Every AI conversation in insurance right now seems to start in the same place. Models, platforms, copilots, automation. So it might sound strange to say the industry's AI problem isn't a technology problem. It's a design problem.

I'm the founder and CEO of a design agency that has worked with insurance companies for more than 15 years, and I keep seeing the same pattern. We invest in technology before we understand the people who are supposed to use it. Then, when adoption is low, we blame it on people.

AI is just the latest, highest-stakes version of that same old mistake.

Whether investments in AI pay off won't come down to what model you pick. It'll come down to whether the people you built it for actually use it.

I am a designer by trade: art school, design school, maker through and through. Cake & Arrow did not start in insurance. We began in retail and e-commerce, designing digital experiences for consumer-centric brands. Then, about 15 years ago, a CIO at an insurance company asked us to help reimagine a sales platform for agents.

We came in as outsiders, and that outside view helped us see something people deep inside the business often can't: insurance experiences are too often built around the business, the policy, and the transactional moments—not around the customers, employees, agents, and brokers trying to navigate them.

Investing in Technology Is Not the Same as Progress

The instinct in insurance is often to start with the technology. A new tool shows up, a new capability emerges, and every executive team wants to show progress. I get the pressure. Boards are asking about AI. Everyone wants to move fast.

But buying technology is not the same as making progress.

A powerful AI tool is still worthless if it isn't solving an actual problem for an actual person. The most advanced chatbot, copilot, or automation platform will fail if it gets bolted onto a broken process. That's the actual risk with AI right now. Making the same old mistake, only faster and at greater expense.

When talking to insurers, I often make a distinction between "design" and "Design with a capital D." When I talk about "Design," I'm not talking about colors, fonts, or pretty screens. Design is the research, the strategy, and the deliberate decision-making underneath every product and experience. It's understanding who a tool is for, what its purpose is, where the work breaks down, and how a solution earns its place in someone's day.

And here's the thing: Design is already happening, whether companies acknowledge it or not. Every agent portal, claims experience, policyholder app, and AI workflow is the result of a decision someone made. The real question is: where and with whom did the decision originate? With the person doing the work, or with an executive mandate, business requirement, vendor pitch, or short-term goal? Too often, the decisions made in insurance have little to do with the human beings they impact.

Agents Are Not the Barrier

In our recent report, The Connective Thread: From Agent and Broker Research to a New Design Vision for AI-Enabled Insurance Work, we spoke directly with agents and brokers about how they are using AI today, where they are finding value, and what is still getting in the way. What stood out was not the agents' resistance, but their resourcefulness.

Agents are already experimenting. They're drafting emails, summarizing policies, comparing quotes, prepping for meetings, and translating complex insurance language into something clients can actually understand. Some are quietly building workarounds because the official systems around them do not support how they actually work.

So the problem is not that agents do not want to use AI. It's that the tools too often do not map to the real friction in their work. The industry keeps talking about AI as an automation story. But when you talk to agents, what they want is integration.

They are not asking for another tab, login, or disconnected assistant. They're already moving between agency management systems, CRMs, email, spreadsheets, carrier portals, and rating tools. They're entering the same information over and over, hunting across systems for context and trying to track what changed, what a client needs, and what follow-up might fall through the cracks. That is not a single-task productivity problem. It is a workflow problem.

AI that helps write an email is useful. AI that understands the context behind the email, pulls from the right systems, shows where the information came from, flags what needs review, and keeps the human in control… that's something else entirely. That is where AI becomes connective tissue, instead of one more tool to add to the pile.

Design Around People, Not Around Replacing Them

For decades, the insurance industry has strived for ways to disintermediate agents. AI has only added fuel to that fire. There's a real temptation to see AI as a way to replace human labor, cut costs, and eliminate the messiness of human relationships.

But that framing misses where the value actually lives.

Sure, AI can create efficiencies. It can reduce administrative burden, help agents manage bigger books, and spend less time on repetitive work. But if your starting point is replacement, you'll miss the bigger opportunity to design tools that unlock capacity, judgment, and relationship-building.

The best agents are valuable because they know what matters. They understand their clients, and they understand risk. They can feel when something is off, and they know what to ask next. That's how they turn complexity into confidence. AI should be making more room for that work, not pushing it to the side.

This is where human-centered design stops being a nice-to-have and becomes a business necessity.

If you want agents to adopt AI, you have to understand how they actually work, not how leadership assumes they work. And that requires more than a survey. It means observing real workflows, listening for friction, and noticing the invisible work that quietly holds the system together. Research embedded in the design process points toward solutions.

Adoption Is the Whole Game

One of the biggest misconceptions about AI is that adoption is what happens after the rollout. It's not. Adoption is the whole game.

A tool is only successful if the people it is built for actually want to use it. People want tools that fit into their world, solve problems they recognize, and make their work meaningfully better in a way they can feel.

Insurance has a long history of underestimating this. The industry spends significant money on technology that never lands because it has never fully accounted for the human experience surrounding it. Then, when usage is low, the conclusion is often that people are "resistant to change."

Most of the time, that's the wrong diagnosis.

People are not resistant to change that helps them. They're resistant to tools that make their day harder, add complexity, create risk, ask them to trust outputs they cannot verify, or worse, are designed to replace them.

AI cannot simply generate confident answers. It has to earn trust. Agents need to see where information came from, verify recommendations, correct outputs, and approve what goes to a client. "Trust but verify" is not just a user preference here. It's a design requirement.

What Leaders Should Do Differently

If a carrier, brokerage, or insurtech CEO asked me where to start right now, I'd say this: Catch yourself before you jump to the solution.

The pressure to move fast is real, and speed does matter. But moving fast doesn't mean skipping the work that makes speed useful. Before you decide what AI feature to build or what vendor to buy, sit with these three questions:

• Who is this for?
• What problem are we solving?
• And what outcome are we actually after?

Then go talk to the people who'll use it. Watch how they work, find where the friction really lives, and let that learning shape the AI strategy before the roadmap hardens. A few focused weeks of research and design up front can save you months, or years, of expensive misalignment down the line.

The Opportunity Is Still Enormous

Despite the industry's habit of chasing tech before thinking about people, I remain optimistic. The opportunity to differentiate in insurance is astounding. The bar for better experiences is still too low.

AI can help agents spend less time searching and re-entering the same information. It can help newer employees get up to speed faster. It can preserve institutional knowledge, make complex decisions more transparent, and free up time for the things that actually build loyalty. Advice, empathy, and relationship-building.

But only if it is designed around people.

The companies that get this right understand that technology alone does not create transformation. People do. It comes down to whether they trust the tool enough—and find it valuable enough—to actually use it.

Insurance doesn't need more AI for the sake of AI. It needs AI that solves actual problems for the people doing the work, in the real flow of their day. That's the design challenge. And if the industry takes it seriously, it's also the clearest path to transformation.

Back in 2013, when Chunka Mui and I were doing some consulting work on innovation for the CEO of a top-five personal lines insurer, he was trying to rewire the compensation structure for his captive agents. He wanted to encourage them to focus more on growth and less on building a book of business and then servicing it ("coasting," in his words). 

He noted that he wasn't trying to cut the total dollars paid to agents. He just wanted to take two percentage points out of the base commission and pay the money out as incentives. 

"But every time I float the idea," he said, "the agents turn around and kick me in the crotch." (He used a more colorful word.)

Having kept an eye on the issue for more than a dozen years now, I believe that State Farm's announcement of a take-it-or-leave-it, incentive-driven compensation model for its 19,000 captive agents marks a turning point. Change always takes time, but I believe the captive agent business will be very different a few years from now.

Let's have a look. 

A smart piece by David Gritz of InsurTech NY provides the backdrop, showing how the industry has been deemphasizing the traditional captive model for years. Noting that the trend predates the generative AI explosion by many years, he writes:

• "June 2020: Nationwide ends its captive agent program.
• "November 2021: Liberty Mutual transitions captive agents to independent agencies.
• "January 2023: Allstate signals a reduction in captive distribution.
• "June 2026: State Farm reduces benefits and commissions for captive agents.

"Viewed individually, each decision can be explained by company-specific circumstances. Viewed together, they reveal something larger: carriers are increasingly questioning whether exclusive distribution remains the optimal model for growth."

Gritz also neatly summarizes what, for me, is the core change that is working against captive agents:

"Consumers can purchase insurance through direct channels, comparison platforms, embedded insurance experiences, independent agencies, affinity groups, digital marketplaces, MGAs, and increasingly AI-powered interfaces.

"Carriers want the flexibility to pursue all of these opportunities simultaneously. Exclusive distribution creates natural channel conflict when a carrier wants to experiment with new distribution strategies."

He gets into other reasons, too, but for me the key is that three decades of development of the internet, led by customer service pioneers such as Amazon, have conditioned us to expect to be able to see all our options, and instantly. We don't just look at what clothes Macy's or Nordstrom might offer us; we look at every seller. Even if we've settled on a brand or a specific item, we still look everywhere for the best prices — in seconds. 

In that sort of world, it just doesn't make sense for someone looking for insurance to walk into the office of the local State Farm agent, even if the agent is a smart and lovely person who sponsors the customer's daughter's soccer team. 

It's not clear how quickly the change away from captive agents will happen. A Silicon Valley truism is that you have to make sure you don't confuse a clear view with a short distance. And the reason for that adage is that so many people make that exact mistake all the time — including, well, me.

I predicted the end of car dealerships 25 years ago, because all you really need is a way to test drive a car. You can then order your choice straight from the manufacturer, get it in a couple of weeks, and not have billions of dollars of car inventory sitting on lots around the country, pushing up costs for everybody. But change has been so slow that we're only now starting to see the sorts of effects on dealers that I expected by 2005 or 2010.

Still, the transition away from captive agents is inevitable. Independent agents will keep growing — witness the interest in the HUB International IPO — while captive agents will have to fight a rear guard action. They will be under pressure from both ends. Their carrier employers will demand more growth and more flexibility to explore other distribution channels. Customers will press for lower prices while also insisting on more options.

And I think State Farm, as one of the last big holdouts relying on captive agents, has pushed the transition past the tipping point, so it should only accelerate from here.

Cheers,

Paul

From rules-based automation in the 1990s and 2000s to machine learning algorithms in the 2010s to generative and agentic AI in this decade, the evolution of AI in insurance has been phenomenal, affecting the areas of underwriting, claims management, fraud detection, and customer engagement. Yet, in the last three years of the industry's AI rush, it's claims management that has become the default AI use case, with its evident ROI. This is possibly due to its visible cycle time, structured first-notice-of-loss data, and well-mapped workflows and exception paths. Very few carriers talk about their underwriting decision latency, their endorsement turnaround, or their fraud triage interval—all of which carry significant value.

And so, a question arises. Should speed be the only outcome of consequence in the new era of autonomous decision making? Be it in claims being processed in minutes, fraud being detected in real-time, or customer queries being answered instantly; speed of autonomy cannot be a destination by itself. We need to govern autonomy that combines speed, traceability, escalation, and accountability to create trust. Agentic AI's contribution to insurance is not throughput. It is compressed decision cycles with an intact audit trail.

Decision velocity is truly what agentic AI in insurance must aim for.

What decision velocity really means

For many industries, and more so for insurance, speed is considered a competitive advantage and differentiator. But in a life-intrinsic domain such as insurance, speed that cannot be explained, reversed or attributed to an accountable owner is an operational and regulatory risk. Without the discipline of correctness, auditability, and escalation, it becomes a liability in many ways.

Decision velocity brings this discipline to speed and scale. The discipline that embeds traceable reasoning and accountable ownership for every consequential decision from the time of the data event to its executed action. With intelligence, it moves the focus to decision ownership, not merely technology ownership. It transparently connects the facts of data, the patterns that analytics uncover, and the recommendations of AI in every business choice made.

Data freshness, reasoning compression and oversight latency — decision velocity thrives only when these three components move in complete unison and understanding. While agentic systems in insurance aim to accelerate decision making, they should not remove the controls that make the decision defensible.

An agentic architecture for insurance decisions

Traditional automation in insurance (and even RPA) is inflexible and deterministic. Rule and rating engines determine monetary thresholds and premium calculations based on predefined variables. And while there are referral workflows to alert and escalate potential risks that fall outside the delegated guidelines, the guardrails are narrow. What's more, they break when there is a shift in context.

Agentic AI can transform the operating model with its ability to ingest and validate multiple sources of data across policy administration systems, geographies, lines of business, and regulatory demands. However, all this pivots on the quality of data and its readiness for agentic AI systems, and this is what the agentic architecture must assure.

A production-grade insurance agent stack should comprise (a) a planning layer, (b) a retrieval layer with policy language, regulatory rules and prior decisions, (c) a tool layer of rating engines, fraud models, claims and policy admin systems, (d) guardrails, (e) a decision logger, (f) an escalation layer, and, above all, a human review console.

The premise of a singular and monolithic "do everything" agent will not work. Work must be bounded by multi-agent systems, where each agent owns one decision class with one accountable human. Remember, agentic does not mean autonomous at all costs. It means delegated work within governed boundaries. Such a model reduces scope risk. However, care must be taken to avoid fragmented decisions by reasoning in isolation. The production architecture must therefore have a unified orchestration layer, shared policy memory, common decision taxonomy, and clear accountability model across agents.

When it comes to data platforms for agentic insurance, the self-adaptive behavior in the user interface calls for real-time event and data streaming, plus real-time curation of enterprise data assets. The traditional enterprise data platform with staged data processing and disjointed data event streaming for specific use cases will not work (see table). Data quality must be uncompromisingly high, and multi-step refinement and generation of machine learning insights must be in real-time, with data features engineered from the ingested and streamed data into the enterprise data platform.

This, then, is how agentic AI brings decision velocity into insurance operations beyond claims management. Be it in underwriting submission triaging, policy endorsement processing, investigation of fraud signals, identification of subrogation opportunities or distribution support, the agentic architecture clearly delineates delegation from human intervention, and shows what the agent can do, where the human stays in the loop and what velocity gain looks like (see table).

Proactive governance for prevention of human oversight failure, agent failure and compliance

Here is a sobering reality. Unless proactively governed, agentic AI can fail while achieving what it was intended to. And this happens due to multiple reasons — stale, biased or narrow data, hallucinated policy interpretation, knowledge drift, conflicting recommendations from multiple bounded agents or complex feedback loops, missed context, overconfident routing and unclear escalation ownership. These are systemic risks that can cascade across the chain to compound uncertainty, opacity, and information asymmetry.

Defining what failure means is absolutely vital, both in business and operational terms. There must be clearly articulated failure controls: confidence thresholds, retrieval-source validation, exception queues, human override reasons, re-playable decision logs, adverse-action safeguards, etc., with temporary kill switches for agents that behave outside tolerance limits. And these controls must be translated into measurable metrics.

Continuous and evidence-based oversight is imperative, not periodical and static testing. Oversight intensity must be matched to consumer impact and reversal cost, and not to a uniform "human-must-approve" rule. It is this fallacy that causes the "rubber stamp failure," where reviewers end up approving almost all agent decisions — a classic instance of minimum oversight and maximum theatre.

Three levels of oversight are recommended, based on decision criticality. The first is the pre-decision review, especially for high-stakes and low-volume instances. The second is the post-decision sampled audit, for medium-stakes and high-volume instances. And the third, for everything else, exception escalation. To add greater effectiveness, we will need to tier systems by both impact and volatility — and ensure that each modification is accompanied by a "change-impact" review.

And oversight must sit above the agent layer, not only inside each workflow. Otherwise, multiple bounded agents can create distributed logic, inconsistent outcomes, and no single view of accountability across the underwriting or servicing process. True governance goes beyond compliance to creating resilient AI systems that assure total trust and safety as they continue to evolve.

The five key governance artifacts that hold up in a market conduct exam include model cards, decision logs with reasoning traces, consumer-impact assessments, bias testing cadence, and third-party model attestations (also see the box on "Five questions a state DOI examiner will ask about your AI").

Five questions a state DOI examiner will ask about your AI:

1. What decision did the agent influence?
2. What data did it use?
3. Which human was accountable?
4. How were exceptions handled?
5. How do you test for bias, drift and inconsistent outcomes across agents?

The following may be referred to as guiding frameworks for governance:

The NAIC Model Bulletin on the use of AI Systems by Insurers (2023) and what it actually requires in terms of governance framework, third-party AI risk management, testing for bias and unfair discrimination, documentation, etc.

The Colorado AI Act and insurance rules that serve as a leading state-level enforcement signal, in terms of algorithmic discrimination testing, governance documentation and consumer disclosures.

The NYDFS Circular Letter No. 7 (2024) on AI in underwriting and pricing.

The EU AI Act for high-risk classification for life and health insurance, which clarifies implications for global carriers.

Seven key implementation lessons in production

The truth is, agentic pilots succeed because they run on narrow data, face relaxed oversight, avoid regulatory scrutiny, and are not integrated into real decision accountability workflows. Production is where the rubber hits the road. It requires governance to be embedded into decision accountability workflows from Day One, not added after a successful proof-of-concept. When governance is an afterthought, the pilot does not survive operational reality.

#1 — Bound the agent narrowly. Broad-scope agents hallucinate decisions. Make it one agent, one decision class, one owner.

#2 — Do not confuse narrow scope with narrow accountability. Narrowly bounded agents still need a shared governance layer so that their decisions do not fragment underwriting, servicing or fraud workflows.

#3 — Instrument before you scale. Observability — input, retrieval, reasoning, tool call, output, override — is the long pole. Carriers that skip this will hit a wall in production.

#4 — Design oversight as a product surface. If your reviewer experience is a spreadsheet, you will get rubber stamping. Treat oversight as a UX problem.

#5 — Data architecture is everything. Without a lakehouse, feature store, and semantic layer, agents work on stale or inconsistent data to produce indefensible decisions.

#6 — Change management is the real constraint. Underwriters and adjusters will not trust a system whose reasoning they cannot inspect. Explainability is an adoption requirement, not just a regulatory one.

#7 — Stress-test agent failure before launch. Simulate bad retrieval, missing documents, contradictory policy language, broker pressure, regulatory constraints, and handoff failures between agents.

Creating decision velocity with agentic AI in insurance is an unambiguous mandate for CIOs and CDOs. The good news is that the steps to do so are equally clear.

Create a 90-day diagnostic: a map of the top 20 consequential decisions, current latency, current oversight model, current regulatory exposure and current failure path.

For each decision, define what can be delegated to an agent, what must remain with a human, what needs pre-decision approval, and what can be handled through post-decision audit or exception escalation.

Pick a non-claims pilot. Underwriting submission triage or endorsement processing are the highest-yield, lowest-risk starting points.

Build the governance scaffolding — model registry, decision log, oversight workflow, escalation rules and accountable decision owner — before the agent, not after.

Define decision velocity as a tracked metric alongside loss ratio and combined ratio.

The message for the insurance industry is loud and clear. Enterprises will not be judged on how swiftly they adopted agentic AI. They will distinguish themselves on whether they made faster decisions without losing control, accountability, or trust. Those that treat agentic AI as a faster claims engine will hit a ceiling within a year. The ones that make it their decision-velocity capability, governed by design, will be the winners.

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

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

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

This should be a teachable moment. 

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

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

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

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

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

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

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

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

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

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

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

Cheers,

Paul

The squeeze in underwriting profits is sustained across the insurance market, driven by persistently high loss and expense ratios. A combination of rising catastrophe activity, social inflation and increasingly complex commercial risks is compounding margin volatility. While the sector has returned to around sub-95 combined ratios, profitability remains highly sensitive to small shifts in the operating environment. This volatility, combined with growth opportunities including cyber risks, climate events, and liability coverage, has made underwriting discipline a necessity rather than a luxury.

This is where AI has become a focal point in the conversation, as insurers are looking to automate more of what matters and embed AI agents into business-critical processes to drive efficiency and protect margins. In theory, AI should help insurers process information faster, detect risk more accurately, and reduce leakage across underwriting and claims. In practice, however, the ambition to extend AI into complex knowledge work that traditionally required human judgment is not yet being realized at scale.

According to Camunda's State of Agentic Orchestration and Automation 2026 report, almost two-thirds (65%) of insurers admit there is a gap between their agentic AI vision and the current reality. While many firms report experimenting with AI agents, only 11% of projects reached production last year. If this pattern continues, insurance firms risk hitting an automation ceiling — one where AI agents compound operational complexity and fragmented IT systems, rather than strengthening underwriting profitability and reducing loss ratios.

Why AI still isn't trusted in insurance

Despite growing interest in how AI can support loss management and improve portfolio steering, trust remains a key barrier to scaling adoption in insurance. This is hardly surprising given the sector's exposure to regulatory scrutiny around customer protection, data security and operational resilience. At its core, insurance is built on accountability and risk transparency — every decision must be traceable, explainable, and auditable.

The majority of insurers (81%) are worried about the business risk of AI systems in day-to-day operations when IT teams lack adequate controls. A further 80% are concerned about a lack of transparency around how AI is used within business processes, while 68% cite compliance concerns and 63% lack internal skills to effectively manage AI.

AI adoption stuck in low-risk mode

While caution is natural in a highly regulated industry like insurance, it significantly affects where AI is deployed. Most agents remain confined to low-risk, isolated use cases, with 81% of insurers saying current deployments focus on chatbots or assistants that summarize or answer questions. These applications may improve efficiency, but they do not improve underwriting profitability or reduce claims leakage.

The challenge is that as AI agents move toward areas that directly influence margin stability, such as underwriting decision-making and claims adjudication, adoption becomes more constrained. These are key financial control points for insurers, where even small errors can lead to significant loss leakage or pricing inaccuracies.

However, if AI remains confined to the periphery of the business, insurance firms will fail to maximize returns on their AI investments. To break through the automation ceiling, insurers need a way to safely embed agents into margin-critical processes and ensure agents operate consistently within clearly defined parameters.

Bringing order to AI in insurance

Closing the gap between AI ambition and reality requires control over how AI behaves inside regulated insurance processes. The majority (90%) of insurers agree that AI must be orchestrated across business processes to get the maximum benefit from AI investments and ensure regulatory compliance.

Insurance operations already rely on structured, deterministic processes to manage underwriting decisions and claims routing, helping ensure accountability and traceability in high-risk financial decisions. The next step is extending that same level of discipline to AI, and this is where agentic orchestration comes in.

Rather than treating AI as a black-box tool, agentic orchestration combines deterministic guardrails with dynamic reasoning. This approach allows AI to participate in underwriting, claims, and servicing workflows while remaining within clearly defined guardrails that preserve auditability.

In practice, this creates a controlled environment where AI can adapt to new information, such as changes in claims severity or risk exposure, without compromising governance. Decision-making remains traceable, controlled, and aligned with regulation, while benefiting from the speed and accuracy of AI.

Scaling AI where it matters most

With combined ratios under pressure and loss volatility increasing, the insurance sector is looking to automation and AI to strengthen underwriting discipline and reduce P&L leakage without sacrificing transparency. There is also growing emphasis on activating a broader vendor partnership ecosystem, such as through insurtech alliances. As a result, insurers are looking to evolve their operating model, which will require a transposable orchestration platform that integrates, coordinates and scales insurance products across the value chain.

Agentic orchestration offers a path to move AI into the core of underwriting, claims, and portfolio management in a controlled and measurable way. This is where AI stands to have the greatest financial impact by improving pricing accuracy, reducing overpayment and strengthening consistency in decision-making.

Those organizations that succeed will not just be more efficient — they will be better positioned to protect margins, stabilize performance and maintain underwriting discipline in an increasingly volatile insurance landscape.

Motor insurers in the UK paid out a record £11.7 billion in claims during 2024, a 17% increase from the previous year, according to the Association of British Insurers. Rising claims volumes are only part of the problem. The disputes sitting inside those numbers, particularly the ones tied to pre-existing damage, represent a cost that cannot be addressed by processing claims faster. They require a different approach at the point of underwriting.

When a vehicle owner files a claim, the insurer has to answer a very fundamental question: Did the damage occur during the policy period, or did it exist before the policy coverage was offered? Answering this question with absolute confidence is next to impossible without verified documentation during policy inception.

This results in disputes that cost both time and money, reduce trust, and, in many cases, result in fraudulent payouts that should never have been made. This gap is now increasingly being addressed through AI-driven inspections at the point of policy inception. It creates a verified, timestamped record of a vehicle's condition before coverage begins, removing the ambiguity that fuels most FNOL disputes.

Why FNOL Disputes Persist

Most FNOL disputes over damage causation share the same root cause: there is no verified baseline record of the vehicle's condition at the point the policy was issued.

When a new policy is written without a photo inspection, the insurer accepts the vehicle's stated condition without verification. If a claim is filed within weeks of inception, the insurer has no objective way to determine whether the damage is new or pre-existing. The policyholder says it is new. There is no evidence either way. The claim is paid, or the dispute drags on.

This is compounded by FNOL data quality problems. Research cited by EasySend found that over 60% of manually completed FNOL forms contain errors, incomplete information, or unreadable data. When the original inspection was also manually conducted and poorly documented, the claims team had very little to work with.

The cost of this gap is measured in claims leakage, adjuster time, and the operational overhead of investigating disputes that should never have reached that stage. It also affects customer trust. Legitimate claimants who face investigation due to a lack of baseline data experience a poor claims journey through no fault of their own.

Why Traditional Pre-Insurance Inspections No Longer Scale

Physical pre-inspection by a field surveyor was the standard approach for addressing this problem. It worked when policy volumes were lower and inspection coverage was more limited. It does not work today.

A field inspection takes two to five days from scheduling to a completed report. For an insurer processing thousands of claims every month, there is a substantial overhead of scheduling and logistics. The cost of each inspection, including surveyor fees and administrative processing, typically ranges from $100 to $300 per vehicle.

Another major problem is the consistency of the reports. Two different inspectors examining the same vehicle will not always produce the same findings. A scratch documented by one inspector may not appear in a report written by another, depending on lighting conditions, viewing angle, and individual thoroughness. When that inconsistency surfaces during a claim, the insurer is in a difficult position.

These limitations are well documented. A growing number of motor insurers are replacing physical inspections with an AI-powered photo inspection workflow that completes the same documentation process in minutes rather than days, at a fraction of the cost, and with consistent output every time.

Creating a Verified Vehicle Baseline Before Coverage Begins

The principle behind AI pre-insurance inspection is straightforward. Solutions such as Inspektlabs have demonstrated how AI-powered photo inspections can generate consistent, timestamped vehicle condition reports remotely, helping insurers establish a verified baseline before coverage begins. The report is timestamped and stored digitally.

When a claim is filed, the pre-policy report is the baseline. If the damage appears in the pre-policy record, it predates coverage. If it does not appear, the claim is consistent with a new incident. This helps eliminate much of the ambiguity that usually drives most disputes.

For underwriters, the same baseline has a direct operational benefit. A verified vehicle condition record supports a more accurate premium rating, particularly for used vehicles or those with a break in prior coverage. Underwriting decisions that were previously based on stated information can be anchored in verified evidence.

For policyholders, the process is faster and more transparent. A guided smartphone capture takes two to three minutes. There is no appointment to schedule and no field visit to wait for. The policyholder submits their photos, receives confirmation that the inspection is complete, and the policy can be issued the same day.

AI Is Transforming Motor Insurance Inspections

The shift from physical to AI-powered inspection is not just about speed. The technology introduces capabilities that physical inspection cannot replicate.

Computer vision and automated damage detection: AI models trained on millions of vehicle damage images identify dents, scratches, glass damage, and miscellaneous damage consistently across every submission. The same detection criteria apply regardless of who submitted the inspection or when.

Guided photo capture and image quality validation: Policyholders are guided through a standardised capture sequence that covers all required vehicle angles. Images are automatically checked for clarity and completeness before the AI assessment runs. Substandard photos are rejected, and the policyholder is prompted to resubmit, ensuring the output is based on usable evidence.

VIN recognition and vehicle identity verification: The vehicle registration visible in the inspection is cross-referenced against the policy to confirm the correct vehicle is being documented. This addresses a common form of pre-inception fraud where a substitute vehicle is photographed in place of the insured one.

Scalable operations without proportional cost increases: A manual inspection operation grows with headcount. An AI inspection workflow handles increased volume without adding staff or extending processing time.

Benefits Beyond Fraud Prevention

The case for AI pre-insurance inspection is often framed around fraud. The operational benefits extend well beyond that single application.

• Faster underwriting decisions: A verified condition report available within minutes of submission removes the inspection bottleneck from the policy issuance cycle. High-risk profiles and break-in renewals that previously required days to process can be handled the same day.
• Reduced claims leakage: A documented baseline at policy inception means fewer ambiguous claims result in unwarranted payouts. The evidence needed to validate or challenge a FNOL submission is present from the start.
• Lower operational costs: AI pre-inspection costs a fraction of a field survey. For insurers processing high volumes, the annual savings are significant. Those savings compound when the downstream cost of disputes and investigations is also reduced.
• Improved customer experience: Policyholders complete the inspection process in minutes, from their own location, at a time that suits them. There is no waiting for a surveyor appointment. Cover can be confirmed the same day.
• Better auditability and compliance: Digital, timestamped reports create a clear audit trail for every policy. Regulators and internal compliance teams have documented evidence of the inspection process and its outputs.
• Data-driven decision making: Aggregated inspection data across a portfolio reveals patterns in vehicle condition, damage frequency, and geographic risk concentration. This feeds directly into underwriting model refinement.

Why Pre-Insurance Inspections Are Becoming a Strategic Imperative

Motor insurance is under sustained pressure from multiple directions. Claims costs are rising. Fraud techniques are becoming more sophisticated. Regulatory expectations around fair treatment and evidence-based decisions are increasing. Policyholders expect faster, more transparent service.

Pre-inspection sits at the intersection of all four pressures. It reduces claims cost by establishing a verifiable baseline. It supports fraud detection by documenting the vehicle's condition before fraud can be attempted. It creates an auditable evidence trail. And it delivers a faster policy inception experience for the policyholder.

Straight-through processing (STP) for motor claims is one of the most discussed ambitions in insurance operations. STP requires reliable baseline data at the point of policy inception. Without it, every ambiguous FNOL submission requires human review. AI pre-inspection is what makes large-scale STP achievable in practice.

Insurers investing in AI-powered inspection infrastructure now are building a capability that will compound in value as the volume of policies processed digitally continues to grow. Those who delay face a widening gap between the speed and efficiency of their claims operations and what the market expects.

The competitive dimension is also real. An insurer that can offer policy inception in minutes, backed by a verified inspection, is providing a meaningfully different customer experience from one that still requires a scheduled field visit. As digital distribution continues to grow, that difference matters at the point of sale.

Final Thoughts

FNOL disputes over pre-existing damage are not a claims problem. They are an underwriting problem that gets discovered at the claims stage.

The answer is not better dispute resolution. It is removing the conditions that create disputes in the first place. A verified, timestamped record of the vehicle's condition before coverage begins provides the evidence that dispute resolution requires. The most effective way to handle an FNOL dispute is to have already made it unnecessary.

As motor insurers accelerate digital transformation, an AI pre-insurance inspection platform is becoming a foundational capability. It supports better underwriting, faster claims processing, reduced fraud exposure, and a customer experience that meets modern expectations. It is not an optional efficiency improvement. For insurers operating at scale in an increasingly competitive market, it is becoming a baseline requirement.

Hailstorms rank among the costliest natural catastrophes in Europe and have become a growing concern for insurers and society alike, with record-breaking events, such as the July 2023 storms in Northern Italy, causing over $3 billion in damage.

While the global rise in hail-related insured losses is primarily driven by increasing exposure and economic growth, the severity and frequency of recent events have amplified concerns about how climate change may be influencing hail risk. Some studies suggest that hailstorms may produce larger hail more frequently in a warming climate, but the science remains far from settled.

A global review by Raupach et al. (2021) summarized the state of knowledge: while small hail may become less common due to increased melting, large hail is expected to become more frequent, driven by stronger updrafts and greater atmospheric moisture. However, Raupach also highlighted that the trends are highly uncertain because changes in other factors influencing hail potential were less understood, such as storm types, storm frequencies and aerosol concentrations.

Proxies reveal important trends despite limited hail observations

One major challenge in understanding hail trends is that no single data set captures all the key aspects of changing hail risk. Hail reports and insurance claims can offer some of the most direct measures of impact, but these are often sparse in rural areas or influenced by changes in exposure and reporting practices. As a result, researchers typically rely on proxy data to infer trends in hail occurrence and severity.

For example, lightning activity over the last two decades shows negligible or even negative trends over large parts of central Europe. However, less lightning does not necessarily mean lower hail risk: if individual thunderstorms become more intense, they could produce larger and more damaging hailstones even as storm frequency declines.

Indeed, the high-density networks of hailpads (a scientific instrument used to measure the size, density, and kinetic energy of hailstones during a storm) in Northern Italy and Western France indicate a slight shift towards fewer but larger hailstones over a similar period.

Another proxy indicator is the atmospheric environment of thunderstorms, which has become more supportive for large hail because a warmer atmosphere holds more moisture and generates stronger convective updrafts, both favoring larger hailstone formation. While this thermodynamic relationship is well understood, proxies cannot fully represent the complex evolution of hailstorms, so additional approaches are needed to develop a robust understanding.

Models point to stronger hailstorms

The development of models with horizontal resolution of a few kilometers enables simulations of individual thunderstorm updrafts, a major step forward in hail research. However, this "convection-permitting" resolution is not yet fine enough to fully resolve the complex structure of storms, which is important because the shape and peak intensities of thunderstorm updrafts are critical for large hail formation. In addition, most models do not simulate the detailed microphysical processes involved in hailstone growth and evolution.

Keeping these caveats in mind, simulation studies focused on specific events or thunderstorm episodes consistently suggest that future hailstorms will feature stronger updrafts, greater water content and thus produce larger hail on average.

Extended-period model simulations, although computationally expensive, are also more recently being conducted by several research groups. Preliminary results for Europe using sophisticated hail diagnostics show regionally varying trends, with some areas indicating an increase and others a decrease in hail risk. A similar pattern has been found in the United States.

This apparent divergence reflects the fact that hail risk is influenced by several competing factors. While stronger updrafts in a warmer climate favor larger hailstone growth within individual storms, changes in storm frequency, storm type and melting processes can vary significantly across regions. Studies disagree on what factors are most important in what region. Moreover, while high-resolution models represent a major advance in hail research, they still have important limitations.

Nevertheless, what these models do consistently show is that thunderstorm updrafts tend to support larger hailstones, especially in already hail-prone regions in Europe such as around the Alps.

In insurance, trust is often determined before a conversation even starts.

Consumers are entering interactions with carriers and agents more skeptical than in the past. Fraud concerns are no longer a niche issue, but a part of everyday life. In 2025, the Coalition Against Insurance Fraud estimates scams cost Americans at least $300 billion annually, and 78% of consumers say they are concerned about insurance fraud. That baseline of caution is shaping how people respond to outreach, offers, and even legitimate communications.

What is often overlooked is how quickly that skepticism carries into the way insurers are actually trying to reach people.

Most outreach still runs through the same channels: email, digital ads, automated follow-ups, and call scripts. While these tools are efficient, they all operate inside an environment where consumers already have their guard up due to the nature of an easily manipulated and inauthentic digital channel. Unknown emails get ignored, unfamiliar phone numbers get screened, and digital ads are easily skipped. Even real messages from insurers often get ignored because they resemble everything else people have learned to avoid.

So, the pressing issue is no longer reach, but credibility at first contact.

That is a tough shift for an industry that has spent years fine-tuning for speed and scale. Automation has made it possible to reach more prospects with less effort than ever before, but that has also made it harder to stand out as real. A lot of messaging feels interchangeable now, with similar subject lines and the same tone. Even when the intent is good, it doesn't always land that way on the consumer end, and they are getting faster at filtering it out.

This matters because insurance is not a casual purchase. The decision is tied to risk, stability, and long-term financial security. They are determining who they can trust if something goes wrong, and that decision rarely begins at the point of sale but instead starts with the very first interaction.

That first interaction is where things are getting lost.

A growing number of consumers are skeptical of anything unsolicited. Not because they've tuned out, but because they've seen too much of the wrong kind of message. Scam calls, phishing emails, fake invoices, and impersonation attempts have made caution the default, with curiosity coming much later, if it comes at all. This creates a difficult dynamic for insurers trying to make well-meaning outreach feel authentic.

Email is the clearest example. It's still a core acquisition tool across the industry, but it is also one of the easiest channels for consumers to ignore. Most inboxes are already crowded, heavily filtered, and judged in seconds from the preview. If something doesn't feel immediately relevant or personal, it's sent to the junk folder. This leaves insurers with a growing gap between effort and perception where more campaigns and outreach does not necessarily garner more engagement.

In response, many organizations have doubled down on automation with AI marketing tools, predictive outreach, and live chat engagement becoming standard across acquisition teams. Although essential at scale, they introduce a quieter issue. The more communication is optimized and automated, the more it starts to feel generic. And when everything feels generic, it becomes harder for any one message to feel credible or intentional.

And that is where credibility starts to erode.

People do not need every message to feel directly tailored to them, but they do at least need some sort of sign that there is a real person behind it. Without that, even accurate or helpful outreach can get filtered out as spam.

This is where smaller, more intentional touchpoints start to matter again.

A handwritten note breaks expectation and sits in a different mental category than automated digital outreach. It does not feel like a spam email or a scripted call, making it a more tangible and deliberate form of outreach in a market where most communication is automated by default. The note itself doesn't need to be long or persuasive, and in many cases it shouldn't be. Its value comes from what it signals: that someone chose to reach out directly rather than through a system designed to do it at scale.

For a prospective insurance customer who is already cautious about scams and impersonation, that signal can be enough to earn a second look.

This is not an argument against automation. The industry depends on it, and consumers expect the convenience it provides. But as skepticism toward digital communication continues to rise, insurers should be careful not to lose the human touch in the process.

Trust has always been at the center of insurance, but the difference today is that trust is often being evaluated before a conversation ever takes place.

In a market where consumers are quick to ignore anything that feels automated, the insurers that stand out may not be the ones reaching the most people. They may, instead, be the ones finding better ways to show there is authenticity behind the message.

Commercial drone use has moved well beyond traditional aviation operators. Today, a drone may be part of a wedding photographer's standard package, a contractor's roof inspection workflow, a real estate agent's listing strategy, an event producer's marketing plan, or a consultant's site-mapping process. For insurers, that change matters because drone exposure can enter a book of business through accounts that were never underwritten as aviation risks.

Market momentum is reinforcing the issue, with commercial and small-business drone use continuing to expand as aerial imagery, mapping, inspection, and delivery applications become more affordable and easier to deploy. Grand View Research projects the U.S. drone market will reach roughly $58.5 billion by 2033, driven in part by commercial uses such as surveillance, precision agriculture, infrastructure inspection, and last-mile delivery. The underwriting challenge is that drones are being folded into everyday operations faster than many applications, endorsements, exclusions, and claims workflows have evolved.

Drone exposure is no longer easy to identify by class code alone

Historically, aviation exposure was easier to spot. A business that owned aircraft or provided flight services generally presented itself as an aviation risk. Commercial drones have blurred that line, and many insureds do not view a drone as an aircraft, but rather as a camera, inspection tool, or piece of jobsite equipment. Looking at it from that perspective creates a gap between how the business operates and how the account is classified and documented.

The exposure can also change during the policy period. A photographer may add aerial footage because clients begin asking for it. A contractor may buy a drone to reduce ladder use. A property manager may hire a subcontracted pilot for seasonal inspections. A business that reported no drone activity at submission may have meaningful drone exposure months later without realizing that the change should be disclosed.

This is where underwriting blind spots appear. Applications that ask only broad questions about business operations may miss important drone variables, including:

• Whether the insured owns drones, rents them, borrows them, or hires third-party pilots.
• How often drones are flown and whether the flights are incidental or revenue-generating.
• Whether flights occur near crowds, roads, airports, schools, residential property, power lines, roofs, or active jobsites.
• Whether operations include night flights, flights over people, controlled-airspace authorizations, or beyond-visual-line-of-sight activity.
• The value of drones, cameras, sensors, batteries, cases, controllers, and other mobile equipment.
• The type of data collected, including high-resolution video, thermal imagery, mapping files, geospatial data, or footage of private property.

For insurance executives, the operational lesson is that drone exposure is often embedded inside otherwise familiar SMB classes. The underwriting process has to surface the aviation component before a claim forces the issue.

Liability can extend beyond the crash

The most visible drone liability claim scenario is physical impact, so think about when a drone strikes a person, vehicle, roof, window, power line, event structure, or piece of equipment. Bodily injury and property damage are still central concerns, especially when drones are used around crowds, venues, residential neighborhoods, construction sites, or client premises. But the liability analysis does not just stop with impact.

Drones increasingly carry equipment that changes both the severity and the nature of a claim. High-resolution cameras, thermal sensors, LiDAR units, and mapping software can turn a routine flight into a privacy, data, or reputational dispute. A contractor filming a roof may unintentionally capture private information or sensitive business activity on an adjacent property. A wedding or event operator may fly over guests, traffic, or a venue property. A real estate professional may publish aerial imagery that includes neighboring property without realizing the potential privacy concerns.

These scenarios can create overlapping allegations of bodily injury, property damage, trespass, nuisance, invasion of privacy, misuse of recorded content, failure to supervise a subcontractor, or breach of a client contract requiring compliant drone operations. They can also create coverage friction when a general liability form, professional liability form, cyber/data form, inland marine form, and drone endorsement all need to be evaluated against the same fact pattern.

Regulatory compliance is part of the underwriting conversation

FAA rules are not insurance policy language, but they are increasingly relevant to underwriting and claims. Part 107 generally governs small drone operations for work or business when the drone weighs less than 55 pounds. Commercial operators need a remote pilot certificate or must operate under the direct supervision of a certified remote pilot, and Part 107 drones must be registered. FAA guidance also highlights operational limits around visual line of sight, altitude, speed, controlled airspace, and accident reporting.

The regulatory environment has also become more operationally nuanced. FAA rules now allow certain Part 107 operations at night, over people, and over moving vehicles without a waiver when specific conditions are met, while controlled-airspace authorization may still be required. Remote ID rules add another layer by requiring registered drones to broadcast identification and location information; Part 107 operators must register each individual device separately. These requirements give underwriters practical follow-up points: Who is the remote pilot in command? Is the aircraft registered? Is Remote ID addressed? Are flights being conducted in controlled airspace? Are operations documented?

Compliance questions do not eliminate loss potential, but they help distinguish casual or undisclosed drone use from a managed operation. They also help claims teams evaluate whether an incident involved an insured employee, an independent contractor, a borrowed aircraft, a noncompliant flight, or a use outside the contemplated exposure.

State-level drone laws are creating additional legal complexity

While the FAA regulates national airspace and flight operations, states continue expanding their own rules around privacy, surveillance activity, trespassing, biometric collection, and permissible commercial drone use. That creates a fragmented legal environment where drone operations that appear compliant from an aviation standpoint may still create liability concerns under state-specific statutes or consumer protection frameworks.

Some states have introduced restrictions tied to recording individuals or private property without consent, while others have expanded protections around critical infrastructure, schools, residential areas, or law enforcement-sensitive locations. In practice, that means the same commercial drone operation may carry materially different legal exposure depending on where the flight occurs.

For insurers, the challenge is not simply understanding FAA compliance. It is evaluating how evolving state-level privacy standards, evidentiary requirements, and surveillance-related allegations may affect underwriting assumptions, claims handling, and policy interpretation across multiple jurisdictions simultaneously.

The issue becomes more complicated as carriers attempt to modernize forms and endorsements at scale. State-by-state filing requirements can slow the rollout of updated drone language, particularly when regulators interpret unmanned aircraft exposure differently across jurisdictions. Product teams may ultimately face approval delays, inconsistent filing expectations, or limitations around how exclusions and endorsements can be deployed within individual states.

That regulatory fragmentation creates operational pressure for national carriers attempting to maintain consistent underwriting standards while adapting to rapidly changing commercial drone usage patterns. As adoption accelerates across SMB classes, insurers increasingly face the challenge of balancing product modernization with a compliance environment that continues evolving at different speeds across the country.

Equipment loss deserves separate attention

Drone insurance discussions often focus on third-party liability, but equipment loss can be material for small businesses. The drone itself may be only part of the value at risk. Cameras, lenses, gimbals, thermal sensors, controllers, batteries, charging stations, data cards, tablets, cases, and other mobile gear can quickly exceed the cost of the aircraft. Equipment can be damaged in a crash, stolen from a vehicle, dropped during transport, or lost during a job.

This matters because liability coverage and equipment coverage respond to different problems. Drone liability coverage is designed to address claims that the drone operation caused bodily injury or property damage to others. Equipment or inland marine coverage is designed to address loss to the drone and related gear itself. If insureds assume one coverage does both, they may discover the gap only after a loss.

Policy language may not match operational reality

One of the most important executive-level concerns is policy architecture. Many commercial general liability policies contain aircraft exclusions, and older wording may not have been drafted with mainstream commercial drone adoption in mind. Some forms exclude aircraft broadly. Some address unmanned aircraft specifically. Some offer limited endorsements. Others may create ambiguity when the drone is incidental to a covered business service rather than the insured's primary business.

That ambiguity affects more than coverage intent. Brokers may believe drone use is incidental and therefore within the customer's existing program. Business owners may believe a drone is just equipment. Claims adjusters may need to evaluate aircraft exclusions, professional services exclusions, personal and advertising injury provisions, privacy allegations, mobile equipment schedules, subcontractor agreements, and certificates of insurance at the same time.

For carriers, MGAs, and program administrators, the issue is not simply whether to offer drone coverage. It is whether underwriting, forms, pricing, claims handling, and producer education all reflect the way businesses actually use drones.

A better underwriting framework

Drone-related exposure doesn't need to be overcomplicated, but it does need to be visible. A practical underwriting framework should separate the exposure into four categories:

• Operational risk: who flies, where they fly, how often they fly, and under what FAA requirements or authorizations.
• Third-party liability: the potential for bodily injury, property damage, premises-related incidents, contractual disputes, and privacy allegations.
• First-party equipment risk: the value, mobility, storage, theft exposure, and crash exposure of drones and related gear.
• Risk transfer and documentation: subcontracted pilot agreements, certificates of insurance, additional insured requirements, waivers, maintenance records, flight logs, and incident reporting procedures.

Questions in these areas help underwriters move beyond a binary yes-or-no drone question. They also support clearer coverage communication with brokers and insureds. For example, an occasional real estate photographer flying a small drone in low-risk environments presents a different profile than a contractor conducting roof inspections near power lines, a venue operator flying around crowds, or a mapping business collecting thermal and geospatial data over multiple client sites.

Why this matters now

Commercial drone adoption will continue because drones solve practical business problems. They can reduce the need for ladders and scaffolding, improve inspection speed, create better marketing assets, document property conditions, and support more efficient site monitoring. Those benefits are exactly why the exposure is spreading across small-business classes that were not historically associated with aviation.

For insurers, the opportunity is to close the gap before claims expose it. Drone insurance is becoming more important because drone use changes the risk profile of ordinary business operations. It introduces aviation concerns, mobile equipment values, privacy questions, data handling issues, subcontractor dependencies, and regulatory compliance considerations into accounts that may otherwise look routine.

The carriers that respond well will be those that make drone exposure easier to identify, easier to price, easier to explain, and easier to adjust. The ones that do not may continue writing policies built for ground-level businesses while their insureds are already operating in the air.