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.

Across rural America, a once-niche building style has become a mainstream housing trend. Barndominiums, which integrate residential space with functional shop or utility areas, are attracting homebuyers looking for cost-efficient and adaptable housing solutions.

Originally popular in central states, barndominiums are now appearing throughout the Southeast, Mountain West, and other high-growth regions. Their broader adoption reflects a combination of lower construction costs and flexible design approaches that appeal to a wide range of uses.

But as barndominiums migrate into new regions and climate zones, insurers face the challenge of understanding how these hybrid structures perform under real-world hazards. While metal construction can provide significant resilience advantages, design, construction, and operational factors can dramatically affect risk.

Carriers and underwriters must evaluate barndominiums on more than their metal shell.

Construction Classification Matters

Because steel is non-combustible, many barndominiums may qualify for more favorable construction classifications than traditional wood-frame homes. In wildfire-prone areas, this is a meaningful advantage. Unlike wood framing, steel does not contribute fuel to a fire and is less vulnerable to ignition from embers and radiant heat.

However, the final classification of a barndominium is often determined by more than its primary structural frame. Interior finishes, wall assemblies, partitions, and mezzanines can significantly alter a building's fire performance.

For example, a structure finished with fire-rated wall and roof assemblies may achieve a higher level of fire resistance than one finished primarily with combustible materials. Conversely, owners who install wood-framed partitions, decorative wood paneling, or other combustible finishes may inadvertently reduce the building's fire-resistive characteristics.

This variability creates challenges for underwriters. Two barndominiums with nearly identical exterior appearances can have very different fire-loss profiles depending on how the interior spaces are built out.

Mixed Occupancies Create Unique Fire Risks

Unlike conventional homes, many barndominiums combine residential and work-related functions under one roof.

Workshops may contain welding equipment, fuel, solvents, or machinery that increase fire exposure. Living spaces may occupy one side of the structure while shop areas remain open and active on the other.

This mixed-use arrangement requires careful evaluation of fire separation and compartmentalization. The effectiveness of barriers between living and working spaces can significantly influence both fire spread and overall potential for property loss.

Water Intrusion Often Goes Undetected

Roof leakage has long been a persistent vulnerability in metal building systems. With the finished ceilings, insulation systems, and drywall present in barndominiums, water intrusion can be concealed for extended periods. By the time there are signs of water intrusion, substantial damage may already have occurred.

The moisture-related problems often originate from roof penetrations added after construction. Post-installed roof penetrations, such as mechanical or utility installations, often create vulnerable points in the building envelope if flashing details are not properly designed and maintained.

Long-term maintenance practices may be just as important as the original roof construction when evaluating water-damage exposure.

Snow Loads Demand Regional Design Adjustments

Pre-engineered metal building systems can perform well under snow loads when properly designed, but construction details matter. It's important to know structural components, such as purlins, may be configured differently depending on the manufacturer, builder, and project requirements.

Variations in support conditions can affect the final performance of the roof system. If field construction differs from engineering assumptions, structural capacity may not align with actual loading demands.

Communication among project stakeholders is essential to ensure snow-load requirements are properly addressed throughout the design and construction process.

Wind Resistance Is About More Than the Building Frame

While metal building systems are often perceived as highly wind resistant, one of the most common vulnerabilities is not the structure itself but the overhead garage door.

The doors on garage or shop openings are frequently supplied by separate manufacturers. If those doors are not properly rated for local wind conditions, they can become a weak link. If large openings such as overhead doors fail under wind pressure, internal pressurization can increase loads on the roof and wall systems, potentially leading to broader structural damage.

Foundations Play a Critical Role

Many pre-engineered building projects rely on a delegated design process in which the manufacturer provides structural loading criteria, while an engineer completes the foundation design separately. Problems can arise when assumptions made by one party are not fully communicated to the other.

In high-wind regions, foundations often serve an additional purpose beyond supporting gravity loads. Their mass helps resist uplift forces that attempt to pull the structure from the ground during severe weather events. As a result, foundation systems may appear substantially larger than expected for a building of comparable size. Those larger foundations are often a necessary component of the building's overall wind-resistance strategy due to the lightweight nature of the superstructure.

Hail Performance Is Generally Strong

Standing-seam metal roofing and exposed-fastener metal panel systems are among the more durable roofing materials available in hail-prone regions. While cosmetic denting can occur during moderate hailstorms, significant punctures that compromise water-shedding performance typically require much larger hailstones.

Even when surface coatings sustain minor damage, repairs are often possible without requiring complete roof replacement. This durability can represent a meaningful advantage compared with some traditional residential roofing materials.

Looking Ahead

Barndominiums are no longer a niche housing product. As their popularity grows, insurers will see these hybrid structures across the country and its diverse risk environments.

While metal construction can provide advantages in areas such as fire resistance, hail performance, and durability, those benefits should not be assumed automatically. Ultimately, evaluating a barndominium requires a holistic view of the structure to ensure policyholders have coverage aligned with the realities of the structure.

As more P&C insurers enter the rapidly growing pet insurance market, many assume they can extend existing platforms, processes, and operational models to support pet insurance as just another specialty line of business.

In practice, this assumption breaks down quickly. Pet insurance is not simply another P&C product. It represents a fundamentally different operating model, one that more accurately reflects health insurance rather than traditional property and casualty.

That distinction matters. While insurers may be underwriting pet risk, they are also being asked to deliver a continuing care experience. Traditional P&C products are built around episodic events. Auto claims happen occasionally. Home claims are infrequent. Even travel insurance is event-driven. Pet insurance is different.

It is defined by continuing, high-frequency interactions between pet owners, vets, and insurers. Chronic conditions, repeat prescriptions, regular check-ups, specialist referrals, and continuing treatment plans transform insurance from a reactive product into an active service. This is not a product extension problem. It is an operating model mismatch.

Traditional P&C platforms were designed for annual renewals, infrequent claims, standardized documents, batch processing, and slower workflows. Pet insurance requires continuous engagement, real-time decision-making, dynamic interactions across multiple parties, and high-volume, non-standardized inputs. Trying to run one on the other creates structural inefficiency.

Why the Traditional Model Breaks at Scale

Historically, reimbursement has been the dominant operating model for much of the pet insurance market. The customer pays the vet bill upfront. They submit paperwork to the insurer. The insurer reviews the invoice, checks coverage, validates the claim, and reimburses the customer days or weeks later. That model is becoming increasingly unsustainable.

Pet parents now expect the same level of convenience and immediacy they experience in other areas of their lives — from real-time payments and instant approvals to transparent decisions. Waiting weeks for reimbursement is no longer just inconvenient; it is misaligned with modern expectations. More importantly, it creates operational strain.

Vet invoices rarely follow standard templates. Different clinics use different terminology, coding structures, and levels of detail. Many insurers still rely on manual processes to interpret invoices, validate treatments, check policy terms, and identify potential fraud or leakage. That creates a long chain of administrative effort.

As volumes grow, this creates a structural problem: operational costs scale with volume. Manual effort increases. Delays become inevitable. Fraud and leakage become harder to detect, and insurers are forced into a false trade-off. Move faster and lose control. Add controls and degrade customer experience. In many cases, they end up losing both.

Pet Insurance Is Increasingly Operating Like Retail Health

Some insurers have already demonstrated what success looks like. The challenge is that much of the market is still trying to scale pet insurance on infrastructure built for fundamentally different products. That matters because the next competitive frontier in pet insurance increasingly resembles a health insurance model rather than traditional P&C.

In modern healthcare, eligibility is checked in real time. Treatment decisions are made at the point of care. Payments are integrated into the care journey. Data is structured, codified, and continuously analyzed. Pet insurance is moving in the same direction. Instead of reimbursement-heavy workflows, insurers are shifting toward real-time coverage validation, point-of-care payment models, integrated vet-insurer-customer interactions, automated adjudication, and structured data capture.

Delivering this is not about digitizing existing workflows. It requires a fundamentally different technology foundation. When invoices are digitized and normalized, it becomes easier to identify anomalies, understand treatment trends, manage fraud risk, and control claims costs. Decisions can be made faster and with greater confidence.

This is where the economics of pet insurance begin to change. Decisions move from reactive to proactive. Control improves without slowing down the customer experience. This is where the real competitive advantage emerges, not just in pricing, but in operational execution.

Why This Matters Now

Pet insurance is growing rapidly, but growth alone does not guarantee profitability.

As more P&C insurers seek to capitalize on the pet opportunity, many risk underestimating how fundamentally different the category is from their existing lines of business. Insurers that continue to treat pet as a standard P&C extension risk carrying forward too much friction, too much manual work, and too much hidden cost.

The firms that win will be the ones that recognize pet insurance for what it increasingly is: not a niche version of home or auto insurance, but a customer-centric health insurance product built around continuing engagement, continuous service, and point-of-care decision-making.

That requires a different mindset, a different operating model, and a new health-oriented, service-driven framework. The next generation of pet insurance will not be defined by who can process the most claims, but by insurers that can most effectively remove friction from the care journey, connect customers, vets, and insurers in real time, enable decision-making at the point of care, and operate efficiently at scale.

This requires platforms built for real-time interaction, continuous data exchange, and event-driven execution — capabilities that traditional P&C systems were never designed to support.

Insurers that continue to treat pet insurance as a simple extension of P&C will struggle to scale profitably. Those that embrace its evolution toward a health-driven, continuous care model will define the future of the market.

America has entered its Peak65 era. With a record 4.2 million baby boomers celebrating their 65th birthday in 2025, the number of people entering retirement age in the coming years is expected to continue rising.

Regardless of whether someone is retiring immediately or continuing to work, the moment demands a new approach to coming lifestyle changes. It's also a time when people reassess their risk tolerance to ensure their savings strategy can support an uncertain retirement horizon.

Annuities have long been a versatile financial tool for this demographic, providing predictable income and financial security at a time when heightened concern for stability is warranted. And demand in the market has followed suit, with last year's U.S. annuity sales surpassing $460 billion for the fourth straight year.

Yet despite the demand, consumers lack confidence in understanding their annuity plans.

For insurers, this demographic shift presents a unique opportunity to engage and educate clients, becoming a trusted source for their future financial security. To capitalize on the momentum, advisors must address the knowledge gap with personalization, transparency, and trust.

The costly impact of miscommunication

Turning 65 is a gateway moment when new financial responsibilities and freedoms emerge – and when high-stakes decisions can have long-term consequences.

But from initial education to continuing policy communications, many consumers are left with a limited understanding of how their annuity works – as well as how it fits into their broader financial plan.

Recent data underscores the scale of the issue. According to Smart Communications' 2026 Customer Experience Benchmark Report, only 50% of consumers rate the communications they receive from insurers as very good or excellent, a 10-point decline from the previous year. This drop becomes significant given that 86% of customers say communications are important to their overall experience with a company. With interactions between insurers and consumers often taking place during emotionally charged, stressful or life-changing moments, communications are not a secondary concern, but central to maintaining an open and trusting relationship.

When communication fails, it creates inefficiency, with 46% of consumers contacting customer support teams when communications are difficult to understand. Confusion can also lead to frustration or disengagement, with many abandoning processes altogether. In fact, 62% of consumers say they would switch providers if communications do not meet expectations.

Shifting from first impression to long-term engagement

As with many insurance products, misconceptions about annuities are not the exception for many clients. As a trusted advisor, it's important to understand where these gaps show up and how to address them.

For instance, it's a common assumption that annuities guarantee fixed lifetime payments, when in reality payouts can vary depending on contract structures or underlying investment performance. Misunderstandings like this highlight a broader issue: too often, information is explained in ways that are technically accurate but not easy to understand. It's here that a personalized approach, which lays out a client's plan and outcome, would drive far better sentiment and results.

Breakdowns in understanding can occur at any stage of the customer's journey, and when they aren't clearly addressed, confidence and trust begin to diminish:

• In the pre-purchase stage, communication often falls short in helping consumers understand what an annuity is and how it fits into their broader retirement strategy.
• At the point of sale, complexity peaks. Contract terms, fees, and payout structures are frequently presented all at once, creating an overwhelming amount of possibly new information at the exact moment a decision is required.
• In the post-purchase stage, communication often becomes more reactive than proactive. Statements, updates, and policy documents may lack clarity or context, making it difficult for customers to interpret performance, understand their income options, or feel confident in their long-term plan.

Rebuilding client confidence through better communication

To sustain growth and retention in the Peak65 era, insurers must fundamentally rethink how they communicate with customers. Communication is no longer a supporting function but a strategic differentiator, where clear, timely, and relevant interactions not only improve customer understanding but also directly reduce operational strain, increase engagement, and build the trust required for long-term customer loyalty.

Technology has been a key enabler of this shift, but only when applied with intention. Modern CRM systems, AI-driven tools, and digital onboarding platforms allow insurers and advisors to deliver more personalized, responsive, and consistent experiences. These capabilities enable meeting customers where they are and tailoring communications to their needs.

At its core, closing the gap requires simplifying complexity without losing meaning. Plain-language explanations and outcome-based messaging make products easier to understand. Consistency across channels ensures clients don't encounter gaps or repetition.

It also means simplifying where and how clients can access information about their plans. Reducing friction in every interaction, such as onboarding or accessing documents, helps eliminate gaps before they arise.

As millions more Americans reach retirement age, insurers should look to deliver clarity at every stage of the customer journey. In a market defined by uncertainty, the ability to turn complexity into confidence will be the defining advantage.

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

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

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

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

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

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

Common Adoption Challenges

1. Lack of Cohesive Education and Marketing

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

2. Inconsistent Terminology

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

3. Cost and Complexity Concerns

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

4. Fiduciary and Litigation Risk

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

5. Plan Design and Portability

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

6. Explanation of Benefits at Scale

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

Driving Adoption with Plan Advisers and Consultants

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

Advisers Filter Ruthlessly for Fit and Ease

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

Fiduciary Risk Perception Is a Major Barrier

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

Education and Hybrid Support Are Now Table Stakes

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

Preference for Thoughtful Choice, Not Overload

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

Retaining Terminated and Retired Participants

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

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

Driving Adoption with Plan Sponsors

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

Develop Turnkey, Simple In-Plan Solutions

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

Offer Flexible but Manageable Menus

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

Tackle Litigation Risk Through Process Excellence

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

Driving Adoption with Plan Participants

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

Design for Radical Simplicity and Portability

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

Close the Explanation-of-Benefits Gap

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

Provide Credible, Independent Comparisons

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

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

Conclusion

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

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

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

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

Cloud-native applications are the right foundations to embrace AI

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

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

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

Effective AI adoption requires a deep understanding of the insurance industry

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

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

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

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