Mergers and acquisitions (M&A) are high-stakes transformations that can redefine a company's structure, strategy, and vendor relationships. Amid the rush to integrate systems and scale operations, insurance brokers often find themselves under scrutiny—and at risk of being replaced.

A recent survey found that nearly one-third (31%) of companies switch insurance brokers post-M&A—a striking figure that underscores the volatility of broker relationships during transitions.

Why Brokers Lose Ground After M&A—and How to Avoid It

Post-deal broker changes aren't just about starting fresh. They often reflect a deeper misalignment between the evolving needs of a newly merged company and the capabilities of their existing broker. Many legacy brokers were a good fit for smaller, regional clients with straightforward coverage needs and personal service models. But after a merger, especially under private equity ownership, companies quickly outgrow that model. They now require enterprise-level support, digital integration, and broader risk expertise. Brokers who can't scale or adapt are often left behind, regardless of how well they served the legacy business.

Several changes brought on by M&A activity commonly trigger a reassessment of broker relationships:

Geographic Expansion Challenges: As the organization grows to operate nationally or globally, new risk requirements emerge, from jurisdiction-specific compliance issues to cross-border liability, international D&O policies, and export-related coverage. Brokers lacking carrier relationships across regions may be quickly replaced by firms with global footprints. The inability to navigate this expanded geography becomes a key disqualifier for regional brokers.

Workforce Growth Strains Coverage Confidence: An increase in workforce size triggers heightened scrutiny of employment-related exposures, such as EPLI, workers' compensation, and health benefits risk. If a broker can't facilitate seamless transitions across multiple employee populations or identify coverage gaps created by rapid growth, HR and finance leaders may lose confidence in their ability to protect the organization through transition

Increased Operational Complexity Demands Expertise: The transition to a more operationally complex enterprise introduces nuanced liabilities that require specialized underwriting knowledge. They need a broker that can handle risks like technology errors and omissions, professional liability, or recall exposures—especially when those risks are tied to revenue-generating units.

Private Equity Expectations Raise the Bar 

Recently merged private equity-backed companies have added intense pressure to deliver efficiencies and returns quickly. Investors seek scalable partners who bring financial rigor, cost containment strategies, and data-driven reporting to the table.

When companies consolidate, they often enter entirely new business landscapes. Expanding geographically, scaling their workforce, and diversifying operations all introduce more complex risk profiles. At the same time, leadership is under intense pressure to realize deal synergies quickly, prompting a reassessment of every vendor relationship.

In this high-stakes environment, brokers who offer only transactional services like renewals or claims processing risk being replaced. Decision-makers now expect strategic partners who can anticipate evolving exposures, identify coverage gaps proactively, and design scalable, cost-efficient programs that support long-term growth.

From Vendor to Strategic Partner: Solving the Benefits Billing Puzzle

In the post-M&A environment, brokers who rely solely on transactional services risk being sidelined in favor of firms that deliver integrated strategy and value. To remain relevant and indispensable, brokers must reframe their role: not just as insurance intermediaries, but as risk and operational optimization partners. One powerful way to do this is by addressing a pain point often overlooked during integration: benefits billing.

Benefits billing is a complex, administrative function that becomes exponentially harder after a merger. Companies may suddenly be managing multiple payroll systems, varied carrier relationships, legacy employee enrollment systems, and disjointed plan structures across locations or subsidiaries. The result? A minefield of billing errors, overpayments, and reconciliation blind spots – issues that quietly erode budgets and create compliance risk.

Brokers who bring outsourced benefits billing solutions to the table position themselves as proactive problem-solvers. Rather than leaving clients to untangle multi-carrier billing chaos on their own, these brokers offer a scalable, centralized solution that consolidates invoices, flags discrepancies, and reports all expensive errors so they can be corrected in the next billing cycle. This shift from transactional to strategic support is not just a value-add—it's a survival strategy in a post-M&A landscape where every vendor must justify their place.

How Outsourced Billing Reinforces Broker Value in M&A Scenarios

Scalability Without Increased Administrative Load: M&A often comes with the expectation of leaner, more efficient operations. Brokers that integrate benefits billing solutions into their service offering empower clients to scale quickly without adding headcount or burdening internal HR teams. By automating reconciliation across carriers and divisions, brokers prove they understand the new demands of growth and are equipped to support them.

Immediate Financial Impact: Outsourced billing solutions often uncover 12–15% in invoice inaccuracies—real money that can be redirected toward growth initiatives or integration costs.

Audit-Ready Accuracy During a Scrutinized Period: Post-M&A periods are often marked by private equity oversight, board reporting, or internal audits. A benefits billing partner ensures clean, centralized records, reducing risk exposure and enabling finance teams to answer questions about benefits spend with confidence. Brokers who provide this visibility elevate themselves from tactical vendors to operational stewards.

Technology-Enabled, Not Just Commission-Driven: In today's market, clients expect more than just access to carriers, they expect access to tools. Brokers who align themselves with benefits billing automation platforms demonstrate a tech-forward mindset, positioning them to retain modernizing clients by sending a clear signal: this broker is built for what's next.

Liberating HR to Focus on Integration Management: HR teams already overwhelmed with culture integration, workforce alignment, and new onboarding processes don't have the bandwidth to chase down invoice discrepancies. Brokers who solve this problem help clients increase HR capacity for post-deal needs: another signal that they understand the broader business context of the deal.

Earning Client Trust = Earning A Seat Post-Deal

In the wake of a merger or acquisition, broker relationships are no longer guaranteed. Those who step up with scalable, tech-enabled, and cost-saving solutions earn more than retention—they earn trust, influence, and a seat at the strategic table.

As the 2025 MIT State of AI in Business report finds, despite $30–40 billion in enterprise investment in generative AI, a staggering 95% of projects have failed to deliver any measurable business value. The authors dub this stark disparity the "GenAI Divide" – a small 5% of AI initiatives are generating millions in value while the vast majority remain stuck with zero return on investment. In short, high adoption has not translated into high transformation. Tools like ChatGPT are widely piloted, yet most enterprise-grade GenAI solutions never get past experimentation. According to the MIT study, these efforts fail not due to model quality or regulations, but due to approach – with common pitfalls including brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.

How can organizations avoid falling on the wrong side of this GenAI Divide? This article offers a practical playbook. We outline five key implementation strategies and five guidelines for sustainable adoption to help enterprises turn promising AI pilots into production-scale successes. The focus is on disciplined execution and organizational alignment – moving beyond one-off demos to deeply integrated, value-generating AI solutions. The goal is to provide senior leaders a concise, HBR-style road map to crossing the GenAI Divide and realizing the business impact that so far has eluded 95% of adopters.

Five Implementation Strategies

To successfully implement generative AI at enterprise scale, leaders should apply the following five strategies. Each principle addresses a common failure point identified in the MIT report and steers projects toward long-term, production-level value rather than superficial wins:

1. Start Narrow, Scale Later – Rather than chasing broad, grandiose AI projects, begin with a focused use-case where AI can solve a defined problem and demonstrate clear value. The organizations on the right side of the GenAI Divide focus on narrow but high-value use cases, integrate deeply into workflows, and scale through continuous learning rather than broad feature sets. Starting small allows teams to learn, adapt, and earn quick wins. Once the AI solution proves itself in one domain, it can then be expanded to adjacent processes or scaled across the enterprise. This controlled approach prevents overreach and tackles the integration complexity that often stalls broader deployments. As the MIT study notes, successful innovators often "land small, visible wins in narrow workflows, then expand" – in contrast to less successful efforts that try to "boil the ocean" and end up overwhelmed by complexity.

2. Data Foundations First – Enterprise AI will only be as effective as the data and context you feed it. Before layering fancy models, ensure robust data foundations: consolidated, clean, and relevant data sources that the AI can learn from. Many GenAI pilots falter because the model lacks domain context or access to up-to-date internal knowledge. Top-performing firms in the MIT research "demanded deep customization aligned to internal processes and data", underscoring that AI must be grounded in the organization's own information and workflows. Investing early in data integration (connecting the AI to your databases, documents, and transaction flows) and data quality (governance, deduplication, lineage) will pay off later. A strong data foundation means the GenAI system isn't operating in a vacuum – it's embedded in your business reality, making its outputs far more relevant and reliable at scale.

3. Human-in-the-Loop by Design – Build human feedback and oversight into the AI workflow from day one. Generative AI shouldn't operate as an autonomous black box in enterprise settings – it works best as a collaborative tool that continuously learns from its users. The MIT report emphasizes that the core barrier to scaling AI is a learning gap: most GenAI systems "do not retain feedback, adapt to context, or improve over time". By contrast, projects that succeed treat AI deployment as an iterative, human-supported process. Establish formal loops for employees to review AI outputs, correct errors, and provide domain input. Design dashboards to capture these interactions and retrain models on this feedback. This human-in-the-loop approach improves accuracy, builds user trust, and ensures the AI evolves in line with real-world needs. It also assigns clear human accountability – critical in regulated and high-stakes environments – without forfeiting the efficiency gains of automation.

4. Governance and Risk Controls – Don't bolt on risk management at the end; bake it into the implementation plan. Enterprise AI adoption must be guided by strong governance: policies and guardrails for ethical use, regulatory compliance, and operational risk. Upfront, define what decisions or content the AI is not allowed to handle, establish approval workflows for sensitive outputs, and set up an oversight committee to monitor AI activities. This proactive stance prevents the common scenario of promising pilots being killed by compliance or security fears. Indeed, teams are far more willing to embrace AI if guardrails are in place during deployment. Effective AI governance includes transparency (knowing why the model produced a result), robust testing for bias or errors, and contingency plans when the AI gets something wrong. By instituting risk controls by design, leaders create the conditions for AI to flourish safely. Governance is ultimately an enabler: it builds the confidence among stakeholders – from frontline employees to regulators – that the new AI can be trusted in production.

5. Productization Discipline – Treat AI initiatives as products, not one-off projects or experiments. This means applying the same rigor to AI pilots that you would to bringing a new product to market: clear milestones, user testing, performance monitoring, and continuous improvement cycles. Many organizations stumble by considering an AI pilot "successful" after a demo, without planning for scaling, maintenance, and integration – the result is a pilot that never translates into operational impact. Instead, instil a product mindset. Develop an MVP (minimum viable product) version of the AI solution, deploy it to real users, gather feedback, and iterate. Incorporate MLOps practices for version control, monitoring, and model retraining. Successful adopters often "partnered through early-stage failures, treating deployment as co-evolution" – recognizing that the first attempt won't be perfect and committing to refining it over time. By expecting and managing iterative improvement, you turn a short-term pilot into a long-term, scalable product with a dedicated team and budget for continuing enhancement. Discipline in productization bridges the gap between prototype and production, ensuring the AI solution delivers sustained business value.

Five Guidelines for Sustainable Adoption

Implementation strategy alone isn't enough – the surrounding organizational environment determines whether AI truly takes root. The following five guidelines are leadership principles to ensure that generative AI adoption is sustainable, cost-effective, and deeply integrated into how the business operates. These guidelines emphasize change management, accountability, and the often-neglected factors that separate a flashy pilot from lasting enterprise transformation:

1. Align AI With Recurring Workflows – Focus on use cases that naturally plug into the regular rhythm of the business. AI solutions should attach to routine, frequent workflows – the monthly report preparation, the daily customer inquiry triage, the weekly financial reconciliation – where they can continuously assist and improve productivity. Aligning AI with recurring processes ensures two things: first, the AI system has a steady stream of real-world practice (and feedback) to learn from, and second, employees incorporate the AI into their normal work rather than viewing it as a novelty. Projects fail when they are mismatched to how work actually gets done. In fact, the MIT report found that many enterprise AI tools were "quietly rejected" because of "misalignment with day-to-day operations" . Leaders should therefore choose GenAI initiatives that map to pain points in existing workflows and design the integration such that using the AI is as natural as using email. When AI augments work that people already do frequently, it stands a far better chance of sticking and scaling.

2. Communicate in Business KPIs, Not Model Metrics – Drive the AI program with business-focused objectives, not just technical benchmarks. Executives and front-line workers alike care about outcomes such as revenue growth, cost reduction, customer satisfaction, and efficiency gains – not model precision scores or the latest algorithm. It's critical to translate AI performance into the language of business value. For example, instead of reporting that a model achieved 92% accuracy, communicate that it helped reduce customer churn by 5% or processed 1,000 more claims per week. This principle was evident among successful adopters in the MIT study, where organizations "benchmarked tools on operational outcomes, not model benchmarks". By linking AI initiatives to key performance indicators (KPIs) that business leaders recognize, you ensure continuing executive sponsorship and cross-functional buy-in. Importantly, framing results in terms of ROI and business metrics forces AI teams to stay focused on use cases that truly matter to the organization's bottom line, closing the gap between technical potential and realized value.

3. Build Cost and Performance Observability In – Once an AI system moves out of the lab, leaders need clear visibility into its usage, effectiveness, and costs. Too often, enterprises deploy generative AI without robust monitoring, only to be surprised later by escalating API bills, latency issues, or drifts in quality. Avoid these surprises by baking observability into the solution. This includes tracking metrics like inference cost per transaction, runtime performance, error rates, and the business metrics influenced (e.g. time saved per task). Set up dashboards that allow both the technical team and business owners to see how the AI is performing in real time. Observability is not just about tech metrics – it ties back to business KPIs. For instance, if an AI customer support bot's handle time creeps up or its customer satisfaction score drops, that should trigger an alert and investigation. Likewise, if monthly usage costs exceed expectations, it should prompt optimization or re-calibration of scope. Building this level of transparency creates accountability and enables data-driven decision-making about the AI's future. It ensures that scaling an AI solution doesn't lead to uncontrolled spending or unnoticed degradation in value. In short, treat your AI system as a living part of the business that needs continuous monitoring, just like any critical infrastructure.

4. Prioritize Security & Privacy – Any enterprise AI adoption must take security, privacy, and data protection as non-negotiable requirements. This goes beyond basic compliance checkboxes – it means designing the AI's data flows and integrations such that sensitive information is safeguarded at every step. Many companies remain understandably wary of generative AI tools because of confidentiality risks (e.g. an employee prompt inadvertently leaking client data to an external model). Address this upfront by implementing measures like data anonymization, encryption, on-premise or private cloud deployment of models, and strict access controls. That sentiment echoes across industries: if stakeholders don't trust that an AI system will keep data secure and decisions auditable, they will simply not allow it into production. Leaders should institute an AI privacy policy, involve the cybersecurity team early, and educate employees on safe AI usage practices. Additionally, consider model-specific risks – for example, generative models sometimes hallucinate (produce false information) or exhibit bias; robust governance and validation can mitigate these. By prioritizing security and privacy from day one, you not only reduce the risk of incidents, you also remove a major barrier to adoption – giving regulators, customers, and your own legal team confidence that the AI initiative is enterprise-ready.

5. Don't Forget the Last Mile: UX and Change Management – The difference between a pilot that impresses in the lab and a solution that succeeds in the field often comes down to the "last mile." This refers to bridging the gap between the technology and the people who use it. A great AI solution must fit seamlessly into users' workflows and be accompanied by effective change management. On the user experience (UX) side, integrate AI into the tools and interfaces employees already use, rather than forcing them to learn a new platform from scratch. Notably, business leaders in the MIT study stressed that if a new AI tool doesn't plug into established systems, nobody will use it – "If it doesn't plug into Salesforce or our internal systems, no one's going to use it." . This underlines the importance of meeting users where they are. On the change management side, involve end-users early, provide training, and appoint AI champions in teams. Many successful deployments began with enthusiastic front-line "prosumers" who tried out AI tools on their own and became internal evangelists. Leverage these power users to help others overcome initial scepticism. Leadership must also set realistic expectations – clarifying what the AI will and won't do – to prevent disappointment or fear. Finally, gather continuous feedback from users' post-launch and refine the solution and workflows accordingly. By investing in user experience design and organizational change management, you ensure that the AI initiative is not just technically sound but widely embraced by the people it's meant to help. This is what transforms a pilot into a scalable solution embedded in the fabric of the business.

The race to capture generative AI's benefits is on, but few have crossed the finish line. As the MIT report warns, the window to cross the GenAI Divide is rapidly narrowing. Enterprises are already locking in AI tools that learn and adapt, creating high switching costs and competitive advantage for the frontrunners. The urgency is clear: organizations that linger in perpetual "pilot purgatory" risk being left behind by more disciplined adopters. Bridging this divide requires more than enthusiasm – it requires executional rigor, deep integration, and a long-term commitment. The strategies and guidelines outlined above all point to a common ethos: treat generative AI as a transformational capability to be woven into the business, not a one-off experiment. Success with enterprise AI is ultimately less about the brilliance of any single model and more about the management practices around it – focusing on narrow value, strong data and governance foundations, alignment with people and process, and relentless iteration towards improvement. With a disciplined approach, cross-functional ownership, and an eye on sustainable value, companies can turn generative AI from hype into lasting competitive advantage. The opportunity is immense for those willing to invest in doing it right – and the cost of failure, in an era of rapidly advancing AI, is an ever-widening gap that no organization can afford.

In today's unpredictable economic climate, business owners are forced to confront many significant challenges—not just managing day-to-day operations of their company. Most are also defending their businesses against significant financial uncertainty due to numerous economic pressures. One of the most pressing concerns, especially for small and mid-size businesses, is the announcement of tariffs and their ripple effect across the U.S. economy.

Recent announcements regarding tariffs on imports—particularly in sectors like automotive, manufacturing, and consumer goods—have brought to the surface an all-too-familiar concern for American entrepreneurs. While tariffs are often positioned politically as a long-term strategic move to level the global economic playing field, the short-term and mid-term consequences disproportionately affect small business owners with limited financial resources and risk mitigation tools. That is to say, traditional insurance won't cover the losses—but alternatives exist.

A salvage yard owner in Boise, Idaho, has expressed how tariff policies have already begun affecting his ability to source and price used car parts for his business. The cost increases weren't hypothetical for him; they were showing up on invoices and cutting into his small business' profits. Like many others in his position, this business owner isn't looking for shortcuts or interested in shortchanging his customers due to his increased costs. He is merely looking for a way to keep doing honest work without being financially blindsided by decisions made far beyond his control.

We need to recognize the impact of tariffs and the lack of tailored and affordable insurance policies to cover the very real risks these small business owners face. Small business owners are tired of being left vulnerable to the kind of political risks they can't predict, foresee or influence—like sudden political policy changes or rising material costs due to global economic uncertainty. Small business owners are seeking smarter ways to build long-term resilience through tools Congress introduced back in 1986.

One of these tools is an 831(b) Plan, or micro captive insurance, found in Section 831(b) of the Tax Code. Micro captive insurance allows small business owners to set aside funds in a formalized, compliant way—reserves that can be used when unexpected damages occur. A micro captive cannot stop a tariff from going into effect; however, it can help provide a cushion when the fallout affects a small business owner's ability to do business.

For most business owners, there's never a "good" time to plan for unexpected challenges. Small businesses are focused on keeping people employed, customers happy, and operations moving. But these last few years have shown us that risk doesn't wait for the right time. Tariffs might be positioned as an intelligent political long game, but they are hurting small businesses today.

Small business owners deserve access to tools that work in the real world where payroll is due Friday and margins are tight. A micro captive isn't a one-size-fits-all solution, but it can offer some breathing room the next time the economic ground shifts unexpectedly.

We don't know how long tariff tensions will last. But we do know this: Reacting to risk in real time is stressful. Planning for it in advance is empowering. Tariffs may be part of a broader strategic game, but your business doesn't have to be the pawn.

Talk about productivity in insurance is often framed around helping people do more: process more claims, sell more policies. Agency leaders, however, know productivity isn't just about doing more—it's about working smarter.

Insurance technology providers are reshaping the tools at the heart of the industry to make that happen. Producers can anticipate what their clients need, servicers can respond to clients faster and spend less time clicking from one screen to another, and agency leaders can make faster, more data-informed business decisions.

A 2024 Vertafore technology survey found that agency staff still say they want efficiency from their tech stack more than anything else and that they would benefit most from reducing manual data entry.

Over the past two years, I've traveled across the U.S. as part of Vertafore's Project Impact to visit agencies and observe how servicers work. The insights gleaned from those visits are driving real technology improvements.

Smart insurtech providers are investing to adapt to the way agency professionals work.

They are designing platforms that connect and work seamlessly, reducing friction and giving users more time back. We're moving from siloed, single-purpose systems to integrated, intelligent ecosystems.

The gap between the promise of productivity and the reality of user experience is closing because of intentional design and investment in three core areas.

Automation built-in

Providers are embedding AI and automation into workflows, not leaving it up to agencies to figure it out.

Agency servicers spend a significant amount of time reentering information from client emails into forms. In some cases, I watched servicers write down policy information by hand while on the phone with a client so they can reenter it later when they aren't doing two things at once.

By building tools around automation, providers are cutting down the number of steps in processes and saving agencies time. Tools built with automation in mind can read the information from an email, predict what it's about and place it into the appropriate workflow.

Automation is improving accuracy, client satisfaction, and agency productivity. Automated processes create the foundation for agencies to pursue new opportunities without adding more tasks to staff workloads.

APIs and interoperability

No single provider can excel at everything—and they don't need to. Today's innovators focus on their strengths while building open ecosystems and partnerships, giving agencies the freedom to choose the best tools for their needs.

For example, many agencies want to leverage best-in-class e-signature solutions to improve their clients' experience and to make it easier to do business. APIs allow the e-signature tool to plug into the agency's core systems and automatically access forms from the AMS. Clients and users both benefit from this integration.

APIs also work to reduce duplicate data entry, facilitate instant document sharing, and make it easy to grow a tech stack by adding solutions without downtime that disrupts workflows or client service. That means less rekeying, fewer delays, and faster client response times.

Providers that build open-platform systems that take advantage of APIs help agencies customize their tech stack. The result is flexibility and scale without operational drag: APIs give agencies the flexibility to grow without breaking workflows.

Turning data into decisions

All the data pouring into an agency can easily become overwhelming or messy as it changes hands from one staff member to another. But providers are making data not just available but actionable.

Data dashboards provide a unified view of an agency and help make sense of the data in real time. Analytics helps uncover opportunities for agencies to grow and for producers to produce.

For agency leaders, a productivity dashboard can provide insights into time spent on tasks and overall performance. This information can then help refine workflows by highlighting individual producers or addressing bottlenecks. For producers, intelligent data analytics tools take into account past quoting, client behavior, and market trends to help find growth and retention opportunities.

Across the board, data analytics are helping agencies move from data overload to strategic insight.

What agencies should look for in their partners

The evolution of the insurance industry means agencies should expect more from their technology. Insurtech tools that listen to users are offering experiences that empower agencies to work smarter. Consider these three questions when choosing a tech partner:

• Are they making continuous investments in their core systems?
• Do they help with integration and interoperability across the tech stack?
• Do they provide automation and insights delivered in ways that reflect my real-world workflows?

The next wave of productivity isn't about agencies adapting to technology—it's about providers delivering smarter, simpler, connected solutions that fit within the systems agencies use so they can work better. Agencies that align with partners that embrace this philosophy will be best positioned for growth.

If your technology partner isn't working this way, it might be time to ask why.

Once a cornerstone of financial safety and legacy planning, life insurance now faces declining relevance among consumers under 40, who will shape the industry's future. This generation isn't looking for traditional "death insurance," instead seeking solutions that deliver tangible, accessible value throughout their lives.

The data show a stark change, where over the last 15 years, life insurance's slice of individual investment wallets has dropped 23%, while equities gained 31%. The driver isn't market volatility – it's a profound disconnect between how insurers design products and how the younger demographic lives.

New research from Capgemini and LIMRA found that 68% of consumers under 40 recognize life insurance as essential for their financial future, but adoption remains stubbornly low. The reason? Traditional life insurance triggers no longer reflect the realities of next-generation policyholders: 63% have no immediate marriage plans, and 84% aren't planning children soon.

Meanwhile, only 33% of insurers recognize the growing competition from investment platforms, digital banks, and wellness subscriptions – alternatives that are rapidly gaining ground by offering immediate value, flexibility, and seamless user experiences.

This gap between consumer expectations and insurer offerings presents a significant opportunity for industry transformation.

The Living Benefits Opportunity

The disconnect is simple: Consumers under 40 want financial tools that deliver value throughout their lives, but what they get from traditional life insurance is value only at death. The solution lies in offering "living benefits" that reframe insurance as a tool for living well, not just dying well.

Younger consumers want products designed for their generation, not their parents' playbook: Cash withdrawals for life events (48%), health and wellness benefits (41%), and critical illness coverage (39%) are main priorities. They want insurance that helps start businesses, pursue wellness goals, or access fertility treatments.

Despite 76% of younger consumers expressing interest in these benefits, offerings remain limited, as the industry continues to treat them as secondary features rather than central value propositions.

Early adopters are proving that the transition to living benefits works. One European insurer redesigned income protection around "employability" rather than "unemployability," focusing on helping people stay productive rather than just covering absences. The result: two months of sales exceeded their previous full-year results. Similarly, wellness programs integrated into living benefits lead to 87% of users reporting improved wellbeing while reducing insurers' loss ratios.

Making Living Benefits Work

The need is clear: living benefits must become part of the core value proposition alongside protection. But delivering them effectively requires three fundamental changes in how insurers develop and distribute their solutions.

1. Flexible, modular solutions are the foundation of this transformation. Modern policyholders need financial solutions that evolve with their changing circumstances, adjusting when they marry later in life or when faced with unexpected career transitions. Insurers should evolve core systems around modularity and simplify underwriting to allow customers to activate, modify, or expand living benefits as their needs change, without requiring full policy rewrites or lengthy approval processes.
2. Enhanced advisory capabilities bridge the gap between product flexibility and customer engagement. While 67% of under-40s want digital access with dedicated advisor support, only 16% of insurers are equipped with these capabilities at scale. This generation wants advisors who can demonstrate how living benefits support their specific goals, which requires updating both technology platforms and compensation models to reward continuing engagement over one-off sales.
3. Ecosystem partnerships extend insurance reach beyond traditional boundaries. Rather than competing solely with other insurers, successful firms embed their offerings into the financial services, wellness, and employee platforms where under-40s already manage their lives. These partnerships transform living benefits from standalone products into integrated lifestyle tools.

The connection between these capabilities is essential. Modular products without advisory support confuse customers. Advisory capabilities without ecosystem partnerships limit reach. Inflexible products disappoint customers when they can't access promised benefits. Addressing these needs together ensures a comprehensive approach that matches how under-40s live and work.

The Moment for Action

Millennials and Gen Z are set to inherit trillions over the next decade, and many already view life insurance as a viable destination for those assets. But they won't engage with products built on outdated assumptions. They expect solutions that reflect their lifestyles, priorities, and financial goals.

Insurers face a crucial moment: Those that embed living benefits, embrace modular design, and build partnerships across wellness and financial ecosystems will earn the trust of the next generation. Those that don't, risk losing relevance to competitors that already deliver quick, lifestyle-aligned value.

The question is no longer whether life insurance matters – it's whether insurers can make it relevant to the people who will define its future.

Headlines about used car loan companies imploding may not get much attention at insurance companies, but this “old news” from the Fed should: Those companies have been making risk assessments based on wonky data and are paying the price. (The Effects of Credit Score Migration on Subprime Auto Loan and Credit Card Delinquencies). 

There is a punch line here for auto insurers. You are relying on the same wonky data, so your risk scores will likely perform worse than historically expected, too.

False negatives

A false negative occurs when a fact you intend to observe is not visible.  A classic in the literature is a pregnant woman who takes a pregnancy test that returns a negative result. In that case, you can blame the test. But a more subtle case is what the Fed is showing now, where the problem is with the data. 

Data is missing that typically weighs down a credit score and is thus driving scores higher—while the riskiness remains the same.   

Imagine a historically stable data process where good and bad observed data drive positive and negative features that calibrate a risk score. If a time or place existed where bad things were not tracked as usual (thanks, COVID) or penalties were simply less enforced (thanks, COVID), then risk scores would rise for no good reason. The COVID timeframe encouraged a period of financial transaction forbearance unlike any we have experienced in modern times.

Auto insurance has other false negatives, too. Having less enforcement of traffic rules (thanks, COVID) and less availability of traffic courts (thanks, COVID) caused similar problems with reporting on motor vehicles. For example, running red lights may have produced no tickets -- still very risky behavior, just with no typical negative indication on record. The same with speeding tickets, which haven’t been issued as frequently in recent years.

The simple equation of score = intercept + good factors - bad factors means that the absence of a bad factor mathematically leaves a score in better shape than it deserves.

Decades of observable data have been used to establish that a credit-based risk score can be useful in describing a risk scenario where the higher the score the lower the risk in an auto insurance relationship. We tend to pull credit data on assessing a new risk in policy acquisition and on a routine basis when we re-score entire portfolios of policies.

But the sort of financial forbearance that is showing up in bad loans for used cars means that people have had virtual clemency. This let many lower-quality risks appear with higher credit-based risk scores, so more risk decisions were made at terms and conditions unwarranted by the true riskiness. 

Another risk – and a new source of data

Credit data is observed backward but applied forward. A forward-looking data stream that has recently been introduced can complement existing credit-based risk assessment methods (JSI CDPI).

The stream, which grew out of work to assess the risks of students seeking loans, tracks risks based on macroeconomic effects on occupational categories. As cars were taking off a century ago, being a buggy whip maker put you at risk. Restaurant workers had a rough time during COVID. Generative AI is currently a threat to clerical workers. And so on.

Incorporating indices linked to wages and wage opportunities can help adjust the false negatives in current credit-based scores. 

For all the insurance decisions linked to credit-based scores, this may be a learning moment. 

Paul Carroll

Amid all the confusion, what can you tell us about the economic outlook and its implications for the insurance industry as we move from Q3 to Q4? 

Michel Léonard

Normally, as we wrap up Q3, we have enough data as economists, policymakers, and business leaders to start thinking about what the year will look like by the end of it. Data sometimes comes in slowly, so for the first half of the year we’re really still forecasting. But by August, normally, we’re 80%, 85%, 95% certain about where we're going to end the year. Well, that's not the case right now.

Traditionally, GDP data takes one to two quarters to firm up, but GDP doesn't move much from one quarter to the next, because an economy is a supertanker. It's not going to move rapidly. But we have tariffs this year. So we have unknowns that we haven't experienced in a developed economy like the U.S. in a long time. As a result, we expect the data to move greatly as we go into Q4. Not only the data for Q4 itself but revisions to the data for Q3 and what we already have. That really complicates things.

We went into 2025 with a shift in economic policy that had no precedent in the last 30 or 40 years. Standard economic theory, which we have to use as a framework, was telling us we could have an economic contraction on the scale of what happened during COVID. But that hasn’t happened. I don't want to tempt fate or the economic gods by saying this, but I think the worst-case scenarios will be avoided. We will end the year with a resilient U.S. economy, both in terms of growth and inflation. 

Paul Carroll

What are the key economic factors we should be looking at? 

Michel Léonard

There's a triad of unknowns: one is about GDP, one is about inflation, and one is about the Fed. And, of course, they interact with one another.

The first one really has to do with data, and, as I said, we're kind of flying blind about GDP at the moment.

The second one is data again, but for inflation. This is different. This is about inventories being depleted and how long it will take for prices to get affected and for consumer confidence to shift. We saw reports earlier this year about cratering consumer confidence, but consumers are still spending because prices haven't been affected yet. That's the second unknown. How much will tariffs increase prices? How much will be absorbed in margins? What's the elasticity of the consumer—to be a bit technical—in terms of whether they're going to keep buying?

That brings us to the third unknown in this triad, which has to do with monetary policy. The Fed is very much waiting for clarity on GDP and inflation to decide whether they should be more concerned with the risk to growth or the risk to price stability. We've been waiting for a Fed rate cut for a long time. [Editor's note: This interview occurred before the recent quarter-point cut in interest rates.] The Fed is in control of the timeline, but, at the end of the day, I think if the Fed does not cut, it's not going to be a good thing. I've been saying this for, I think, a year and a half now. 

Paul Carroll

What implications are you seeing for the insurance industry, particularly regarding replacement costs and demand? 

Michel Léonard

Traditionally, P&C lags the economy. We are slower to go into a downward cycle, and we're slower to get out of it. Right now, P&C underlying growth is outperforming overall GDP, and it's forecast to do so through next year and into 2027. But the outlook is a bit confusing. We know we're heading into a contraction of some sort. Whether that just means slower growth or means a contraction in GDP, that's unknown. The question comes down to: Is the data off, or are people actually being more resilient here in the face of tariffs and other economic issues? I'd say it's probably about the data, and we're probably going to see those forecasts for 2026 and 2027 change significantly and not for the better. [Editor’s note: This interview occurred on Sept. 5, four days before the Bureau of Labor Statistics said the U.S. economy created 911,000 fewer jobs than previously thought in the year that ended in March 2025, meaning growth was only about half as robust as previously believed.]

Paul Carroll

How is tariff policy affecting replacement costs in insurance, particularly for materials like lumber for homes and steel for automotive parts? 

Michel Léonard

On tariffs, there is no clarity whatsoever. That's part of the goal of this performative policy. We have to try to deduce the effects without necessarily passing judgment. We should continue to see a significant weakening in our expectations for homeowners insurance and for auto. On the homeowners side, prices for construction materials have increased in 2025, for the first time in four years. At the same time, folks aren't buying or expanding or renovating. With personal auto, we’ve had a great year. American consumers knew there would probably be trade tariff uncertainty and bought cars ahead of time. That buying put auto in a great place, but we probably borrowed growth in Q1 of this year and even Q2. That likely means less growth in the second half of the year and certainly next year. 

Paul Carroll

Does it even make sense to try to make a longer-term projection about the effects of the tariffs? 

Michel Léonard

It's not my job to read the president’s mind, but I do think the administration is very clear that it intends to continue using trade as tools for what it believes is best for American workers and the U.S. economy in the longer run. All economists agree that these changes will take three, four, five, six, 10 years. In the meanwhile, no matter whether the tariffs worsen or improve—by which I mean whether the tariffs get higher or lower and whether more countries are involved—they're going to remain. And once inventories from before the tariffs are depleted, we'll feel the full impact.

Paul Carroll

I’m skeptical that much manufacturing will return to the U.S. because of tariffs. Factories take years to plan and construct. An aluminum smelter requires nearly a decade. In the meantime, nobody knows whether Trump’s unilateral setting of tariffs will last.

The Supreme Court is hearing a case in November on whether Trump has unconstitutionally usurped authority that belongs to Congress; as of now, courts have ruled that Trump doesn’t have that power. Even if he escapes that threat, he faces the mid-term elections next year. His tariffs are very unpopular, meaning that Republican congressional representatives facing any kind of threat to their seat may feel pressure to reassert their authority on tariffs and force Trump to back off. And the next presidential election is two years beyond the mid-terms. Trump has promulgated all his tariffs via executive order, so the next president could undo all of them on Day One.

How many companies will build factories that are meant to last decades when Trump’s tariffs face so many challenges just in the next few years? Not many, I don’t think. 

Michel Léonard 

Reshoring was already underway before the Trump administration. It's a result of the decrease in affordable labor globally, along with rising production costs and shipping expenses. The cost difference between producing abroad versus domestically was reaching equilibrium. Now, reshoring is being accelerated by uncertainty regarding trade agreements. But people don't want the same old factory jobs. They want better jobs as a result of new factories, which will take five to 10 years to establish. And not everyone can wait five to 10 years to get to a better economic place.

Our members at the Triple-I often ask if we’ve reached a tipping point that changes the world order. Well, that's a big philosophical question. But we’ve certainly passed some milestones.

Paul Carroll

I got us off on a bit of a tangent. Any final words about the general outlook for the economy and insurance?

Michel Léonard

Just that we're going to end the year most likely in a better place than we expected, and we should be very happy about that.

Paul Carroll

Thanks, as always, Michel.

Commercial properties carry an array of risks: structural failures, natural disasters, liability claims, tenant defaults, and shifting market conditions. The margin between profitability and financial loss often comes down to how effectively risks are identified, prioritized, and mitigated. The key question is how quickly you can translate risk signals into execution.

Where Commercial Real Estate Risk Management Breaks Down

Organizations consistently face the same challenges:

• Fragmented data - Building systems, tenant records, and claims histories often sit in silos. No one has the full picture.
• Reactive approaches - Too many organizations still wait for losses to occur before acting.
• One-size-fits-all strategies - Generic risk frameworks ignore the unique exposures of high-value commercial portfolios.

These breakdowns create a gap between what risk managers know on paper and what actually protects capital. When the gap widens, losses multiply.

Execution-First Risk Management for Your Real Estate Investment

Effective strategies do not start with more technology or more checklists. They start with execution that actually works:

• Clear ownership of risks at the operational level.
• Data that feeds decisions in real time, not after quarterly reviews.
• Partnerships that understand both insurance and property operations, not just one side of the equation.

Execution-first means risk management is not a binder on a shelf. It is processes embedded into daily operations, monitored, tested, and adapted.

Core Risk Management Strategies to Protect Your Capital

● Property-Specific Risk Assessments

Standard models are not enough. Every building has unique exposures: roof condition, HVAC performance, fire suppression, and occupancy patterns. Assessments that go beyond regulatory minimums help you see where risks threaten net operating income (NOI) the most and where you should invest in mitigation efforts. Clear execution, ownership, data, and insurance reduce losses and strengthen your position as an investor in commercial properties.

Outcome: Targeted investment in mitigation where it matters most.

● Data-Driven Predictive Modeling

Claims history shows where losses have already happened, not where they will occur. Predictive models powered by IoT data, weather analytics, and building sensors let you spot emerging risks earlier. This approach helps you address issues like water intrusion or system failures before they escalate into major claims. It also ties operational data more directly to financial exposure, reserve planning, and market risk trends that can influence portfolio performance.

Outcome: Reduced surprises and better capital allocation.

● Integrated Business Continuity Planning

Risk events damage property and interrupt cash flow. Continuity plans must be tied to debt service coverage ratios, interest rate exposure, and lease obligations to reveal the full impact. Asking what happens to debt service, leases, and cash flow if a building is down for six months makes the stakes clear. Embedding continuity into contracts with vendors, lenders, and tenants helps stabilize obligations even when property damage disrupts operations.

Outcome: Faster recovery, less erosion of enterprise value.

● Tenant Risk Screening and Management

Properties are only as stable as their tenants. Credit strength, industry and market trends, and operational practices all influence property risk. Screening tenants before lease agreements helps limit exposure to weak credit and unstable businesses. Monitoring then allows you to anticipate changes early and reduce the financial risk of a market downturn or sudden rental income volatility hitting portfolio performance.

Outcome: Stronger portfolio resilience and reduced volatility in rental income.

● Insurance Optimization

Conventional property insurance programs often fail to reflect the complexity of diverse portfolios. Structuring coverage with captives, parametric triggers, and layered policies creates both flexibility and protection. A well-designed program not only manages premium-to-risk ratios but also ensures claims are paid quickly, providing liquidity at the exact moment it is needed to maintain cash flow and asset valuation.

Outcome: Balanced cost control and guaranteed capital protection.

What Leaders Should Be Asking

The best risk strategies as part of your commercial property management succeed not because of the tools but because of the people and processes around them. You should be challenging your teams and partners with questions like:

• Who owns the execution of these strategies at the asset level?
• How are we turning real-time data into decisions, not reports?
• Are our insurance structures aligned with our actual risk profile, or just market convention?
• Which potential risks could destroy the property value fastest, and how are we mitigating them today?

Protecting What Matters - Your Investment

The need to manage risk in commercial properties is not a compliance exercise. It is a capital protection strategy. When you focus on execution-first delivery, clear ownership, timely data, and strong insurance structures, you prevent losses from becoming financial shocks. Following the best practices to mitigate risk gives you an edge in commercial real estate investment, where disciplined execution separates growth opportunities from setbacks.

AI now sits at the center of the insurance conversation. Algorithms screen submissions, models calculate probability distributions, and natural language systems draft clauses and summaries. For some, this marks the beginning of a future in which machines displace the human broker, underwriter, or claims handler. That reading, however, misjudges both the nature of insurance and the limits of technology.

Insurance has never been simply about processing data. It has always been about managing uncertainty, interpreting meaning, and defending promises under pressure. AI accelerates calculation, but coherence rests on interpretation, and while algorithms may process inputs at speed, it is the broker who preserves alignment. The visible form of insurance is being reconfigured with remarkable velocity, while its strategic core remains intact.

At that core are three irreducible functions. Representation is more than the mechanical capture of data. To represent a client is to absorb their operational logic, commercial anxieties, and appetite for risk, then to embody these in a form the market can understand. No system, however advanced, can grasp what is withheld, unspoken, or only tentatively expressed. An algorithm can record information, but it cannot perceive silence. Representation is therefore an act of disciplined interpretation, giving strategic shape to a business so that it can be recognized in the language of the market.

Translation also resists automation. AI may generate summaries, but translation is not a matter of summarizing; it is about deciding what truly matters. The broker reframes exposures that are complex, partial, and ambiguous into terms the market can underwrite, and then recasts the market's response into consequences the client can act upon. This is not the simple transmission of information but the transformation of meaning. It requires knowledge of how underwriters price ambiguity, how narratives influence actuarial models, and how risk is negotiated in practice. Translation is the conversion of uncertainty into actionable form, and it cannot be mechanized.

Defense is the crucible in which insurance proves itself. When a claim is contested or a clause tested in practice, AI can accelerate document retrieval but it cannot argue meaning under pressure. A model can reference precedent, but it cannot persuade a reluctant underwriter, untangle jurisdictional complexity, or arbitrate between interpretations. Defense is existential, the moment in which alignment is reasserted precisely when promises come under strain. It is here that the difference between system and substance becomes most visible, for no technology can substitute the human broker as the final line of defense.

Representation, translation, and defense form a triad that defines the sovereignty of broking. Each carries its own risks, but only together do they hold alignment in place. If one is surrendered to automation, the others are weakened. Intake forms may simulate representation, machine outputs may mimic translation, escalation protocols may approximate defense. The outcome is substitution, not coherence.

The greater danger, then, is not that AI will disrupt insurance, but that it will induce drift. New systems accelerate transactions without recalibrating purpose. Dashboards glow green even as coverage gaps widen. Efficiency becomes a proxy for alignment. The industry risks mistaking the sophistication of AI for the substance of strategy, rehearsing coherence while quietly dissolving it.

Such drift rarely appears in the form of sudden collapse. It accumulates gradually, layer upon layer. Each unchecked assumption, each templated response, each unexamined upgrade displaces interpretive discipline a little further. The structure may remain intact, but stripped of reflection it ceases to think, functioning only in appearance rather than in substance.

The broker's task in this environment is not to resist AI but to guide its use. This responsibility involves ensuring that systems serve alignment rather than substitute for it. It requires the ability to frame, escalate, and sequence risk independently of machine logic. It also means maintaining coherence across time: managing the immediacy of renewals and claims while anchoring decisions in the slower rhythm of trust, continuity, and coverage design.

This responsibility does not mean resisting change, nor does it mean blocking progress. It is the discipline of choosing what must persist and what may be abandoned as systems evolve. It demands discernment, knowing when AI strengthens function and when it distracts from it. Sound judgment lies in the ability to hold ambiguity without rushing to premature closure, to absorb friction without reducing it to a checklist, and to defend coherence even when speed and surface efficiency suggest otherwise.

AI will remain, and its presence will expand, shaping the visible architecture of insurance with increasing force. What endures, however, is the deeper structure of the industry itself. Representation, translation, and defense cannot be automated without dissolving the very coherence they sustain. These are not optional roles; they are the functions that give insurance meaning. The firms that thrive will not be those who chase every new model first, but those who recognize what must not be surrendered at all. The future of insurance will be shaped not by the intelligence of machines, but by the discernment of those who know where technology ends and responsibility begins.

Fertilizers and pesticides have increased crop yields for centuries, but they also create broader problems that increase pollution liability risks for farmers. Legislative and legal developments in the past several decades have led to unfortunate consequences: agricultural runoff is polluting rivers and oceans, and farmers and agribusinesses are exposed to environmental liability from everyday activities that were intended only to improve food production. Retail insurance agents can play an important role in advising agribusiness clients on loss prevention practices and working with wholesale specialists to develop tailored risk transfer solutions for agriculture risks.

Risks from agricultural runoff

Irrigation and precipitation cause runoff from fields and pastures, taking on chemicals and animal waste. Eventually, this runoff finds its way into streams and rivers, and drains into oceans. The runoff can cause algal blooms that spawn low-oxygen spots and disrupt marine ecosystems.

An analysis by North Carolina State University found a complex chain of effects from the addition of nitrogen and phosphorus – common ingredients in fertilizers. Published in the Biological Review, the analysis "combined the results of 184 studies drawn from 885 individual experiments around the globe that investigated the effects of adding nitrogen and phosphorus, the main components of fertilizer, in streams and rivers. While the analysis only included studies where scientists added nitrogen and phosphorus experimentally, nitrogen and phosphorus pollution can run off from farms into streams, lakes, and rivers – as well as from wastewater discharge. At high levels, fertilizer pollution can cause harmful algal blooms and can lead to fish kills."

A prime example of these consequences is the "dead zone" in the Gulf of Mexico. The Science Education Resource Center at Carleton College says it "is primarily a result of runoff of nutrients from fertilizers and manure applied to agricultural land in the Mississippi River basin. Runoff from farms carries nutrients with the water as it drains to the Mississippi River, which ultimately flows to the Gulf of Mexico. If the number of nutrients reaching the Gulf of Mexico can be reduced, then the dead zone will begin to shrink."

The National Oceanic and Atmospheric Administration (NOAA) has studied the dead zone since 1985 and notes it varies in size. In Aug. 2024, NOAA estimated the Gulf of Mexico dead zone was more than 6,700 square miles – about the size of the state of New Jersey. Subsequently, the Hypoxia Task Force formed in 1997. Led by the U.S. Environmental Protection Agency and consisting of five federal agencies and 12 states, it has been working to implement policies and regulations to reduce the size of the zone.

Initiatives include:

• Better management of nutrient application can reduce nutrient runoff to streams.
• Planting of certain grasses, grains or clovers, called cover crops can recycle excess nutrients and reduce soil erosion, keeping nutrients out of surface waterways.
• Reducing how often fields are tilled reduces erosion and soil compaction, builds soil organic matter, and reduces runoff.
• Keeping animals and their waste out of streams, rivers, and lakes keep nitrogen and phosphorus out of the water and restores stream banks.
• Reducing nutrient loadings that drain from agricultural fields helps prevent degradation of the water in local streams and lakes.

Source: NOAA/Louisiana Universities Marine Consortium/Louisiana State University

Waste minimization and pollution risks

An analysis of United Nations' Food and Agriculture Organization data shows that global meat production quintupled from 1961 to 2023. More than 360 million tons of meat are produced worldwide from various kinds of livestock. Managing the waste produced by the livestock required to fulfill global demand is an enormous challenge.

Since 1976, when the Resource Conservation and Recovery Act (RCRA) was enacted, the U.S. has sought to address the increasing volume of municipal and industrial waste. The 1984 passage of Hazardous and Solid Waste Amendments to RCRA "required phasing out land disposal of hazardous waste, corrective action for releases and waste minimization. Waste minimization refers to the use of source reduction and/or environmentally sound recycling methods prior to treating or disposing of hazardous wastes," according to the U.S. Environmental Protection Agency.

In recent years, additional legislation has extended to the agricultural efforts of farmlands that have the continuing potential to pollute and harm the very land on which they reside, in addition to the surrounding environment, groundwater and wildlife that share these spaces. The National Resources Defense Council defines agricultural pollution as "the contamination we release into the environment as a by-product of growing and raising livestock, food crops, animal feed, and biofuel crops."

Protecting farmers' right to farm, not pollute

State and federal court decisions in agricultural pollution cases have consistently found in favor of protecting the environment and adjacent lands. Courts have generally ruled the right to farm does not grant a right to pollute. Commonly, courts levying fines have issued findings of negligence, nuisance and trespassing related to the storage, hauling, treatment and disposal of waste of all kinds.

In 2014, the Wisconsin Supreme Court ruled that "manure applied to fertilize a field in the usual course of a farming business was transformed into a 'pollutant' when it seeped into adjoining neighbors' wells. As such, the Court ruled that a 'pollution exclusion' clause in the farmer's insurance policy eliminated the insurer's duty to defend in lawsuits seeking damage for the contaminated wells."

In the underlying case, the court noted the farmer used "manure from his dairy cows as fertilizer for his fields pursuant to a nutrient management plan prepared by a certified crop agronomist and approved by the Washington County Land and Water Conservation Division. Several months later, the Wisconsin Department of Natural Resources notified the farmer that the manure had polluted a local aquifer and contaminated neighboring water wells. The well owners demanded compensation, and the farmer sought coverage from his insurer under his farm owners' policy."

The insurer sought a declaratory judgment that it had no duty to defend or indemnify the farmer because "manure was a 'pollutant' subject to exclusion under the policy. The circuit court agreed with the insurer, finding that the pollution exclusion in the policy applied to exclude coverage for damage caused by the application of manure because 'a reasonable person in the position of the (farmer) would understand cow manure to be a waste,'" the state supreme court ruled.

Mitigating pollution liability

Farmers today face many difficult issues, from the effect of climate change and weather volatility to the continued urbanization of rural areas, to how to manage runoff and waste. What can farmers do with all the manure as their operation grows? How can farmers protect themselves from pollution laws even when they've done their best to alleviate the problem?

Fortunately, tailored pollution insurance solutions are available to retail agents and brokers to help curtail the potential effect of exposures and liability confronted by their clients, today's farmers and agricultural community. Some insurance options include:

• Sudden & Accidental (Time Element) Insurance. This typically covers sudden pollution incidents that happen on an insured's land. Incidents typically need to be found in about three days and reported to the insurance carrier within two weeks. It must begin and end in a certain timeframe and is meant to exclude true gradual pollution damage claims that occur accidentally over months or years. Gradual damage tends to be the most costly, hence why Sudden & Accidental pollution coverage for agriculture risks is so inexpensive.
• Pollution Legal Liability (PLL). This is a risk management tool for property owners that is typically designed to address premises pollution exposures. This claims-made coverage, in our experience, consistently manages the on- and off-site cleanup/remediation expenses; third-party bodily injury and property damage; and defense expenses associated with industries including the agricultural sector. However, because it is an expanded gradual coverage compared to sudden and accidental, it comes with a much higher price tag.
• Transportation Pollution Liability. This type of cargo pollution insurance generally covers pollution conditions caused during transportation, loading or unloading, and sometimes mis-delivery of this cargo including waste such as manure, or a product such as fertilizer.
• Non-Owned Disposal Site Liability (NODS) typically covers disposal of waste to a non-owned disposal facility. RCRA provides for joint and several liability for waste generators, meaning a farmer that uses a non-owned disposal facility can still be deemed a potentially responsible party for cleanup and remediation by the EPA. NODS coverage exists to indemnify insureds in this situation.

The key to helping protect the agricultural community from potential pollution conditions starts with the careful understanding of the client's workplace, practices and processes. A one-size-fits-all solution may not take into account the specifics involved in an insured's work and daily practices. Other solutions include the tailoring and blending of various policy forms to create an a la carte coverage form that is designed to help cover the specific pollution conditions of this unique marketplace.