be allocated to revenue-generating activity.

At the same time, such core processes as policy checking, endorsements and data entry can consume more than 20 hours per week per employee, much of it non-differentiated work. The result: Growth is constrained not by demand, but by how the agency runs.

Industry has moved to AI — but not far enough

Insurance is no longer lagging in AI adoption, it is leading. AI adoption in insurance is outpacing most industries and accelerating rapidly. According to market research firm Zipdo, more than 61% of insurers have embedded AI into workflows or are actively piloting it. Additional research from global consulting firm Deloitte notes that 76% are using AI in some form across their operations.

The impact on the insurance industry of using AI is clear. When AI is applied correctly, insurance organizations have found the following results:

• 30%+ productivity gains for operations teams using AI-enabled tools
• 40%-50% reduction in manual workload across key agency tasks
• 30% cost savings through automation in brokerage operations 

For fast-moving insurance organizations, quoting is where service quality and operational efficiency either scale together or break apart. By implement AI technology, agencies have attained up to 55% faster quoting and 50% faster renewals, improving client satisfaction.

Despite widespread adoption, only a small minority of insurers have scaled AI across their full operating model with most deployments remaining siloed or tactical.

Structural problem: Fragmentation

The majority of agencies have not redesigned workflows. Instead, they have layered AI onto disconnected agency management systems. They continue to use siloed workflows across new business, renewals and servicing. In addition, agencies maintain inconsistent data structures and use manual handoffs from department to department. 

The outcome is predictable: Automation improves isolated tasks, but not end-to-end throughput. Agencies find that they have key data somewhere in their system, but the data is missing context across a policy’s lifecycle. Producers find they continue to be constrained by service dependencies, and the agency’s leadership finds it lacks any visibility into real-time operations.

This is why many agencies experience a paradox: more technology, more automation, and yet limited improvement in growth capacity.

What leading agencies do differently

The agencies capturing disproportionate value are not only adopting AI. They are restructuring the way their agency operates. They are building operating models where:

• Data flows seamlessly across the policy lifecycle,
• Work moves across functions with shared context, and
• AI is embedded into workflows, not added on top. 

This shift is where performance begins to compound. By using AI-driven information, producers can find cross-sell and renewal insights that can increase revenue by 20%-30%. Agencies have found that error rates in operations can drop by up to 90% with integrated validation and data workflows. Most importantly, customer engagement and service response times improve materially, driving retention and growth.  

At an industry level, the upside is significant. According to the well-known consulting firm Bain & Company, AI-enabled transformation represents about $100 billion in value for the insurance industry, driven by productivity, growth, and improvements in operational efficiency. 

The Cogneesol perspective: Extending the agency system

This is the shift Cogneesol is focused on enabling.

The agency management system remains the system of record, but it was never designed to coordinate the full intricacy of modern agency operations.

Cogneesol provides an intelligent operational layer that:

• Connects data across systems, workflows, and stakeholders,
• Standardizes and coordinates how work moves across new business, renewals, and servicing,
• Embeds AI at the points of highest operational leverage, and
• Creates a unified, real-time view of agency performance.

This is not incremental optimization. It is structural alignment. When agencies move from fragmented operations to a coordinated system many benefits accrue:

• Producers gain capacity without adding headcount.
• Service teams operate with greater consistency and scale.
• Renewal cycles become more predictable and analytics based.
• Leadership gains actionable visibility across the book. 

Most importantly, the agency transitions from absorbing growth to enabling it.

The bottom line?

The industry has already proven that AI works by reducing costs, accelerating workflows and improving productivity. But those gains plateau quickly when applied to a fragmented operating model.

The next phase of competitive advantage in insurance distribution will not come from deploying more AI tools. It will come from changing the way the agency operates so that intelligence can move across the entire business. That is the difference between simply automating tasks and building an agency that continually improves the way it performs. 

AI is a multiplier, but only if the operating model it sits on is designed to scale it.

About the author

Soumojit Ghosh is Head of Technology Services at Cogneesol.

For more thought leadership from the Cogneesol team, please visit our blog at Cogneesol Blog – For an Ecosystem of Digital Transformation

Sponsored by Cogneesol

While oil prices crested at a bit over $110 a barrel In April, some 65% higher than when President Trump began his second term in January 2025, they came nowhere close to economists' forecasts when it became clear that Iran would close the Strait of Hormuz. They expected prices of $150 to $200 a barrel. Some economists feared we might hit $250. 

That oil prices didn't cause a global economic crisis shows that the world has become more resilient in the face of oil shocks. I remember, as a kid, seeing lines around the corner at gas stations after OPEC imposed an oil embargo in 1973, but we came nowhere close to that this time.

The world will now move to become even more resilient, especially to possible events in the Middle East, given that we've seen that supplies can be curtailed just with some mines, speedboats and drones in the world's major oil thoroughfare. The shift toward a global economy no longer dominated by oil considerations will take many years to play out and won't be complete for decades, if ever, but the change will put the world on a new trajectory with profound changes for every industry, including insurance. 

So it's a good idea to start shifting to a new mindset as soon as possible.

My thinking about a post-oil world traces back to 2010, when I was part of a sort of SWAT team at the Department of Energy distributing tens of billions of dollars to drive innovation and help stimulate the economy as part of the Recovery Act following the Great Recession of 2008-9. The leader of the project, Matt Rogers, was the leader of the oil and gas practice at McKinsey, and he had developed a thesis that was radical, especially for the time. While the conversation among economists was about "peak oil" (when global oil supplies would max out and start to diminish), he calculated that the issue was peak demand (when demand, not supply, would crest).

Matt and Stefan Heck, also a senior partner at McKinsey, developed the peak demand idea much more broadly in their 2013 book "Resource Revolution," which I helped them write. So I was intrigued when a columnist in the Washington Post declared yesterday that: "The 53-year-long oil crisis is now over, leaving the world in a better place.... The black, sticky stuff just doesn’t matter as much as it used to."

The soaring output from fracking in the U.S. over the past two decades did a lot to cushion the oil shock this time around. So did impressive flexibility by China, which reduced its imports by some 4 million barrels a day, to less than 8 million, while barely tapping its oil reserves. In coming years, Venezuela may become a major oil exporter again, and the growth of power from renewable sources will keep surging. The Post column notes: "Renewables account for 47 percent of electricity generation across the European Union and 26 percent in the U.S. The share in America is rising fast, with solar last month overtaking coal in power generation."

The columnist says the end of the oil crisis will have three major consequences, and I think he's mostly right:

"First, the Middle East won’t matter so much anymore.... Next, inflation will be subdued. Oil was a crucial enough input in a wide range of products that it helped determine their prices. The U.S. should now expect a decade or more of the cost of living barely changing from one year to the next. Finally, and most significantly, the global economy will be far more stable. Over the last half-century, soaring oil prices accompanied five major recessions or stock market slumps (1973, 1979, 1990, 2008 and 2022).... Chaos was always just around the corner."

I'm not quite as sanguine as the Washington Post writer is, mostly because I've seen too many long-term assurances get derailed. Remember Francis Fukuyama's 1992 book, "The End of History and the Last Man," which assured us that liberal democracy and free market capitalism represented the final stage of human evolution? Look around today and tell me how that worked out.

I can also see plenty of short-term hurdles. The Trump administration is actually paying companies NOT to develop renewable energy, compensating companies for canceling off-shore wind projects. This administration has also recently announced some $700 million to support coal-burning plants, even though coal is on the way out as a source of electricity. In addition, the Trump White House is trying to halt or even reverse the transition to electric vehicles, even though the shift in strategy has caused U.S. car companies to take tens of billions of dollars in write-offs and will help Chinese companies widen their already considerable global lead on the cars of the future.

But I'm still convinced that the half-century-long oil crisis is, in fact, dissipating. 

The geopolitical implications will extend well beyond the Middle East. Other major oil producers, including Russia, Norway, Mexico and, perhaps, Venezuela. will lose huge amounts of revenue and, thus, influence. The world's largest producer, the U.S., won't be immune. Industries, such as chemicals, that rely on cheap energy prices in the U.S. will also lose out. 

Meanwhile, China should gain influence because of its dominance in the rare earth metals that are needed for batteries, in solar panels and in electric vehicles.

The changes should, in fact, reduce global tensions in some areas, though that doesn't mean that peace is at hand. China's increasing influence could, for instance, embolden it to try to retake Taiwan, which could touch off a geopolitical crisis. 

Insurers will have to tack continually as the geopolitical winds shift, and will have to guide clients wisely as they assess their risks. Insurers will likewise have to adapt as industries evolve — probably including shorter, more local supply lines at least for now, in reaction to the Iran war, and to the rise of isolationist politics, including America First. 

Again, there will be lots of twists and turns. Look at how fast Trump reversed U.S. policy in support of electric vehicles. But I think the move toward a post-oil world — at least one far less vulnerable to oil shocks — is accelerating because of the Iran war, and it's never too early to start reshaping our brains to prepare for such a profound change.

Cheers,

Paul

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

For me, the clear breakout star is Freddy. 

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

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

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

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

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

.           

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

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

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

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

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

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

Cheers, 

Paul

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

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

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

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

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

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

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

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

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

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

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