The notion that there must always be a "human in the loop" has become de rigueur in any discussion of AI implementation in business. The cynical side of me suspects that part of the reason is to reassure everyone that AI won't lead to mass layoffs, but there are also plenty of practical reasons to have humans check the work of AIs.

Or maybe not. At least not for long.

A recent article in Harvard Business Review argues that human supervisors quickly become numb and simply approve the recommendations of the AI as it becomes increasingly accurate. And I think the author has a point, based on what I've seen of human/AI interactions in semi-autonomous cars. 

There needs to be a better way.

Let's have a look.

Early in the development of its driverless cars, Google gave demo models to employees. Google warned them that the AI wasn't perfect and instructed them to have their hands on the wheel, always ready to take control. That worked... for a little while. A very little while. 

As the AI proved to be reliable, the drivers began to relax their hold on the wheel, then started to take their eyes off the road, then began reading or looking at their phones. When the car signaled that the drivers had to take over, they needed perhaps 10 seconds to fully reengage. Given that a car going 60mph travels nearly 300 yards in 10 seconds, Google realized its hybrid approach wouldn't work and committed to producing only cars that could be fully autonomous. 

Other makers of autonomous vehicles, most notably Tesla, continue to offer cars that take a hybrid, human/AI approach, but I think that's irresponsible (as I wrote at length a year ago). 

Now, an article in Harvard Business Review offers a similar view about the dangers of having a "human in the loop" to supervise work done by AIs. The author, a senior fellow at Cal, writes:

"Decades of research on automation show that better automation systems lead to worse human safety layers; as automation grows more reliable, the people monitoring it grow complacent, their skills atrophy, and they become less attentive.... AI compounds the problem. Its failures sound as confident, fluent, and well-structured as its successes....

"Even when validation works and people catch errors, every correction is captured as training data. Each exception a person handles teaches the system to handle that exception; the better the data, the smaller the scope of human validation needed in the future."

The article says the lack of reliability of the humans in the loop is actually just the start of the problem. He writes:

"The larger problem with the human-in-the-loop organization, though, is that the entire blueprint reads the transformation backwards. It organizes the company around the shrinking frontier of what machines can’t yet do. But there’s a larger opportunity here. Because AI removes constraints, it enables new combinations of work, new decision rights, and new forms of value. Organizations, accordingly, have an opportunity to reorganize and 'rebundle' around those new decisions rather than the shrinking residue of the old work."

He goes on to lay out how to think about rebundling: "Instead of asking which tasks the machine can take over, leading organizations should ask which new roles the technology makes possible, and then redesign the organization around them." He lists several steps that can lead you to new approaches, which seem intriguing and which you may want to explore.

For now, though, I just want to make the point that "human in the loop" isn't a panacea, certainly not in the long term. The reviewers of AI work can quickly become as reliant on the AI as those Google employees who found themselves with their hands off the wheel, their eyes off the road and their minds far away from driving when a problem popped up in front of them.

Cheers,

Paul

Last year, the Justice Department brought the largest healthcare fraud case in its history, which included charges that criminals stole the identities of more than 1 million Americans to submit $10.6 billion in fraudulent Medicare claims for urinary catheters. While Medicare blocked nearly all of it, private Medicare supplemental insurers paid out about $900 million.

Americans lost a record $20.9 billion to internet crime in 2025, up 26% in one year, according to the FBI. Insurance fraud, including healthcare fraud, costs Americans more than $300 billion a year, and health insurance scams pick up during open enrollment, the FTC says.

Seniors are the hardest-hit age group, losing $7.7 billion in 2025, up 59% in one year. In fact, Michigan officials warned on Oct. 12 of impostors claiming to be from Medicare to dupe older adults.

As co-founder of Spokeo, a people search company, and former co-chair of the W3C Credentials Community Group, I have witnessed firsthand how scammers have grown increasingly sophisticated over the last 20 years.

Insurance scams are evolving and costly

We are seeing many insurance-related scams in 2026. Fraudsters are buying sponsored search ads with web addresses and logos that look very similar to HealthCare.gov and Medicare.gov. When Americans click on the link, they end up buying a plan that is not real insurance and also fork over their personal information, which can lead to identity theft.

Even large health insurers are being duped, as the $900 million loss in the catheter case shows.

The Coalition Against Insurance Fraud calls insurance fraud "the crime we all pay for," because it comes back to honest policyholders as higher premiums, which can hurt retention.

In a September survey by BrandShield, 30% of U.S. adults said they would stop buying online from brands scammers had impersonated. For an insurer, that is a renewal lost to a scam it never ran.

Here are three tips for insurers to help prevent insurance fraud

• Say what you will never do. Tell customers you will never ask for a one-time code or take payment by gift card, wire or crypto. Put the statement on the ID card, in the app, and on every renewal notice.
• Explain what numbers to trust and verify. Scammers can spoof caller ID so your company's name appears on a customer's phone. In every fraud alert, encourage customers to use the number on their card and check the identity of incoming callers with a reverse phone search.
• Report fake ads and websites and warn consumers. Search regularly for impostors using your name and report them to the FBI IC3, the FTC, and the online platform where they live. Send alerts to customers in real time.

We've built an extraordinarily sophisticated financial system around industrial failure. We can price it, insure it, reserve capital for it, and finance recovery when it happens, but preventing that same failure often requires an entirely different standard of proof.

Insurance is exceptionally good at mobilizing money after something goes wrong. Natural catastrophes caused an estimated $100 billion in economic losses in the first half of 2026. Insurance covered approximately 42%, leaving nearly $60 billion to be absorbed elsewhere. Insurance's ability to fund recovery is indispensable. The shortfall also exposes a contradiction in how risk is funded.

Premiums, deductibles, and recovery costs have established places in corporate budgets. A proposal to prevent an asset failure faces a tougher test. Someone must prove the threat is serious, determine what intervention it warrants, and defend spending money on an event that might never happen. Failure has a funding mechanism. Prevention has a business case to make.

Insurance can transfer part of a financial loss. It cannot restore missed production, repair damaged customer relationships, or undo the wider consequences of an industrial disruption. The challenge is making prevention as intelligible and investable as recovery.

Knowing the risk isn't the same as managing it

Industrial organizations rarely lack information. They receive risk-engineering reports, inspection findings, maintenance records and alerts from connected equipment. Yet information doesn't guarantee action.

A temperature change, unusual vibration, or a deteriorating component has little meaning in isolation. Its significance depends on the asset's role in the operation. Is there redundancy? What production depends on it? What would a shutdown cost? Could a failure disrupt customers, supply chains or other facilities?

These questions turn technical observations into business consequences. Without that translation, recommendations can remain in reports while maintenance teams address more immediate demands and capital committees fund projects with clearer returns.

Industry has invested heavily in detecting changing conditions and predicting potential failures. Those capabilities matter. Still, knowing what might fail doesn't tell an organization which emerging risks deserve attention first.

A relatively minor condition on a critical asset may represent far greater exposure than a severe condition on equipment with sufficient redundancy. The challenge is understanding what matters most, what the consequences could be, and where intervention will create the greatest value.

Prevention needs to compete for capital

Consider a manufacturer that identifies deterioration in a critical component on its main production line. Although the component hasn't failed, its condition is changing. The organization must compare the cost of replacing it during a planned shutdown with the lost output, repair time, and disruption that could follow an unplanned failure.

The consequences, however, may extend well beyond that production line. A shutdown could affect customer deliveries, contractual commitments, supply-chain continuity and financial performance. What appears to be a maintenance decision may represent a much larger business risk.

The appropriate response might be immediate repair, closer monitoring, a change in operating parameters or acceptance of the exposure. Someone must have the authority to make that decision and commit the necessary resources before the opportunity to intervene passes.

Now multiply that challenge across hundreds of assets and facilities, each presenting different levels of exposure. The question is no longer simply whether an individual intervention is justified. It's which interventions deserve priority when resources are limited, and the consequences extend across the enterprise.

For decades, industry has developed sophisticated ways to measure losses after they occur. We need comparable discipline in understanding the economic value of preventing those losses before they happen.

Capital should not only be deployed to absorb loss. It should be possible to direct it intelligently toward preventing loss.

The standard for resilience must rise

Risk assessments, inspections and engineering recommendations are essential. Completing those processes, however, doesn't necessarily mean an organization has addressed its most consequential exposures. An inspection can establish that a process was followed. A risk assessment can document a vulnerability. Neither, by itself, demonstrates that the organization is becoming more resilient.

Industrial risk isn't static. Asset conditions change, operational dependencies evolve, and an exposure that appeared manageable during a periodic assessment may become significantly more consequential.

A recommendation without a named decision-maker and deadline is information, not control. Someone must have the authority to accept the exposure, fund the response or explain why another course is justified. Following the process is important, although it's not the same as preventing the loss.

Insurance can help change the economics of prevention

Insurers can make recommendations easier to act on by explaining the severity and urgency of an exposure, not simply recording that it exists. Brokers can connect risk-engineering findings with an organization's insurance and resilience priorities. Operators must determine whether to intervene or accept the exposure and take responsibility for the outcome. There is also a bigger opportunity here.

Insurers and brokers possess extraordinary expertise in understanding, quantifying, and pricing risk. Much of that expertise has historically helped organizations prepare financially for potential losses. What if more of it could be applied to making prevention economically compelling before those losses occur?

Imagine insurers, brokers, and industrial operators working from a more continuous, consequence-based understanding of emerging physical risk. Rather than relying primarily on historical losses and periodic assessments, they could better understand where exposure is developing, which conditions present the greatest business consequences, and where intervention could materially reduce risk.

This wouldn't eliminate uncertainty or diminish the need for insurance. It could, however, create a stronger economic foundation for prevention and a more productive relationship between risk financing and operational decision-making.

For decades, the insurance industry has helped businesses understand the financial consequences of what can go wrong. That expertise will remain indispensable, but the next opportunity is to help make the economics of what doesn't have to go wrong equally visible.

We've become extraordinarily good at financing industrial failure. It's time to become just as disciplined about investing in its prevention.

Bill Gates recently said he is using generative AI as part of the annual, two-week strategy session that determines how the Gates Foundation spends its next $10 billion. He'll be "taking the strategy notes and actually engaging in a dialogue with ChatGPT, Claude, Copilot, and even having them talk with each other," he said.

I don't have $10 billion that I'm considering how to spend, and you may not, either, but genAI has now improved to the point where it can at least be an aid in setting strategy at almost any level. 

An old boss, Mel Bergstein, used to say: "Strategy is what you don't do." The idea worked for Mel, who founded a consulting firm, Diamond Management & Technology Partners, that pioneered the notion of digital strategy in the mid-1990s. Chunka Mui and I took Mel's adage further in our book, "Billion Dollar Lessons," for which we had 20 researchers spend two years looking at 2,500 major failures to identify the warning signs.

Our recommended solution boiled down to having a "devil's advocate" identify all the underlying assumptions for a strategy, to keep problems from being glossed over as an executive team rushed to realize the CEO's plan. GenAI, used right, can now do a pretty good job of filling that role for anyone.

Let's have a look. 

Gates told the New York Times that, "in a few of the reviews, we’ll actually have the A.I. sit in. In a few cases, we’ll tell it: Hey, only speak if we ask you. And then in a few other cases, we’ll say: Hey, if you hear something you think is wrong or you hear us thinking about, for example, what are these statistics? Please engage. So we’ve gone from it being a joke [when someone suggested using genAI a year ago] to it will be a peer — not making any final decisions, but it will be a peer in deep, complex strategic discussions, making a significant contribution."

I'm not willing to go that far just yet, partly because I'd need to test the domain knowledge that the large language models have about a subtle, heavily regulated business like insurance and partly because genAI has shown itself to be a sycophant. The biggest, overarching pattern of failure that Chunka and I found in the research for "Billion Dollar Lessons" was that people were reluctant to point out obvious problems that could derail initiatives that had the imprimatur of the CEO, and we don't want to replace human sycophants with AI ones. 

But you can describe your strategy to an AI, then pepper it with questions, going deeper each time while demanding evidence for its thinking and insisting it be blunt.

Let's say you're thinking of buying a company. You could start with a general question: What could go wrong? Then you probe. How will our competitors react? What if interest rates increase? The economy goes into recession? The war in Iran escalates? Will we actually be able to cross-sell to our customers? Will the efficiencies from scale be as great as we expect? Who's done something similar and had problems? What were the problems? And on and on and on. 

You can do the same exercise if you're thinking of a new line of business, a software overhaul, adopting AI chatbots — or if you want to explore the dangers of standing pat, of not undertaking a major initiative that is under consideration. 

Major corporations have staff do extensive analysis along the lines I'm suggesting and, in the case of a possible acquisition, draw on their investment banks for advice — but the bias is always toward acting. If the perception is that the CEO backs an idea, the staff strives to make it work, not to kill it. Investment banks only collect their hefty fees if the deal happens, so why would they recommend against it? 

When Chunka and I did a consulting project with a major company that was considering launching autonomous vehicles, we saw elaborate studies it had commissioned from consulting firms that said the robotaxi business was a slam dunk and that the first to enter would win big. That was more than eight years ago. 

It turned out, however, that the executives working on the AV project internally were aware of all the sorts of operational and safety issues that we've all seen play out and that have slowed the robotaxi rollout. Those concerns were brought to the surface in the interviews for our devil's advocate review. 

Warren Buffett used to set aside two, equal pots of money when asking investment banks for advice on possible acquisitions — one for a bank that argued in favor of a deal and one for a bank that argued against it. Whichever carried the day collected its fees, while the other got nothing. You can be sure the "anti" bank worked as hard as possible on its arguments and evidence. 

In my experience, many big ideas should fall apart at the slightest scrutiny. As we wrote in "Billion Dollar Lessons," we thought 47% of the failures we investigated were based on strategies that never should have made it out of the strategy room — and not because those involved were stupid or inexperienced. 

The fifth-biggest cement maker in the world reasoned that it was really in the home products business because so much of its cement was used there. It began also making furnaces and lawn mowers... then went bankrupt and was bought by a competitor. Avon decided that its core competency was its culture of caring — not, you know, its line of cosmetics and its devoted salesforce — so it bought a maker of medical equipment and an operator of retirement homes. It had no idea how to operate the new businesses and quickly sold them at a major loss. In the early days of fax machines, FedEx spent hundreds of millions of dollars to set up a business that would collect a document from you, drive it to the nearest office, fax it to the office nearest your destination, then drive the fax to the recipient. The company somehow missed that Moore's law would soon make the devices so inexpensive that every company would have its own.

It may take time to learn how to use genAI to avoid costly strategic mistakes — and that's even before we start using it as broadly as Gates is planning to. You'll likely also need to introduce to the LLM information specific to your market, including regulatory issues that it likely doesn't have even as these models try to Hoover up everything available on the internet. Insurance is a complicated market.

But I think we're now at the point where genAI can, if managed correctly, start to catch strategic errors. We won't get that 47% figure for obviously bad ideas down to zero, but even heading toward 40%, maybe even 33%, would mark real progress.

Cheers,

Paul

Year-end audits don't create cash problems. They expose the ones that were already there.

Every year, a carrier's finance team enters audit season relying on the cash position assembled during the last reconciliation cycle. Then, it starts reconciling current account activity, and the numbers do not line up. The hours that should go toward supporting the audit go toward confirming where the money is and understanding what changed.

Insurers already reconcile regularly due to expected controls and audit standards. The trouble is that even a well-run reconciliation process cannot close the gap between checkpoints. Discrepancies can build quietly for weeks. By the time audit season arrives, the deadline forces a reckoning with a gap that has been growing all along.

The real cost of not knowing your cash position

For carriers working with even a handful of delegated partners, cash reconciliation means comparing records held in separate systems and checking them against each other. Add MGAs, third-party administrators (TPAs), and claims administrators, and each brings its own claim and payment feeds as well as fund accounts to reconcile with the carrier's books. The process works as designed. The systems on each side were never designed to communicate automatically.

The reconciliation burden grows with the number of delegated relationships a carrier maintains. A carrier working with three or four TPAs faces a manageable set of comparisons. A carrier working with 20 or 30+ faces the same problem across more formats, timelines, and reporting styles.

The underlying issue is scale, and it grows with the network.

Better process helps at the margins, but as a carrier's delegated network grows, the reconciliation work grows with it. Periodic checks struggle to keep up. The number a CFO reports to the board, and the number an auditor verifies, depends on seeing activity across those accounts in one place.

Why this hits harder at year-end

Delegated claims accounts can be a blind spot. Money held by an MGA or TPA may not be visible until the reconciliation process begins. Differences in format between carrier statements, bank feeds, and internal ledgers take time to resolve before anyone can analyze the transactions that do not match.

Most transactions reconcile without trouble. The hours go to chasing the exceptions, and that work lands when time is shortest, with the calendar leaving little room. Auditors typically start in January, and findings reach the audit committee and board in late Q1 or Q2 for those on a calendar year close.

In banking, finance leaders are expected to know where cash sits and how it moves. Insurance should expect the same clarity, even when claims funds pass through delegated partners.

Accountability without visibility

A less discussed part of the problem is that accountability and control have separated. The carrier holds financial and regulatory responsibility for claims paid on its paper. The day-to-day work of paying claims, holding funds, and reporting on both has been delegated to TPAs, MGAs, and claims administrators.

That arrangement can make operational sense, and many delegated partners run it well. A carrier relies on others to do that work, but it remains responsible for the outcome. That means a CFO may be signing off on numbers assembled from someone else's systems, on someone else's schedule.

The consequences reach beyond audit preparation. When bordereaux arrive late, or in formats that do not consolidate cleanly with bank data and the carrier's ledger, finance teams work with information that may already be weeks old. Without a single view, loss reserves can run too low on some programs and too high on others, without a clear view of which is which.

A carrier should also know where its claims funds are held, and whether they sit in segregated accounts or are pooled with other money at the administrator. Risk committees ask the same question after recent bank failures: where is the cash, and is it safe?

Consider a carrier with $10 million held by a TPA that fails. If that money sits in a segregated titled account, it is generally identifiable and better protected. If it has been pooled with the administrator's own money, the carrier may have to file a claim against a failed business alongside it and other creditors. This is a hypothetical, but the question it raises is real, and CFOs need the answer before a partner runs into trouble.

That visibility matters most if a delegated partner runs into financial distress. The longer it takes the carrier to understand where its funds are, the harder it is to respond. Board and risk committees also watch whether claims are paid on time, because late payment can trigger penalties under many state prompt payout rules. Claimants waiting for payment feel the consequences, too.

What CFOs should do before, not during

• Shorten the reconciliation cycle. Surface discrepancies between scheduled closes so they can be investigated and resolved earlier.
• Build one view across delegated accounts. Bring account activity together with claims, payment and ledger data in real time.
• Standardize data formats and reporting expectations. Agree on the information carriers need from partners while there is time to address gaps.
• Clarify where funds are held. Confirm account structure, segregation, how quickly the carrier would learn of a partner in financial distress, and who is responsible for reporting changes.

Auditors will keep asking whether the year-end number is right. The better test for a CFO is whether the carrier could produce that number with the same confidence in any month of the year with much more efficiency and fewer headaches. The carrier's accountability does not wait for audit season.

Imagine the customer of a 20-year-old life insurance policy trying to figure out if it makes sense to surrender the policy. Do they call the insurance company first, hoping to talk to a knowledgeable agent? Probably not. They will upload the policy contract, riders and most recent statement to the AI assistant with a list of questions: What benefits would be lost by surrendering the policy? What happens to the outstanding loans? Are there other alternatives to surrendering the policy? Which policy provisions are the most important to consider before deciding?

This AI might be right, or might be partly right, or might be very wrong. But what is becoming abundantly clear is that this customer arrives at their insurer with a level of analysis and a body of knowledge that once cost time, or cost money, or both.

This leads me back to the question insurers have been asking for years about how AI will change what we do: underwriting, servicing, operations, all inside the enterprise. So what happens when the policyholder is AI-enabled as well?

The information gap is starting to change

The Society of Actuaries Research Institute is now seeking research on policyholder behavior in the artificial intelligence era. According to the proposal request, it is possible for consumers to use AI to understand a policy's provisions better to improve their premiums, loans and withdrawals, and claims through improved product features.

That isn't proof that AI has changed lapse rates, loan behavior, or benefit usage yet. It's on the research agenda. The difference is significant. But the formal asking of such questions by the actuarial community indicates that consumer AI requires attention before the results become apparent in experience studies.

Up until recently, this has created a bit of a predicament: an insurer has product specialists, actuaries, underwriters, policy administration systems, and decades of institutional knowledge; a policyholder has a lengthy contract written in technical language and maybe the occasion to review it a couple of times.

Generative AI won't remove that imbalance entirely, but it certainly lowers the cost of asking sophisticated questions about a complex contract. That alone changes the dynamic of that interaction.

A long-lived contract can meet a very different customer

Life insurance is unique because the contract could go on for decades, and the technology available to the user would be different from the present one.

A policy issued in the age of paper statements and telephone calls may one day be managed by a consumer carrying a persistence layer of AI that summarizes documents, compares alternatives, remembers every previous conversation, and prepares detailed questions before every interaction.

The policy's bones haven't altered one bit. But a policyholder's ability to kick the tires and check them out has.

All this matters because many decisions and actions can occur throughout the lifecycle of a single life insurance policy: premium payment policy, loans, withdrawals, changes of beneficiary, conversion, surrender, and more. By making those decisions easier to understand and execute throughout the life of the product, there's no guarantee that customer behavior in the future will continue to be like the historical behaviors that informed product assumptions.

Policyholder behavior may become an AI issue

SOA's current research request explicitly identifies potential effects on lapses, loans, withdrawals, and benefit usage. A separate 2026 SOA call for essays on AI and the potential for antiselection in life insurance distribution asks whether AI could help applicants or intermediaries anticipate underwriting outcomes, compare carriers, select underwriting pathways and apply sophisticated placement strategies at greater scale.

Once more, these are questions under investigation, not established outcomes. But, one thing appears clear: certain presumptions in life insurance are based on how people behave.

Making it easier to evaluate the timing and consequences of a lapse, loan, withdrawal, conversion, surrender, or benefit election changes behavior in ways that historical behavior doesn't predict. The negative reaction to that realization is to predict material disruption. The right reaction is to begin measuring whether AI-assisted behavior is emerging, where it's concentrated, and if it's big enough to matter.

From a more strategic perspective, for our actuarial and product teams, consumer AI could one day become another behavioral variable to study, rather than just a way to service customers better.

AI-enabled policyholders can also be good for insurers

This should not be framed only as a new risk.

When a customer understands the policy, it allows them to ask the right questions, know what benefits they have, prepare the right documents, and know what decision they are making. Beneficiaries will use AI to organize materials for a claim, point out relevant policy language, and understand what the insurer is asking for. A policyholder who walks up to the window to service their policy will know the difference between a loan, withdrawal, and a surrender.

This is consistent with the financial services research. In July 2026, LIMRA reported that AI is increasingly being studied not only as a tool that assists advisors but as a potential source of analysis and financial guidance directly to consumers.

While more knowledgeable buyers are good for insurers, they also bring heightened expectations. A customer who has just received an instantaneous explanation of a policy provision may be less patient about waiting for days to get a generic response from a service agent.

The customer's AI is not the insurer's AI

Yet there is a boundary—the policyholder's AI is outside of the insurer's control.

The model was not chosen, trained, fed, or even provided with its system prompt by the insurer. Your AI could be reading an outdated contract, skipping an endorsement, not grasping a rider, getting guaranteed vs. non-guaranteed values mixed up, or just making up a provision out of whole cloth that does not exist.

The assertion could be false, or at least inaccurate.

The enterprise response is to make verifying what the authoritative policy information says more accessible. Suppose a customer says, "My AI says this rider gives me a different option." It should be easy for a service person to say, "Let's review what your current contract and its effective endorsements, transaction history, and applicable rules say about this."

The more external AI becomes part of customer decision-making, the more valuable clear, accessible, and authoritative policy context becomes.

Soon, the customer may send the AI instead

The next step is not difficult to imagine: the policyholder does not merely use AI before contacting the insurer. The AI contacts the insurer.

That scenario is beginning to appear in insurance discussions already. An October 2026 Insurance Thought Leadership article described consumer AI agents calling insurers and other businesses on behalf of users. At the same time, NIST is developing work around identity and authorization for software and AI agents as agent-to-agent interactions move from concept toward practical infrastructure.

This brings us to a much bigger question for life insurers: what if a chatbot does work? How would your service channel know that it's talking to software working for a policyholder? What data could be returned through that channel? What happens when a customer AI and your system don't agree on a policy provision? When does it become important to switch the interaction over to a person?

There's nothing in those two scenarios that necessitates banning the use of AI in customer service. It just means that we, as users of these services, will need to create different channels for them to go through.

Build policyholder AI readiness

Insurers do not need to predict exactly how fast consumer agents will mature, but they do need to prepare for the direction of travel.

First, make authoritative policy context easier to retrieve and explain. A policyholder's AI will only be as useful as the documents and information it can access. Clear digital contracts, current riders, transaction history, and understandable definitions reduce the chance that outside AI starts from incomplete context.

Next, test servicing journeys with customers being assisted by AI in mind. When customers arrive having 10 specific questions, they should not follow the same process as someone who has only general questions.

Third, watch how behavior changes. Teams — actuarial, product, servicing — that rely on lapses, loans, withdrawals, conversions, benefit usage, and channel usage need to understand what constitutes a significant deviation. We want to avoid blaming AI if another force is at work. The goal is to build evidence that indicates that AI is a cause before rewriting our assumptions about behavior.

Fourth, prepare digital channels for external agents. Identity, consent, disclosure and escalation will become practical. We do not want to automatically trust an outside agent. We do want to ensure legitimate customer assistance can reach us and not diminish customer protection.

The next AI transformation may happen outside the insurer

When asked about the insurance AI strategy, carriers invariably identify the same things: automated underwriting, automated servicing, automated claims, automated document processing, and automated decision support.

However, when it comes to the impact of artificial intelligence on enhancing service innovation, customer capabilities are also affected.

The increased understanding could allow them to compare things to other products, which could open them up to asking difficult questions.

However, what I think we have not talked about and probably is an elephant in the room is that insurers are not going to be in control of all this.

They can only prepare for it.

Will the next phase of life insurance AI be defined by how intelligent the insurer becomes? Or by what happens when the policyholder becomes intelligent at machine speed?

Paul Carroll, Editor-in-chief of Insurance Thought Leadership.

Pricing gets far less attention than claims or underwriting, yet is important. How is the pricing function being modernized in insurance? And how should it be?

Kumarappan Sundaram, Managing Director, PwC US

Pricing gets less attention partly because a lot of the work is infrastructure: platforms, data flows and deployment processes. It is not a front-office workflow that a new tool can visibly transform.

Modernization can be uneven. Personal lines are well on their way. Commercial lines are earlier in the journey and still rely heavily on underwriter judgment and rating engines embedded in policy administration systems.

How pricing should be modernized can largely be a question of sequence. The foundation comes first: decoupling rating from the policy admin system. At the moment, actuaries produce pricing logic, which they hand to IT, which embeds the logic in a rating engine that sits within a policy admin system. So, making a change to the pricing logic means waiting for a new release of the policy admin system. If you decouple, you can make changes to ratings as and when you want.

Once you’ve decoupled, you should build reliable data flows and establish clear governance.

You can then go to the next level of capabilities, like advanced analytics and AI. Let’s say you have a $10 billion book. You’re considering changing a particular rate factor from X to Y, maybe related to driver age. Advanced analytics can help you understand the impact of the rate change on the whole book. The actuaries can then decide whether to make that rate change. Carriers can help deploy and monitor pricing changes quickly and safely.

Paul Carroll

From a technology standpoint, what are the most important shifts in P&C pricing and rating?

Kumarappan Sundaram

Pricing is moving from a periodic project to a continuous capability. With catastrophe volatility and shifting market conditions, treating pricing as an occasional exercise is hard to sustain.

Two shifts stand out. First, dedicated pricing platforms. Rating, models and rules increasingly sit on purpose-built platforms rather than inside the core policy system. Second, modern software discipline. Version control, automated testing and continual monitoring of live models are becoming standard.

Paul Carroll

What is driving carriers to invest in sophisticated pricing platforms now?

Kumarappan Sundaram

Five things.

First, speed-to-market of rate changes. Moving from quarterly or annual releases, gated by IT and policy admin deployment, to something near-continuous is the key value. Decoupling rating logic from the core policy system is also a practical starting point for core modernization, delivering value without an entire core replacement.

Second, loss cost volatility and trend uncertainty. Social inflation, repair and parts costs, tariffs and secondary-peril catastrophe losses make trend assumptions go stale faster. Carriers want faster feedback from emerging claims experience that they can incorporate into price.

Third, segment-level control. Carriers want to grow where they are well priced and hold the line where they are not, which means testing, deploying and monitoring changes at a granular level.

Fourth, regulatory and governance demands. Filing, documentation and explainability requirements keep growing. Audit trails can be far easier to build in than to retrofit.

Fifth, data ecosystem expansion. Telematics, geospatial and property-level data, cat model output and third-party enrichment should flow into rating at quote speed, which can often be the real integration challenge.

A CFO will often ask how the benefit is measured, so programs should define measurement up front.

Paul Carroll

Underwriting and pricing are tightly linked conceptually, but loosely coupled in practice. Why is that, and can they be brought into phase?

Kumarappan Sundaram

The link is strong in theory but weak in practice, mainly for technology and operating-model reasons.

Start with systems and data. Pricing logic sits in a rating engine or pricing platform, while underwriters work in a different workbench based on the data in the submission, so the two rarely share a view of the risk. Then there are different rhythms. Pricing changes on a release cycle, while underwriting decisions are made submission by submission. Overrides and discounts help fill the gap. Finally, the feedback loop is weak. Why underwriters override a price rarely flows back to pricing in a structured way, so a valuable signal is lost.

Modern pricing platforms increasingly aim to close this gap. Bringing them into phase is mainly an integration and governance task. Pricing can be exposed as a service that the underwriting workbench calls in real time, connected to appetite, distribution, retention and claims signals. Overrides can be captured as data, and authority and guardrails can be made explicit. In complex commercial risks, underwriter judgment legitimately plays a larger role, so the aim there is visibility and learning, not automation.

Paul Carroll

How can modern pricing technology help carriers price emerging risks?

Kumarappan Sundaram

Emerging risks, such as cyber, climate-related perils and new auto and energy technologies, may not come with deep loss history. A modern platform helps in four ways.

First, new data sources as proxies. Outside-in cyber scans, geospatial and property-level data, and vehicle and telematics data give carriers signals where claims history is thin. The platform brings them together with internal experience and subject matter specialist judgment, with clear rules for how the weighting shifts as claims data builds.

Second, scenario testing. Teams can test prices against stress scenarios, such as a major catastrophe or a systemic cyber event.

Third, fast, controlled learning. Small, measured changes show what a new risk is worth sooner and with less exposure than a book-wide change.

Fourth, flexible product design. Configurable rating makes it easier to adjust deductibles, sublimits and endorsements as understanding of the risk develops.

The limit can be that technology cannot create data that does not exist. For novel risks, the platform helps carriers learn and adjust quickly, alongside accumulation controls, reinsurance and underwriting oversight.

Paul Carroll

What impact is AI having on pricing and rating, and where should leaders be skeptical?

Kumarappan Sundaram

I would separate what is emerging with clear value from what is still unproven.

The clearest near-term value is GenAI around the pricing process. The more underrated opportunity is in the work surrounding the price: drafting and reconciling rate filings, translating pricing specifications into platform configuration, documenting changes, summarizing competitor filings and preparing data. These steps slow the path to market, and errors are easier to review than in a pricing decision itself.

What is still unproven is AI acting on pricing decisions. The idea of agents that monitor performance, propose rate actions and test them is compelling. But pricing is regulated and auditable, and many carriers are not ready for autonomous rate changes. For now, I expect AI to propose and people to approve, so the quality of that review is the real control.

As AI capabilities become widely available, proprietary data, speed of deployment and governance are likely to matter more as differentiators. Carriers that treat AI as a standalone upgrade, without the platform and operating model underneath it, risk building capability they cannot put into production.

Paul Carroll

What would you advise a carrier to do first?

Kumarappan Sundaram

Measure your cycle time, from the point a pricing change is approved to the point it is live throughout the various channels. Many carriers have never seen that number, and it shows where value is being lost. Then prioritize platform and process changes that shorten it. Put governance in place before scaling AI, and start with one or two lines where faster pricing has a clear, measurable payoff. Resist modernizing everything at once.

Paul Carroll

Thanks. This is all super helpful.

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Kumarappan (Kumar) Sundaram is a Managing Director in PwC’s Property and Casualty Operations and Technology practice and a leader of its Insurance Core Transformation and Pricing/Rating offering. With more than two decades of consulting experience, he partners with US and UK P&C carriers, from large international insurers to small regional ones, across personal, commercial, and specialty lines, with depth in commercial and specialty. He has led numerous large-scale programs modernizing policy, claims, and pricing/rating operations, and works with PwC’s strategy, management, technology, and actuarial teams to deliver integrated solutions, thought leadership, and sales.

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Sponsored by PwC

Collaboration. It's what every leader hopes for in her team. It's the purpose behind much of our communication at work. And it can be a challenge.

There is a collaboration platform in the digital workplace for almost everything. Salesforce. Asana. HubSpot. Figma. Git. ServiceNow. Too many to name them all. Each tracks and assigns work items and enables us to collaborate through a system. The systems sit between us. They enable us to tell one another what to do online. Want something done, open a ticket. Want to feel productive, pound through your task list, and assign "work items" to the next person in the workflow. Fire, then forget.

Most of the time, this works just fine. The efficiency can't be disputed. But ask yourself a couple of questions. Are there times you assign a task in Salesforce and get back a result you didn't expect? Have you ever opened an Asana task and not known what you were being asked to do? Our experience tells us it happens all the time.

Communication scholars and system architects make a distinction between synchronous and asynchronous messages. Synchronous communications occur in real time and involve immediate back and forth between participants. Asynchronous communications allow for time delays. Workflow assignments are asynchronous. They happen independent of an immediate response. Sometimes, in fact, responses don't happen at all. Workflow makes it easy to make an ask of a colleague, assign a task to a subordinate, offer feedback to a collaborator. They also eliminate the synchronicity of a person-to-person "handoff."

Evidence suggests synchronous interaction makes us feel connected at work. It even fosters relationships, which make asynchronous communication on workflow platforms more effective. An intriguing study of software engineers proves it. The engineers worked in two buildings several blocks apart. In some instances, teams worked together in the same building. In others, the team members were spread across the locations. As is common in the software industry, the engineers used a platform to share code, give one another assignments, and provide feedback. Researchers found that teams of engineers working in the same building—that is, colleagues who had frequent synchronous interactions with one another—received 23% more online feedback—an asynchronous interaction—on their code than did those on multi-building teams whose members had less frequent face-to-face contact with one another. "Sitting near workers increases not only in-person but also digital collaboration," the authors write.

The collocated software engineers enjoyed the benefits of social capital: mentorship, coaching, learning, and collaboration. All were achieved through a digital tool that, when coupled with the opportunity for synchronous interaction, worked for and not against social capital. Everyone benefited, especially those newest to the team. Retention among newer joiners was significantly higher among collocated teams than dispersed teams. Young workers and women, the authors tell us, are "substantially more likely to quit the firm when they lose proximity to coworkers." Less feedback. Less connection. Lesser results. It's a lesson the system architects at Amwins, led by Ben Sloop, took to heart when they designed their collaboration platform.

Ben knows his way around a spreadsheet. A boardroom, too. Harvard undergrad. Stanford MBA. A stint as a management consultant at Bain, then a few years in private equity. The perfect pedigree for his current role as president and chief operating officer of Amwins, the nation's largest wholesale insurance broker. But there's more to Ben's success than his stellar resume. Ben told us, "One of the core requirements to gain more responsibility here is having deep, real relationships with our salespeople. I spend a disproportionate amount of my time talking to and staying connected with people in the field. Of all the people that I'll respond to, first is if one of our brokers reaches out to me. That's the top of my inbox."

Amwins' clients are retail insurance brokers and agents looking to find homes for complex, hard-to-place risks—think workers' compensation insurance for circus performers. The insurance carriers who advertise on the Super Bowl don't want the hard stuff. And so, retail brokers work with wholesalers like Amwins to get complex risks placed with one of a large legion of specialty insurance carriers. Each occupies a well-defined niche within the commercial insurance market. Each is expert in a particular type of risk. This creates a classic many-to-many problem. Thousands of customers, thousands of carriers and thousands of Amwins brokers sitting between them.

The challenge is having the right expertise at the right time. "Our brokers have to be specialists," Ben told us. "Our retailers don't want to work with someone that does everything a little bit, because that's kind of what they do. They want to deal with someone that's a deep expert." Enabling his brokers to always find that one expert within their large, decentralized organization became a key part of Amwins' founding mission. It's the hardest part of what they do, Ben told us. "In order to be a deep expert, what that means is that, well, if my client comes to me and they ask me for a construction wrap policy in Scottsdale that I know nothing about, I've got to know where to get them what they need inside our own firm. So, we don't lose that client, right? And so, this created this huge coordination problem."

The company built Amlink to solve it. Amlink is the wholesaler's proprietary relationship management system. It is a workflow platform, but it's different. Ben beamed with a creator's pride as he told us how it works. "I can look in the system and say, well, actually, we have a guy who lives in Scottsdale and does only construction wraps, right? And so, I can reach out to that person who I usually know personally, because we have all sorts of sales events. The point is to build connectivity with an internal coordination mechanism."

We both have seen lots of systems that tried to solve similar problems. All did it the same way. Type in "construction wrap in Scottsdale" and you'd get deal listings, PowerPoint presentations, maybe a white paper or two. Lots of stuff to wade through, maybe some good slides you can reuse, but probably not the answer you need. Amlink is different. It delivers names and contact information and forces conversations to happen. It's effective at both delivering expertise when it's needed and building relationships across the firm. Knowing who to turn to can make or break a deal. Amwins brokers don't just compete on expertise—they compete on connectedness.

Amlink is a unicorn. "I've never seen another system in the industry like this. Everyone says it's a people business. but they tend not to track [data] at the people level," Ben told us. He was clear to put the tech in its proper context: "We are a technology-enabled business. The thoughtful use of technology makes our people better. But we are fundamentally a person-to-person business."

Social capital matters at Amwins. It drives market results. It also creates an environment where people want to stay. "You know, it's funny. I joined 19 years ago, and I still think of myself as one of the newer people," Ben confessed, explaining that Amwins was the kind of place "good people stay for a long time. People typically don't leave just for money. People tend to leave because they just don't feel connected, don't like who they're working with, don't feel compelled by the strategy."

We probably should have introduced you to Ben at the very beginning.

"Social Capital at Work," by John Burrows and Seth Rachlin, will be published by Simon Acumen on Nov. 10. This article is excerpted from their book.

It's little wonder that "AI governance" is a term on everyone's lips wherever business technologists gather. With great power comes great responsibility, and we are all aware of the tremendous capacity for change that is inherent in AI, and agentic AI especially, but also the potential for rogue agents and human errors that can cause catastrophes. That goes double for an industry like insurance that has so much systemic impact for individuals, enterprises, countries, and economies, and, as is appropriate, is highly regulated as a result. The good news is that I believe insurance can move very fast so long as players don't neglect necessary defensive measures.

I stress the relevance of AI governance specifically to agentic AI because it is so much cleverer and more capable of true business reengineering than generative AI. Generative AI lets us parse content and ask and answer questions with blistering speed, but agentic AI provides an opportunity that is without precedent. It has the reach, performance, and access to resources to create better organizational (and inter-organizational) workflows and processes.

The results of this are amazing even to veterans of the sector that may feel they have seen everything. I'm referring to the removal of human drudgery, as the gears move faster and act with greater accuracy, alongside the chance to delight customers and partners, and liberate people to work more closely with customers while igniting creative, collaborative thinking.

Barriers to progress

But there are challenges and constraints. Making relevant data available to AI agents involves a certain level of sensitivity and understanding of data protection, privacy rights, and ethics.

Bill Gates is surely right when he argues that:

"We have no experience with a technology that can be adopted quickly or that can think and move like a human. When the PC came along, it took twenty years to significantly change how we worked because the software had to be developed, the price had to come down, and people had to learn how to use the tools and incorporate them into their business processes. AI, on the other hand, runs on the devices we already have, and it uses natural language. We don't have to adapt to it because it can adapt to us. It can watch the same training video that is used to train human workers and learn from existing data."

Gates says this new AI era will be "one of the most turbulent times in human history," and he calls for "human-reserved" roles to ensure that chaos does not ensue, thanks to bad prompts or machines acting in ways we had not anticipated.

I echo all of these thoughts, and I think we can have it both ways. That is, transformation without trauma.

Crash barriers must be in place

Essentially, the remarkable autonomy of AI agents means we need more guardrails, and we will always need the 'human in the loop' to oversee what our brilliant digital minions are doing.

But we can't be Luddites. A concise shortlist of the advantages of agentic AI includes turbo-charged productivity, better-prepared (and auditable) executive decision-making, faster claims processing, and customer experiences that delight customers and encourage them to evangelize on behalf of the insurer serving them.

And it's hard to overstate the scale of what is possible in the agentic AI world. This isn't about trimming a fraction of a cent or penny off costs, and neither is it about incremental change. We are talking about new insurance products coming to market in a couple of days (or, say, a month when marketing campaigns and everything else is aligned) rather than six, eight, or 12 months. That is the sort of acceleration and cost arbitrage that even the dotcoms didn't see with the advent of e-commerce.

So, we have an opportunity for seismic change in how insurers operate and accelerate. Brokers are more likely to go in steps, but we are still talking about genuine transformation, offering faster response times to inbound sales queries and service desk calls, as well as an ability for brokers to absorb sharp growth without adding to the employee roster.

There are other benefits. Standardization of processes becomes simpler, and continuous improvement will occur organically as agents learn. And of course we know that agents excel in other ways that humans don't: their lack of mood swings, health issues, need for holidays (or even breaks), and so on.

Building trust

As processes become refined and we become more trusting of our robo-companions, we will be able to confidently rely on agents to act in autonomous ways, with the human in the loop always there as a check and balance and ultimate arbiter.

To reach that point, though, we need to school our agents by providing them with the institutional knowledge the insurance sector has built up over years, decades and centuries. By creating a super-brain of knowledge management and making that available in machine-readable data, we can reduce the human element so that we can act more like observers and referees seeking out anomalies and red flags rather than micro-managers.

We will reach the point where we only require human oversight for bigger deals or to handle exceptions and 'black swan' events. My estimate is that we will reach that stage in up to four years; although, such is the rate of progress, it could be more like one year.

Guardrails will still apply and that's why we advise customers to spread workloads across agents to validate stability and create a separation of duties. Just as we wouldn't want to have one member of staff only holding the keys to the kingdom, we must use agents in tactical ways, restricting their movements and allocating unique tasks so the risk of a single point of failure is eliminated.

We also want alert human beings who use their experience to detect risks as opposed to the narrow focus of an agent on task completion. We will rely on a combination of humans and agents to understand regulatory change and what are likely to be the next steps of industry lawmakers.

We need to ensure the insurance industry can manage risk, and there is an equitable relationship between technology provider, buyer, and end-user. Today, we have a legislative grey area, so we must be in listening mode and have the sorts of interpersonal meetings that are not (yet!) the domain of agents.

Similarly, we must be honest with our people. Agentic AI will take some jobs, but it will enable others, so we need to create a learning environment where new skills can be added.

And finally, insurers must be certain their choice of AI technology providers is good. Not all providers in what has become a febrile space will last, and not all will have the ethics or reach across the IT ecosystem to succeed as long-term players.

We need to understand the trade-off at play. The power of technology always comes with a price tag that goes beyond the cost of acquisition and maintenance. Technology is not a silver bullet, and it can be used for good and bad. We need to move fast to gain an early-market advantage but without sacrificing protections that require the human touch.