Recently, there's been talk from AI-native insurance startups telling incumbents they'll never catch up. The argument goes like this: The barrier isn't technology; it's organizational DNA. Boards resist. Agent networks resist. Incentive structures resist. Even superintelligent AI can't rewrite a captive distribution network or a CEO's risk tolerance.

We built one of those AI-native insurers. We've spent nearly a decade learning where AI actually works in insurance - and where it doesn't. So we'll say what most people in our position won't: 

The critics are only half right.

The organizational immune system is real

We've watched it operate from the inside.

AI threatens more than processes. It threatens people, hierarchies, and decades of institutional knowledge that leaders built their careers on. The more powerful the technology gets, the more threatening the disruption feels, and the harder the organization pushes back.

The execution gap is genuine, too. Deloitte surveyed 3,200 enterprise leaders this year and found that executives feel strategically ready for AI but not operationally ready. Every insurance business we talk to confirms this. The board said yes. The pilot worked. But not much actually changed. They tripped in the last mile.

If you're reading those blog posts and feeling uneasy, trust your instincts. Standing still is falling behind.

Where the thesis breaks

The "incumbents are dead" argument assumes the only way to win with AI is to have been born with it. That organizational barriers are permanent. That traditional insurance businesses are evolutionary dead ends waiting for the asteroid.

This confuses two problems.

The first is building AI technology. AI-native startups have a real advantage here. Clean architectures, ML engineers who learned to work alongside actuaries, feedback loops from day one.

The second is having the operational context that makes AI actually work in insurance. Here, traditional businesses have an advantage no startup can replicate.

A startup can build a great claims model. But it doesn't know that your Florida team handles litigation differently than your Texas team because of venue-specific judicial considerations. It doesn't know that your underwriting knowledge base says one thing but your senior underwriters do another - and the deviation is actually producing better results. It doesn't know which of your 50 state regulatory constraints are real compliance requirements and which are institutional habits nobody has revisited in a decade.

That operational context - the messy, human, state-by-state reality of how insurance actually works - is the raw material AI needs to generate value. Technology is the engine. Context is the fuel. Insurance businesses have been accumulating this fuel for decades.

The startup pitch is: "We have the engine, and we'll figure out the fuel." The honest answer is that the fuel is harder to build than the engine.

The real question is speed

Can you close the execution gap before it shows up in your results?

The gap closes by connecting AI to the operational reality of how your business actually runs - across claims, underwriting, distribution, and compliance - in ways that compound over time.

Every month of operational AI data makes the system smarter. Every feedback loop accelerates the next one. This is an exponential curve, not a linear one. The businesses that start building now aren't just catching up. They're beginning a compounding process that gets harder to replicate with every cycle.

We spent nearly a decade building these feedback loops inside our own company. That experience made one thing clear: The distance between an AI demo that works and an AI system that changes how you operate is almost entirely about understanding the insurance underneath.

What I'm telling insurance executives right now

Your data is an asset that will appreciate with use. Your operational context can be youradvantage. The AI-native startups telling you it's over are talking their own book.

Some businesses already know this. The ones investing seriously in operational AI - not pilots, but production systems touching real policyholders - are proving the thesis wrong in real time.

We're seeing this from carriers, MGAs, and specialty businesses alike.

But the window is real. AI feedback loops compound. The businesses that start building them in the next 12 to 18 months will pull away from those that don't. You'll see it first in expense ratios, then in loss ratios, and then in competitive position.

The businesses that win won't become AI companies. They'll stay insurance companies that figured out how to make AI compound inside their operations before the window closed.

For independent agents, the to-do list never gets shorter. New clients to win, policies to place, and revenue to grow. But there's one conversation that doesn't always make it onto the planning agenda, and it might be the most important one of all. What happens when it's time to hand things off?

Succession planning has long carried a reputation as something to worry about later. A conversation for agents nearing the end of their career, not those in the thick of building their business. But that thinking can be costly. The agencies that make the transition most smoothly aren't the ones that started planning at the last minute. They're the ones that built transferable tech operations from day one.

Here's the good news: if you're already using an agency management system to run your daily operations, you're likely closer to succession-ready than you think. The tools that help you manage client account data, track performance metrics, and stay on top of renewals can do double duty. Used consistently, they build the kind of organized and documented operation that makes handing things off far less daunting.

Performance Metrics Tell Your Agency's Story

When it comes time to demonstrate value, data speaks louder than anything else. Potential successors and buyers will want a clear picture of your agency's performance, including which lines of business are driving the most revenue, which producers are performing, and where coverage gaps exist across the book. Those answers need to be readily accessible.

A robust agency management system gives you this performance visibility in real time. Dashboards and reporting tools surface the metrics that matter most, from total annualized premium and active policies per customer to a detailed breakdown of your book of business by transaction type, often presented in intuitive visual layouts.

You can customize these reports too, filtering and drilling down into the data points that matter most. Some systems even let you benchmark your performance against peer agencies, giving you a clearer sense of where you stand. That kind of insight doesn't just serve a future transition. It sharpens your decision-making today, helping you spot growth opportunities and course correct before small issues become bigger ones.

Over time, these reports build a compelling picture of your agency's health and trajectory, one that tells a clear story to a successor and makes you a stronger agency today.

AI Keeps Client Knowledge Transferable

Serving clients without missing a beat is one of the first challenges any incoming leader faces. That means being able to find policy details quickly, understand coverage history, and get up to speed on the relationship without having to track down the person who used to handle it. When information is scattered across inboxes, desktop folders, and spreadsheets, that handoff becomes harder and more costly than it needs to be.

AI-powered agency management tools change that, and not just when a transition is on the horizon. Picture this: a newly onboarded staff member pulls up a long-standing client account in their first week. Rather than digging through months of email threads and agent logs, they get an instant summary of the relationship, enabling more knowledgeable client interactions and a much faster path to getting up to speed.

Clients expect continuity. They don't want to repeat themselves or re-explain their history with their agency. They expect whoever picks up the phone to already know them. AI makes that possible whether you're onboarding a new hire, navigating a leadership change, or simply trying to deliver a better client experience every day.

Renewal Tracking Protects What You've Built

Retention is the metric that tells the clearest story about an agency's health. A consistent renewal process signals that clients are being taken care of and that the book of business is stable.

A good management system gives you and any future leader a single view of every upcoming renewal, what has changed between the current policy and the renewal offer, and which clients are most at risk of shopping around. Predictive analytics flag at-risk policies before they become problems. Automated remarketing workflows retrieve updated rates and surface them alongside renewal details, so whoever is managing the book can act quickly and make informed recommendations.

For a buyer or successor, a clean and consistent renewal process is one of the most compelling things they can walk into.

No Matter Where You Are in Your Career — Start Now

Whether you're just launching your agency, in the middle of a growth run, or beginning to think seriously about the future, the time to invest in technology-enabled workflows is now.

The efficiencies you gain today will compound over time, and when the moment comes to pass the pen and the policies, you'll be glad you started early.

Despite seeming predictable in hindsight, Black Swans are unexpected or unforeseen events that are highly disruptive and economically damaging. Examples include the 9/11 attacks of 2001 in the U.S., the 2008 global financial crisis, and the COVID-19 pandemic. Allianz Research estimates cumulative global GDP losses from the pandemic between 2020 and 2023 to be in the region of $12 trillion.

In addition to the huge financial and business costs, such events typically have long-lasting implications, resulting in geopolitical and societal shifts that continue many years after the initial event.

According to new Allianz Risk Barometer analysis, more than half of the 3,000+ respondents (51%) identify a global supply chain paralysis due to a geopolitical conflict as the most plausible Black Swan scenario globally that could affect their company in the next five years. Fear of a global Internet outage ranks second (47%), which reflects the increasing awareness of cyber and artificial intelligence (AI) risks among business leaders.

In the United States, global supply chain paralysis tops the list (52%) followed by global Internet outage (45%) and sudden collapse of a major financial institution (33%).

Geopolitics is a key driver for Black Swans

Given the current geopolitical environment, it is no surprise that supply chain paralysis resulting from a geopolitical conflict is regarded as the most plausible Black Swan scenario. The threats of tariffs, trade wars and protectionism, as well as disruption to supply chains and shipping caused by regional conflicts in the Middle East and Russia/Ukraine are on the top of every board agenda.

Allianz Research estimates that cumulative GDP losses over a two-year horizon triggered by a global supply chain disruption on the scale of the war in Ukraine could total $1.5 trillion. In fact, political-related risks stand out as a leading potential trigger for Black Swan events, according to respondents. Mass social unrest and political instability is regarded as the fourth most plausible scenario globally (29%) and is a top three risk in the Americas (31%) and Africa and the Middle East (41%) regions, as well as in France (42%), for example. A sudden collapse of a major financial institution or a sovereign debt crisis, leading to a global liquidity crisis and severe market volatility ranks third (30%).

Interconnectivity and interdependency of both physical and digital supply chains are potentially increasing vulnerability at a time of geopolitical uncertainty, rapid advances in technology, and climate change. Businesses and global supply chains are also more vulnerable to Black Swan events due to growing concentrations of economic activity reliant on a limited number of critical suppliers and products in areas like AI and digital services, semiconductors, rare earth processors and transition technologies.

Company size influences risk perception

Global supply chain paralysis due to a geopolitical conflict halting the movement of goods and raw materials ranks top for both large (>$500 million annual revenue, 55% of responses) and mid-sized companies ($100 million+ to $500 million, 52%). In contrast, smaller companies (<$100 million) are most concerned about the impact of a global Internet outage (45%), which is the No. 2 scenario for larger and mid-sized businesses.

The third most plausible Black Swan for mid-sized and smaller companies is the sudden collapse of a major financial institution, while larger companies are more concerned about the risk of simultaneous climate disaster and energy grid failure, such as a heatwave triggering wildfires and widespread blackouts.

Multinational enterprises have the advantages of bigger budgets and more diversified portfolios and therefore feel they are better prepared to mitigate the risks of an event such as a major Internet outage than their smaller and medium-sized counterparts.

The top global Black Swan scenarios

Preparing for a Black Swan risk involves bracing for the improbable possibility, necessitating a deep comprehension of the intricate web of interconnected risks, the probabilistic nature of modeling tools, and a touch of imaginative foresight. The future risk environment will continue to evolve, and the organizations best prepared are those that continuously assess, adapt, and embed resilience at every level of their operations.

To read the Allianz Commercial Business Black Swans report, click here. 

Insured losses in 2025 broke the $100 billion barrier for the sixth consecutive year — a number that has long been considered a measure of escalating natural catastrophe risk. Without a major U.S. hurricane landfall, however, losses came in below the average incurred during the past decade. While the market may take a moment to exhale, context is crucial. A below-average loss year is not an anomaly; statistically, it is the expectation. Because catastrophe risk is heavily skewed by tail events, the average will always be driven by a handful of very active years, leaving the majority of years falling below the mean.

What makes 2025 stand out is that losses were modest despite the backdrop of elevated hazard potential. Sea-surface temperatures in the North Atlantic have been among the warmest on record, while global mean temperatures continued to test the upper bounds of the satellite era. Exposure concentrations in hazard hotspots are higher than ever, and rebuilding costs continue to rise. The ingredients for large losses were present in 2025, yet the atmosphere chose not to combine them.

This situation is best described as transient meteorological luck: the temporary alignment of atmospheric and oceanic conditions that suppresses loss activity without altering the underlying risk. A quiet year does not signal that the underlying hazard or vulnerability has reduced. Rather, it represents a fortunate gap in natural catastrophes. When viewed through this lens, 2025 stands out as a moderate loss year in a high-risk situation.

While 2025 was the first year in a decade where no hurricanes made landfall in the United States, other places were not so fortunate. Hurricane Erin followed an eastward track well away from the Caribbean but still delivered heavy rain to Guadeloupe and Puerto Rico and high winds to the Bahamas. Hurricane Imelda passed directly over the northern coast of the Dominican Republic and Haiti and brought heavy rains and severe flooding to both countries. The storm also caused heavy rain and landslides in Cuba and strong winds (with gusts up to 100 miles per hour) over Bermuda.

The most consequential event of the past season was Hurricane Melissa, which made landfall over Jamaica on Oct. 28 as a Category 5 storm. It was previously uncommon for major hurricanes to form in October, but as the North Atlantic has warmed, the environmental conditions that are favorable to hurricane formation are lasting later in the year. And that same warming also allows storms to become much stronger very quickly. Compared with the late 20th century, the number of storms undergoing "explosive" intensification (winds strengthening by almost 60 miles per hour in less than a day) has almost doubled.

Long term, the climate is warming, which continues to load the dice toward greater volatility and more complex extremes, from wildfire behavior outside historical norms to record rainfall and the rapid intensification of tropical cyclones. Even if the financial tallies appear muted, the physical risk remains on an upward trajectory.

For example, future wildfire losses in California are likely to exceed what recent fire footprints suggest. Fires can now occur across more of the year, more people and assets are located in flammable areas and rebuilding is materially more expensive. With much of the state's high-value wildland-urban interface yet to experience fire under these conditions, historical loss experience alone is no longer a reliable guide for pricing or portfolio steering.

Meanwhile, flood events in 2025 illustrate the accelerating trend of hydrological intensification driven by continuing global warming. A preliminary global review shows extreme and often record-breaking rainfall on every continent, with many events producing exceptional sub-daily intensities. Crucially, severe flooding is increasingly occurring in locations not historically classified as high risk, prompting renewed scrutiny of exposure and preparedness.

Recent flooding illustrates how once-in-a-lifetime events are now occurring in rapid succession. The United States' Texas Hill Country floods, with more than 500 millimeters of rainfall in two days, exemplified this shift, resulting in substantial loss of life and revealing gaps in emergency response and insurance coverage. Further evidence of this intensification has been seen in Pakistan, Spain and elsewhere. The persistence and clustering of such extremes align with trends clearly established in 2023 and 2024. Climate change is increasing rainfall intensity and expanding flood hazard footprints, while societal exposure continues to outpace preparedness.

Quiet years often breed a false sense of security. The 2006–2016 drought of major hurricane landfalls in the U.S. created an illusion of reduced risk. But the inevitable return of high-impact events in 2017 (including hurricanes Harvey, Irma and Maria) taught us that lucky streaks always end. As the reinsurance market softens, the temptation to chase premium can erode discipline, leading to the silent accumulation of risk. In this environment, strong scientific judgment, rigorous model evaluation and robust exposure management frameworks will be essential safeguards.

Now is the time to dig into the data. Leveraging research to develop bespoke views of risk, such as climate-conditioned event sets or vulnerability functions based on recent claims experience, allows cedents to distinguish portfolio resilience from temporary good fortune. Today, (re)insurers increasingly expect not only robust numbers from models, but also a transparent account of the science and limitations behind them.

Meteorological luck can delay the inevitable but does not offer lasting protection. The question is whether the industry uses this pause to relax or to prepare for when the pendulum inevitably swings back.

For more than a decade, insurers have worked from a shared assumption: Spreadsheets are legacy applications that modernization will eventually solve. Excel was the stepping stone. You were supposed to outgrow it as systems matured.

That assumption is breaking down.

Across carriers accelerating AI adoption, spreadsheet usage isn't declining. It's increasing. Spreadsheets remain central to the insurance value chain from actuarial modeling and rating and pricing, to underwriting, reserving, and financial reporting, especially in specialty lines. Even as AI takes the spotlight with copilots, conversational interfaces, and automated workflows, under the hood, the actual calculations remain in Excel.

Microsoft CEO Satya Nadella has described Excel as effectively Turing complete. It can express complex logic in a way business users, auditors, and regulators already understand. Decades of institutional knowledge are encoded there. When an actuary needs to build, test, and revise pricing logic without involving a dev team, Excel is still the fastest path from idea to production.

None of this is a temporary gap in modernization. AI actually increases the need for deterministic, explainable calculation engines. In insurance, those engines already exist — in spreadsheets.

So the real question for carriers isn't whether Excel will persist. It's whether spreadsheet-based logic can be governed, automated and deployed at the scale AI now demands.

The Compounding Challenge

Large language models (LLMs) can now generate sophisticated spreadsheet models on demand. We saw this firsthand when our head of sales engineering tested ChatGPT to build a term life underwriting rules engine in Excel. It worked surprisingly well.

His reaction: "There are going to be a lot more Excels in the world soon."

But these AI-generated spreadsheets introduce a new kind of opacity.

When an actuary builds a pricing model by hand, they own it in every sense. They can walk you through every assumption and defend every edge case. That model is an extension of their thinking. When AI generates the same model, the formulas might be cleaner, but the reasoning that makes the logic defensible in an audit isn't embedded in the file.

Why AI Acceleration Creates a New Class of Risk

As spreadsheets get wired deeper into AI workflows, a different kind of risk surfaces. One most governance structures were never built to handle.

Consider a common scenario: an AI application calls a rating spreadsheet to generate a quote. But that spreadsheet was updated last week by someone on a regional team, and the production version hasn't formally been approved. Now the AI is using knowledge and logic nobody reviewed and nobody approved.

The gap between the logic you think is in production and the logic that's actually in production widens without anyone noticing. In a regulated industry, that drift has real consequences.

Pricing and underwriting decisions still need to be reproducible, documented, and defensible long after execution. Yet spreadsheet controls at many carriers remain manual and inconsistent. A single "fat-finger" error can misstate a rate, and by the time anyone catches it, the exposure is already on the books.

Without confidence in spreadsheet governance, organizations default to one of three paths. They slow approvals down to reduce risk. They push them through and let risk accumulate unnoticed. Or they treat spreadsheets as the problem itself and launch a costly transformation program, only to find the rebuild consumes years of IT bandwidth while the spreadsheets never quite disappear. Regardless, it's governance that sets the ceiling on AI velocity, not model quality or compute power.

Governing Spreadsheets as Enterprise Infrastructure

The insurers making real progress have accepted that spreadsheets aren't going away. Not in the medium term, and possibly never for some lines and use cases.

Rather than waiting years for a full platform replacement, they've moved to a more strategic question: how do we treat this logic like the infrastructure it actually is?

The urgency is justified. Only about 10% of firms are using AI in any meaningful way, according to a recent U.S. Census Bureau survey, and nearly half of respondents in a UBS survey cited compliance and regulatory concerns as a top barrier.

Governed spreadsheet logic offers a way through.

When an actuary can hand a state regulator an Excel-based rater that's been converted into a governed, callable service, with the AI output and the rater file one-to-one matched and documented, the conversation changes. Regulators get what they need. Domain experts stay in control. And AI adoption gets what it usually lacks: a foundation people will actually stand behind.

What does that look like in practice?

Versioning, change history, validation evidence, and audit trails get enforced automatically. Spreadsheet calculations get deployed as services that downstream systems, including AI applications, can call directly.

The logic stays in Excel. Governance wraps around it.

Coherent, for example, does exactly this — transforms those spreadsheets into governed, API-driven, enterprise-grade assets, without asking the user to leave Excel.

The Strategic Choice for Insurers

Your pricing logic, your underwriting rules, your reserving models.

That's not technical debt sitting in Excel. That's your IP.

The carriers pulling ahead aren't the ones rebuilding everything in proprietary systems. They're putting governance infrastructure around what already works, so AI initiatives can leverage approved calculations directly, without translation risk or months of development delay.

That's the real divide.

Not between companies that use spreadsheets and companies that have "moved past" them. But between companies that govern their logic as infrastructure and companies that let it sprawl.

AI isn't replacing spreadsheets. It's raising the stakes on every one of them.

Walk into any bank branch, insurer's office, or contact hub, and you'll see the same challenge playing out: frontline teams juggling conversations, systems, and expectations in an environment where customers expect answers now -- and on the channel of their choice.

Consumers today benchmark banking, financial services and insurance (BFSI) service not against competitors but against the best digital experiences they see anywhere. The organizations that succeed in 2026 won't simply add more tools; they will blend human advice with smart, invisible technology, so their advisors, agents, relationship managers, and service reps can engage faster, personalize better, and build trust at scale.

What Customers Want (And Where Organizations Still Fall Short)

Customers want digital convenience, but they also want expert human guidance for financial decisions that carry risk:

• Choosing an insurance plan
• Applying for a loan
• Completing know-your-customer (KYC) requirements
• Making investment decisions
• Resolving service or dispute issues

McKinsey's customer-experience research across financial services shows why this matters:

CX leaders achieve stronger revenue growth, lower costs, and higher employee engagement than their peers - and generate meaningfully higher shareholder returns.

But many journeys across BFSI remain fragmented.

Customers begin online, switch to chat, walk into a branch or connect with an advisor, only to repeat themselves at every step. Follow-ups may be inconsistent, and context often gets lost between channels.

Yet the human element remains critical: across financial services; trusted advisors and relationship managers consistently rank as the most valued touchpoint, so much so that switching behavior rises when advisors leave or service becomes inconsistent.

The message is universal across BFSI:

Customers don't want to choose between people and technology. They want both, seamlessly connected.

Why Frontline Productivity Is the New Customer Experience

Customer experience and employee experience are inseparable.

When advisors and agents spend time hunting for information, switching screens, or reentering data, customers feel the friction immediately.

McKinsey finds that organizations that engage frontline teams as co-creators in CX transformation see:

• Higher customer satisfaction
• 20% improvement in employee satisfaction
• Faster turnaround time
• Stronger cross-sell and retention

In BFSI, this link is especially clear:

• A banker who has instant access to a customer's full financial picture can advise better.
• A field officer with mobile access reduces turnaround times.
• An insurance advisor with contextual prompts can personalize without delay.
• A contact center agent with unified history avoids escalations.

Technology doesn't replace expertise - it amplifies it.

Accenture's latest research reinforces the urgency: customers increasingly report difficulty reaching a human when needed, or navigating service journeys. In a landscape where attrition is high and expectations are rising, BFSI organizations must remove friction, not add new layers of complexity.

Where AI Fits: Practical, Not Hype

AI's role is not to automate away financial conversations - it is to shorten the distance between a customer question and a confident, compliant answer.

IBM's Institute for Business Value says that:

75% of financial services executives believe AI will improve personalization and CX but warn that fragmented systems and weak data foundations remain barriers.

The promise of AI becomes real when paired with clean data, integrated workflows, and empowered frontline teams.

In BFSI, this looks like:

1. Advisor / agent assist

Real-time summaries, next-best-actions, explanations of product rules, contextual prompts - helping frontline teams spend more time advising and less time searching.

2. Smarter routing

AI triages inbound requests to the right channel:

• Self-service for simple tasks
• Human support for complex or high-value conversations

This reduces drop-offs and improves first-contact resolution.

3. Engagement

Detecting repayment cycles, renewal windows, eligibility changes, life events, and portfolio gaps - then nudging advisors with timely reasons to reach out.

4. Faster, cleaner onboarding

AI-assisted KYC checks, document classification, real-time validation, multilingual support, especially important for banks, insurers, and non-banking financial centers (NBFCs).

Designing the "Human + Tech" Operating Model Across BFSI

To make this transformation real, BFSI leaders are anchoring around a few core principles:

Human-led, tech-accelerated journeys

Map the moments where customers need reassurance (loan approvals, investments, claims, disputes). Then use tech to handle the rest - context-gathering, data prep, routing, and follow-ups.

Modernize the service core

Accenture's "Service on the Brink" findings highlight a striking gap: customers don't feel technology has meaningfully improved service quality yet.

That gap closes only when:

• Data is unified
• Systems are connected
• AI is grounded in real rules
• Handoffs are seamless

Measure what matters

Beyond cost KPIs, leading BFSI organizations track:

• Advisor time saved
• First-contact resolution
• Customer effort scores
• Quality and consistency of advice
• Employee adoption and satisfaction

AI and automation must serve people, not distract them.

The New Standard of Service in BFSI

Picture a loan customer messaging a query.

A virtual assistant validates identity, surfaces repayment options, and flags eligibility for a top-up offer.

The customer asks a nuanced question; within seconds a relationship manager joins, armed with a clean summary of past interactions, risk profile, and suggested talking points.

The conversation finishes quickly, the customer feels understood, and the advisor moves confidently to their next engagement.

This is where BFSI is heading:

Frontline expertise supported, not overshadowed, by technology.

When AI, automation, and connected systems fade into the background, advisors and agents can lead every interaction with clarity, empathy, and insight.

That's the real frontier.

References

● McKinsey & Company. “Elevating customer experience: A win–win for insurers and customers,” 2023.
● IBM Institute for Business Value. “Insurance in the AI era,” 2025.
● Accenture. “Customer Service on the Brink,” 2025.
● McKinsey & Company. “The Human Advantage in Banking Customer Experience,” 2023.

In the legal profession, the work is only as strong as its support. A good argument isn't just persuasive, it's backed by citations. You can point to the contract clause, the case, the exhibit, and the chain of reasoning that got you to the conclusion. That's not academic formality. It's how legal work survives scrutiny in a court of law. 

Insurance is moving in the same direction.

For decades, insurance operations could lean on experience and muscle memory. A tenured underwriter knew what "this form usually covers." A claims leader knew the standard response posture. A broker knew which markets were flexible. That knowledge still matters, but the environment has changed. Regulators, legal teams, and procurement groups now expect decisions to be documented, explainable, and consistent over time.

"Trust me" is no longer a reasonable operating model.

Why legal workflows look the way they do

Legal work is predicated on the ultimate potential that it will end up in front of a judge. This fear shapes all the work lawyers do. 

A motion, an opinion letter, or a contract position might get scrutinized months or years later by a judge, with millions of dollars at stake. The only way to truly prepare for that situation is if the work product is structured to be audited. That's why legal workflows emphasize three things:

Citation. Show exactly where the claim comes from.

Reasoning. Make it possible to retrace the reasoning steps from source to conclusion.

Conclusion. Make it easy for another expert to validate or challenge the conclusion by having a very clear and articulate conclusion.

These practices aren't about slowing work down. They're how the legal industry moves quickly while staying defensible when the stakes rise.

Insurance is discovering the same truth, especially in claims and coverage interpretation.

Insurance is already under similar scrutiny

Insurance has always been regulated, but scrutiny is broader now and comes from more directions, including clients. Decisions can trigger omissions, bad-faith allegations, and liabilities that far exceed a coverage dispute.

State-by-state variability adds another layer. A defensible decision in one jurisdiction may be incomplete or risky in another.

At the same time, the work is deeply document-driven. Policies, endorsements, submissions, claim files, and correspondence are still stored in PDFs, scans, and formats that were created for manual review.

That means insurance decisions are often anchored in unstructured language that must be read carefully, compared across documents, and defended later.

In short, insurance faces legal-like constraints whether it realizes it or not.

The AI factor raises the bar, not just the speed

AI is often discussed as a productivity lever, but in insurance, the real challenge is credibility. When an AI-supported decision gets scrutinized, you need to show the basis for it. If the answer is a black box, you've created a new type of risk.

That's why the industry is increasingly prioritizing accuracy, explainability, and consistency over speed alone.

It's also why "model drift" matters. If a tool's behavior changes over time, it undermines consistency and auditability in regulated workflows.

This is one place where legal has a head start. Many legal technology workflows were designed around precedent and review. The focus is less about generating text and more about interpreting documents with citations and a clear path from source to conclusion.

Insurance now needs the same.

The future of insurance work

This shift isn't theoretical. It changes how teams should define quality.

In an insurance workflow built to withstand scrutiny, a good outcome isn't only correct—it's defensible. That means:

Your conclusions should point back to the policy language. Coverage positions and claim decisions need to be anchored in the actual text, not just institutional memory. Insurance has long depended on expert judgment in how professionals read policies, interpret exclusions, and apply precedent. The next step is making that judgment visible and reproducible.

Your reasoning should be transparent enough for peer review. If a colleague can't follow how you got there, an auditor or regulator won't either. Transparent reasoning isn't a luxury in high-stakes decisions, it's a requirement.

Your process should be consistent across teams and time. Insurance is full of niches and specialized expertise, but inconsistency is costly. As experienced practitioners retire, decision quality can decline if judgment stays trapped in individuals. Understanding the policy is the hardest problem in insurance. You can't solve it with expertise that only exists in people's heads.

Your documentation should be structured for "future you." Claims files, coverage analyses, and underwriting notes should read like work that's expected to be examined later. That's the legal mindset, and it's becoming the insurance mindset.

This is also why many leaders are talking less about flashy AI and more about repeatable operating models. The most valuable AI in insurance will look like consistency, documentation, and fewer surprises.

The practical takeaway for insurance leaders

If you're leading claims, legal, compliance, or brokerage operations, the question isn't whether your teams will use AI and automation. They already are. 

The real question is whether you're building systems that will hold up when scrutiny arrives. That means setting standards for citation, traceability, and reviewability in the work itself, not as an extra step at the end.

It also means resisting tools and processes that optimize for speed while sacrificing transparency. In insurance, "close enough" is often where the risk begins. 

Insurance is learning a lesson the legal industry learned long ago: when the stakes are high, the work must show its sources.

The AI market in the insurance industry is set to hit $80 billion by 2032, yet nearly two-thirds of carriers have a gap between their AI vision and reality. In essence, carriers understand what they want from AI and see the significant value it can drive but are struggling to actually put this into practice, especially in the property sector.

Marrying vision and reality is critical, however, as carriers look to scale automation, re-orchestrate and transform workflows, and fully realize the potential of AI across the lifecycle of a claim. There is a substantial downstream impact on claimants and clients, as well. 

There are a number of steps a carrier needs to take to find success with AI in 2026 and ensure they are future-proofed and ready for the next era of property claims.

The AI Journey for Property Carriers

First and foremost, carriers need to understand where they are on their AI journey. This is important in identifying what the next steps are, what's feasible in the short term, and eventually in the long term, and aligning company communications, operations, workflow, training, and more.

At the moment, anywhere from 58-82% of carriers are leveraging AI tools in their operations, but only 12% claim to have fully mature capabilities, and only 7% have achieved scalable AI success. This means that 93% of carriers are still in the part of their AI journey in which they are identifying how to scale AI to a point at which it is driving real, measurable outcomes. What we've seen so far is that adoption of AI has been popular in areas such as intake, triage, and documentation, but fully integrated technology and end-to-end AI workflows are still far away for most carriers. This, in turn, results in a fragmented technology experience, rife with different tools, vendors, and solutions. This limits AI's impact. It keeps value confined to each step in the lifecycle of a claim, can lead to inconsistent data or silos across systems, and weakens output.

Reliance on pilot programs or point solutions is the first step in an AI journey, but it certainly can't be the last. Most importantly, this technology is rapidly advancing, and the longer it takes carriers to find value and scalability, the further they'll fall behind the competition.

The Challenges Facing Carriers

There are three main challenges facing carriers. First is integration of AI into legacy technology. The majority of claims systems weren't built with API connectivity in mind, which introduces difficulties immediately into scaling this technology across workflows. Before integration even begins, carriers need to ensure that their claims systems can support orchestration.

Second is training a disconnected property workforce. An often-overlooked aspect of AI in the property space is preparing for the challenges that can arise when adjusters are managing heavy caseloads and working in the field. Support systems are critical to success in this area, and AI and any other new tools cannot feel like a burden to them. Training and communication in best-use cases are important in presenting these tools as benefits. This can be streamlined through rollout plans that align with day-to-day workflows, prioritize flexibility, and implement continuing training opportunities.

Finally, expecting AI tools to drive perfection is a key challenge. This technology won't deliver perfect outcomes from day one, but through gradual improvements can drive real change in processes. If too much focus is placed on perfection, then widespread implementation can be delayed. Instead, carriers should prioritize progress first and perfection second when measuring AI against real-world baselines, with the goal of refining capabilities over time.

What Real Impact Can Look Like

When challenges are addressed and overcome, and carriers understand how to progress from point A to point B in their AI journey, real impact can be achieved. This will be seen in a number of ways across operations.

Main points of impact will be evident in faster claims reviews in which AI is helping adjusters summarize claims, extract data, and capture notes more efficiently, as well as in improved program outcomes, smoother workflows across internal and external systems, and smarter claim routing. AI tools can evaluate loss severity, complexity, and fraud risk at intake, assisting in routing claims to the right recovery resource sooner.

In addition, adjusters will see stronger field operations through enhanced drones, sensors, and tablets, which enable faster mitigation, better assessments, and quicker resource mobilization.

The data backs up this impact. Intake automation has reduced average claim processing from 10 days to 36 hours, AI photo analysis boosts claim handling efficiency by 54%, and much more.

Prioritizing Human Expertise

A key consideration in the integration of AI is the increasingly important role that humans play, and will continue to play, in this process. AI should be seen as a tool to augment and support workforce expertise, rather than replace it.

This technology is powerful, but cannot be used to replace human judgment, empathy, or real-world experience in the claims process. Losses can be ambiguous, emotionally sensitive, or require nuanced, complex coverage decisions that AI cannot handle, and require human professionals to consider context, communicate clearly, and advocate for policyholders throughout each stage.

In essence, AI is a catalyst, not a cure-all, and carriers must aim to apply AI selectively while keeping people at the center of their claims decisions. Striking this balance will be the difference in staying ahead and remaining competitive in a rapidly changing technological and regulatory landscape.

For Sedgwick's full report on "Future-Ready Property Claims," click here.

Consolidation is often presented as progress. In group benefits today, it may prove to be the opposite.

As consolidation continues to ramp up across the insurance technology landscape, mergers and acquisitions are being framed as a way to deliver broader capability, stronger platforms, and more complete ecosystems. For carriers under pressure to modernize quickly, the logic is appealing. Consolidation promises a larger vendor with a fuller suite of functionality that should be better equipped to support long-term transformation.

But for group benefits insurers, in particular, this assumption deserves scrutiny. Behind many of today's platform mergers lies a perfect storm. One that risks locking carriers into greater rigidity at precisely the moment they need to become more adaptable.

Building on a Fragile Foundation

To understand why platform consolidation can be problematic, it helps to start with the technology foundations many group benefits platforms already rest on.

Unlike some other insurance lines, group benefits technology did not evolve over decades on stable, purpose-built architectures. Much of it emerged more rapidly, often adapted from adjacent markets such as life, pensions, or individual products. Vendors re-engineered existing platforms to meet growing demand for employer-sponsored benefits, then layered on new functionality as customer expectations evolved.

Over time, these platforms became highly customized to individual carriers and employer requirements. New features were added to meet immediate needs. Integrations were built to support emerging distribution and service models. Documentation rarely kept pace with delivery pressure. What began as pragmatic adaptation gradually accumulated into significant technical complexity.

In other words, many group benefits platforms entered the current consolidation wave already carrying structural fragility. At the same time, market expectations have accelerated. Employers and employees increasingly expect flexible benefit design, digital enrollment, ecosystem integration, and personalized experiences. Delivering on these expectations requires technology that can be configured and extended quickly. Not simply maintained.

This is the environment into which consolidation has arrived.

The Consolidation Illusion

When technology vendors merge, the narrative is straightforward. Customers of the acquired platform are told they are becoming part of a larger, more advanced organization with greater investment capacity. Customers of the acquiring platform are told they will gain new functionality and broader capabilities. Both sides expect improvement.

In reality, consolidation is often driven first by market share and coverage, and only second by technological unification. This is not the result of poor intent. Vendors pursue acquisitions because they believe it is the fastest and most cost-effective way to fill capability gaps and respond to market opportunity. Building new functionality from scratch is expensive and time-consuming. Acquiring it appears faster and less risky. Larger scale also reassures risk-averse insurers, who often prefer established vendors with financial strength and broad offerings.

On paper, the logic holds. In practice, the technical challenge of integrating two heavily customized, architecturally distinct platforms is frequently underestimated. Particularly when decisions are driven primarily by commercial leadership rather than engineering reality.

From Safety Blankets to Patchwork Quilts

Every mature insurance platform reflects years of client-specific configuration, integration, and adaptation. No two are the same. Data models differ. Product logic differs. Workflow structures differ. Some critical functionality may exist in legacy code written decades earlier and never fully documented. Each system has evolved around the needs of its existing customers.

When two such platforms are combined, true unification requires deep re-engineering: rationalizing data structures, redesigning core services, and often rebuilding significant functionality. This is expensive, disruptive, and difficult to justify commercially. As a result, most merged platforms evolve through accommodation rather than transformation. New layers are added. Interfaces are built. Functionality is duplicated rather than consolidated.

The result is what many in the industry privately recognize: a frankenstack. Or, more accurately, the merging of two frankenstacks.

Over time, more and more IT investment is directed toward supporting this complexity rather than advancing capability. Roadmaps slow. Innovation competes with maintenance. What was intended to be a safety blanket for customers becomes a patchwork quilt that grows heavier and harder to adapt.

The industry has seen versions of this story before. Large technology estates built primarily through acquisition can become extraordinarily difficult to modernize, leaving both vendors and their customers managing accumulated complexity for years afterwards. Consolidation promises acceleration. Too often, it results in gradual technological stagnation.

Why This Matters in Group Benefits

Group benefits carriers are particularly exposed to this dynamic because their business demands constant configuration and change.

Benefit structures vary by employer. Employee expectations continue to evolve. New partnerships and services must be integrated rapidly. Distribution and engagement models are shifting toward more digital, personalized experiences. Technology must support continuous adaptation rather than periodic transformation.

Yet consolidation often redirects vendor focus inward. Engineering effort moves toward integration of acquired platforms and preservation of existing revenue streams. Transformation initiatives slow while complexity is stabilized. For customers, the experience can be subtle but significant: fewer meaningful enhancements, slower responsiveness to new requirements, and growing difficulty introducing new products or partnerships.

The risk is not immediate disruption. Most consolidated platforms continue to function adequately. The real danger is long-term loss of adaptability and a slow erosion of the ability to respond as the market evolves.

AI Raises the Stakes Even Further

If adaptability was already becoming the defining factor in group benefits, the rapid evolution of AI has raised the stakes significantly.

There is growing consensus across the industry that AI will transform underwriting, claims, service, and product design. But meaningful AI adoption is not achieved by layering point solutions onto legacy cores. Injecting AI "at the edges" of rigid platforms may create isolated efficiencies, but it does not fundamentally change how the business operates.

To unlock AI's full potential, insurers require something far more foundational: open, data-fluid architectures where operational and analytical data are unified; governance and controls are embedded by design; and experimentation can occur safely within defined guardrails. AI needs to sit at the core of the platform, not be welded onto the perimeter.

In consolidated environments built from multiple legacy estates, this becomes extraordinarily difficult. Data models remain fragmented. Core logic is tightly coupled. Every meaningful change requires an IT project, often with significant coordination across integrated systems. Instead of enabling experimentation, the architecture restricts it.

The consequence is subtle but powerful. Rather than allowing business teams to test new processes, deploy new journeys, or refine models quickly, innovation becomes dependent on complex technical programs. What should be controlled experimentation turns into multi-quarter initiatives. Governance becomes reactive rather than embedded. AI becomes a feature to manage, not a capability to leverage.

As group benefits carriers look to modernize, the question is no longer simply whether a platform can support today's products. It is whether it can support continuous experimentation and governed AI-driven evolution. In this context, architectural rigidity is not just a technical limitation, it is a strategic constraint.

Challenging the Plumbing Assumption

Part of the issue lies in how technology decisions are evaluated. Platform choices are sometimes treated as infrastructure decisions, where scale and vendor stability appear more important than architectural flexibility. There can be an implicit assumption that technology is interchangeable. That one platform can be merged into another without fundamentally altering its capacity to evolve.

But insurance technology is not generic plumbing. Every system reflects years of bespoke configuration and embedded business logic. Integrating two such environments is not a simple exercise in connection; it is a complex process of reconciliation that shapes what can and cannot be changed in the future.

As group benefits enters a period of accelerated transformation, that distinction becomes critical.

A Unique Opportunity to Reassess

For insurers whose technology partners are entering a merger or acquisition phase, this is not necessarily a cause for concern. Consolidation can deliver benefits when approached with architectural care and sustained investment. Consolidation is also a natural moment to reassess.

Carriers should seek clarity on how platforms will actually be integrated, where investment will be directed, and how innovation roadmaps may change during the process. Critical questions include: Will resources be focused primarily on maintaining and connecting existing systems, or on enabling new capabilities? How will duplicated functionality be rationalized? What will this mean for the speed of change over the next three to five years? Most importantly, will the combined platform become easier or harder to evolve?

The group benefits market is entering a decisive phase. As existing technologies approach end of life and expectations continue to rise, adaptability will define competitive advantage. Encouragingly, the technology now exists to support more flexible, staged transformation, allowing carriers to modernize incrementally rather than through high-risk, "big bang" replacement.

Consolidation may expand capability on paper. But in a market defined by constant change, it is adaptability, not scale, that will ultimately determine who wins.

Independent agencies are always looking ahead, but they're still focused on how much work it takes to place and service a policy today. Underwriting remains tight, carrier appetites change, and remarketing eats up time and resources. At the same time, clients expect clear, timely communication and fewer surprises.

In a recent Vertafore survey, independent agencies told us what will shape their operations in 2026. Three trends emerged:

• Using AI automation to reduce manual work
• Preparing for uncertainty in carrier appetite and placements across admitted and E&S markets
• Prioritizing proactive, consistent client communication as a defining trait of high-performing agencies.

What connects these trends? Operational efficiency supported by the right technology.

High-performing agencies aren't trying to predict every market shift, and they're not just adding headcount to keep up with more work. Instead, they're focused on how the work gets done. The agencies that win in 2026 are using technology designed for their workflows to reduce manual effort, adapt quickly to appetite shifts, and communicate consistently with clients.

Using AI to reduce manual work in insurance agency workflows

Carriers always ask for more information, pose follow-up questions, and make adjustments as requirements change. When agencies run that work through manual steps and disconnected systems, it quickly turns into duplicate entry, rework, and resubmitting applications.

To manage this workload, agencies are turning to more AI automation because they can't scale this work by hiring alone. In the survey, nearly 30% said they expect AI-driven process improvements to deliver the strongest return on investment in 2026, and more than one-third said the greatest value will come from AI embedded into the solutions they already use.

Today, agencies are successfully using AI-powered solutions to pull data from ACORD forms and carrier documents so the information can be reviewed instead of re-entered. AI supports everyday service work, validating information before it reaches service or sales, and even determining whether it makes sense to move forward before spending time investing in a full rating.

Over time, these improvements will expand further. AI takes on more of the manual work—redundant clicks, document routing, and non-licensed processes that consume time. This automation works in the background so teams can focus on licensed work and client conversations.

Managing carrier appetite shifts and E&S placements efficiently

Carrier appetite changes are expensive when agencies aren't structured to adapt. Every shift means rebuilding submissions, re-entering information, and spending more time redoing work that was already completed.

In the survey, nearly half of agencies said they expect to place about the same amount of business in the E&S market this year, and 40% expect to place more than they did in 2025. That tells us E&S isn't going away. Agencies are already seeing movement in both directions—some accounts shifting back to admitted markets as appetite expands, and others moving into E&S as underwriting tightens.

When an agency uses systems that are not integrated, a carrier decline can mean significant manual work—re-entering data, rebuilding documents, and recreating submissions from scratch. In the E&S market, where supplemental applications often appear late in the process, this friction adds up fast.

To manage these changing processes, agencies are using submission and application management tools to collect and organize risk information once by integrating with their agency management system. They pair these tools with benchmarking solutions to understand which carriers or MGAs will be most likely to write the risk, then carry the information out to the market without someone having to re-enter it. Solutions that talk to each other keep the work moving and teams don't have to rebuild the same submission over and over.

Why consistent client communication separates high-performing agencies

After years of pricing pressure, underwriting changes, and continuing remarketing, clients rely on access to their agents more than ever. They want to talk about coverage shifts and rate increases before they happen. When they have to call and ask why something's changed, it's often too late—they're already shopping.

This emphasis on consistent communication showed up in the survey. More than half of respondents agreed that providing proactive, timely communication will set high-performing agencies apart in 2026.

But for busy agencies, it's difficult to provide a higher level of outreach. Juggling renewals, remarketing risk, and doing the daily work that keeps the doors open keeps teams from picking up the phone or sending an email. When delivery depends on individual effort, important messages become reactive and clients get frustrated.

That's why using a marketing automation tool for communications is so important. These solutions can either be integrated within your existing management system or built into your CRM or AMS tool. Agencies that use these tools turn routine messaging like renewal updates, document readiness, and general market info over to technology, and use the time savings to have deeper conversations about more important issues. Automating second-tier communication means clients stay informed and agency professionals aren't spending time manually writing and sending emails.

Using this technology also means clients receive intentional touchpoints that show a higher level of attention and care. Agencies can automatically send focused reminders to homeowners about fire season, educational materials about important topics, or alerts about upcoming trends. Templated messages go out on time and people step in where judgment and empathy are required.

It may feel counterintuitive, but automating routine communication often improves satisfaction. Clients feel informed and have clearer expectations. Teams aren't overwhelmed trying to reach everyone at once. With the right solutions carrying some of the load, agencies can elevate their clients' experience and reduce workloads.

Operational efficiency matters for independent agencies in 2026

Across the three themes—AI-driven workflows, E&S readiness, and automating client communication—the common thread is operational efficiency.

The agencies that will perform best in 2026 won't predict every market move. They will invest in how work gets done—making sure their systems talk to each other, their processes are disciplined, and their teams aren't buried in manual tasks.

It's not really a technology story. It's about how agencies manage their business day to day. And the agencies that focus on that now are going to be in a much better position in 2026 and beyond.