Generative AI (GenAI) promised insurers quick wins, yet most pilot programs stall out before they can deliver real business value. In fact, a recent MIT study found that nearly 95% of generative AI initiatives never move past the initial pilot stage. Datos Insights, an insurance research firm, concurred.

This isn't just a missed opportunity, though. It's a red flag. With AI moving faster than any technology that insurers have adopted before, failing isn't an option. Companies that can successfully adopt AI are those that learn to fail fast, iterate quickly, and focus on practical applications.

Four Common Pitfalls Holding AI Back

If AI is so powerful, why are nearly all pilots failing? For insurers, it often comes down to making the same missteps over and over again. These aren't technical failures as much as strategic ones. Below are the four most common mistakes insurers make when rolling out AI — and how to avoid repeating them.

Mistake 1: Confusing Building for Innovation

Many insurers believe success requires building AI capabilities in-house. Yet MIT's research shows vendor-built solutions succeed twice as often as internal builds.

That doesn't mean "buy and forget." Startups carry vendor risk, and custom development is rarely worth the cost or maintenance. The smarter path is to work with enterprise platforms that insurers already use. Providers like Microsoft, Salesforce, and Amazon continue to expand their AI services, offering reliable, secure, and scalable options without requiring teams to reinvent the wheel.

Mistake 2: Chasing the Shiny Object Instead of the Sure Bet

Too often, AI budgets flow into customer-facing applications such as chatbots, lead-scoring tools, or digital assistants. These may look impressive in a board deck but are difficult to validate and introduce risks insurers aren't prepared to manage.

The fastest return on investment (ROI) is usually in the back office. Automating document ingestion, workflow routing, and data extraction saves thousands of hours and frees staff to focus on higher-value work. These high-volume, repetitive processes are exactly where AI performs best, yet they're often ignored in favor of harder-to-execute, more glamorous projects.

Mistake 3: Expecting Perfection Out of the Gate

We often judge AI by unrealistic standards. We'll allow a junior underwriter a learning curve, but if AI makes mistakes on day 1, it's branded a failure. Like a new employee, AI improves with use, with accuracy and efficiency increasing over time.

Workforce anxiety compounds this challenge. If employees fear AI could replace them, they're quick to dismiss its early missteps. Leaders need to reframe the narrative: AI isn't about replacing jobs but about removing repetitive tasks so people can focus on higher-value decisions. Success depends on setting reasonable expectations and building trust in the process.

Mistake 4: Overthinking Instead of Taking Action

Lengthy RFPs, demos, and vendor evaluations can consume months or even years, creating the illusion of progress while manual processes remain unchanged.

Ironically, many insurers already own the AI tools they need through enterprise licenses. Capabilities like Azure Document Intelligence, Power Automate, and Copilot services can automate document intake, claims routing, and workflow support right now. The fastest path to value is often activating existing capabilities rather than prolonging procurement cycles.

Taken together, these missteps explain why so many pilots stall out — but they also highlight the path forward. The insurers that are breaking through have a very different playbook.

What Successful AI Programs Do Differently

Not every insurer is struggling. A small percentage — 5%, in fact — are moving beyond pilots and seeing measurable results. Here are some common traits they share:

• They narrow the focus. Instead of chasing enterprise-wide transformation, successful organizations zero in on a single pain point. Solving one operational problem creates quick wins, builds credibility, and sets the stage for expansion.
• They start with the obvious, not the flashy. Rather than automating complex underwriting decisions, they tackle the "boring stuff" first, such as email routing or workflow handoffs. These repetitive tasks are high volume and easy to supervise. And while they deliver clear ROI, they also provide a non-threatening way to introduce AI to the organization.
• They execute quickly. Long planning cycles kill momentum. Successful programs prioritize speed by testing, validating, and deploying in weeks. Short feedback loops enable them to refine models in real-time and maintain value flow.
• They partner with proven providers. Rather than betting on untested startups or building everything internally, they lean on the AI services offered by large, established cloud vendors. This reduces risk, simplifies integration, and ensures security and compliance standards are met.
• They set realistic expectations. AI doesn't need to be flawless to be transformative. If it outperforms the manual process, then it's a win. Successful insurers measure against that baseline, not against perfection.

The bottom line: These organizations succeed because they've redefined what success looks like. They don't expect AI to reinvent the business overnight. Instead, they use it to quietly, steadily strip out inefficiency, building both measurable ROI and organizational trust in the process.

Creating a Way Forward

Insurance leaders have a choice: keep overengineering and overpromising, or simplify, act, and deliver. The technology is ready, the platforms exist, and the use cases are obvious.

What's missing is the discipline to execute with focus and speed. Insurers that figure this out in 2025 will gain compounding advantages in efficiency, cost savings, and customer satisfaction. Those that don't will still be explaining to boards why their AI initiatives haven't moved the business metrics that matter.

Don't be part of the 95%. Join the 5% who are making the right choices.

The global insurance industry is built on the concept of helping people at a time of great need. This extends beyond assisting policyholders by helping to protect their homes, businesses and so much more to include becoming a force for good across our communities through charitable giving and volunteerism. Every day, professionals across our industry roll up their sleeves to offer their time and talent, and to give generously, in support of the communities where they live and work.

Our industry provided $1.3 billion in charitable contributions in 2023, along with more than 500,000 professionals giving of their time to volunteer, according to the most recent data gathered at the Insurance Industry Charitable Foundation. These figures reflect 100 insurance and insurance-related organizations.

The insurance industry also recognizes that helping others is not only good for the community but good for business. Positioning our board member companies at the forefront of community involvement and highlighting their social impact programs can help showcase the good the industry does while focusing attention on those in need.

Ours is an industry that appreciates the power of collective strength in working together to make a greater impact. At the heart of many influential efforts is our organization – The Insurance Industry Charitable Foundation (IICF), which, by working with the global insurance industry for more than 30 years, provides grants, volunteer service and leadership programs throughout the U.S., U.K. and, beginning this year, in Canada.

Each October, IICF hosts a global Month of Giving, celebrating insurance industry volunteerism throughout the year and highlighting philanthropic commitment in action. In this article, we'll talk about some insurance industry initiatives that make a difference in local communities and provide best practices to build a successful philanthropic program.

The New Normal

The evolution of work over recent years has had a significant impact on philanthropy and charitable giving. Whether operating in a remote or hybrid environment, a daily 9-to-5 or in-the-office structure is no longer a reality for many insurance professionals. As such, the ability to find and connect with people is critical, along with turnkey avenues for philanthropy given that people are spread across multiple locations, geographies and time zones.

IICF's Fill the Truck Food Drive is an example of a turnkey initiative that meets people where they are. Created and implemented during the pandemic as a safe, socially distant way to donate much-needed food, Fill the Truck also provides a pathway for various areas of connection in the form of in-kind donations and financial contributions from an organization to individuals donating and facilitating the collection and delivery of food. Since its inception, this program has grown to deliver thousands of meals across the West and Southeast U.S. regions through the IICF's food bank partners.

Another distinct challenge when navigating philanthropy in today's business environment is striking the right balance between corporately supported initiatives and causes and the varied, and often more locally based, charitable passions of individual employees. Both are important and carry a considerable role in shaping corporate culture, and the quality of business, employee and community connection. Embracing both a top-down and bottom-up approach is important, helping to ensure that the philanthropic avenues are not only strategic, but also authentic in their connections.

For instance, one long-serving IICF board company, Brown & Brown, had been seeking to engage its summer interns with philanthropy in a meaningful way. Through the organization's Next Gen Connect Program, IICF helped facilitate connections with nine nonprofits from around the country. The 61 Brown & Brown interns developed social media campaigns, created impact videos and supported several functions that small and medium-sized nonprofits do not have the capacity to coordinate. The project instilled a sense of purpose among the group of interns and goodwill toward our industry, while providing fulfilling and relevant experiences.

This is just one of thousands of volunteer efforts from our tremendous partners and their colleagues. Each year IICF features the contributions of our Key Partner Companies, those companies supporting us at the highest leadership level, in an annual publication that shines a spotlight on their extraordinary contributions through charitable giving, volunteerism and innovative industry leadership. The impact can be viewed in the 2024 IICF Insurance Industry Philanthropic Showcase.

Setting the Foundation

I have always believed the building blocks of a successful community outreach program already exist within each organization; the key is properly identifying this foundation through the organization's collective values. For example, a company's clients – and their own employees – are already connected within their own communities and are acutely aware of their neighbors' needs. To that end, insurance leaders should aim to gain a deeper knowledge of programs already in place and expand on them to benefit the community.

Second, I like to urge organizations to benchmark participation and effectiveness of any programs in place, the same as any successful business would benchmark operational goals. Good measurement of outcomes will enable a company to understand its current position and determine how to effectively develop or expand its philanthropic strategy.

Finally, companies should identify the top performing champions of philanthropic programs and empower them to drive and grow those efforts. These "doers," are the champions who are truly passionate about connecting the community and the selfless purpose behind charitable giving. Organizations that can effectively harness that employee passion will reap the rewards – including a positive reputation across the community, and enhanced employee recruitment and retention. Some thoughts for consideration to develop a greater employee connection:

• Cultivate connections. Effectively communicate the reason for launching a charitable campaign or a coming volunteer event to drive employee engagement.
• Make involvement easy. Create a variety of options for engagement and opportunities to connect. Not everyone has the time, ability or financial resources to contribute to every single cause. Make it easy for people to engage by giving them opportunities to amplify a philanthropic message via social media, or throughout the community without needing to commit time or financial contributions.
• Share the successes. Contextualize the impact of a particular donation or volunteer effort and celebrate the successes. Make your teams aware of how their contributions have made an impact on a particular campaign, mission or nonprofit organization. This can be done through sharing success stories or clearly identifying how donated funds will be deployed in the community.

The IICF is privileged to have nearly 300 board companies, and more than 800 individual insurance professionals serving on its boards and committees across the U.S., Canada and the U.K. These include organizations, individuals and teams passionate about doing good in the world and making our communities better for all. Building even stronger connections among employers, employees and the community benefits all involved. And participation in our Month of Giving is a great start - find out how you can get involved by visiting https://www.iicf.org/.

For decades, insurers used a broad-brush approach. They categorized people based on limited and generalized information. But this old model doesn't help today, as no two individuals are alike. From driving habits to home security, everything is unique. How can insurance businesses accurately price risk when the world is changing faster than a spreadsheet can track?

Thankfully, AI-based underwriting software systems have got insurers covered. These are changing underwriting from a slow, manual process into a dynamic conversation, one that includes actual risk and ensures the system is fairer, faster, and more accurate for everyone.

To truly appreciate the power of AI, let's first understand the challenges of old-school underwriting processes. It is through these issues that we recognize what AI-based underwriting software offers insurers and how they can capitalize on this opportunity.

Why Are Traditional Insurance Underwriting Methods No Longer Effective?

For decades, underwriters have been the backbone of the industry, making careful judgments based on their expertise and available data. Though this approach served the purpose well for years, it presents hurdles in terms of accuracy and profits. Here are some of the major hurdles insurance underwriters face:

1. Data Silos and Manual Entry

Important information often remains trapped in separate departments, either in physical files or online folders. What's more frustrating is manually entering and reconciling this data. Besides consuming time, this manual process makes room for human error, which can lead to incorrect risk assessment from the very start.

2. Static Risk Models

Insurers have always relied on historical data, which has undoubtedly proved valuable. But by looking backward, insurers may miss out on emerging risks entirely. A model built on past weather patterns, for example, may not be the right option for assessing a property's risk in the face of today's changing climatic scenario.

3. Assessing Everyone Using the Same Lens

One-size-fits-all approach worked really well when data was simpler and sources were limited. The problem arises when insurers have to differentiate between individuals who appear the same on paper but have totally different risks in reality. This often leads to homogenized premiums, causing insurers to miss opportunities to attract and reward low-risk customers.

4. Slow Turnaround Times

In times when customers can get a loan or book international travel in minutes, waiting for days and weeks for the underwriting process feels unrealistic. Such long waiting times frustrate potential customers, putting insurers at a competitive disadvantage.

Given all these factors, it's clear that insurance underwriting needs a new approach. It should add to human expertise. Insurance underwriting automation software aptly serves the purpose.

How Does AI Affect Underwriting in Insurance?

AI makes processes intelligent, and underwriting in insurance is no exception. At its core, AI allows insurance underwriters to make choices that are backed by data. Let's see how:

I. Machine learning

This is the engine of AI. ML algorithms are fed vast amounts of historical data, such as millions of past applications, claims records, and outcomes. Instead of being explicitly programmed, these algorithms learn to spot the complex patterns and correlations that lead to a claim. They continuously improve their predictive accuracy as they process more data.

II. Predictive analytics

This is the primary output of ML. By understanding the patterns of the past, the software can predict the future probability of a claim for a new applicant. It answers the basic underwriting question, "What is the probability of a loss?" with a much higher degree of detail.

III. Natural language processing

A huge portion of valuable risk information is buried in unstructured text. This includes doctors' notes in a medical record, detailed descriptions in a claims report, and even regulatory filings. NLP allows the software to "read" and "understand" this text, extracting relevant facts and sentiments that would be impractical to evaluate manually. The best part? All this is done while adhering to strict privacy protocols.

IV. Alternative data

This is where AI truly expands the horizon of risk assessment in insurance. Advanced models go beyond traditional sources, such as credit scores and motor vehicle records, to also consider non-traditional data points. For auto insurance, this could be telematics data showing actual driving behavior. For property insurance, it could be IoT sensor data indicating the quality of a building's maintenance. This creates a much richer, more dynamic picture of risk.

Insurance underwriting platforms with all these capabilities have the power to turn underwriting from a static, form-based exercise into a dynamic, multi-dimensional analysis. This is equally beneficial for both insurers and insureds. Explore what more insurers can do with such advanced solutions in the next section.

How Does AI-Powered Software Improve Risk Assessment in Insurance?

The theoretical advantages of the latest underwriting software are undoubtedly compelling. But what convinces the stakeholders to actually adopt is measurable results. By using AI-powered underwriting software, insurers can speed up old processes and enhance the quality and fairness of risk assessment. Here's what they can do:

i. Personalized Risk Profiling

AI lets insurers shift from grouping people into risk buckets to evaluating each applicant as a unique individual. By synthesizing thousands of data points, the software creates an individualized risk score.

The result? Two 40-year-old non-smokers living in the same ZIP code can receive vastly different life insurance premiums. Why? Because the model considers one's fitness routine and regular health check-ups versus the other's inactive lifestyle. This fairness benefits both the insurer, which can price risk more accurately, and the customer, who pays a premium truly reflective of their individual situation.

ii. More Accurate Fraud Detection

The human eye is excellent, but it can miss subtle, complex patterns that might point toward fraud. AI algorithms excel at spotting anomalies and correlations that are invisible in a manual review. By assessing an application against millions of previous ones, the software flags inconsistencies. For example, a discrepancy between stated income and spending patterns obtained from alternative data, or a claims history that follows a suspicious pattern.

Thus, insurers can spot potentially fraudulent applications at the point of underwriting, preventing losses before a policy is even issued and protecting honest policyholders from bearing the cost of fraud.

iii. Higher Efficiency and Speed

One of the most immediate benefits is efficiency. AI-powered underwriting software solutions handle the routine, repetitive tasks that eat up a human underwriter's time. It can instantly validate data, run checks against external databases, and even make straight-through processing decisions on low-risk, standard applications.

This slashes turnaround times from days to minutes, meeting the need for instant gratification. Crucially, it also elevates the role of the human underwriter. Freed from mundane tasks, they can focus their expertise on complex, high-value cases that require nuanced judgment and empathy.

iv. Risk Insights

The latest underwriting software enables insurers to prevent risks instead of crying over them later. By connecting real-time external data feeds, such as climate models, geospatial imagery, and economic indicators, these solutions can tell what the future holds.

For instance, they can spot properties at increasing risk of wildfire due to changing vegetation density and drought conditions. Alternatively, they can also identify commercial properties in a supply chain that are more susceptible to specific geopolitical disruptions. This allows insurers to work with clients on risk mitigation strategies before a loss occurs, turning the insurer from a simple payer of claims into a genuine risk management partner.

Wrapping Up

The way insurers assess risk today is way different from how it was done, say, three to four years ago. It is no longer a craft meant only for experienced underwriters but has become a fair practice, one that ensures accuracy and transparency. And AI-powered underwriting software for insurance helps businesses take the jump.

Not long ago, managing money meant sitting down with a paper checkbook, a monthly bank statement, and a pencil. Each month, you'd receive a paper statement via post, manually tick off cleared payments and try to reconcile the numbers. This process was slow and error-prone and gave you little-to-no real-time visibility into your finances. It feels like a relic, but claims funds are still managed with the same outdated mechanics.

Claims funds remain scattered across stakeholders and systems. Banking structures are split between insurers and TPAs, while MGAs often sit in the middle, reliant on reports that arrive late or don't align. Across all parties, reporting lags behind reality. Reconciliation still depends on manual processes, and payments are often handled in batches rather than in real time. What should be a seamless financial flow instead resembles balancing a giant, industry-wide checkbook.

This fragmentation is more than just inefficient. It is a structural problem that drains liquidity, hides cash, slows payments, and consumes valuable time across the entire insurance value chain. Tens of millions of dollars often sit idle, reducing efficiency and yield. Accounts are routinely underfunded or overfunded. Teams spend hours reconciling balances when they could be focused on strategy and performance. The result is higher costs, lower margins, lost opportunities for investment, and a poorer customer experience at the very moment policyholders expect speed and certainty.

Hidden Costs Identified

The fragmentation of claims funds creates a series of hidden costs that ripple across the entire insurance ecosystem. While these costs may not appear on a balance sheet, their impact is felt in liquidity management, operational efficiency, profitability, and customer satisfaction.

1. Liquidity inefficiencies

When funds are spread across multiple accounts and stakeholders, balances are rarely optimized. Some accounts sit underfunded, limiting the ability to settle claims smoothly. Others hold excess cash that sits idle instead of being deployed or invested. For many insurers and TPAs, this can mean tens of millions of dollars not only tied up unproductively but, in some cases, effectively invisible, hidden across fragmented accounts with no clear line of sight.

This is not just a theoretical issue. A recent analysis of U.S. property and casualty insurers found that companies have increased their holdings in cash and other short-term liquid assets by more than 3% year over year, largely in response to rising catastrophe losses and unpredictable claim demands. While holding liquidity is prudent, the need to maintain such large buffers often reflects the limitations of fragmented systems and delayed reporting. Without clear visibility into cash positions, insurers lock up capital that could otherwise be put to more productive use.

2. Lost real-time visibility

Fragmentation obscures the true financial picture. With accounts scattered and reporting delayed, it is difficult for carriers, MGAs, and TPAs to know exactly where cash sits at any given moment. According to one recent industry commentary, insurers often struggle with "lack of balance visibility," especially when funds are handled across different accounts, in restricted currencies, or segmented by TPAs and insurers. This lack of real-time oversight hinders decision-making and increases the risk of errors, especially in times when claims surge and liquidity is needed most.

3. Operational inefficiencies

Reconciliation is still largely a manual process. Teams spend hours chasing reports, matching payments, and trying to balance accounts—much like the old days of reconciling a paper checkbook. According to the State of Claims Finance report, nearly eight in 10 insurers (79%) see these internal inefficiencies as a major barrier to timely claims payments, and 78% point to coordination breakdowns with brokers, TPAs, and banks as a source of friction. Only 1% describe collaboration between claims and finance teams as "highly effective," a telling sign that silos persist across the value chain. At the end of the day, this represents thousands of hours lost to administration, diverting talent away from analysis, strategy and improving performance.

4. Profitability impact

When money sits idle, balances are unclear, and teams spend hours on manual work, profits suffer. Insurers lose out on opportunities to put cash to better use, everyday costs remain high, and the gap between revenue and expenses narrows. Competitive pressures in the market make these inefficiencies even harder to absorb. Across U.S. insurance markets, rate dynamics are increasingly uneven. Some lines continue to see upward pressure, while others, such as property and certain financial lines, are beginning to soften as more capacity returns. Catastrophe exposure, such as the impact of California wildfires, adds another layer of volatility that puts further strain on margins. In this environment, inefficiencies in claims fund management are no longer a minor inconvenience. They have become a direct drag on profitability.

5. Customer retention costs

Perhaps the most damaging consequence lies in the policyholder experience. Delays in claims payments erode trust. For example, in the 2025 J.D. Power Small Commercial Insurance Study, customers rated "problem resolution" and "ease of doing business" as among the top drivers of overall satisfaction. Small commercial customers who experience poor problem resolution are much less likely to renew.

In an environment where even small mistakes or delays can push buyers to shop elsewhere, this becomes expensive. The cost to acquire a new policyholder far exceeds the cost to keep an existing one—with churn tied to claims experience being an accelerating risk, especially in specialty and smaller commercial lines where margins may already be thinner.

The Path Forward

If fragmentation is the problem, simplification is the solution. The industry faces a choice: continue investing in bespoke systems that are costly to build and slow to adapt, or embrace shared, modern infrastructure that enables real-time visibility, automated reconciliation, and faster payments. The goal is not to rip out everything that exists today, but to rewire the financial foundation of claims in a way that reduces friction across the value chain.

Progress will come as more stakeholders, carriers, MGAs, TPAs, and their partners, work from connected platforms rather than isolated silos. When that happens, cash is deployed more effectively, reconciliation becomes less resource-intensive, and policyholders receive funds with greater speed and certainty. The benefits go beyond efficiency. Faster, more transparent claims payments improve trust, and at scale can strengthen the resilience of the entire insurance ecosystem.

AI is transforming insurance, but most organizations aren’t prepared to take full advantage. CLARA’s Data Engineering as a Service (DEaaS) solves the data readiness challenge by converting fragmented inputs into clean, AI-ready intelligence.

Discover how leading insurers are:

• Unifying siloed data across systems and vendors
• Improving claims outcomes with agentic reasoning
• Future-proofing their AI investments with trusted data foundations

Learn how CLARA DEaaS can help your organization unlock the full potential of AI.

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Sponsored by: CLARA Analytics

Recent reports on the improving dexterity of robotic hands have raised the prospect that humanoid robots will show up in big numbers in workplaces and homes within the next few years, with obvious implications for workers' comp and homeowners insurance. Many investors are all in on the idea: Securities analysts say some 75% of Tesla's $1.5 trillion of market value stems from optimism about its prospects for "embedded AI," in its cars and Optimus humanoid robots. Hypemeister-in-chief Elon Musk said this summer that the robots could generate $30 trillion (that's trillion, with a "t") in annual revenue for Tesla.

Color me skeptical. I think specialized robots, a la the roughly 1 million in use in Amazon warehouses, will continue to proliferate rapidly but believe it will be decades before humanoid robots can function like people in the home and workplace.

And it just so happens that a thorough takedown began making the rounds recently, explaining in a far-more-learned way than I could just why human-level dexterity remains so far off for our machines. 

I'll share. 

The bullish case for humanoid robots goes more or less like this article from Bain, which begins:

"Dexterous, bipedal robots with general intelligence are advancing faster than many expected, and they’re quickly becoming economically viable. Within five years, robots will likely be able to perform a wide range of physical tasks at a cost that rivals or beats human labor. Adoption is poised to accelerate across industries, from manufacturing to food service, healthcare, and even construction."

The piece adds that "robotic mobility and dexterity are reaching human levels" and that "cost parity is within reach" for robots and human labor.

Rodney Brooks, a professor emeritus of robotics at MIT, counters that, while "the general plan is that humanoid robots will be 'plug compatible' with humans and be able to step in and do the manual things that humans do at lower prices and just as well..., believing that this will happen any time within decades is pure fantasy thinking."

He zeroes in on the dexterity issue, where he says humanoid proponents are making a false analogy to other AI-based systems — image labeling, speech to text and large language models (LLMs) — that have made exponential progress. He says those three benefited from decades of research that provided detailed, digital descriptions of what constituted an image, speech and the text that LLMs have been trained on. That baseline needs to be there, Brooks says, before you can turn machine learning loose and get the kinds of near-magical improvements we've seen with images, speech/text and LLMs. Yet researchers and developers are, he says, simply showing their robots videos of people handling objects and counting on the AI to figure out how to copy the movements. 

Brooks adds that humanoid robots have nowhere near the sensitivity of a human hand, with its "about 17,000 low-threshold mechanoreceptors in the glabrous skin (where hair doesn’t grow) of the hand, with about 1,000 of them right at the tip of each finger." 

He says walking is the other main issue with humanoid robots. He acknowledges that he's seen robots about half the height of humans maneuver smoothly among people in somewhat chaotic environments but notes that robots don't (in fact, can't, he says) walk as smoothly as humans and says the issue becomes far more complicated if the robot is the full height of a human, as it needs to be to take over most human tasks. The tendency is to think that doubling the height merely doubles the complexity, but you're also doubling the width and the depth, so you're actually having to deal with roughly eight times the volume and mass.

I'll add the cost issue. Musk's claim that Tesla can generate $30 trillion a year in revenue from humanoid robots is based on a price of $30,000 apiece. I don't like doing laundry or putting away dishes any more than the next person, but I'm not going to spend $30,000 (plus some annual fee for maintenance) just to have a robot do the chores, then go stand creepily in a corner until I assign it another task. 

I'm not at all suggesting that robots won't play a huge role in our future — just that they will be in the workplace, not the home, and that the robots will be specialized, not humanoid. I think the future for manufacturers, retailers, fast-food restaurants and many others looks a lot like what Amazon is pioneering with the sort of array of task-specific robots described in this New York Times story. 

As this related article in the Times shows, the results will still be profound for insurers, because hundreds of thousands of workers won't be hired for the sorts of jobs that run relatively high risks of injury — and that's just at Amazon. The article says Amazon expects to sell twice as many products by 2033 but will need some 600,000 fewer workers in its warehouses because of robotic automation (while needing some thousands of robot maintenance workers who, Amazon notes, will earn higher pay). 

Just don't expect the robots to look like you or to run your home like Rosey the Robot did for the Jetsons.

Cheers,

Paul

Financial planning for those with disability needs has often been viewed as an "advanced sales support" capability for carriers, useful for when an advisor runs into a client with more sophisticated needs than traditional sales training provides. This commonly occurs in situations where families have children with disabilities, whether that is physical, cognitive, or mental. As a whole, the insurance industry is immature, often referring to this as "special needs planning," terminology that is not in line with disability advocacy best practices. Many carriers have no formalized special considerations planning capabilities, while less immature models may have trusts specialists who may be able to assist as a part of a sales desk. Leaders will have formalized home office support in the form of a dedicated disability considerations team. This may include needs calculators, marketing materials, or even a dedicated sales representative that is a Chartered Special Needs Consultant (ChSNC) in the home office.

But most specific needs planning capabilities are based on the assumption that disability needs planning is a secondary consideration to broader estate planning through an advisor. These assumptions are simply no longer accurate. First, disability needs planning is no longer a niche market reserved only for ultra-high-net-worth individuals. Mental health and broader disability consideration services affect all markets, many of which may not traditionally have advisors. Second, specific needs services and mental health providers represent significant spend on their own. While the average child is estimated to cost $13,000 a year to raise, a child with disability needs is estimated to cost $30,000 a year to raise. And unlike other children, children with specific needs typically need support well beyond the age of 18. Mental health spend may be less (~$1,100 a year) but affects a significantly larger number of individuals. Third, needs are not quite as simple as effective estate planning. Consumers need products and advisors that can reasonably understand disability needs scenarios to ensure that coverage is accurate. For example, simply identifying an individual as bipolar is not sufficient – the long-term care for that individual must include medication, plans/treatment, and monitoring.

Growth In Specific Needs and Mental Health Markets

The number of individuals who qualify as specific needs has significantly grown over the last 20 years. From 2017 – 2023, the number of individuals three-21 years who qualified as disability needs under the Individuals With Disabilities Education Act (IDEA) grew 7%, with overall growth from 2013-2023 showing 14-15% growth.

In addition to the growth in the disabilities market, there has been an increase in both frequency and severity of mental illness . Not only are coming generations experiencing mental illness at a significantly greater rate than older generations, they are experiencing more severe diagnoses, as well.

This population is not just receiving diagnoses – they are receiving mental health treatment, defined as having received inpatient treatment/counseling or outpatient treatment/counseling or having used prescription medication to help with mental health challenges. In 2022, among the 15.4 million adults with a significant mental illness (SMI), 10.2 million (67%) received mental health treatment in the past year .

Most projections show significant growth in the mental health space. A combination of destigmatization, proliferation of mental health and behavioral resources, and technology to assist in serving historically underserved communities is likely to drive growth in this space. Some estimates put behavioral and mental health service growth at 18% over the next decade.

Opportunity for Carriers

The significant growth in the number of individuals with disabilities and mental health presents a growth opportunity for insurance carriers. Existing products simply do not meet the needs of families attempting to ensure care of their dependents when they are no longer able to care for them themselves.

1. Government Benefits Are Uncertain – Historically, the bedrock of benefits for individuals with disabilities were government programs, such as SNAP or housing benefits. But budget concerns affecting discretionary spending, both at the federal and state levels, put these programs in jeopardy. Parents who need long-term planning cannot reasonably rely on these programs to remain.
2. Trusts Are Complex – Third-party trusts are often complex, costly, and not easily administered. Consider that the trustee must have strong understanding of legalities of specific needs programs, particularly government benefits, to avoid jeopardizing them in the long run. A trust is also expensive to set up and an incorrect legal structure can ruin the beneficiary's benefit eligibility. There is also the practical administration – smaller families and weakened familial ties often mean there is no obvious individual to serve as a trustee.
3. Insurance Products Are Not Fit For Purpose – While it is possible to leverage life insurance and other financial products to fund a trust, this again requires significant planning and expense to properly set up the trust and administer it long-term. While long-term care can support dependents with disability needs, the policies are often expensive and may not actually cover what you need (e.g., supporting speech therapy versus mental health sessions).
4. Group Benefits Only Work For Employees – While there has been an expansion of mental health benefits in the group and voluntary benefit space, many dependents will be unable to have traditional jobs to qualify for the benefits.

Customer-centric product design suggests a simple solution – creating products specifically for dependents with specific needs and developing distribution and operational support necessary to enable the products. A growing market, limited alternatives, and a consistent need for the services all support further research and expansion into this market.

For carriers to succeed, they will need to address five critical challenges:

1. Underwriting Operational Support – Most critical of these challenges is the development of underwriting processes and risk management necessary to accurately price the product. This will require the mental health equivalent of a chief medical officer, comprehensive training, and trial and error to ensure proper pricing discipline.
2. Sales and Distribution Enhancement – Product sales can be driven through direct channels or through advisor-led channels, each with significant advantages and disadvantages. Designing the right distribution channel strategy will be critical – one key consideration will be the carrier's ability to successfully market and educate consumers on the product. Strong marketing capabilities could favor a direct model that reduces commission costs and helps ensure profitable growth.
3. Technology Enablement – This relatively new insurance space has one other upside – it is not bound by legacy systems or processes. The best carriers will leverage AI and automation to streamline product development, new business processing, and servicing. Because the dependent population may or may not be able to advocate for themselves, this presents an opportunity for carriers to heavily invest in technology so that the process does not require heavy intervention from the beneficiary, who may have limited decision-making capacity.
4. Advisor and Consumer Awareness – Advisors are typically ill-equipped to advise families in these areas. The sales scenario is not usually practiced in formal training programs, and advisors typically have little knowledge of mental health or disabilities to truly understand risks and client needs. Consumers are often not much more aware. Carriers will have a significant awareness challenge, but can develop this capability in conjunction with mental health and disability institutions to drive broader education efforts.
5. Regulatory and Compliance Frameworks – Insurance carriers can provide consumers the ability to administer trusts and handle the legal complexity associated with them. While this would likely involve third-party administrators, the challenge will be the legal standard applied to value-based care and the liability limits associated with them. For example, will there be a simple suitability standard applied to providing mental health care or will there be a heightened best interest standard? How is this standard monitored and enforced?

The good news for insurers is that they do not have to rush into this market. A macrotrend in insurance is the move toward hyper-personalization. The more tailored a product is to a need, the easier it is to underwrite the risk and meet the consumer's needs. This market is so broad that carriers can begin by offering adapted versions of existing products before eventually developing net new products. The important aspects are having the right individuals that understand both mental health/disability needs (e.g., therapists, caretakers) and those who understand insurance to marry the two to create a viable model that can serve a significantly growing population.

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Sponsored by: Origami Risk

The 2025 Canadian Reinsurance Conference featured a panel discussion with representatives from an insurer (Manulife), a reinsurer (RGA), and a leading industry organization (LIMRA/LOMA) on the topic of bridging the life insurance coverage gap in Canada. This article summarizes that discussion – outlining the current situation, describing factors, and offering potential solutions. 

The positive side of emerging challenges is they generally bring opportunities. That certainly holds true for today's Canadian life insurance industry. 

The challenge: Canada is facing a significant life insurance coverage gap, with insurance ownership declining across the nation.

The opportunity: By identifying underlying issues, developing and prioritizing strategies to address them, and working together across all parts of the insurance value chain, the industry can build a more secure future for all Canadians.

A growing gap

First, a quick look at the numbers.

Figure 1: Life insurance gap in Canada 

The gap spans all income levels but is most pronounced in households earning below $50,000 annually. Accordingly, the decline in insurance coverage is particularly evident in term policies, which dropped from 397,000 in 2019 to 340,000 in 2024. Meanwhile, 56% of 2024 premium sales were participating (whole life) policies, indicating a shift toward higher-end products. The result: While the current market generates relatively positive financial results, underlying declines in policy ownership suggest significant growth opportunities have yet to be realized 

Underinsured rates are especially prevalent among younger generations, with Gen Z showing a 44% need gap, more than double that of Baby Boomers. Gender analysis reveals clear, though less prominent, disparities, with women having a 32% insurance need gap compared to 28% for men.

As insurance ownership drops, the need for financial protection only grows. Increasing housing costs and associated mortgage debt, for example, amplify the protective benefits of insurance coverage, especially for younger people taking on new loans. On the other end of the age spectrum, Canadian seniors already at risk of outliving their own retirement savings also risk leaving loved ones unprotected. 

Consider this: The crowdsourced fundraising site GoFundMe, a popular source of support for individuals in need, recorded its highest annual payout total in 2024, reflecting the critical need for financial protection despite declining insurance ownership.

Contributing factors

Several connected factors contribute to the growing coverage gap. 

Within the industry itself, a shift in focus toward high net worth clients has led to an emphasis on larger and more sophisticated policies. This trend, coupled with a decline in the number of insurance advisors, has resulted in reduced attention to the mass market and term policies. A lack of young advisors is particularly troubling as many industry veterans near retirement age. 

Changing demographics and consumer attitudes also play a crucial role. Immigration, including an estimated 395,000 people coming to Canada in 2025, has increased the number of households who may need insurance coverage. 

Post-pandemic shifts in consumer priorities and financial strains have further exacerbated the issue, with higher cost of living fueling competing priorities for after-tax dollars and making insurance seem less urgent. Younger generations are delaying life events – home ownership, marriage, children – that typically trigger insurance purchases. In addition, fallout from the pandemic-triggered "Great Resignation" and accompanying expansion of the gig economy, which saw a 44% surge in the number of digital gig workers in Canada in 2024, has left many without employer-sponsored plans.

Misperceptions about insurance remain prevalent. Only 32% of Canadians trust their insurer, according to Statistica Canada. Additionally, a general lack of awareness about life insurance benefits and costs reduces perceived value. Complex product offerings and jargon-filled communication can magnify this issue and create added barriers to insurance adoption.

Five keys to future growth

Addressing the life insurance coverage gap in Canada requires a multi-faceted approach. 

1. Education and awareness. This tactic should be at the forefront, with efforts to simplify insurance language, highlight affordable premiums (see Figure 2), and emphasize the universal benefits of life insurance – such as income replacement, debt protection, and legacy planning.

Figure 2: How much does life insurance really cost? 

In a recent study, Canadians overestimate the cost of insurance by more than 300%. Actual premiums for $250,000 of life insurance coverage for 30-year-old nonsmokers in Canada (represents 5X income coverage for someone making $50,000 – a commonly recommended ratio):

2. Digital engagement. Expanding marketing efforts to platforms like TikTok, Instagram, and YouTube; developing user-friendly interfaces for needs analysis and applications; and implementing hybrid advice channels that combine online tools with advisor support can make insurance more accessible and appealing, especially for younger people. In LIMRA's Insurance Barometer Study, 46% of respondents indicated, "I would research life insurance online, but ultimately buy in person." Companies embedding immediate contact support with online tools are having success, and accelerated underwriting processes can remove additional barriers to entry. 

3. Targeted strategies. Innovation in products and services tailored to changing life stages and demographics can make insurance more relevant and attractive to a broader range of Canadians. This includes developing "bite-sized" entry-level products for underserved markets, such as new Canadians and gig economy workers. 

4. Advisor recruitment. The industry must focus on advisor recruitment and training, particularly from underserved communities. Promoting insurance advisor as a viable career path and conducting outreach through job fairs and community events can help address the shortage of advisors serving the mass market.

5. Industry collaboration. Partnerships with financial planners to include insurance in overall financial literacy efforts, collaborations with "finfluencers" in investment and banking sectors, and industry-wide initiatives to improve accessibility and trust, can create a more robust and inclusive insurance ecosystem.

A call to action

The opportunities are there – but only if we take collective action as an industry to seize them.

As the insurance landscape evolves, we must ensure we are meeting the needs of all customers. And the time to act is now. For example, protection for a mortgage is a common reason people acquire term insurance, and more than 1.2 million mortgages are renewing in 2025. This significant opportunity is just one of many.

Despite the urgent need for sufficient coverage and the continued advances in direct digital sales, insurance remains primarily a sold-not-bought product. Awareness of need without advice does not result in action; awareness with advice does result in action. To serve a broader population, the industry must reimagine distribution models and design innovative products that bridge the gap between awareness and action.

By diversifying the customer base and making financial protection accessible to all Canadians, the industry can fulfill its social responsibility while enhancing its resilience. Together, we can bridge the gap and create a more financially secure Canada for generations to come.

The traditional two-dimensional method of plotting portfolio risk and return, easy to illustrate and comprehend, remains a starting point for building most investment portfolios. For insurers, however, this method may not be sufficient.

An investment portfolio should be designed to serve an insurer's unique needs: asset-liability management, liquidity needs, regulatory constraints among others. Adding these factors to a risk-return analysis would likely add complexity, but expanding beyond the two-dimensional risk-return graphic may provide valuable insight to help insurers develop a more appropriate and efficient custom strategy.

As an example, consider risk-based capital (RBC) charges. A variety of investment strategies may offer similar or even equal risk-reward profiles, but they can vary significantly in RBC charges (see Figure 1). Though they share similar risk-return profiles, Portfolio 1's higher allocation to highly-rated structured securities results in a lower overall capital charge, while Portfolio 2's higher charge is primarily due to its greater equity allocation. Higher RBC charges can lead to higher hurdle rates and inefficient use of capital. A strategy with a lower RBC charge would enable insurers to free capital that can be used either for business expansion or returning to shareholders or policyholders. The added perspective on RBC charges can be a significant benefit.

Insurers face a growing array of challenges in all aspects of their business, and their investment portfolios are not immune. As interest rates in the past few years climbed back from an extended period of historic lows, insurers have an even broader menu of options worth considering in developing portfolio strategies. Given the other factors that can affect an insurer's investment choices – each firm's distinct book of business, risk tolerance, preferences, etc. – the more extensive the review of potential outcomes, the better an insurer's decision-making is served.

Modeling for Insurers: A Higher Standard

Investors typically view portfolio optimization through a framework known as the Markowitz Efficient Frontier. It helps balance risk and reward, often represented by standard deviation and the average of historical returns, respectively, although there are a variety of metrics to measure both. Insurers, however, require a broader set of considerations.

Markowitz's mean-variance optimization assumes that asset returns follow a normal distribution, but that is often not the case. This can lead to inaccurate risk assessments, especially during extreme market conditions.

A more sophisticated level of modeling risk and return includes the use of a stochastic economic scenario generator to project multiple scenarios for asset returns, volatilities, and correlations based on historical data and probabilistic models, capturing the non-normal behavior of asset returns. In this approach, risk and reward are defined as the average and standard deviation of portfolio returns across scenarios at a long-term steady state.

Figure 2 shows optimization results when applying the Markowitz framework. Each dot represents a portfolio with unique asset compositions, leading to different risks and rewards. However, this classic focus on two variables (risk and reward) does not capture all the specific nuances that insurers must consider when making asset allocation decisions.

If we add in the risk-based capital aspect – one of many other metrics that might be important to portfolio construction, but hardly the only one – we immediately add greater complexity. While the relationship between risk (expected volatility) and capital charge has a positive correlation, it is not linear.

To build upon Figure 2 and adding a third component – the aforementioned RBC metric – we would need to plot a three-dimensional graph which covers risk, reward and capital charge (see Figures 3 and 4).

Figures 3 and 4 show the same underlying data as Figure 2 – each point represents a portfolio - except these illustrations includes a third aspect: RBC charges. Rather than a smooth surface, we observe hills and valleys indicating nonlinear relationships among risk, reward and RBC charge, precisely the type of information that is lost in using the traditional Markowitz framework. What concerns us is no longer an efficient frontier line, but rather an efficient surface.

One way to generate capital-efficient portfolios is to solve for efficient surface portfolios, in our example that being the efficient frontier portfolios for the desired capital charge. This is shown in figure 5, where efficient portfolios for a given capital charge are marked by 'x'. The figure also highlights the inadequacy of considering only risk and reward where two very similar portfolios can be vastly different in terms of capital efficiency as shown in the back rectangle above at Figure 5.

Tailored Solutions for Unique Challenges

Of course, not all insurers are alike. Two different insurance companies holding identical asset portfolios will have different RBC ratios because of differences in other components of the RBC formula, namely insurance risks, interest rate risk and business risk. And of course, RBC is just one consideration among many. As such, we cannot have a one-size-fits-all portfolio for insurers.

Our narrative thus far has ignored the liability side of the puzzle. In practice, insurers need to consider the interplay between the assets and liabilities; indeed, a portfolio that appears appropriate from an asset-only perspective can be completely inappropriate from an enterprise perspective. A Strategic Asset Allocation (SAA) exercise then should consider cash flows generated by both assets and liabilities under different economic scenarios, in addition to the considerations discussed previously. This requires sophisticated dynamic financial modeling systems that can incorporate assets and liabilities as well as regulatory capital requirement. Last but not least, the model should be able to capture the impact of selling assets from an existing portfolio and buying into a new one.

To summarize, insurers are different from the "average" investor type because of the myriad of considerations insurance asset management demands, which can be better served by sophisticated insurance-focused modeling capabilities. Given the demands of the competitive environment, insurers may wish to work with an asset manager with deep knowledge of the insurance and investment environments, as well as the experience and tools to help them develop more sophisticated approaches to asset allocation, to assess the risks and rewards of optimizing their investment strategies.