For years, much of insurance technology has been sold in the language of capability: better systems, faster models, smarter automation, more conversational interfaces. But capability alone does not create business value. In insurance, what matters is not whether an AI system can generate activity, but whether it can help produce accountable, measurable results. That is where the next real shift in insurance AI is taking place.

That distinction matters. The insurance industry does not need more demonstrations of what AI can say. It needs more evidence of what AI can actually help organizations achieve.

The real shift: from tools to outcomes

Most insurers have already seen what the first phase of AI adoption looks like. Vendors offer tools that promise efficiency gains, faster response times, more personalized engagement, or better support for frontline teams. Those improvements matter, but they are still only intermediate indicators. A faster conversation is not the same as a better business result. A more fluent recommendation is not the same as a bound policy. A lower handling cost is not the same as sustainable growth.

That is why the transition from a software mindset to a results mindset matters so much.

In my view, the difference can be captured through a baseball analogy. In baseball, the ball itself never scores. You can hit it hard, place it well, and create momentum for the team. But until someone actually reaches home plate, the run does not count.

AI in insurance increasingly resembles the batter. It can open the play. It can improve timing, precision, and opportunity creation. But creating the conditions for a result is not the same as completing the result itself. That distinction becomes especially important in insurance, where trust, accountability, compliance, and human judgment still sit at the center of value creation.

Why agentic AI changes the conversation

What makes the current moment different is that AI is no longer being asked merely to assist. It is increasingly being asked to participate in execution.

In recent years, the international technology sector has increasingly begun to describe the same shift in different terms: AI is no longer just being sold as a tool, but is starting to take on work and deliver outcomes. Sequoia gave this shift one of its clearest formulations by arguing that "services" are becoming the new software, as AI moves from enabling work to increasingly performing it. Sierra has spoken more directly about outcome-based pricing, arguing that customers should increasingly pay not for seats or usage, but for concrete results achieved. The terminology differs, but the direction is consistent: in the global technology market, "selling results" is no longer just a company-specific experiment. It is increasingly being recognized as a broader AI commercialization trend.

What is new, in other words, is not the existence of this model but the language now available to describe it. For years, versions of outcome-based delivery existed without a widely shared market vocabulary. What the current moment provides is not merely stronger technology, but a clearer way of naming and recognizing a shift that had already begun.

A recent QbitAI report on Lingxi Technology illustrates that shift in concrete terms. The report describes how the company applies large-model technology in sales environments through an outcome-based model it calls Results as a Service, or RaaS. According to the report, one insurance client that adopted Lingxi's sales agent generated RMB 2 billion (about US$295 million) in additional premium in one year. Whether one focuses on the number itself or on the broader pattern, the more important signal is this: the conversation is moving beyond whether AI can interact, and toward whether AI can consistently deliver business outcomes.

The Lingxi case is relevant not because it proves that one company has found the universal formula, but because it reflects a broader evolution in enterprise expectations. According to QbitAI, Lingxi's model is built around causal reasoning, post-training, closed-loop feedback, and real business performance rather than generic conversational ability alone. Its ACE (Agentic Customer Engagement) system is positioned not as a lightweight digital tool, but as a customer engagement agent designed to plan tasks, coordinate sub-agents, and support conversion in complex sales environments.

That does not mean outcome-based models became possible only now. A more accurate way to frame the shift is that they are only now beginning to meet the conditions for large-scale viability. Earlier versions could work, but they were heavier, slower, and far more dependent on human effort. What has changed is not the basic logic of the model, but the extent to which AI can now support its repeatability and scale.

That is a meaningful shift for insurance leaders.

Traditional large models are often impressive at generating plausible responses. But in insurance, plausibility is not enough. When a customer hesitates on a product, the critical question is not only what to say next. The deeper question is why the hesitation exists in the first place. Is the issue product misunderstanding, distrust in the claims process, pricing anxiety, family financial pressure, or uncertainty about long-term need? If AI cannot help uncover that underlying logic, then even its most polished recommendation may still be little more than statistical guesswork.

This is where causal reasoning becomes strategically important. The real value of AI in insurance does not come from sounding more human. It comes from being more useful in situations where the outcome depends on good judgment, the right sequence of actions, and clear explanation.

Causal Sovereignty: the leadership question AI does not replace

Whenever high-performing AI enters a business process, leaders often worry that they are losing control. I would frame the issue differently. In fact, when AI begins to take on more of the burden of execution, leaders are forced to return to a more important responsibility: defining what success means, where the boundaries are, and what must remain under human accountability.

That is what I call Causal Sovereignty.

AI can help identify pathways. It can surface patterns, test strategies, adapt interactions, and improve the odds of success. But it does not determine why an organization should pursue a particular outcome, what ethical constraints should apply, or what trade-offs are acceptable in getting there. Those remain leadership questions.

In insurance, this distinction is especially important. AI may help improve conversion, optimize engagement, and support premium growth. But it cannot decide how far persuasion should go, how trust should be protected, how fairness should be interpreted, or what role insurance should play in a broader social safety framework. Those are not merely technical questions. They are governance questions.

So the rise of agentic AI does not eliminate human responsibility. It clarifies it.

Seen this way, the shift is not simply about a new pricing model. It marks a deeper reallocation of responsibility. In the software era, vendors delivered capabilities while clients remained responsible for turning those capabilities into results. In an outcome-based model, providers begin to take on part of that burden directly. What changes, therefore, is not only how value is priced, but how work, accountability, and responsibility are divided.

Three rules for insurance leaders in the RaaS era

If this outcome-based model continues to spread, then insurance leaders should adjust what they demand from AI.

First, demand causality, not just performance.

If a system can tell you that something worked, but cannot help explain why it worked, then you are still managing a black box. In insurance, that is not enough.

Second, define the human-AI boundary explicitly.

Leaders need to decide where AI can act with speed and autonomy, and where human judgment remains non-negotiable. This is particularly important in areas involving compliance, brand risk, ethical sensitivity, and high-stakes commitments to customers.

Third, evaluate AI by business outcomes, not by technical spectacle.

The key question is not whether a model is advanced. The key question is whether it helps produce durable, governable, real-world results.

Insurance executives do not need to become programmers to lead through this transition. But they do need to become better judges of where AI creates real leverage, where it creates hidden risk, and where responsibility still needs a human owner.

The bottom line

AI is becoming more capable of producing measurable commercial value. That is real progress. But the more important development is not that machines are getting stronger. It is that the division of labor between humans and machines is being rewritten.

In traditional baseball, the batter hits the ball and then runs the bases himself. In the age of agentic AI, those functions are beginning to separate. The machine becomes increasingly effective at creating the conditions for progress. The human remains the one who defines the meaning of success, sets the boundaries of action, and ultimately owns the result.

That is why the future of insurance AI should not be framed simply as a story of better software. It is a story about governed outcomes.

AI may create the opening. But in insurance, humans still bring the result home.

The federal court ruling regarding a dispute under the No Surprises Act (NSA) has been subject to debate throughout the healthcare and insurance sectors. Although the decision was based primarily on the extent of judicial review available for independent dispute resolution (IDR) determinations, its impact extends far beyond the context of this particular case. This ruling signifies a much larger truth for insurers, self-insured employers, TPAs and stop-loss carriers. The next stage of the NSA will be shaped by risk management strategy.

When Congress created the NSA, the intention was to protect patients from receiving unexpected medical bills while creating a method for both providers and payers to resolve payment disputes through a formalized process. However, the federal IDR process has developed into a large-volume operational process that currently affects a payer's forecasting of expenses, compliance responsibilities, potential liability due to litigation and resource usage.

Since dispute volumes continue to grow nationwide, organizations can no longer consider IDR solely as a claims-related function.

A System Developing Scale

The federal IDR process, originally designed as a mechanism for resolving select payment disagreements, has significantly exceeded usage estimates, developing into a national system processing 5.1 million disputes since its inception. The unexpected usage introduces additional risk concerns that include new administrative burdens, such as increased costs associated with managing cases and maintaining consistency within documentation, eligibility reviews and reimbursement strategies.

Consequently, participants in the IDR system have become increasingly sophisticated. As such, there exists greater amounts of data supporting each dispute filed. Filing strategies are advancing and parties are gaining experience and familiarity with the procedures and the decisionmakers. Therefore, the amount of risk involved with each IDR determination extends beyond the dollar value of each determination. Each determination will potentially influence broader reimbursement trends, reserve levels and future dispute activities.

For insurance organizations, the question goes beyond whether IDR will be a permanent feature of the healthcare landscape and shifts focus to how they will manage the associated risks.

Litigation Uncertainty Creates Another Layered Problem

The federal court decision underscores the boundaries of judicial review under the NSA. While the ruling suggests a limited role for courts in revisiting certain aspects of IDR determinations, similar disputes are continuing in various federal courts across multiple jurisdictions. Future appeals and litigation may shape the interpretation and application of judicial review under the law.

For insurers and plan sponsors, this creates a real-world concern. Traditionally, litigation provided an opportunity to clarify the applicable laws and determine whether actions were improper. Without a defined level of judicial intervention, it is more difficult for organizations to predict outcomes and assess their legal exposure.

This does not suggest litigation will cease to exist within the IDR landscape. Instead, it indicates organizations need to plan for a time when litigation results may differ across jurisdictions and unanswered legal issues may persist for lengthy periods. Under such conditions, reliance on courts to resolve operational challenges becomes increasingly impractical.

The ruling also raises broader questions regarding oversight. As dispute volume continues to grow, confidence in the long-term integrity of the IDR process depends upon consistent eligibility standards, transparent administration and meaningful accountability mechanisms. If judicial review is limited, the importance of getting those processes right at the administrative level increases substantially.

The Paradigm Shift from Reactive to Proactive Risk Management

As litigation pathways become less predictable, risk management must begin looking forward. Many organizations historically regarded payment disputes as independent events that occurred individually and therefore could be resolved post-dispute. Such a philosophy is becoming less viable as disputes are occurring in the thousands and each IDR determination can have cumulative financial implications.

A more sustainable model of risk management begins prior to arbitration. Organizations should adopt clear governance models for eligibility review, reimbursement methodology and dispute documentation. Similarly, internal processes should ensure that positions taken during dispute resolution processes are supported by consistent data, defendable payment rationale and adequate documentation.

Equally important is identifying patterns, such as large volume filing activity, frequently disputed categories and geographic trends, which provides useful information relative to future exposure. Organizations that understand where future exposures may occur are better able to allocate resources and respond proactively. The objective of this paradigm shift is not merely winning individual disputes but rather developing an architecture that allows for consistent decision making within increasing complexity.

IDR Has Become an Enterprise Risk Issue

Perhaps the most critical takeaway from recent developments is that IDR cannot be assessed solely from a claims administration perspective. The financial impacts relate to budgeting, reserving and managing health care related costs. Operational impacts relate to staffing, workflow and administrative efficiencies. Legal impacts relate to compliance with regulations, dispute resolution practices and litigation strategy. Collectively these factors raise IDR from a reimbursement issue to an enterprise-wide risk management issue.

As such, greater organizational involvement is required. Allowing IDR to be managed from an enterprise-risk perspective involves collaboration among all relevant departments including legal teams, claims professionals, financial leadership and executive-level stakeholders.

If organizations continue to treat IDR as a normal administrative process, they may find themselves responding reactively to emerging problems. Organizations that integrate IDR into their overall risk management program will be best situated to manage future uncertainty.

Looking Forward

This federal court decision may ultimately be remembered more for what it reveals about the evolving nature of the IDR system than for any immediate legal ramifications. As IDR continues to scale nationally, the healthcare sector is entering a period where operational discipline, data-based oversight and proactive governance will become increasingly essential.

Regardless of how future courts choose to interpret the scope of judicial review under the NSA, one fact appears evident. Those organizations most likely to successfully navigate this evolving regulatory landscape will not be those who focus on individual IDR disputes. Rather, those who recognize IDR as a strategic risk management challenge and invest in the infrastructure, expertise and oversight needed to address it.

Here's a sentence that should alarm every carrier deploying AI:

Most insurers adopting AI never explicitly decide which decisions the model gets to make.

We decide on the vendor. The budget. The implementation timeline. The model-risk review. The filing. Then we switch the thing on and let the boundary between machine decisions and human decisions get negotiated by accident — one workflow at a time, by whomever happens to be configuring the rules engine that day.

It's like hiring a brilliant new chief underwriter, never defining the role, and then acting surprised when they're quietly setting appetite 18 months later.

In a regulated industry, that drift isn't just operational risk — it's the kind of thing a market-conduct examiner asks about. The fix is a discipline I call Decision Rights, and it sorts every decision in your operation into one of three tiers.

Tier 1: DELEGATE — the machine decides, humans audit

Delegate-tier decisions share three traits: they're reversible, low-stakes individually, and high-volume. Routing a first notice of loss to the right queue. Flagging a claim for SIU review. Straight-through processing of small, reversible claims within preset limits. Triaging submissions against appetite. If the model gets one wrong, you catch it, fix it, and move on.

Delegating these isn't surrender — it's strategy. Every hour an adjuster or underwriter spends on a call the machine can make safely is an hour stolen from the calls only they can make.

Two rules keep Tier 1 honest. First, sample audits: humans review a random slice of the model's calls, because unwatched systems drift — and drift in rating or claims handling is how you end up explaining a disparate-impact pattern you never intended. Second, escape hatches: any policyholder or producer can pull a decision up a tier. The insured who says "I want a human" gets one. Always.

Tier 2: AUGMENT — AI drafts, a named human decides

This is the default tier for consequential calls, and it should be the biggest tier in your charter. The model does what it does best: gathers, scores, surfaces options at speed. Then a named human does what only humans can do — weighs it against context, exposure, and fairness, and makes the call with their name on it.

The operative word is named. Augment-tier decisions have a single accountable owner, and that owner must be able to defend the decision from a blank whiteboard, no model in the room. In insurance that isn't a metaphor: if an underwriter can't explain a decline without pointing at a score, you can't issue a defensible adverse-action notice — and you can't answer the regulator who asks why. If you can't explain it without the tool, you haven't decided. You've subscribed.

Tier 3: RESERVE — human-only; the machine may inform, never recommend

Some decisions should never carry an AI recommendation at all — not because the model couldn't produce one, but because the act of producing it contaminates the judgment. A recommendation is an anchor. Once the room has seen "the model suggests deny," every human thought gets measured against deny, and dissent starts to feel like arguing with math.

Reserve-tier in insurance includes: decisions that materially affect a person's livelihood or access to coverage, large-loss and potential bad-faith determinations, anything touching unfair discrimination or vulnerable policyholders, and the company-shaping strategic bets — entering a line, acquiring a book. Plus a catch-all worth memorizing:

Anything you must personally defend to a regulator, a policyholder, or a court.

If you'd be uneasy saying "the model recommended it" under oath, the decision was Reserve-tier all along. AI can still inform — pull the loss history, model the scenarios — but the synthesis, the weighing, and the recommendation stay in human hands from start to finish.

The three-question tier test

For any decision in front of you right now:

1. Is it reversible? Can you unwind it without harming a policyholder or breaching a filing? If yes, it might be delegable. If no, climb a tier.
2. Who answers for it? If you can't name the human — and an examiner asks "who decided this?" — you've found a problem in progress.
3. Would I sign my name to it, in a market-conduct exam? If the thought makes you flinch, it's Reserve-tier.

What happens when you publish one

The first time a program lead publishes a charter, something unexpected tends to happen: half the political fights on the team simply evaporate. The friction was never really about the technology. It was unspoken anxiety over who actually owned what. The charter doesn't restrict anyone — it relieves everyone. People fight hardest in undefined territory. Draw the map, and the shooting mostly stops.

A governance note, because insurance lives and dies on it: AI's arrival tempts organizations to give the algorithm a letter on the RACI chart. Fine — let the model be Responsible or Consulted on plenty of rows. But the A never goes to the algorithm. Accountability requires someone who can be promoted, demoted, thanked, or fired — and someone a regulator can hold answerable. The day your RACI has an A with no heartbeat behind it, your governance is decorative.

Build yours before the tool builds it for you

You can stand up a first draft in an afternoon: list your highest-volume AI-touched decisions, sort each into Delegate, Augment, or Reserve, name an owner for every Augment and Reserve row, and write down the audit cadence for Tier 1. Then publish it, and defend it the way you'd defend any filing — because in this industry, sooner or later, you will.

Legacy businesses are feeling the pressure. Pressure to modernize, to adapt, to adopt this new AI tool or that new automated process. Pick up the pace. Cut production time in half — and do it all with half as many humans on the payroll. The message is loud and clear: Get on board or get left behind.

But the real threat to legacy businesses isn't AI. It's not a massive influx of new technology or dramatic changes in the workforce.

The real threat is resistance to change. It's the belief that what worked for the last 50 years will continue to work for the next 50.

I understand the instinct to operate from an "If it ain't broke…" mentality. In many legacy businesses, long-standing systems and processes aren't simply operational choices; they're often tied to memories, milestones, or moments of success and growth in the business. But in an era where the newest technology is driving the conversation and shaping the industry, it would be naive to believe a legacy business can thrive by simply maintaining the status quo.

As a second-generation leader, I feel this conflict personally. I grew up in The Brokerage Inc., watching my parents build it from the ground up, making difficult decisions along the way, never losing sight of their purpose. I saw their work ethic, their dedication, and the sacrifices they made to turn their dream into a reality — a legacy that would outlast them.

When your business also carries personal, emotional ties, leadership comes with a different kind of weight. Every decision must be viewed through several lenses: Will this make the business stronger? Will it help us better serve our customers? Does it require us to sacrifice our core mission or ideals? Can we adapt and stay competitive without losing the values that made the company worth preserving?

So how do legacy business leaders keep up with rapidly changing technology, shifting workforce expectations and a competitive marketplace, while preserving what our predecessors built and entrusted to us?

Reinvent Without Replacing

Reinventing your business does not mean abandoning your identity. In fact, the strongest companies are often the ones that know their identity well enough to inherently understand what should and shouldn't be changed.

Insurance is still a deeply relationship-driven business, especially during major life, health, and financial decisions. Trust matters. Service matters. The ability to guide people through major decisions matters. Those are not outdated values. They're intertwined with our core beliefs and a major part of who we are.

But the way we support those relationships can and should evolve.

With AI bursting onto the scene, many legacy business leaders see it as a disruptor — the antithesis of that deeply personal, human connection that makes up the very foundation of their business. Customers need more than just information. They need guidance. They need confidence. They need someone who can help them understand their options and make the right decisions.

The fear of losing that personal touch is causing some leaders to dig their heels in and double down on their "business as usual" attitude. But what if we could shift our perspective a bit? If we view AI as a multiplier for strong organizations, instead of a replacement for human connection, it could be that extra ingredient that makes already-strong teams even stronger, helping them make an even bigger impact, be more responsive, more efficient, and more focused on meeting the needs of their customers.

Supporting Tradition Doesn't Mean Stagnation

The same balance applies to tradition. I sometimes hear people talk about legacy businesses as if their longevity came from doing things the same way forever. But no business survives for decades without adapting. The founders of long-standing companies have all faced disruption at some point. It may not have been AI, machine learning, or automation, but they had to navigate change, take risks, adjust their thinking and make hard decisions in real time.

A company that is still standing in its second or third generation is not proof that change can be avoided. It is proof that someone before us was willing to change when change was needed.

This perspective has helped me reframe modernization. Change is not a betrayal of what previous generations built. In many cases, it is the most respectful thing we can do for it.

If my parents had not been flexible, determined, and willing to evolve, there would be no legacy for me to help carry forward. So, the question for leaders like me is not, "How do we keep everything the same?" The better question is, "How do we protect the purpose — the reason we do what we do — while adapting to new methods of doing it?"

Guiding the New Generations

That question applies to people, too. There's concern in our industry about whether younger generations are even interested in insurance, sales, agencies, or ownership anymore. I understand the concern, but I choose to view it differently. In my experience, younger generations are interested in this industry, particularly when they see opportunity, mentorship, entrepreneurship, flexibility and purpose. It's our responsibility to provide these opportunities, help nurture their curiosity, demonstrate our purpose, mentor and guide the new generation coming in.

Last week, an old friend and teammate from my college volleyball days called me out of the blue. She coaches a young woman who is about to graduate and start her career in life insurance sales. She and a few friends will be joining a captive agency.

But my friend didn't call to ask if I would recruit the student or entice her to come work with us instead. She called because she knew I would be genuinely excited to hear that a college graduate was entering the industry and that I would love the opportunity to speak to her and encourage her.

She was right. I was excited.

Here's this young woman, fresh out of college, choosing to pursue our industry. She wants to learn how to network, build relationships, generate leads, and maybe one day open her own agency. I told her I thought starting captive could be a great path, because it can provide structure, training, and exposure to the business.

I hung up feeling hopeful. Excited for the future.

There are important moments when we, as leaders, need to recognize our role in preserving and growing this industry. If we only make time to mentor young talent working for us, we're doing ourselves, the industry overall, and this up-and-coming generation a disservice. The industry needs capable, ethical, motivated people entering the business, regardless of where they begin.

And who knows? Maybe one day, when she decides to go independent, I'll be her first call. Maybe not. Regardless of where she ends up, the industry is better off with her in it.

Succession and Stewardship

Another big question that surrounds legacy businesses today is succession. But it's about more than transferring ownership. Of course, ownership matters. Governance matters. Titles, roles, and responsibilities matter. But those are only part of the handoff.

The harder question is whether the next generation understands why this business exists, what core values we uphold that should never be compromised, and how to keep evolving when the market changes again. Because it will.

My parents wanted me to have the opportunity to lead one day, but that opportunity came with the expectation that I understand every aspect of the business, from the ground up. Starting in an entry level position was not a formality. It was crucial for me to see, experience, and respect every moving part of this business, the people doing the work, and the responsibility that comes with every decision.

That experience shaped how I think about change. The farther you are from the day-to-day work, the easier it is to talk about transformation in theory. But when you understand the people, processes and relationships inside a business, you realize change has to be both bold and thoughtful. It must move the company forward without dismissing the people who helped build it.

The Balancing Act for Legacy Businesses

We have to ask ourselves difficult questions. Are we protecting our values, or are we protecting our comfort? Are we honoring our history, or are we hiding behind it? Are we preparing the next generation to preserve what we built while also pushing it forward?

The future will not belong only to the newest companies or the most technologically advanced ones. It will belong to organizations that can combine trust and tradition with flexibility and adaptability. It will belong to leaders who understand that legacy is not a reason to resist change. It's a reason to embrace it and use it to shape the future of your company — so the relationships, trust, and culture it was built on can endure for another generation.

A convergence of forces is reshaping the U.S. retirement landscape. With only about 15% of private-sector workers covered by traditional pensions—and continuing concerns about Social Security's long-term solvency—the burden on individual savings has never been greater.

Asset managers and carriers have responded with in-plan annuity solutions designed to convert savings into guaranteed lifetime income, most commonly embedded within target-date funds (TDFs)—the path of least resistance for sponsors and participants.

Recent Alvarez & Marsal research among participants aged 40–60 highlights the latent demand: Only 3% had heard of in-plan annuities, yet 92% want their employer to offer income solutions, and 84% would be more likely to purchase if automatically included in their plan. Seventy percent ranked guaranteed lifetime income as "most important," with fear of outliving savings scoring 4.29 out of 5.

Despite this, adoption remains low. Only 6–16% of plans currently offer guaranteed in-plan income solutions, with uptake concentrated among larger employers. Participants remain largely unaware.

Yet 2025–2026 marks a potential tipping point. Assets in TDFs with annuity components reached $42 billion by March 2026, up nearly 70% year-over-year. Broader multi-asset portfolios with embedded annuities now exceed $115 billion. TIAA research shows 76% of defined contribution plan sponsors expect demand to grow significantly by 2030. JPMorgan's 2025 survey found 79% of sponsors believe their plans should help participants generate retirement income, and 61% of non-offerers are likely to consider adding an option this year.

Interest is rising. Adoption is not. Here's what's holding it back—and what can accelerate progress.

Common Adoption Challenges

1. Lack of Cohesive Education and Marketing

Industry efforts remain fragmented and provider-specific. A broader coalition is needed to build foundational awareness before stakeholders can meaningfully compare solutions. Without a shared understanding of what in-plan annuities are and how they work, differentiation efforts fall flat.

2. Inconsistent Terminology

Confusion persists between standalone in-plan annuities and TDFs with retirement income features or managed payout strategies. Advisers have historically preferred embedded solutions within the QDIA. The absence of a common taxonomy breeds hesitation and slows adoption across plan sponsors and consultants.

3. Cost and Complexity Concerns

Sponsors cite administrative and operational costs, especially for mid-size and smaller employers. Participants worry about fees and ROI. High-net-worth segments are less cost-sensitive but place greater emphasis on inflation protection and customization. For most plans, perceived complexity remains a significant deterrent to even evaluating solutions.

4. Fiduciary and Litigation Risk

Even with SECURE 2.0 safe harbors for annuity provider selection, fiduciary concerns remain high inside organizations. Only 37% of sponsors feel confident explaining annuity value to decision-makers. The psychological weight of potential litigation often outweighs the regulatory protections currently available.

5. Plan Design and Portability

Participants want simplicity and portability—especially given an average of 13+ job changes over a career. They want a clear answer to: "If I contribute $X, what monthly income will I receive at age Y?" Older participants tend to prioritize flexibility and portability, while younger participants are more interested in accumulation incentives. There is no one-size-fits-all design.

6. Explanation of Benefits at Scale

Participants trust advisors most (4.35 out of 5). Their confidence in researching financial products is high (4.33 out of 5), but confidence in independently purchasing an in-plan annuity drops sharply to 3.42 out of 5. Scaling credible, trusted guidance—whether through financial advisors, trained benefits consultants, or well-designed hybrid models—remains one of the largest barriers to mass adoption.

Driving Adoption with Plan Advisers and Consultants

Advisers and consultants are the most influential gatekeepers. Roughly 90–92% of sponsors work with them, and their recommendations heavily shape what gets evaluated and ultimately adopted. Conversations with experienced plan consultants reveal several practical realities that providers must address.

Advisers Filter Ruthlessly for Fit and Ease

Solutions that feel complex, poorly integrated, or hard to explain to plan committees are quickly dismissed. Recordkeeper-bundled solutions have a clear advantage due to seamless data flows, participant experience, and lower operational lift—if the underlying product delivers clear value. Direct-from-carrier or asset-manager solutions must demonstrate materially better outcomes or stronger fiduciary support to overcome the added friction of an extra vendor relationship.

Fiduciary Risk Perception Is a Major Barrier

Even with safe harbors, fiduciary exposure feels real inside sponsor organizations. Many plan sponsors view involving a trusted fiduciary advisor—whether their existing consultant or a specialized third party—as one of the cleanest ways to satisfy their duties when introducing complex income products. Providers that offer robust due-diligence packages, clear participant outcome data, continuing monitoring tools, and transparent governance support see significantly higher win rates.

Education and Hybrid Support Are Now Table Stakes

Advisers consistently identify participant education and communication as one of the largest remaining hurdles. Hybrid models that combine a plan-level income solution with targeted, personalized guidance at key inflection points (age 55+, termination, or when participants begin thinking seriously about drawdowns) are gaining traction. Pure "do-it-yourself" approaches are viewed skeptically for most participants. Well-designed "do-it-for-me" solutions with smart guardrails and limited, meaningful choice are generally preferred.

Preference for Thoughtful Choice, Not Overload

Sponsors and participants like the idea of having two or three well-designed income pathways rather than a single rigid option. However, the choice must feel meaningful—different risk profiles, liquidity features, or guarantee levels—rather than confusing. True personalization (factoring in age, health, outside assets, and spending goals) is conceptually appealing but often loses to simplicity, cost transparency, and ease of explanation in real plan committee discussions. Winning solutions offer an intuitive core experience paired with a small number of high-value options.

Retaining Terminated and Retired Participants

As plans become more income-focused, attitudes toward allowing former employees to remain in the plan for continuing drawdown are evolving. Advisers see this as both an opportunity for deeper engagement and greater scale, and a new set of fiduciary and operational considerations. Solutions that handle portability, in-plan income, and former-participant servicing seamlessly gain favor.

Adviser guidance is clear: stop leading with product features and start leading with how solutions reduce perceived risk, simplify decisions for sponsors and participants, and improve measurable outcomes.

Driving Adoption with Plan Sponsors

Securing sponsor acceptance is essential—especially through TDF or QDIA defaults—so participants can actually enroll.

Develop Turnkey, Simple In-Plan Solutions

Participants want formulaic transparency: a clear mapping of contributions to future monthly income at a specific retirement age, with limited customization. Standardized, streamlined solutions with straightforward administration are especially attractive to mid-market sponsors who lack the resources or expertise for complex implementations. The rapid growth of TDFs with annuity components demonstrates that embedding income solutions in familiar structures significantly reduces friction.

Offer Flexible but Manageable Menus

Borrowing from the voluntary benefits "cafeteria" model, sponsors can offer a shelf of options that address different participant segments—while avoiding choice overload. Strong defaults paired with decision-support tools are essential. The goal is meaningful choice without overwhelming participants or plan committees.

Tackle Litigation Risk Through Process Excellence

Carriers can help by offering simplified product designs, clear governance frameworks, and options such as funding in-plan income solutions exclusively through employer match or non-elective contributions (reducing participant decision risk). Full use of SECURE 2.0 safe harbors—combined with strong insurer financial ratings, transparent selection processes, and continuing plan committee education—further de-risks adoption for sponsors.

Driving Adoption with Plan Participants

Participant adoption, contribution levels, and long-term retention should be core KPIs for any in-plan annuity provider. Research and adviser feedback point to several high-impact levers.

Design for Radical Simplicity and Portability

Participants value clarity and mobility. Products must deliver an intuitive understanding of the value exchange and accommodate the reality of frequent job changes. Portability features and rollover-friendly design are table stakes for building trust across life stages.

Close the Explanation-of-Benefits Gap

Participants trust financial advisors far more than digital tools or chatbots. Human expertise—whether through financial advisors for higher-net-worth segments or trained benefits consultants and hybrid models for broader populations—will be required in the near term. Clear calculators, scenario modeling, and "If I contribute $X, what monthly income will I receive at age Y?" illustrations are non-negotiable for building confidence.

Provide Credible, Independent Comparisons

Participants want to understand how in-plan annuities compare—not only to other annuity products but also to alternatives such as indexed universal life insurance, real estate, or systematic withdrawal strategies. Credible, easy-to-understand independent or third-party illustration tools will be essential for informed decision-making and for demonstrating relative value.

Demographics matter: younger participants tend to respond to long-term accumulation combined with a guarantee, while older participants and pre-retirees prioritize decumulation security, flexibility, and portability. High-net-worth participants, who already work with advisors at much higher rates, represent an important beachhead for advisor-distributed solutions.

Conclusion

The in-plan annuity market has reached an inflection point. Macro tailwinds—erosion of traditional pensions, heightened longevity awareness, surging assets in TDFs with annuity components, and the SECURE 2.0 framework—have created the conditions for acceleration. But translating interest into widespread adoption will require deliberate, coordinated action across advisers, sponsors, and participants.

Further federal policy clarity (building on SECURE 2.0) and industry collaboration on common terminology, education standards, and best practices would remove significant friction. Carriers and asset managers, however, control the most important variables. They can define clear KPIs for each stakeholder group—adviser recommendation rates, sponsor adoption percentages, and participant enrollment and satisfaction—measure performance rigorously, and iterate quickly with simpler, more trustworthy, and better-designed solutions.

Those who move decisively now—armed with clear evidence of massive latent demand against a backdrop of only 3% awareness—will be best positioned to capture meaningful share of the trillions in defined contribution assets moving into decumulation. The window for leadership in this next chapter of retirement plan innovation is open, but it will not remain open indefinitely.

There has been much wild speculation about the future of software-as-a-service (SaaS) applications in light of artificial intelligence (AI), with suggestions that a "SaaSpocalypse" will occur due to AI sweeping away the need for core SaaS-based systems. This is not very helpful for insurance companies weighing up their future IT adoption strategies. It is clear that AI tools will be valuable, but customers need a calm, rational discussion about how best to implement the technology in enterprise IT settings. If SaaS vendors want to prove they still matter, they must demonstrate that SaaS is not just relevant but is the indispensable foundation of every successful IT transformation.

Cloud-native applications are the right foundations to embrace AI

The crucial questions customers need to address when deciding how best to transform their IT systems are not whether they should replace their SaaS applications with AI, but what is the best transformation strategy to minimize disruption and provide measurable returns. Having extensive contextual understanding of the specialist workflows and language that different applications use means SaaS vendors are better placed to ensure AI tools work effectively for insurers. The leading AI vendors understand this point, because they know building enterprise software for mission-critical applications is hard and not something an AI agent can unpick overnight.

However, this does not mean SaaS vendors can rest easy. I would agree with Insight Partners: "Trust, user stickiness, and existing ROI may provide some breathing room for cloud-natives to build new AI features that cement value, but they have to hustle to keep that position."

The key phrase here is "cloud native." Applications built for the cloud are better placed to help customers embrace AI, because this approach requires interoperability between systems. This means AI tools can access a single view of all the data across an organization, which is critical if insurers are to have confidence in the decisions these tools make.

Effective AI adoption requires a deep understanding of the insurance industry

That said, being able to integrate AI more easily is not the main reason customers turn to SaaS vendors instead of going to the AI tools providers. Customers are worried about the squeeze on margins due to current economic conditions, and the unprecedented unpredictability in markets around the world. They only care that their business applications help them address these issues. SaaS vendors must show they understand this driver for IT transformation and be prepared to take risks to prove their commitment.

This requires a dramatic rethink of how value is delivered. The agentic AI model switches the focus to achieving business outcomes rather than simply processing transactions. The technology can proactively interpret objectives, break them into tasks, make decisions within defined boundaries and monitor progress.

This resets how users interact with applications. Consequently, SaaS vendors must leverage their specialist industry knowledge to demonstrate they can deliver outcomes insurers want.

It is somewhat ironic that these vendors, who themselves a mere 15 to 20 years ago were seen as the big upstarts, must prove their worth in the face of new technologies. The winners will be those who seize the opportunity, are ambitious and don't just try to defend their existing position. This is likely to be one of the most exciting chapters in the evolution of enterprise software so buckle up and enjoy the ride!

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.