We talk a lot about smartphones and even smart homes, but let's not sleep on smart cities. They are getting more wired all the time, in ways that don't just provide convenience — such as alerting drivers to open parking spots — but that make people safer and reduce insurance claims.

The two latest examples that caught my eye are the deployment of sensors on Chicago streets that can detect flooding and a General Motors patent application for a way to use its cars to sense road condition. The Chicago sensors will relay warnings to property owners and to authorities. The GM data will flow to drivers to let them avoid trouble and to local governments that can fix the roads. 

But there's a lot more besides, and progress will likely accelerate from here.

The Chicago and GM stories demonstrate both the promise of innovations in cities and what pieces still need to fall into place so those innovations can be deployed widely and deliver major benefits.

In Chicago, 50 sensors will be deployed over the next 18 months on bridges, above roads and in sewers. Wireless and powered by solar, the cylindrical sensors will use sonar to measure the depth of water beneath them. If levels are rising at a threatening pace, the sensors will create instant flood maps for city authorities, who will alert property owners in affected areas. 

Chicago is a big place, so a lot more than 50 sensors will eventually need to be deployed. Bugs will also surely need to be worked out of the technology. Costs will need to come down — each sensor currently costs about $1,500.

But all those issues feel manageable, based on the cost and performance curves that are normal for this type of technology. Sensors for water leaks in homes, for instance, began as elaborate devices, shrank to about the size of hockey pucks, and now, according to an announcement from Hartford Steam Boiler, can be as thin as four credit cards stacked on top of each other.   

As it happens, as I was about to publish this commentary, the New York Times published a piece this morning about a system that is similar to Chicago's, that is further along and that underscores the need for the final, key piece: getting the word out, and rapidly. 

Spurred on by damage from Hurricane Ida four years ago, New York City has installed 250 sonar-based sensors whose cost is just $300 apiece and plans to double the number of sensors by 2027. The Times reports a big improvement in understanding flash flooding in real time — previously, authorities only learned of problems based on emergency calls, social media posts and news reports.

But the notification system is passive. You have to monitor a city website to see where flooding may be happening. If you're alert, likely because you've suffered damage in the past, you're fine. If not, you're as vulnerable as ever. 

The leaders of FloodNet, the sensor network New York uses, say they're piloting a system that can alert people via email, which will mark a huge improvement. An even bigger one will be when FloodNet and similar sensor operators can ping the cellphones both of those who've signed up for flooding alerts and of any others in an area, such as drivers, who might be affected.

The GM system has much further to go than the flooding sensors but could also deliver major safety benefits if GM can, in fact, gather useful information about road conditions from sensors that track the traction that a car's tires get and the movement in its suspension system. I grew up driving in Pittsburgh, where potholes seemed to show up everywhere during the spring thaw, and would have loved to know to be prepared to skirt a big one just ahead. A whole lot of drivers suffered damage to their cars and put in insurance claims that could have been avoided if a system like GM's were in place.

GM will face the same notification issue that the flood sensors do. It's one thing to have one car detect an anomaly. It's quite another to collect data from enough cars to be sure there's a problem with a road surface, and even more difficult to communicate that finding to a driver in real time. 

I'm optimistic that the connection challenges can be addressed relatively quickly because of a system called Sidewalk that Amazon has introduced that is an inexpensive backbone for communication with sensors. 

One of the challenges for "connected cars" has been that they typically communicate with their hosts via cellular networks, requiring lots of relatively expensive bandwidth. Sidewalk, by contrast, is low-bandwidth and low-cost. 

It operates via a mesh concept: A sensor doesn't need to send a signal so strong that it will reach a cell tower miles away. The signal just needs to be strong enough to reach any other device that is in the mesh network and within half a mile. That next device can then forward the data to any other device and so on until it reaches a major node that can send the data to its final destination. 

A mesh network can get overloaded if lots of data needs to be sent, but something such as a water-depth sensor is just sending a single number, perhaps every few seconds. The system Hartford Steam Boiler announced is, for instance, transmitting its data via Sidewalk. 

As Sidewalk and potentially similar communication backbones are developed, sensor networks will no longer need to worry about how to transmit their data. They just have to get it to a Sidewalk/etc. node. Similar standards will develop on the back end, handling the notifications to those who want them. 

So we can let our imaginations run wild. What else, beyond warnings about flooding and bad roads, should be sensed in a city and relayed to interested parties in real time?

If you step back a bit, you can see that smart cities have made real progress in recent years and decades. Some of that progress is mostly convenience. Traffic lights are synchronized so you generally don't have to stop on a main road if you're driving at the speed limit. Signs or phone alerts tell you just when that bus or subway will arrive. Your phone lets you know of traffic jams and can reroute you. Sensors in the pavement and in streetlights can monitor parking spaces and let you know when one is empty. 

Some of that convenience leads to safety. Knowing that there is an accident ahead of you makes you less likely to plow into something. Getting people into parking spaces faster reduces traffic in cities — a remarkable amount of which is people looking for spots — and decreases the number of accidents. 

And with the flood sensors and, perhaps, GM's sensing of road conditions, we're seeing even more opportunities for safety. 

What's next?

Let's have at it.

Cheers,

Paul

Artificial intelligence has moved past the proof-of-concept phase. Businesses are integrating AI into operations at a record pace, from customer service and logistics to medical diagnostics and HR decision-making. But as the benefits of AI grow, so do the risks, and most companies have not adequately addressed who will bear the legal and financial consequences when things go wrong.

The problem isn't the potential for harm alone. It's that the liability landscape for AI is undefined, shifting and increasingly litigious. When an algorithm produces biased results or a chatbot dispenses incorrect medical advice, it's not always clear who should be held responsible: the business deploying the tool or the developer behind the code. For companies that own or rely heavily on AI, especially those with captive insurance companies, now is the time to scrutinize these risks and evaluate how captives can help fill a widening gap in risk management.

AI failures already have consequences — and lawsuits

The assumption that AI risks are futuristic or theoretical no longer holds. In 2024, a federal judge allowed a class action to proceed against Workday, a major provider of AI-driven hiring software, after a job applicant claimed the platform rejected him based on age, race, and disability. The suit, backed by the EEOC, raises thorny legal questions: Workday argues it merely provides tools that employers configure and control, while plaintiffs claim the algorithm itself is biased and unlawful. 

The case highlights the growing legal gray zone around AI accountability, where it's increasingly difficult to determine whether the fault lies with the vendor, the user, or the machine. In another case, an Australian mayor threatened to sue OpenAI after ChatGPT incorrectly named him as a convicted criminal in a fabricated bribery case. The mayor wasn't a public figure in the U.S., and the false output had real reputational consequences.

These incidents are no longer rare. In 2023, the New York Times sued OpenAI and Microsoft for copyright infringement, claiming their models used protected journalism content without permission or compensation. The lawsuit reflects a growing concern in creative and publishing industries: generative AI systems are often trained on datasets that contain copyrighted material. When those systems are then commercialized by third parties or used to generate derivative content, the resulting liability may extend to businesses that integrate those tools.

More recently, the Equal Employment Opportunity Commission issued guidance targeting the use of AI in hiring decisions, citing a spike in complaints tied to algorithmic bias. The guidance emphasized that employers, not vendors, would typically bear responsibility under civil rights laws, even when the discriminatory impact stems from third-party software.

These examples reveal a pattern. AI is being used to make decisions that carry legal weight, and the consequences of failure (reputational, financial and regulatory) often fall on the business deploying the system, not just the one that created it.

A legal and regulatory framework is forming

The global regulatory environment is evolving quickly. In March 2024, the European Union formally adopted the EU AI Act, the first comprehensive legal framework for artificial intelligence. The law classifies AI systems into four risk categories--unacceptable, high, limited and minimal--and imposes stringent obligations on businesses using high-risk systems. These include transparency, human oversight and data governance requirements. Noncompliance (related to high-risk AI systems) can lead to fines of up to 7% of a company's global annual revenue.

While the U.S. lacks a national AI law, states are moving ahead with sector-specific rules. California's proposed Safe and Secure Innovation for Frontier Artificial Intelligence Models Act would require companies to test for dangerous capabilities in large language models and report results to state authorities. New York's Algorithmic Accountability Act aims to address bias in automated decision tools. Several federal agencies, including the FTC and the Department of Justice, have also made it clear that existing laws, from consumer protection to antitrust, will apply to AI use cases.

In Deloitte's Q3 2024 global survey of more than 2,700 senior executives, 36% cited regulatory compliance as one of the top barriers to deploying generative AI. Yet less than half said they were actively monitoring regulatory requirements or conducting internal audits of their AI tools. The gap between risk awareness and preparedness is widening, and businesses with captives are in a unique position to act.

The role of captives in addressing AI liability

Captive insurance companies are not a replacement for commercial insurance, but they provide an essential complement, particularly for complex, fast-evolving risks that the traditional market is hesitant to underwrite. AI liability falls squarely into that category.

For example, a captive can help finance the defense costs and potential settlements tied to AI-generated errors that fall outside the scope of cyber or general liability policies. This might include content liability for marketing materials created using generative AI, or discrimination claims stemming from algorithmic hiring tools. In some jurisdictions, captives may even fund regulatory response costs or administrative fines where allowed.

Captives can also provide coverage when a third-party AI vendor fails to perform as promised and indemnification clauses prove insufficient. In such cases, a captive can reimburse the parent company for business interruption or revenue losses that stem from the vendor's failure: a growing risk as more companies integrate third-party AI into core workflows.

Because captives are owned by the businesses they insure, they offer flexibility to craft tailored policies that reflect the company's actual AI usage, internal controls and risk tolerance. This is particularly valuable given how little precedent exists in AI litigation. As case law develops, businesses with captives can adjust coverage terms in near real time, without waiting for the commercial market to adapt.

Building AI into captive strategy

To incorporate AI risk effectively, captive owners must begin with a clear-eyed assessment of their own exposure. This requires collaboration across legal, compliance, IT, risk management and business units to identify where AI is in use, what decisions it influences and what harm could result if those decisions are flawed.

This analysis should include:

• Inventorying all internal and third-party AI systems
• Mapping potential points of failure and legal exposure
• Quantifying financial impact from regulatory enforcement, litigation or reputational damage
• Evaluating existing insurance coverage for exclusions or gaps
• Modeling worst-case outcomes using internal data or external benchmarks

Once this assessment is complete, captive owners can work with actuaries and captive managers to design appropriate coverage. This may include standalone AI liability policies or endorsements to existing coverages within the captive. It may also involve setting aside reserves to address emerging risks not yet fully insurable under traditional models.

Risk financing alone is not enough. Captives should also be part of a broader governance strategy that includes AI-specific policies, employee training, vendor vetting and compliance protocols. This aligns with the direction regulators are taking, particularly in the EU, where documentation, explainability and human oversight are mandated for many high-risk systems.

Boards are paying attention

AI is no longer just a back-office issue. In 2024, public companies and shareholders sharply increased their focus on artificial intelligence, especially on board-level oversight and shareholder proposals. According to the Harvard Law article AI in Focus in 2025: Boards and Shareholders Set Their Sights on AI, the percentage of companies providing some disclosure of board oversight grew by more than 84% year over year and more than 150% since 2022. This trend spans all industries. Meanwhile, shareholder proposals related to AI more than quadrupled compared with 2023, mostly calling for greater analysis and transparency around AI's impact.

This intensifying scrutiny signals a clear mandate for risk managers and captive owners to deliver solutions. Captives offer companies a flexible tool to fund, control and adapt their responses to the rapidly evolving AI risk landscape and regulatory environment.

Conclusion

AI is changing how businesses operate, but also how they are exposed. As regulatory frameworks tighten and litigation accelerates, businesses must prepare for the reality that AI-related liability is no longer speculative. Captive insurance companies offer a powerful tool to manage that exposure, not by replacing traditional coverage, but by addressing what lies outside its bounds.

For companies that rely on AI, the question is no longer whether liability will emerge–it's whether they are positioned to handle it. Captives provide a path forward, giving businesses the ability to design, fund and control risk management strategies that evolve as fast as the technology they are built to protect.

Our fast-changing world has become increasingly connected. Risks such as geopolitical instability and climate change continue to escalate on the world stage. With both risk dependencies and risk drivers surfacing and evolving more and more rapidly, so much is changing so quickly that by the time this article is published it may well be (slightly) outdated.

Yet capital modelling techniques have barely changed. Current models are unreliable, under-used and time-consuming. The insurance industry needs tools that are dynamic and built for cross-business and cross-functional collaboration.

Capital models have a clear advantage over long-term projections, as they are reassessed at least every year and so reflect the latest changes in risk drivers. A key advantage for the modeling of climate risks is that the uncertainty is much reduced compared with that resulting from long-term projections of climate trajectory.

How the insurance gap affects climate losses

It is worth first highlighting the fundamental difference in metrics considered when looking at climate losses. Total losses or ground-up losses consider the total cost of a catastrophe, while insurers consider the insured value -- for example, the cost they are contractually liable to. The difference between the two (the 'insurance gap') effectively measures the difference between the real costs incurred versus the costs covered by the insurance industry.

For climate losses, this means total losses could increase while the insured costs may decrease, in particular following the ramping up of climate mitigation measures, such as flood and fire defenses protecting inhabited areas, or carriers' stopping the writing of lower tranches or pulling out of a region altogether. How the insurance industry chooses to tackle the insurance gap issue will affect the level of climate losses insurers will see coming through.

Current approaches unfit for purpose

Insurers frequently adopt a bottom-up and granular approach. The wider aspect and connectedness of global events is generally considered to some extent when calibrating dependencies. However, this requires tedious monitoring and updating of parameters that are extremely difficult to estimate. Traditional approaches also tend to give considerable weight to past data, which is not necessarily the best basis to form an opinion on risks that are evolving fast.

Cyber is a good example of such a risk. While it is not a new risk, data remains too sparse for robust modeling, and the various types of cyber attacks continue to rise in volume and severity, with new defenses constantly being designed and built to try to keep pace with these threats. Furthermore, insurers need to ask themselves whether the dependency of cyber risk with other global risks, such as climate change and geopolitical conflicts and terrorism, is being appropriately reflected in their current models.

More generally, chief risk officers and other senior stakeholders responsible for the capital model should be asking their teams:

• Can they rely on the output from their capital model? Is this complex risk landscape reasonably reflected in a transparent, dynamic way to enable challenge and communication?
• Are they able to confirm that the view of risk implemented in their capital model is consistent with that established in other parts of the business (e.g. pricing and exposure management)?
• Are the traditional approaches they are using providing value given the extensive work required for setting parameters?
• How much compounded uncertainty – stemming from limitations and uncertainty at granular risk levels that are then aggregated – is too much? Does this uncertainty make the model outputs unreliable and useless?

Identifying risks and drivers

Traditionally, the risks assessments conducted by companies have been focused on their own risks, with little consideration for global socioeconomic factors. The past few years have shown us that this approach is no longer suitable and that risks should be assessed more holistically.

The changing dynamic of the risk landscape does not necessarily translate into a new, distinct risk category but often results in additional losses simultaneously affecting several risk components. Creating a map of all the risks and their drivers affecting the company should be a key step before trying to model this complex risk environment.

The advantage of short-term modeling for long-term risks

Modeling long-term risks over a short time horizon can seem challenging. Climate change risks (typically described as comprising physical, transition and liability risks) are not only long-term but also highly uncertain in how they will develop over time.

Climate change risks are also affected by geopolitical instability, supply chain issues and inflation, as well as national government policies and the emerging threats and benefits from AI. Therefore, it is not the isolated, long-term impact of climate change that we should be focusing on for capital modelling purposes but its combined, short-term impact with other risks. And short-term modeling undeniably provides greater flexibility.

The evolution of physical risks from climate change is already baked-in for decades. For example, our actions now won't affect catastrophes in the short term. The year-on-year impact on physical risks (resulting from human activity from long ago) remains limited, albeit trends are emerging when looking at the past decade. "Secondary" catastrophe risks are a good example, having become key catastrophe risks, as demonstrated by recent flood and severe convective storm losses.

However, current catastrophe models remain suitable provided they are regularly reassessed to confirm they capture all plausible scenarios, with in- and out-of-model adjustments applied to ensure this.

All of the above needs to be considered alongside an evolving insurance gap and other global socioeconomic variables - these can change relatively quickly and are difficult to capture in long-term scenarios, although less challenging over the next year or so.

Liability risks are difficult to model, as the uncertainty around whether, when and to what extent climate litigation claims will emerge is incredibly high. However, it is important to distinguish between estimated average losses over the next 12 months, maximum losses (which, according to contracts' terms and conditions, will also contain important exclusions such as criminal behaviors and those drive by personal profit) and the uncertainty around average losses. Given the slow pace of court judgments and the money big companies can spend on lawyers to defend them, it seems highly unlikely that we will see an unsustainable level of losses emerging very quickly. Therefore, it is reasonable to assume that yearly reassessed allowances in the capital model for "known unknowns" would be sufficient to absorb losses that have emerged over the year beyond the estimated average losses. However, liability risks are also affected by other global, socioeconomic factors.

A holistic view of climate risks to capital models

As highlighted in the Bank of England paper, "Report on climate related risks and the regulatory capital frameworks," climate risks require a forward-looking approach. Past data would not appropriately reflect future trends and impacts from climate-related risks.

Although scenarios are already used to validate models, their use as a calibration approach remains limited, as this requires moving away from traditional models and a big leap into innovative approaches.

A natural answer to this would be to consider a "catalogue" of scenarios that provide a good representation of plausible events, with each scenario being decomposed into its risk drivers (not unlike how event catalogues are constructed in catastrophe models).

For this reason, we have been working on an approach in which current-era dependency models can be expressed in terms of explicit, dynamic, and possibly systemic risk drivers that would model the combined impact of climate risks and other global risks in a transparent and holistic manner. Each risk would be decomposed into its (systemic) risk drivers and a variable specific to this risk representing the non-systemic aspect. Then risks or portfolios can be combined and aggregated using relatively simple mathematic formulae and distributions.

This approach could also be extended to all the risks the company is exposed to, as well as to the ultimate time horizon basis, in which case Monte Carlo simulations and their inherent uncertainty would not even be required. This would also help moving from discrete time or single-time-step models into a continuous time setting for "always-up-to-date" risk analysis. The key advantage of a continuous time model is that coverages are only modeled in between their exact start and end dates.

This would turn a time-consuming, under-used model hindered by compounded uncertainty into a dynamic risk, pricing and portfolio management tool. It would also replace onerous, isolated, parameter guesstimation with a collaboration across business functions that provides a consistent view of risk with reduced effort.

In the world of insurance, brand strength is a formidable asset. However, even the strongest brands must adapt to meet the evolving needs of consumers and the digital-first world.

The insurance market is at a pivotal juncture. The next generation of insurance isn't just about offering policies; it's about delivering an exceptional customer experience, optimizing operational efficiency, and demonstrating agility. While a strong brand provides a solid foundation, resting on past successes is not an option in today's fast-moving, tech-driven environment. 

Opportunities for Established Insurers in a Digital-First Landscape

Today's policyholders expect more than just financial products—they demand seamless digital experiences, quick and transparent claims processing, and personalized interactions. This shift requires insurers to rethink traditional models and integrate modern solutions.

A strong brand is only as powerful as the experience it delivers. Insurers must prioritize exceptional customer experiences across every touchpoint, particularly during the claims process, which often serves as the ultimate test of a brand's promise. By focusing on digital-first experiences, insurers can ensure that interactions are not only efficient but also empathetic and tailored to meet evolving expectations.

Established insurers, with their longstanding reputation for trust and stability, have a unique opportunity to lead this evolution. By leveraging their brand strength, they can introduce digital transformation initiatives that not only improve internal processes but also enhance the overall customer journey. The transition to digital claims processing, for instance, offers a chance to replace paper-based, manual workflows with automated, intuitive systems that resonate with both digital-native generations and more mature policyholders.

Evolving With Broader Product Offerings and Market Reach

The insurance market is becoming increasingly diverse. As carriers introduce broader product offerings—from hybrid life and long-term care policies to new investment and annuity options—they must also adapt their operational frameworks to support these innovations. 

For insurers looking to expand their market reach, particularly into underserved segments, operational efficiency is crucial. A strong brand can open doors, but delivering on that promise requires back-end systems that are as sophisticated as the products themselves. This is where a robust claims administration platform can provide a competitive edge, enabling carriers to scale efficiently while maintaining high service standards.

Maintaining Agility in a Changing Regulatory Environment

Regulatory requirements in the insurance industry are ever-changing. Whether adapting to new compliance standards, managing data privacy, or ensuring accuracy in claims processing, established insurers must remain agile. Strong brands often come with a legacy of traditional processes, and adapting these to meet modern regulatory expectations can be challenging.

The integration of automation and data analytics within claims systems offers a path forward. By automating compliance checks and streamlining document verification, insurers can reduce regulatory risks and maintain transparency with both policyholders and governing bodies.

Embracing Ecosystem Planning for Sustainable Growth

As insurers seek to broaden their reach and future-proof their operations, building a strategic ecosystem is crucial. This involves forging partnerships with technology providers, financial service firms, and even healthcare and long-term care organizations to create a holistic approach to customer service and operational efficiency.

An effective ecosystem allows carriers to integrate new technologies seamlessly, offer broader and more tailored product portfolios, and respond quickly to market changes. By building a robust network of partners, insurers can diversify their offerings and strengthen their position as leaders in the insurance sector.

Unlocking Opportunities Through Claims Experience

A well-orchestrated claims process does more than resolve claims quickly—it plays a strategic role in asset retention and business generation. When beneficiaries experience a smooth, empathetic claims journey, they are more likely to view the insurer as a trusted financial partner. This opens the door for insurers to engage beneficiaries during pivotal life transitions, potentially converting them into new clients.

For example, a streamlined digital claims experience combined with personalized outreach can guide beneficiaries through financial planning opportunities, including annuities, life insurance, or investment products. Insurers that engage during the claims process can turn an administrative necessity into a powerful sales and retention channel.

Bringing Brand Promise to Life Through Claims Innovation

Ultimately, the claims process is where the brand promise is truly tested. It is often the most critical touchpoint in the customer lifecycle, and a positive claims experience can turn policyholders into lifelong advocates. Conversely, a slow, opaque, or cumbersome process can erode even the strongest brand equity.

Modernizing the claims process through digital tools and automation is not just about efficiency—it's about delivering a compassionate and transparent experience that reflects the brand's values. For insurers that have built their reputation on trust and reliability, enhancing the claims process is a natural extension of their brand ethos.

Positioning for Growth: Where Technology Meets Tradition

While technology plays a vital role in evolving claims and servicing processes, it is the thoughtful integration of this technology into established business models that drives success. Strong brands do not need to overhaul their entire infrastructure to modernize. Instead, they can take strategic, incremental steps that offer immediate benefits while preparing for long-term transformation.

At Benekiva, we blend tradition with innovation through human-centered automation—what we call Humanomation—to help insurers optimize claims processes without losing the personal touch that their brands are known for.

The insurance industry is poised for significant change, and those who act now will lead the market forward. Strong brands have the advantage, but sustaining that advantage requires a commitment to evolving with the times, embracing technology, and keeping the customer at the heart of every decision.

General opinions about AI are split. Even among the most knowledgeable, 31% think AI does more harm than good. Just 22% see its benefits outweighing its risks. People are wary about AI's potential risks, from spreading false information to stealing jobs.

This goes for the insurance industry, as well. Even though 57% of insurance organizations have identified AI as the most important technology over the next three years, there's still plenty of hesitance when it comes to the more complex and high-stakes areas of the business, such as policy review and interpretation.

These concerns are often rooted in a misunderstanding of how AI operates in the context of the insurance workflow; for complex cases, humans should lead the way with support from AI. It's up to leaders to make this part of the picture clear within their organizations. Here are three ways to do that.

Tout AI as a Copilot

One of the biggest myths about AI in insurance is that the technology is meant to replace people. Much of AI, after all, is marketed on its ability to increase productivity and efficiency, which are often codewords for reducing headcount.

But the goal of AI in policy review and interpretation is not to replace your highly experienced and knowledgeable experts. AI is instead a copilot that adds a second set of eyes to the process.

A good AI copilot, for example, might allow your people to upload a policy, ask a question, and instantly get a precise, citation-backed answer. The user could then click straight to the source text to check the AI's work.

Your experts are still in control and still responsible for the finished product. AI is there simply as another tool to accelerate their work. Now they can spend their time thinking strategically instead of combing through endless text.

In its copilot capacity, AI might also inspire your policy experts to look into issues they might not have considered otherwise. Those new ideas will require additional human research and refinement, of course. But just planting that seed is AI demonstrating its value to amplify and accelerate their work.

These examples are not cases of AI stealing jobs. Human reasoning and verification and interpretation remain integral to the process. This is simply technology helping to make complex tasks a little more manageable.

Manage Expectations Around AI

Policy review and interpretation work requires stringent accuracy, which is another area where doubters question the viability of AI. Analyzing, synthesizing, and reasoning upon the information in a 300-page policy is complex work. And AI isn't perfect at it right now. But the nuanced nature of policy review isn't an argument against using AI. It's simply another endorsement for treating AI as an aide rather than a full-time standalone solution.

Think of it this way: The right AI for this context is not one that makes complex decisions. It's one that can analyze and summarize hundreds of pages of documents, identify the relevant portions, and surface them for a human to review, then make a decision.

In this case, AI is helping to make the initial work go much faster. Even with a few oversights that may need citations and corrections by humans in a follow-up review, that added upfront speed is well worth the investment.

AI will get closer to 100% accuracy as the technology continues to develop and advance in the coming years. But even short of that goal in its current form, it can greatly benefit insurance carriers in their policy review work.

To Drive AI Adoption, Start by Raising Awareness

Defining AI's role and acknowledging its limitations are two great ways to help build trust around the technology. But leaders also need to foster an environment of experimentation and innovation in their broader effort.

For example, seeing is believing for many AI skeptics. So show them what it can do. When employees are encouraged to sample AI, they're more likely to grasp its time-saving possibilities and its ability to generate fresh ideas. Now they can picture how AI will affect their workflow, making them more likely to embrace it – and less likely to worry about losing their jobs.

Getting more comfortable with AI will also likely open their eyes to how the technology can help them better serve customers. From policy to claims, AI can provide fresh insights and new ways of leveraging data. That helps the entire organization work at a higher level, which is something everyone can get behind.

Make the Case: Modernization and Trust Aren't Mutually Exclusive

AI is like any other new technology. Some people will be inclined to focus largely on its exciting possibilities. Others will mostly see its faults and threats. Satisfying both groups means highlighting AI's potential to work alongside humans to unlock greater creativity and productivity.

For insurance providers, highlighting that message means focusing clearly on the role that AI can and should play. It's not a doomsday solution that aims to replace the human expertise so vital to policy review and interpretation. Rather, it is a tool that can speed up and enrich that important work.

Because in the end, people are and always will be the key to success.

Slips, trips and falls make warehouse jobs high-risk. Innovative tech — such as real-time telemetry, edge sensors and smart automation systems — can reduce hazards.

As connected material handling equipment improves operational safety and performance, the insurance industry is recalibrating how it measures and prices risk in warehouses and logistics hubs.

More Dynamic Risk Considerations

Traditional loss models relied on historical data, annual inspections and policyholder declarations to assess risk. This dated approach struggles to keep pace with automated warehouses, where operational conditions can change by the hour.

The MIT Warehouse of the Future Initiative has identified 26 technology-related vulnerabilities spanning software, hardware, networks, infrastructure and human-machine interfaces. These include:

• Robots running outdated firmware that cannot receive security patches
• Automated storage systems without mechanical overrides for jammed loads
• Conveyor control units with no backup power supply

These risks are not hypothetical. Picking operations in facilities could easily halt just because of one server outage. Even the lack of fire suppression access between tightly packed automated racking can escalate minor incidents into significant losses.

In modern facilities, material handling equipment functions as a distributed network of sensors, recording everything from forklift speed on wet floors to the temperature of battery charging bays. For insurers, this means static assessments are no longer enough.

Real-time telemetry can flag a spike in conveyor motor temperatures before overheating leads to a belt fire, detect abnormal acceleration patterns that suggest unsafe autonomous vehicle routing, or issue a micro-risk score for a specific shift when environmental humidity levels exceed design tolerances.

Data-Rich, Risk-Dense Environments

One key driver of change is the sheer data granularity that is now available. The latest market overview reveals that industry leaders are fitting about 1.3 million commercial vehicles with GPS intelligence systems to track their real-time location. These technologies capture high-resolution metrics such as shock events, proximity violations, battery levels and idle time.

From an insurance perspective, this means acting before accidents happen and setting prices based on real risk instead of industry averages. For example, a warehouse whose automated guided vehicles often collide or make sudden stops might pay higher premiums or be required to improve its safety systems.

New Liabilities and Coverage Gaps

Precision scanning and other forms of automation help prevent mistakes during nonstop order processing, which translates to fewer human error claims and lifting injuries, as robots do much of the heavy lifting. However, the automation also introduces risks that many policies do not yet cover. These vulnerabilities span:

• Software platforms
• Automated equipment
• Network connectivity systems
• Facility infrastructure
• Human–machine interactions

For example, although OSHA Standard 1910.178(l) requires all lift truck operators to complete certified training, incidents still occur when operators bypass built-in safety features like dynamic stability systems or proximity sensors. Such behavior blurs the line between human and machine fault, making coverage definitions more complex.

Policies need clear terms and limits for these risks, and insurers must assess the combined exposure from connected devices, especially in multi-site operations where one failure can trigger others.

Real-Time Pricing and Usage-Based Coverage

As connected warehouse equipment becomes more common, usage-based insurance also becomes more viable and practical. Real-time data from machinery can set premiums based on actual risk, not on traditional and theoretical estimations of facility size or equipment count.

Insurers in Brazil and Fiji are already testing parametric models in a regulatory sandbox in various sectors before applying permanent changes. While they focus on natural catastrophes, it is only a matter of time before the flexibility of agreed triggers trickles into the industry. These models give clients clarity and insurers flexibility but require tight integration with warehouse systems and the ability to process live data.

Operational Resilience and Policy Flexibility

Automation has not eliminated human error. It will always be possible wherever there is an interaction between a machine and workers. Much of the failure may occur at the system integration level, often caused by poor programming, misconfigured interfaces or inadequate employee training.

A recent report highlights that one of the five categories of disruptions in highly automated warehouses involves human–machine interactions, alongside risks like cyberattacks, power outages and technology failures.

This means that blanket exclusions for tech failures no longer work. Policies must be flexible, tailored to each system's setup, and consider both the technology and human behavior behind them. Only then will policies reflect real-world risk complexity and ensure minimal gaps in insurance protection.

Underwriting in the Age of Automation

Insurers now need deeper technical expertise that extends well beyond traditional risk assessment. Evaluating a modern warehouse requires an understanding of the following new tech:

• AGV fleet management software
• Operational technology cybersecurity protocols
• Edge computing architectures
• Warehouse control system integrations.

Additionally, risk teams should have direct access to real-time telemetry dashboards showing asset performance, error rates and operational anomalies, as well as digital maintenance logs that reveal component wear, firmware update histories and vendor service responsiveness.

This shift demands upskilling existing staff and recruiting talent with industrial automation, data analytics and cybersecurity backgrounds. Actuarial models must incorporate IoT-derived operational data, not just historical loss records. Underwriting must evolve to factor in equipment vendor track records, system design resilience, and update or patch management practices. A technically sound installation with poor update discipline can be riskier than an older system maintained to perfection.

Regulatory and Privacy Concerns

With more operational data flowing through automated warehouses, the stakes for compliance and cybersecurity are rising. Legal questions around privacy, data ownership and breach liability are increasingly relevant to the insurer–client relationship.

In late 2023, a cyberattack on Ace Hardware's key IT systems shut down distribution before the holiday rush, delaying online orders and store restocking for weeks.

For insurers, that incident signals several risks they must account for:

• Business interruption exposure: Even a short downtime in warehouse systems can cause prolonged revenue loss for the insured party, leading to large claims.
• Cyber risk overlapping with property and liability: The Ace Hardware attack targeted digital infrastructure, but its effects rippled into physical supply chains, creating blended loss scenarios.
• Seasonal risk amplification: Timing near peak retail periods can multiply losses, increasing claim severity.
• Vendor and system dependencies: Reliance on specific warehouse management system platforms means a breach at a single point can halt operations across multiple sites.
• Underwriting complexity: Insurers must evaluate risk beyond warehouse fire or theft, including the cybersecurity resilience of operational technology.

Insuring in the Age of Continuous Operational Risk

Connected material handling equipment is pushing the insurance sector toward a risk model that is dynamic, data-driven and deeply embedded in client operations. Warehouses are no longer static facilities but fluid ecosystems of people, machines and data flows. Only insurers that recognize this and build the infrastructure to support continuous assessment will gain a significant edge.

Across the South, businesses are at a crossroads. Despite rising investment in employee benefits, talent continues to slip through the cracks. Job boards are saturated. Turnover is high. Retention costs are climbing.

Yet one overlooked truth is reshaping how smart Southern employers are approaching the problem: Benefits aren't just HR's domain anymore, they're a strategic business lever. And Q4 is your last, best chance to get it right.

The Disconnect Is Real and Costly

Earlier this year, OneDigital commissioned the Employee Value Perception Study, surveying 2,000 professionals across industries, age groups, and income brackets. The results were eye-opening:

• 56% of employees said they wouldn't be able to afford a major unexpected expense, even among those who budget responsibly.
• While wages remain a core driver of satisfaction, employees increasingly want holistic, human-centered support: financial education, mental health access, caregiver support, and flexibility.
• Employees reported that while benefits are a critical reason they stay, the ones offered often feel generic, misaligned, or poorly communicated.

This is more than a missed opportunity. It's a direct threat to retention, morale, and bottom-line performance.

Why the South Requires a Different Playbook

Southern employers face unique dynamics. Tight-knit communities. Rising cost of living. Rural workforces with limited access to care. A growing blend of multigenerational workforces, all with starkly different expectations.

Here's what's also true: Southern employees are fiercely loyal when they feel seen. When benefits reflect their reality, they don't just stay. They engage.

Q4 open enrollment isn't just a compliance deadline. It's the most strategic window you have to recalibrate, rehumanize, and reclaim your edge in the talent market.

Three Executive-Level Strategies for Q4 Benefits Redesign

These are not incremental tips. They are fundamental shifts in how leadership should think about people investment.

1. Approach Benefits Like a CEO Approaches Product Design: Start With Discovery, Not Assumptions

Most employers build benefits like a catalog: pick, price, push.

High-performing organizations treat benefits like product-market fit. They gather real data through surveys, interviews, and direct employee conversations and build accordingly.

This is especially important in the South, where life stages vary widely and local context matters. For example:

• Early-career workers want help with student loans, mental health access, and first-home planning.
• Mid-career professionals prioritize family support, flexible schedules, and financial planning tools.
• Late-career employees are focused on retirement readiness, long-term care, and estate planning.

The message: Don't guess. Ask. Segment. Then serve. Just like you would with your customer base.

2. Design a Modular Benefits Strategy, Not a One-Size-Fits-All Plan

The days of static benefits menus are over.

Today's workforce expects flexibility, personalization, and choice. You don't need to expand cost; you need to expand relevance.

Consider these high-impact options already gaining traction in Southern markets:

• Lifestyle spending accounts (LSAs): Let employees decide where wellness matters—gym memberships, childcare, personal development, or caregiving expenses.
• Voluntary benefits with bite: From pet insurance to legal assistance, voluntary offerings drive perceived value without increasing employer cost.
• Telehealth and virtual primary care: A must-have for remote and rural workers, especially across the Southeast's vast geographic footprint.

The winning play? Invest in flexibility, then communicate with precision. Benefits only retain people if employees understand and trust them.

3. Treat Q4 as the Launchpad for Year-Round Engagement

Too many companies treat open enrollment like a once-a-year event. The best employers treat it as a kickoff.

Here's what that looks like in practice:

• Personalized education at scale: Interactive decision support tools, digital explainer videos, and guided one-on-one benefit coaching sessions.
• Pulse surveys and real-time feedback loops: What's working? What's being ignored? Where is confusion highest? Adjust accordingly.
• Enrollment analytics and user behavior insights: Use data to drive smarter messaging and midyear plan refinements.

Benefits engagement is not a communications problem; it's a design and delivery problem. And the companies that solve it will lead the pack in talent retention.

Final Word: The Talent War Is Won with Design, Not Perks

If you're a Southern employer looking to retain your top 20% (your difference-makers), here's the reality:

They're not looking for ping-pong tables, free snacks, or another app.

They're looking for stability. Autonomy. Family support. A workplace that sees them as people, not just producers.

Q4 is not a fire drill. It's a moment of leverage.

Get this right, and you don't just improve benefits, you shift culture, loyalty, and business results.

The South doesn't need more perks.

It needs more purposeful design. And it starts now.

The modern consumer expects seamless digital interactions, effortless convenience, and personalized experiences, regardless of the brand or industry with which they interact. For the insurance industry, decades of technical debt – much of it in the form of legacy systems – combined with challenging market conditions, make these expectations harder to meet. Each year, we commission an independent, global survey of 3,000 consumers to understand what they want from their insurers, their attitudes toward technologies, and what insurance organizations need to do to win and retain their business.

This year, we learned that insurance customers aren't just warming to artificial intelligence – more than half are ready to see what it can deliver. However, beneath the headlines, our research revealed four key takeaways for insurance companies.

1. Insurance customers are open to AI, but with conditions

The standout finding from our research is that one in two people (51%) said they would value AI being used to suggest changes to their insurance plan. That number rises significantly for younger respondents, with Millennials leading the pack at 62% and Gen Z following closely behind at 58%.

We also saw a more comfortable attitude toward AI when it comes to their personal information: only one in three expressed hesitation over AI handling their information securely (32%) or ethically (31%).

But insurers should not take this as carte blanche for their AI plans, especially when it comes to customer communications. Consumers are more likely to support the use of AI when it results in faster response times (53%), more accurate communications (44%), or cost savings (42%). Without those tangible benefits, new AI features could receive a cooler welcome.

Generational differences should also be taken into consideration. Silent Generation consumers were less likely to value AI's suggestions for their insurance plans (32%) and were more hesitant about AI handling their information securely or ethically (52%). To address these concerns, older customers could be given more opportunities to opt out of AI use. Insurers could also invest in communications campaigns that help reassure these customers of the safeguards in place when AI is used, and demonstrate the tangible benefits they can experience.

2. Customers want digital data collection options, not just fillable PDFs

While consumers may be warming to AI in their interactions with insurance companies, they are already expressing a widespread preference for digital tools when providing information. Over three-quarters (77%) said it's vital that insurers offer digital data collection or forms, instead of manual processes that involve printing, scanning or mailing.

This wasn't just a majority opinion of younger consumers. 71% of Baby Boomers and 63% of Silent Generation respondents also favor digital processes.

And insurers can't just digitize their paper forms and be done with it. When given the choice between completing a fillable PDF or a guided digital form, consumers chose the guided option by a margin of almost two to one (63% vs. 37%). Even more surprising was the generational preference: Respondents from the Silent Generation tied with those from Generation X as the most likely to prefer guided digital forms (67% each).

Accommodating these data collection preferences will be critical to winning and retaining customers. Insurers that fail to do this will risk losing business, as two-thirds of people (65%) said they would likely end their interaction with an insurance company if the data collection or forms process is too difficult.

3. There's room for omnichannel improvement

Opinions about omnichannel communications are a mixed bag for insurance customers, with slightly over half (54%) saying they are satisfied with their insurers' omnichannel experience. A similar proportion agreed that insurance companies always or almost always communicate with them on their channel of choice (55%). Given that omnichannel communications have been part and parcel of customer experiences for the better part of a decade, merely having a passing grade should be a cause for concern among insurers.

To improve on those scores, organizations should start by ensuring they're communicating on preferred channels. On this front, the data is clear: The more channels, the merrier. While email was the resounding favorite for 44% of all respondents, the majority were split between a mix of old and new technologies. Encrypted messaging tied with print/mail, each ranking as a preference for 12% of consumers, while SMS was preferred by just 17% of respondents and web/applications by only 14%.

Insurers also need to check they're not making assumptions based on their customers' ages. Despite being the oldest group surveyed, Silent Generation respondents were the most likely to prefer email (48%). Just 39% of the youngest group, Generation Z, shared the same sentiment.

The lesson here is to prioritize choice and consistency. Because insurers cannot presume their customers' communication preferences based on age, they must offer a wide range of options and ensure that each channel delivers at a level that exceeds consumer expectations.

4. Insurers have upped their game on customer communications

Despite some mixed feelings on specific areas of communications, insurers should take pride in their overall performance. 60% of consumers now rate their insurance companies' communications as good or excellent, a 41% increase from 2024. Adding to the good news is that nearly two-thirds (65%) feel they can trust insurance companies, a valuable attribute in an increasingly discerning consumer environment.

These findings aren't just crucial to improving customer experiences and retaining business. Good communications drive business growth. Four out of five respondents (78%) said they were likely to recommend an insurance company to a friend if their communications exceeded expectations.

Our research shows that today's customers have clearly defined preferences: They're ready for AI but demand real, visible benefits; they favor intuitive, guided digital experiences over outdated paperwork; and they expect insurers to deliver consistent communications through their channels of choice. Insurers must focus their digital investments on these critical areas to build stronger, trust-driven customer relationships, differentiate themselves in an increasingly competitive marketplace, and position themselves for sustained growth.

Jaime Henry is the vice president of product at Origami Risk, driving product strategy and execution for the platform and each of the markets Origami serves. She has been a proud Origamian since 2015 and brings 20 years of client support, product management, and strategy experience to her work with both clients and colleagues. In her time at Origami, Jaime has worked as the director of market strategy, healthcare market strategy lead, and service delivery manager. Jaime received a bachelor’s degree in management information systems from Saint Mary’s College of Notre Dame, Indiana.

Paul Carroll

So much of the focus on artificial intelligence concerns the rapid advancements in technology. But let’s start with what you’re seeing in terms of uptake.

Jaime Henry

This is something I've been particularly focused on: adoption. Generative AI represents a fundamental change in how we all work and live. It will make the timeline. When you look back at cloud, mobile, the internet becoming a thing, email—those are timeline moments. And we are absolutely in a timeline moment here with generative AI.

It does require a significant change in behavior, both at a consumer level as well as professionally. A comparable situation that I think about a lot is when GPS became mainstream.  We started to transfer from getting used to maps and specific directions from MapQuest online that you'd print out. Then GPS arrived, and you went out and bought that separate device for your car. Now GPS is second nature for us. We know how to get from home to work, but I'll still use my GPS because it provides information about what’s happening on the route.

It makes my life a little bit easier. It saves some of my decision-making power for something bigger. I think about generative AI that way, as well.

But while interest over the past two-plus years has been very, very high, adoption has been very, very low.

We had those very early adopters, as you do with any sort of change management curve, and I think the masses are starting to use generative AI capabilities in their daily life, both personally and professionally.

Paul Carroll

How can you help users become more comfortable with exploring and adopting generative AI?

Jaime Henry

First of all, they have to feel safe. They have to feel like the results they're getting back are accurate. They also need to be confident that information they're putting in isn't going to be used in a way they're not expecting. For example, the most common question we get from our clients is, Are you going to use our data to tune or train models? And the answer is no.

To talk about that more tactically, we’ve had two launches so far this year that provide a sort of microacceleration for the adoption of AI. The first uses natural language processing within our Total Cost of Risk (TCOR) AI Analytics module. Users can literally ask a question in English about their data and get their answer. They don't really have to think about using AI. We just served them a better way to get access to their data.

The other thing we've done is an AI assistant for email. You can say, "Help me write this email." The AI automatically pulls in data from the claim record and populates the body and subject line of the email. One thing we did that kind of takes it to the next level, and again, provides that microacceleration by saving a little bit of time, saving some of that decision energy that we all need to conserve, is to help you figure out that subject line.  

How much time do you spend trying to get the perfect subject line? I feel like there's a lot of brain processing power that goes there.

Paul Carroll

It feels to me like we’re all on a voyage of discovery with AI, that we’re collectively feeling our way toward the best answers. How do you collaborate with clients on AI implementation?

Jaime Henry

We’re working on what we're calling democratization of AI. We are building out capabilities that allow our clients to bring their use case to the table. For example, we know generative AI is spectacular at summarization and generation of content. I hear of a lot of use cases that are just specific variations of "I need to summarize this" or "I want to generate x, y, and z."

What we're working on doing is saying, Okay, you want to summarize, and this is the point in your business workflow where you need this summarization, and this is where you need the summarization to go, and this is how you need to interact with it. Perfect. I'm going to give you the technical workflow tools that allow you to define your business workflows and inject AI at the specific points where you need it.

We're going to give you some of those hard-coded AI capabilities, natively integrated into our platform, but we're also going to give you the capabilities to build out your own AI-infused workflows so you're not having to wait to deliver on the use case that makes the most sense for your business.

We'll have what we're calling "tiles." We may have a summarization tile, an email generation tile, or a tile to summarize a group of records. We might also have tiles to ingest a policy record or ingest a policy binder, for example.

We know there will be these broad areas with a number of preconfigured capabilities that each client will be able to put their own touch and spin on.

Let me give you an example from our insured clients. An executive committee needs regular readouts on high-profile claims, so a risk manager has to do summarizations. You have a week or two where everyone's getting their claim summaries updated in the expected format for the executive team, and the risk manager spends a lot of administrative time generating and reviewing that information manually.

With generative AI, we can fast-track that in an incredible way. The risk manager or claims adjuster needs their claim summarization to do their day-to-day job, but there's typically also a different set of information or a higher-level view that the executive team needs. Now we can automatically generate that with the latest information by putting together a workflow that says, "Generate the executive summaries for this group of claims."

I can target claims that have a certain flag, or maybe they're open with a reserve amount over $1 million, whatever the threshold might be.

We're taking what is a two-week process with numerous people involved down to an hour or two, all by inserting AI.

Paul Carroll

I love the tiles idea. It sounds like the object-oriented programming approach that has allowed developers to produce so many apps, so fast for our phones.

Any final thoughts?

Jaime Henry

We should talk about curiosity. With any software system, users typically sit on an incredible amount of data, and everyone struggles with how to gain truly actionable insights from it that allow meaningful change.

In the world of risk management and insurance, real people are involved, and real harm can occur. We know that with proper data analysis, you can reduce that harm.

We need to allow risk managers, users, adjusters, and executive teams to approach their data with curiosity and simply ask questions.

Compare this with today's approach: A typical dashboard or report is put in front of a risk manager, who looks at it and says, "This is great information, but I have a question."

They want to know more about a specific location during a particular time frame under certain circumstances. So the analyst digs in and perhaps a week later returns with updated reports and visualizations. The risk manager reviews this and says, "This is helpful. Now I have another question."

This cycle continues—we're getting insights, but it takes a long time. We want to reduce the need to be technically proficient to get reports and insights. Users need to be able to simply follow their curiosity, ask a question, drill deeper with follow-up questions, and get immediate answers.  

Paul Carroll

That is a profound change. It’s already under way, and I can’t wait to see where we go from here.

Thanks, Jaime.

The most startling article I've read in a very long time ran in The Atlantic last week, about euthanasia in Canada. The headline reads, "Canada Is Killing Itself." The deck headline says, "The country gave its citizens the right to die. Doctors are struggling to keep up with demand."

Canada's parliament legalized euthanasia in 2016, and the article says it now "accounts for about one in 20 deaths in Canada—more than Alzheimer’s and diabetes combined."

Perhaps you knew that fact, but I certainly did not, and I suspect a high percentage of you who don't live in Canada were also unaware of that trend.

In any case, it raises all sorts of moral and philosophical questions, probably moreso for those of us who've watched elderly parents gradually die. The trend will also have implications for insurers, notably life and health insurers. Those implications could grow, too, if Canada turns out to be a bellwether, and the trend spreads to other, even more populous countries.

I thought I should share. 

The Atlantic article says:

"It is too soon to call euthanasia a lifestyle option in Canada, but from the outset it has proved a case study in momentum. MAID [short for Medical Assistance in Dying] began as a practice limited to gravely ill patients who were already at the end of life. The law was then expanded to include people who were suffering from serious medical conditions but not facing imminent death. In two years, MAID will be made available to those suffering only from mental illness. Parliament has also recommended granting access to minors."

What's happening in Canada is not just an increase in number but in kind, because the country is allowing what's known as active euthanasia, not just passive euthanasia or assisted suicide. In active euthanasia, a doctor administers drugs that will end someone's life. In passive euthanasia, a doctor ends attempts to keep a terminally ill person alive, while assisted suicide is helping someone end their own life.

A few smaller countries—the Netherlands, Belgium and Luxembourg—have allowed active euthanasia since the 2000s, and a few more populous countries (Colombia and Spain, as well as Canada) have allowed the practice during the past decade. More countries and a dozen U.S. states, including California and Texas, allow some form of passive euthanasia or assisted suicide.

The potential implications for insurers seem pretty clear, especially if access to active euthanasia spreads. Life insurers will pay out on policies earlier than they would have. They will also pay out on more term life policies, the vast majority of which now lapse without a claim. By contrast, health insurers, whether private or governmental, will see claims decline. Studies vary but tend to find that 10% to 12% of healthcare costs come in the last year of life, and decisions by people to end their lives will presumably obviate many of those expenditures.

Euthanasia is a thorny subject that gets into all sorts of questions about the sanctity of life. The final years of an aging relative can also stir up bitter family arguments, some based on hard feelings that had lain dormant for decades. While my seven siblings, our parents and I were all aligned about care decisions leading up to my father's death at 82 and my mother's at 93, I've seen ferocious arguments erupt in some in-laws' and friends' families in the final weeks and months of a dear one's life.

While the insurance issues seem clear and quantifiable, if you've wrestled with the broader questions, as I have, I very much encourage you to read the piece in The Atlantic. It digs deeply into the tough stuff.

I'll just add one observation from someone who led a large, national organization, speaking to a group of healthcare CEOs. He said he'd found that a lot of the fighting over the care of dying parents occurred because of a misunderstanding. The parents felt they needed to hang on for the sake of their children, while the children felt they owed it to their parents to prolong their lives as long as possible.

He added, with chagrin, that he and his siblings had just made that sort of mistake as their father died. They had authorized something like $250,000 of care, which had lengthened their father's life by a week. During that week, their father was rarely conscious and, when he was, was incoherent and seemingly in pain.

Perhaps Canada, if it does nothing else, will bring issues about end-of-life care to the surface in ways that will allow for better conversations, in time to make a difference.  

Cheers,

Paul