As we progress through 2025, several groundbreaking technologies in artificial intelligence (AI) and machine learning (ML) are emerging. Each is poised to address complex challenges and unlock unprecedented opportunities. 

Below, we explore the most important trends shaping the AI landscape.

1. Domain-Specific Generative AI Models

Generative AI has demonstrated remarkable versatility, but its future lies in specialization. Enterprises are increasingly adopting domain-specific generative AI models tailored for industries or business functions. These models leverage vast amounts of specialized data to produce highly accurate outputs, making them invaluable in areas like healthcare (e.g., personalized treatment planning) and finance (e.g., risk analysis). In the insurance space, this includes automated policy generation, risk assessment and underwriting, fraud detection or customer profiling.

By 2027, over 50% of generative AI models used by enterprises are expected to be domain-specific, a sharp rise from just 1% today.

By leveraging domain-specific generative AI, insurers can significantly improve efficiency, reduce costs, and deliver superior customer experiences across various aspects of their operations.

2. Multimodal AI as the New Standard

Multimodal AI assimilates diverse data types—text, images, audio, and video—into cohesive models capable of delivering more personalized and sophisticated user experiences. Applications range from healthcare diagnostics using combined visual and audio inputs to automotive assistants that respond to voice commands while analyzing visual cues. This capability is revolutionizing customer interactions across industries by enabling seamless, context-aware solutions.

3. Optimization of the AI Stack

2025 marks a shift from experimentation to optimization in AI deployment. Organizations are focusing on maximizing the value of their AI investments by refining infrastructure for training and inference. For instance, advancements in hardware like GPUs and TPUs have reduced processing times by over 50%, significantly cutting costs while improving efficiency. The emphasis on optimization also extends to selecting the most suitable models for specific use cases, ensuring long-term relevance and effectiveness.

4. Agentic AI: Autonomous Collaboration

Agentic AI refers to systems capable of performing tasks independently with minimal human intervention. These autonomous agents are expected to collaborate across networks to execute complex workflows efficiently. While still evolving, agentic AI holds promise for automating routine tasks and enabling human-in-the-loop systems that boost productivity and innovation across sectors like logistics, customer service, and software development.

5. AI for Sustainability

AI is playing a pivotal role in addressing global sustainability challenges. From optimizing energy consumption in smart grids to enhancing climate modeling accuracy, these technologies are helping industries reduce their environmental footprint. AI-driven solutions are also being employed in agriculture for precision farming and in manufacturing for waste reduction.

6. Quantum Computing Meets AI

Quantum computing is beginning to intersect with AI, offering exponential processing power for specific tasks such as cryptography and molecular simulation in drug discovery. Although still nascent, this technology has the potential to solve problems that are currently intractable for classical computers, further expanding the horizons of what AI can achieve.

7. Breaking Down Silos With Generative AI

Generative AI is democratizing access to advanced tools within organizations by breaking down departmental silos. This fosters collaboration and accelerates innovation by enabling non-technical users to leverage AI for creative problem-solving. For example, Generative AI-powered chatbots and virtual assistants can access information from various departments to provide comprehensive customer support, bridging gaps between sales, service, and claims teams.

Conclusion

The emerging technologies in AI and ML for 2025 underscore a shift toward greater specialization, enhanced collaboration, and optimized performance across industries. From domain-specific applications to the integration of quantum computing, these advancements promise not only to drive business growth but also address broader societal challenges like sustainability and efficiency. Organizations that embrace these trends early will be well-positioned to lead in an increasingly competitive landscape shaped by intelligent systems.

Protecting data, closely adhering to pertinent rules and regulations, cleanly importing existing architecture, and controlling costs are all imperative for insurance companies when they migrate to and work in the cloud.

Recent survey data shows that 91% of banks and insurance firms are migrating to the cloud. And no wonder: Cloud migration offers significant benefits, such as better security, effective resource management, and cost optimizations. But these benefits don't come without challenges. 

Addressing Data Security and Compliance Concerns

Properly managing the vast amounts of personal data insurance companies handle – which are so critical to day-to-day operations – involves changing the infrastructure, networks, access controls, and firewalls, among other things. All of these changes create big security challenges. Fortunately, cloud providers offer multilayered security measures, such as advanced network protections, continuous monitoring, end-to-end data encryption, secure backups, and rigid user permissions.

Combining these approaches with employee best practices such as multifactor authentication, secure awareness training, and role-playing scenarios around social engineering can give insurers a multilayered data defense posture.

Cloud providers also help insurance companies adhere to industry-specific regulations (e.g., GDPR and HIPAA) that require detailed security audits to monitor access to restricted data. They do this by automatically creating and updating those logs to better prepare them for quarterly or annual compliance audits.

Clearing Legacy System Integration Hurdles

Insurance firms have huge hurdles to clear to successfully integrate legacy systems into the cloud. That's because a typical company's traditional infrastructure is weighted down with a mainframe administration system that may contain decades of policy information, claims and customer data. Systems that use obsolete programming language, unique architecture, or ancient data formats face a formidable challenge.

Maintaining legacy systems can waste time, retard digital transformation, and impair network performance. This is a priority concern for insurers, which say they spend 70% of their IT budget on that task. Moreover, per-policy IT costs can be 41% higher on legacy platforms.

Installing a modern system lowers maintenance costs by making legacy skills less necessary, fostering automation, cutting the time and energy businesses need to introduce new initiatives, and making IT and business teams more efficient.

There are ways to effectively migrate these systems:

1. Moving one or two systems at a time, in phases, reduces the likelihood of downtime and improves customer satisfaction. Testing and validating each migration ensures the highest-level performance.
2. By adopting a hybrid cloud approach, insurers can keep immovable, critical systems on-site or in legacy infrastructure while moving testing environments, data warehouses, or customer-facing software-as-a-service (SaaS) applications into public or private clouds. This lets insurers scale more modern systems cost-effectively without importing traditional architecture before it's ready.
3. Microservices disassemble siloed, legacy infrastructure into smaller, independent applications, making it easier to modify outdated software. APIs take it from there, communicating in real time to cloud servers or third-party vendors so insurance companies can improve their reliability, build faster deployments, and conduct a controlled, well-paced cloud migration over time.

Optimizing Cloud Costs

Migrating to the cloud requires insurers to apply careful IT cost management to efficiently store data and process workloads. Most cloud providers have built-in cost management tools, mostly user-friendly, easy-to-understand interfaces that provide high-level pre-configured views and granular customer reports. This way, companies can see what they're spending, how to better control costs, and project costs as their businesses grow.

By right-sizing cloud resources, insurers can auto-scale to accommodate peak demand periods (e.g., the major increases in server capacity that health insurance providers experience during open enrollment season when potentially hundreds of thousands of users try to register all at once). Dynamically expanding server capacity makes it easy for customers to sign up for new policies – which cuts downtime and boosts revenue.

There are two most effective ways to optimize cloud costs. The first is reserved instances – which strengthen core business functionality with a stable, consistent system purchased for longer periods. The second is spot instances, which help use idle cloud capacity at a lower cost for testing or data processing tasks.

Taking a Strategic Approach

Providers confront unique data security, legacy systems, and cost management challenges in moving to the cloud. But the long-term benefits – faster, nimbler digital transformation, a competitive edge, greater employee productivity, and more secure network infrastructure – frequently outweigh the short-term process frustrations.

The adage, "There's just not enough hours in a day," often resonates with commercial underwriters. In addition to policyholder expectations, they grapple with mounting competition, limited visibility into risk factors, and resource constraints that hinder comprehensive exposure analysis. 

These challenges persist as insurers and program administrators/managing general agents (MGAs) strive to enhance risk-assessment capacity and proficiency. Fortunately, generative AI advancements offer solutions and relief, enabling underwriting teams to alleviate capacity constraints and improve operational efficiency.

Generative AI is already making waves in the insurance industry by automating repetitive tasks, extracting insights from documents, and supporting critical functions like risk evaluation, fraud detection, and claims adjudication. The next evolution of this technology—reasoning and self-learning AI assistants—promises to revolutionize commercial underwriting. 

Agentic AI in the form of cognitive, persona-based assistants can be tailored to specific roles, automating workflows and executing complex tasks with precision. By functioning as digital co-workers, they empower underwriters to focus on high-value activities while gaining reliable, data-driven AI assistant support to manage essential responsibilities such as classification, risk appetite determination, and portfolio management.

Enhancing Underwriting Efficiency

AI assistants offer scalable solutions for business classification, risk analysis, and decision-making, bolstering risk assessment across various lines of business. Take Lessor's Risk Only (LRO) policies, which can be challenging to underwrite due to the dynamic nature of tenant occupancy. 

Insurers and MGAs can mitigate reliance on policyholder or agent inputs by deploying a role-based AI assistant. The digital teammate can ingest underwriting guidelines and apply them alongside real-time occupancy data, enriching workflows with expedient insights. The AI assistant can be trained to immediately alert underwriters if a high-risk tenant—such as a sports bar, liquor store, or gun shop—moves into a property. This enables timely reviews of the property owner's policy (and potentially adjacent tenant policies), ensuring adequate coverage and risk mitigation.

AI assistants also enhance productivity in high-volume segments like small and medium business (SMB) policies. Insurers and MGAs must rapidly assess risk and bind policies to maintain profitability in this competitive market. AI assistants streamline this process by swiftly determining whether submissions align with risk appetite and consistently addressing risk-assessment queries. By automating these tasks, underwriters can dedicate more time to complex risks, accelerate quote-to-bind timelines, and improve submission throughput.

Strengthening Premium Audit Processes

Beyond underwriting, AI assistants can optimize connected processes, such as premium audits, critical for ensuring accurate coverage and pricing based on actual exposures. Premium audits are particularly valuable in assessing business exposures, where operational changes can occur rapidly. For example, when purchasing a workers' compensation policy, a small construction company might initially classify employees in less risky roles. However, if the company pivots its operations mid-term, a reclassification may be necessary, affecting coverage scope and premium pricing.

Traditional premium audit processes are often time-consuming and prone to backlogs. A premium audit expert AI assistant can alleviate these inefficiencies by automating tasks such as applying insurer guidelines and state-specific regulations to submission documents. They can identify missing or incomplete information and autonomously conduct audits, ensuring compliance with standards. Insurers can enhance customer experience, foster loyalty, and improve retention rates by expediting audit completion and providing policyholders with timely advisory guidance.

Fostering Collaboration Across Functions

AI assistants are not confined to isolated tasks; they can be trained to collaborate across insurance functions, creating a cohesive ecosystem. For instance, an AI assistant supporting underwriting teams in risk analysis and eligibility determination can seamlessly integrate with a claims-focused counterpart. Those assistants can share information and learn from each other, establishing a valuable feedback loop. If a claims AI assistant detects an unusual volume of submissions in a specific risk category, it can alert the underwriting team to review and potentially adjust eligibility guidelines. This approach enhances decision-making and ensures consistency across workflows.

Driving Enterprise Growth Through AI Integration

Integrating autonomous AI assistants into underwriting and operational workflows represents a consequential shift in the operational advancement of insurers and MGAs. By streamlining processes, enhancing risk assessment capabilities, and fostering cross-functional collaboration, smart digital co-workers enable organizations to scale operations and achieve cost-efficient growth. From expediting SMB policy underwriting to improving premium audits and providing real-time insights in a rapidly evolving risk landscape, AI assistants can enable efficiency and precision in commercial lines underwriting.

As generative AI technology evolves, its potential to boost competitive advantage and reshape insurance operations grows exponentially. Commercial insurers and MGAs that embrace these advancements will be better positioned to navigate the complexities of an increasingly dynamic market, fortify their risk-assessment proficiency and agility, and deliver superior value to policyholders.

The insurance industry is at a crossroads. Consumer resentment about insurance affordability and fairness of premiums is brewing. The insurance industry is taking needed action on rates, but insurers find it difficult to inform and educate a customer base that views pricing as opaque and overly complicated. 

All of this raises the question: Can adequate premiums and trustworthiness coexist?

Eroding Trust

As consumers grow increasingly skeptical, the industry faces mounting challenges. Recent lawsuits involving individuals’ driving data from connected cars and consumer apps being sold to insurers without clearly informed consent has struck a nerve. Allegations of improper home insurance cancellations based on flawed aerial imagery and related concerns of insurers “spying” on their customers have surfaced. Even more attention has been attracted by the recent reports of non-renewals for many homeowners just before the Los Angeles-area wildfires, generating a mix of angst and wake-up calls. But each of these actions is deemed necessary for carriers to ensure viability, despite the obvious criticism that the decisions lack transparency.

Concern about insurer profitability has been in the forefront as the P&C industry experienced significant underwriting losses over the last three years. Attendant rate increases and tightening underwriting practices are having the desired outcome with, at least, financial recovery in personal auto lines. However, increasing rates are a new reality as climate exposure, repair cost inflation, social inflation, and fragile supply chains persist and costs are passed on via premiums.

Premium Leakage

Premium leakage, a problem fueled by outdated and inaccurate data, continues to contribute to underwriting losses and undermine profitability. Such leakage occurs when insurers are unable to align premiums with the actual risks of their policyholders. This often stems from reliance on stale, incorrect, or incomplete data. The problem doesn’t stop there. After policy inception, specifics like garaging location, undisclosed drivers, vehicle use and mileage can become a moving target. Inaccuracies create a ripple effect—insurers lose revenue, or customers may pay more than they should.

Adding to the complexity is the industry’s reliance on third-party providers that create a snapshot of information based on public records and other sources. Data providers sell information to counteract insurance application shortcomings. Insurers needed another way to size up risk with confidence beyond a short list of application questions and biased responses. These third-party sources, while in standard use, are not real time, can be flawed and can lead to broken feedback loops where errors compound.

In some cases, policyholders’ details are “grandfathered in” from the initial application snapshot without updates for years, further widening the gap between actual risk and premium pricing.

Personalization and Policyholder Engagement for the Win

As the outlook for auto lines improves, competition is intensifying in early 2025 and is expected to stay heated for at least two years. At present, carriers are addressing high shopping rates and seeking to grow market share in the long term. Customer retention has suddenly been reprioritized to the top as carriers see a profitable growth pathway. With the right approach, current conditions can become opportunities to build stronger, more transparent relationships with customers while improving operational efficiency.

Direct engagement with policyholders offers a pathway to address premium leakage and rebuild trust at the same time. By going directly to policyholders and offering them incentives to share their verified information, insurers can ensure accurate, up-to-date information that informs their underwriting decisions. This approach not only improves underwriting decisions and pricing accuracy but also creates a more transparent relationship with customers.

• Gather verified first-party data: Policyholders can directly share critical details, such as real-time mileage and the condition of their vehicles. Unlike with third-party reports, this data is both accurate and timely.
• Address stale or missing data points: Insights into garaging locations, prior vehicle damage, and undisclosed drivers can close significant gaps in risk assessment.
• Enhance customer participation: Encouraging policyholders to share their data while giving them full control fosters engagement and trust. When customers understand that their data directly contributes to fairer pricing, they are more likely to participate willingly.

The foundation of successful engagement and personalization is trust. Many consumers view the insurance industry as opaque and unresponsive, often associating it with unwelcome surprises like non-renewals or claims disputes. To counter this perception, insurers must prioritize transparency.

Imagine receiving a clear message from your insurer: “Here’s why your policy is changing, and here’s how we calculated your rate based on verified data you provided.” Such communication demonstrates fairness and builds confidence. Customers feel valued and empowered, which can translate into long-term loyalty.

Transparency also serves as a deterrent for misrepresentation. For instance, policyholders who understand the importance of accurate garaging information are less likely to provide misleading details, knowing it could affect their claims or coverage down the road.

A Win-Win for Insurers and Policyholders

Personalization and transparency are not just customer-centric strategies; they also drive profitability and operational excellence. By adding personalization and direct engagement with policyholders, insurers can:

• Reduce premium leakage by optimizing pricing accuracy, leading to fairer premiums for all customers
• Reduce friction and administrative costs of handling disputes and errors
• Build a loyal customer base that values engagement and honesty

For policyholders, the benefits are equally compelling. Fair pricing, clearer communication, and the assurance that their data is used responsibly creates a more positive experience. These factors foster trust, making customers more likely to renew their policies and recommend their insurers to others.

Looking Ahead

The year ahead offers a pivotal opportunity for the insurance industry to redefine itself. By prioritizing transparency and personalizing policies, insurers can address premium leakage while restoring trust. Companies that lead with these values will not only strengthen their bottom lines but also reshape the industry’s reputation for the better.

Joe Powell joined Gallagher Bassett in 2014. He serves as chief digital officer, overseeing data, analytics, and product innovation functions. His team manages GB's innovation road map, including Luminos RMIS, Waypoint decision support, and GB's suite of AI technologies. The team also provides a wide range of analysis, reporting, and insight services, from basic loss runs to state-of-the-art machine learning-based benchmarking. Previously, Powell served as a management consultant at Bain, focusing on initiatives involving growth strategy, corporate investment planning, IT strategy, post-merger integration, and cost reduction across numerous industries. He holds a bachelor of science degree and a master of science degree in management information systems, both from the Kelley School of Business at Indiana University.

Insurance Thought Leadership

How would you describe the transition from basic generative AI to agentic AI?

Joe Powell

I'll start with AI as it existed when ChatGPT was launched in 2022. It was this raw foundation model that you could ask questions of, and it would give you a cogent -- maybe not completely accurate, but at least coherent -- response. It sounded like a human. It was magical.

From there, the progression has been a relatively natural evolution toward better and better accuracy, to the point where eventually that accuracy becomes so good that you can begin to use it to take action. That's the nature of agentic AI -- you're going from something that is error-prone to something that hopefully is highly accurate and is driving action for your organization.

You could imagine the progression: When we first got into AI, having a chatbot like ChatGPT was impressive in itself. Eventually, it evolved to allow handling documents and asking questions about them. The next step was allowing the AI not just to get you the answer from that document but to show you where in that document it found the answer, thus getting a little bit more action-oriented. Eventually, the AI gets to a place where you've implemented this seamlessly in an organization's process, and through that process, it's handling a step or helping with a step and presenting information to a person to make a decision. Finally, you get to a place where the AI is taking that action out of a person's hands, and operating autonomously. That’s when you achieve the true goal of agentic AI.

Insurance Thought Leadership

What are some examples of AI taking actions traditionally performed by humans, either at Gallagher Bassett or in the industry at large?

Joe Powell

In the industry, we're generally not at the point where AI can make major decisions yet. There are two important dimensions to consider when thinking about agentic AI and what actions it should take. First, how critical is the decision? Another way to think about this is: What are the consequences if the decision is wrong? And second, how autonomous do you want the AI to be?

As decisions become more critical, you're less likely to want AI making them. For instance, you're not going to have AI deciding the settlement on a $10 million claim. Instead, you'll start with AI handling relatively routine, day-to-day decisions. As it becomes more sophisticated and accurate, you'll trust it with increasingly critical decisions.

The same principle applies to autonomy. The right approach is to begin with AI providing information to help you make decisions. Then, as it progresses, AI will help make recommendations. Eventually, you'll reach a stage where AI is making decisions with human oversight, and finally, it will make decisions with full autonomy.

Insurance Thought Leadership

What are some current examples of AI applications that meet appropriate risk and reliability standards?

Joe Powell

Around the industry, we're seeing organizations take a cautious approach by starting with low-risk decisions. Much of what we're seeing involves having AI review work that humans have already completed – for example, reviewing completed policy work or double-checking the coding on a claim.

At Gallagher Bassett, we're developing a tool to help flag urgent emails. This addresses a micro-decision that people make every day when reviewing their inboxes -- determining which messages are critical. Having AI serve as a second set of eyes to identify important demand letters and determine what needs immediate escalation is highly valuable.

These are the types of low-risk applications where we're starting to see AI step in -- relatively small day-to-day decisions where having an automated helper or second set of eyes can provide significant value.

Insurance Thought Leadership

It seems AI could be a good verification tool, given the recent story about AI quickly detecting a mathematical error in research about black plastic spatulas that was overlooked by human researchers?

Joe Powell

Absolutely. There are analogs for our industry, as well. You could imagine somebody entering a plan of action on a claim and having AI double check to make sure it aligns with what was prescribed in the prior plan of action -- what was going to be done, what was going to be followed up on, and whether those action items were addressed. 

AI can also be a huge help in ensuring adherence to best practices and consistent product delivery. At Gallagher Bassett, we use both generative AI and machine learning models to help us make more consistent decisions in various ways.

We tend to leave the power in the hands of our claims experts but still have AI offering recommendations or acting as a check. A simple example is determining the right reserve at a given time. We ultimately equip our adjusters to make a decision on the most likely ultimate financial outcome on that claim, but we have AI models running in the background that are constantly checking to say, "Is this what the AI would come up with?" If not, we have a conversation about why, and whether a reserve change is needed.

Insurance Thought Leadership

How do you ensure that AI implementation genuinely helps adjusters and customers rather than becoming technology in search of a problem?

Joe Powell

A few ways. One is getting those people involved early in the decision-making process. We have a team of former adjusters, whom we call AI specialists, that are engaged in our AI design team. These individuals advocate from the very beginning of an AI project in terms of how to make the AI effective in the adjuster’s workflow.

Second, as the product progresses to something usable, you want to begin testing it in multiple ways. There's automated testing for accuracy, which includes head-to-head tests with actual people to see which one's more accurate. You do automated tests to see how consistent the AI is in its decision-making and whether that reveals areas where we might need to improve the model. Finally, you gain feedback early in the process from a pilot group.

As an example, we launched a tool that summarizes claim files. You can imagine, if you're an adjuster handling an insurance claim that's been open for a few years and is highly complex, it can have hundreds of pages of documentation. It can be extremely helpful to have the AI scan through those hundreds of pages and give you a tight summary.

The summary covers what happened with the claim, the medical situation, how legal has progressed, whether we're nearing settlement, and what next steps have been documented. It also provides the ability to drill into any one of those areas to find out more. The user can then dive deeper into specific pieces of information, like all the medical visits that have happened.

We had the AI specialists involved upfront, but we also piloted it and got phenomenal feedback from adjusters in the field. We also asked what more we could provide. The input was key to the tool’s success when we launched this across our entire North America operation.

Insurance Thought Leadership

How do you envision teams of AI agents working together, particularly in claims processing?

Joe Powell

This is an interesting problem because as an organization and industry we're launching more and more AI tools, and they all tend to report back to the human user -- typically the adjuster, in our world. 

I like to use the analogy of a basketball team. If you're playing basketball and you can only talk to the coach, you as a team are not going to communicate well. The players need to be able to communicate with each other, not just with the coach.

I think that's the next stage, especially as we see more agentic AI that's actually taking action. Those actions, just like human decisions, need to be based on information from other agents and what they're seeing. For example, if you have an AI that handles claim intake and asks various questions, being able to distill that information down and perhaps having a back-and-forth between that AI agent and one that's concerned with detecting fraud could equip the fraud agent to do an even better job.

The interplay between these AI agents is something we're just beginning to experiment with, but it's really powerful. In the same way that a human would be much less productive if they could only work by themselves and never ask anybody questions, AI agents will be much more productive when they can begin to interact and share knowledge.

Insurance Thought Leadership

How will you approach integrating AI agents that span different organizations, ones that maybe start with Gallagher Bassett and eventually expand to carriers and brokerages?

Joe Powell

For the foreseeable future, it's much more realistic to do this within a controlled environment. But we're laying the groundwork for broader implementation.

For example, we have that AI that engages with email -- flagging important messages and gleaning critical information from them. We've got an AI that covers claim documentation and another AI that listens to phone calls, creating transcription summaries, checking if we're following best practices, and analyzing the sentiment of the person on the other end of the call.

When you've got these various sources of information available to AI, we can begin to pull relevant pieces from each to make things like litigation predictors or reserve predictors even more powerful. Whatever you could dream up becomes possible when you can access and integrate these various sources of information.

Insurance Thought Leadership

What are your thoughts on the trend of moving from large language models to small language models that are specific to industries and business functions?

Joe Powell

It'll be interesting to see how this develops. There's a lot of experimentation happening around whether to use large language models (like ChatGPT 4o), small language models (smaller, more efficient models), or large language models that have been fine-tuned for specific industries.

Which approach fits best with which use case is something we'll likely gain better insights about in the coming months and years. Right now, there’s a bias toward large language models because they tend to be more accurate and thus are lower risk, but there could theoretically be a place for small language models for very low risk use cases.

Insurance Thought Leadership

AI processing capacity is reportedly doubling every 3.4 months, so there’s certainly a lot of runway in front of us. 

Joe Powell

There are some interesting rumblings about whether AI is hitting a plateau. While I don't have a strong opinion on that, I think this discussion misses a crucial point: There's a tremendous amount of value we can derive from applications of AI even if the underlying technology itself doesn't get dramatically better.

It's similar to what happened with the internet. At a certain point, even if bandwidth doesn't massively increase, there's still so much you can do with it simply by connecting people -- you just have to figure out creative ways of using the technology. That was true 30 years ago, and it's still relevant today.

We've got the foundational AI technology now.

Insurance Thought Leadership

I'm sure some amusing situations will come up as AI agents talk to each other. I remember moderating a panel in the late 1990s, when a senior partner at a major VC firm talked about using early versions of speech recognition in his car and in his phone. At one point, he said, his car said something unprompted. The phone responded, "I don't understand you." The car started talking back, the phone responded, and so on. His car and phone had this long conversation that he couldn't figure out how to stop.

Joe Powell

You do have to tightly prescribe those interactions. I'm probably making it seem like you just put the two together and say, "Have a conversation," but there's going to have to be a lot of forethought in terms of the architecture. You need to consider what information you want them to share and how it will be shared so you don't have these surprises, but instead have the right information flowing from the right agents to the right agents.

Insurance Thought Leadership

What advice would you give readers looking to begin their AI journey?

Joe Powell

There are several ways companies can get started. One is to build your own, which is what big, well-resourced claims organizations are going to want to do. You build a team of AI experts and invest in the infrastructure -- specifically a private and secure in-house AI environment. It's quite an investment, which is why this path is typically for larger organizations.

Smaller organizations can take different approaches. One option is to work with AI startups and organizations that are selling their services on an ad hoc basis. This is a fast way to get to value. However, longer term, this option presents some risks in terms of what I described -- if you eventually want your AIs taking action and working together, you need them to be cohesive. If you've gone with a multi-vendor approach where you're taking very specific skill sets from each of them, the question becomes whether they'll be able to work well together in the future.

The other option, and probably the better option for most organizations that have claims or claim handling needs, is to partner with an organization that offers an end-to-end solution -- one that has a vision that you share. The key then becomes a matter of making sure you do indeed share a vision -- in terms of their adherence to privacy and security, accuracy, where you think the technology will go, and the organization’s capability to improve your outcomes in terms of better claims results, better communication, and better employee experience.

Insurance Thought Leadership

How do you determine which functions to outsource? The case of Borders Bookstores outsourcing their online book sales to Amazon in the 1990s seems like a cautionary tale.

Joe Powell

Yeah, 100%. That's a fantastic point. At Gallagher Bassett, when we think about what we should build, we look at our core competencies.

The claims summarizer is a great example. We're experts at handling claims -- it's a huge part of what we do as an organization. So naturally, when we want an AI to help us digest claims into tight summaries, that's something we feel we're better than anybody else at taking on. But when you look at something like building the foundation model, that's not something we're going to try to do. There are other examples where we might say OpenAI or Google would be better because that's their game.

Insurance Thought Leadership

This is great. Thanks, Joe. 

Today’s complicated, fast-moving risk environment is forcing changes to traditional roles across claims and insurance management, safety, and Governance, Risk and Compliance (GRC). How will you stay ahead?

While these roles may historically have been heavily weighted toward administration and reporting functions, the modern risk professional is increasingly a strategic role looked to for insight and context that factors heavily in the decisions made by organizations’ leadership teams.

The transformation to a more strategic and impactful approach to risk requires a move toward 4 key ways to level up.

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Sponsored by: Origami Risk

Munich Re’s global medical team recently collaborated on a wide-ranging thought leadership project intended to help life insurers better understand and navigate the most prevalent emerging medical trends and risks across five critical topics: AI in Healthcare, Improving Cancer Outcomes, Prevention, Obesity, and Climate Change. 

The Obesity chapter examines how new anti-obesity medications can reduce obesity-related mortality and morbidity risks. As many adverse medical conditions are associated with obesity, if these medications can stop or reverse the upward obesity prevalence trends, the potential impacts on mortality and morbidity could be significant, with an impact on life, disability, and critical illness portfolios.

Key takeaways

• Forecasted obesity trends: Obesity prevalence continues to rise, with forecasts predicting that more than half of the global population will be obese by 2035. This trend poses significant risks to health outcomes and insurance portfolios.
• Impact of new medications: If these medications can stop or reverse the upward obesity prevalence trends, the potential impacts on mortality and morbidity could be huge.
• Weight loss outcomes: The current literature on newer drugs for adults with obesity supports weight loss outcomes never seen with prior weight loss medications.
• Impact on insurance portfolios: This chapter provides a plausible estimation of the revolutionary impact weight loss medications may have on life, disability, and critical illness portfolios.

Visit our Life Science Report page for more information and to access additional chapters. 

Sponsored by ITL Partner: Munich Re

Two key factors suggest that the 2025 hurricane season won't be as active as the one that caused such heavy damage in the U.S. last year. The water in the tropical Atlantic isn't as warm, meaning less energy to fuel massive hurricanes like Helene and Milton. And this season isn't expected to see a La Niña weather pattern — a cooling in the Pacific Ocean that makes it easier for tropical storms to form in the Atlantic. 

At the same time, a study finds that severe convective storms are becoming a bigger problem, and the Trump administration has announced cuts that could imperil key federal capabilities for weather forecasting.

But let's start with the good news, even if the hurricane forecast is preliminary. 

As this Washington Post article explores in detail, the early indications are that the conditions during the Atlantic hurricane season could be much more benign than in 2024, when there were 18 named storms and five hurricanes that made landfall in the U.S. Conditions might even be close to opposite of what they were a year ago. 

Last year, an El Niño weather pattern held into the early part of the hurricane season, leading to record temperatures in the Atlantic waters where tropical storms form. An El Niño pattern interferes with the winds that coalesce into tropical storms... but a La Niña pattern took over as the summer progressed and removed the obstacles. Hurricane Helene blasted through Florida, Georgia and western North Carolina in late September, and Hurricane Milton slashed across Florida two weeks later.

This year, a La Niña is currently in place, reducing water temperatures and thus the energy that is available to fuel hurricanes. Yet that La Niña is expected to fade as the summer progresses. It's not clear that an El Niño and its hurricane-preventing effects will replace that La Niña, but at least the weather pattern that is conducive to major storms should be absent.

As I say, the hurricane forecast is preliminary. Much more definitive information will be available in about a month, and even better forecasts in May. But I'm really hoping for a break, for everyone's sake.

If only severe convective storms would cooperate....

A recent study finds that the storms continue to pose increased dangers. The season for such storms is starting earlier and ending later, and they are becoming more powerful. A derecho — a long-lasting band of rapidly moving thunderstorms that causes straight-line wind damage that can stretch for hundreds of miles — hit downtown Houston in May and caused more destruction than Hurricane Beryl, which struck the area in July 

Citing a study by CoreLogic, the Triple-I Blog writes:

"The traditional severe convective storm (SCS) season is expanding, with storms appearing earlier in spring and continuing later into fall. Tornado impacts are also shifting much farther east than historical norms, impacting Midwest states such as Illinois, Indiana, Michigan and Ohio."

Triple-I says:  

"Hailstorms pose a threat to 41 million homes at moderate or greater risk, representing a reconstruction cost value (RCV) of $13.4 trillion, according to CoreLogic’s risk score models. For tornadoes, 66 million homes are at risk, valued at $21 trillion RCV. Straight-line winds affect 53 million homes with an RCV of $18.6 trillion....

"[Insurers need] refined risk models and improved infrastructure in emerging high-risk areas. Geographic risk exposure management will become increasingly important as SCS events evolve, according to CoreLogic."

Yet in Washington DC...

The Trump administration has fired some 880 staffers at the National Oceanic and Atmospheric Administration (NOAA). CBS reports that an administration official said critical employees, such as National Weather Service meteorologists, were largely spared. But CBS adds: "A source at the National Weather Service disputed this, however, telling CBS News some meteorologists were impacted, including radar specialists, as well as staff of the Hurricane Hunters crew, which fly airplanes into storms to help forecasters make accurate predications during a hurricane."

Given the chaos that has surrounded the budget cuts by Elon Musk's DOGE team — the claim of an $8 billion cut in spending that turned out to be $8 million, the claims of major savings on contracts that actually ended almost 20 years ago while George W. Bush was president, and so on — I, for one, have become skeptical of any claim DOGE makes. We'll have to wait to see what's really going on. 

(I confess to a bit of a personal stake in the fate of NOAA. The rock-climbing partner and close friend of my younger daughter is a contractor for NOAA, and her boss was among those fired. My daughter's friend, a very bright and impressive woman, says the boss is the best she's ever had, but the boss had the temerity to be promoted a few months ago and thus is officially "probationary" in her new position and easy to fire quickly. I've covered major businesses for a very long time, and I assure you that going after the easy fires is never the right way to go.)

The Trump administration is also reportedly planning to close two key facilities for weather forecasting. According to Axios, "One of the buildings is the nerve center for generating national weather forecasts.... The modeling center runs the computer models used in day-to-day weather forecasting and ensures that weather data correctly goes into these models and that they are operating correctly."

Axios says "another building on the list... is the Radar Operations Center [in Norman, Oklahoma], a centralized hub for technicians and researchers to work on improving and repairing the nation's aging fleet of Doppler weather radars."

The firings and moves to close facilities come amid considerable speculation that Trump wants to eliminate or at least neuter NOAA. The thinking is that he may have it in for NOAA because it talks about and tracks climate change. Trump may also want to privatize weather forecasting, along with many other other parts of the federal government. There's even speculation that he's still fuming because a NOAA official told Alabamans not to worry about a hurricane in Trump's first term, shortly after Trump had said publicly that they were in the line of fire — the NOAA notification that led Trump to use a Sharpie to alter a NOAA map and falsely claim that he'd been right.

Whatever his plans for NOAA, I'm in favor of top-notch forecasting and hope no permanent damage is being done. 

I'm also in favor of fewer hurricanes and severe convective storms, and if these early predictions bear out then we can perhaps all have a more relaxed summer. Here's hoping.

Cheers,

Paul

Long-term care (LTC) planning stands as one of the final frontiers in retirement strategy, a true wildcard that continues to challenge retirement planning for both families and advisers. Traditional methods, whether national averages or basic Monte Carlo simulations, have never truly addressed the unpredictable costs and complexities of aging. As lifespans extend and healthcare needs evolve, these outdated tools fall short in preparing families for what's ahead. In this rapidly changing landscape, artificial intelligence (AI) potentially emerges as the solution we've been waiting for.

See also: The Crisis in Long-Term Care

The Persistent Challenges of LTC Planning

Historically, long-term care (LTC) has been the "elephant in the room" for retirement planners. Conventional models tend to paint with broad strokes, leaving critical questions unanswered:

• Delayed Conversations: Many families wait until a crisis hits before discussing LTC, often scrambling to put a plan together at the last minute and getting rejected by the most favorable LTC protection insurance products.
• Generic Data: Relying on average statistics and standard simulations rarely reflects the unique circumstances of any given family, causing clients to feel unmotivated in addressing LTC today.
• Missed Opportunities: Without tailored insights, advisers can struggle to translate preliminary discussions into concrete strategies, causing clients to experience confusion today and frustration when it's too late to plan.

LTC planning is one of the few remaining unsolved challenges in retirement planning. Its inherent unpredictability creates significant risk, but it also presents a prime opportunity for advisers to differentiate themselves by offering bespoke, forward-thinking solutions.

AI: A New Lens on an Old Problem

Unlike outdated traditional approaches that rely on broad averages, AI-driven platforms analyze a vast array of data, ranging from regional cost variations and healthcare inflation to individual health profiles and family dynamics, to generate a truly personalized projection of a client's LTC journey. 

AI platforms for LTC planning can streamline the process with quick intake forms that produce tailored predictions about a client's future care needs. These platforms can deliver detailed projections on the timing and duration of care, anticipated costs, and even estimates of the caregiving hours that family members might need to provide. With such personalized insights, advisers can move well beyond vague "what if" scenarios and instead initiate rich, meaningful conversations that address the specific realities of each client's situation.

• Initiate Rich Conversations: Rather than relying on generic averages, advisers can discuss specific care projections tailored to each client's circumstances. This not only clarifies the planning process but also helps clients grasp the real implications of their choices.
• Accelerate Decision-Making: When clients see a clear, actionable plan outlining expected timelines and costs, they're more likely to act, whether that involves purchasing the right policy or adjusting their savings strategy.
• Unlock Premium Growth: By overcoming the emotional barriers that often stall LTC discussions, personalized planning converts tentative ideas into high-value, concrete action plans, opening up new opportunities for advisers.

AI can transform a complex, often intimidating subject into a clear, relatable narrative that clients can understand and act on. Advisers can now replace vague "what if" discussions with detailed, personalized projections that spark more meaningful conversations.

Beyond Traditional Insurance: A Broader Perspective

Today's LTC planning goes far beyond traditional long-term care insurance. The market now offers a diverse range of products, from indexed universal life (IUL) policies and hybrid solutions to annuities and even short-term care options. By harnessing AI insights, advisers can create holistic strategies that not only forecast future costs with precision but also tailor the ideal mix of insurance products and self-funding plans to meet each client's unique retirement needs.

Despite the sophistication of AI, the human element remains irreplaceable. LTC planning is deeply personal, requiring emotional considerations and family dynamics. While AI provides the hard data needed to forecast costs and timelines, it's the advisor's empathy and insight that translate those numbers into a plan tailored to each family's unique situation. In essence, AI complements the adviser's expertise, offering clarity and precision while leaving room for the nuanced, human guidance that clients rely on.

See also: The Future of Long-Term Care Insurance

A New Era for Retirement Planning

As digital transformation continues to reshape the financial services landscape, integrating AI into LTC planning is quickly becoming standard practice. In a market where long-term care remains one of the last unsolved frontiers in retirement planning, AI tools are not only bridging the gap between uncertainty and clarity but also paving the way for a more secure, confident future.

For families facing the realities of longer lifespans and rising care costs, having a clear, tailored LTC plan is a necessity now more than ever. By combining the precision of AI with the irreplaceable human touch, advisers can ensure that their clients are prepared both financially and emotionally for whatever the future may hold. In doing so, they not only protect their clients' financial well-being but also reinforce their own role as trusted, forward-thinking leaders on one of the most critical aspects of retirement planning: long-term care.

In today’s fast-paced insurance landscape, where data is abundant but often fragmented, a robust Master Data Management (MDM) strategy is no longer optional—it’s a critical driver of business growth and operational efficiency. MDM capabilities help insurance carriers optimize their operations by creating a unified, consistent view of essential business data. 

Here's how MDM transformation can enable insurers to improve efficiency, compliance, risk management, and more.

1. Single Client View for Risk Pooling

Insurance carriers often deal with multiple clients, brokers, and partners across different regions, lines of business, and systems. By implementing an MDM solution, insurers can create a single, consolidated client view that enables them to:

• Accurately pool risk profiles across diverse markets and geographies, ensuring that underwriting decisions account for the full risk exposure of a client.
• Eliminate duplicate or fragmented client records, which reduces the risk of underwriting errors and enhances decision-making.

2. Cross-Sell and Up-Sell Opportunities

A unified view of customer data, facilitated by MDM, can also drive significant revenue growth through targeted cross-selling and up-selling. With accurate, consolidated customer profiles, insurance carriers can:

• Identify potential cross-sell and up-sell opportunities by gaining a comprehensive understanding of customer needs and behaviors across different lines of business.
• Personalize offerings based on client profiles and past interactions, improving customer engagement and satisfaction.
• Enhance sales and marketing effectiveness by providing insights that help agents and brokers offer the right products to the right clients at the right time.

3. Enhanced Partner Relationship Management

Insurance carriers work closely with a wide network of brokers, agents, and reinsurance partners. MDM plays a key role in streamlining partner data management by:

• Creating a centralized repository for partner profiles, facilitating consistent onboarding, contract management, and performance monitoring across the partner ecosystem.
• Providing a 360-degree view of partner relationships, enabling insurers to optimize contract terms, pricing, and performance based on accurate, up-to-date data.

See also: A Different Slant on Operational Efficiency

4. Single View of Reinsurance Contracts

Reinsurance contracts are often complex, with terms and conditions spread across multiple systems. With MDM, insurers can:

• Maintain a centralized repository of treaties that serves as a single source of truth for contract terms, obligations, and renewal dates.
• Ensure automated synchronization of terms across underwriting and claims departments, minimizing errors and discrepancies in claims settlements.

5. Improved Catastrophic Event Response

During catastrophic events (e.g., natural disasters, pandemics), insurance carriers need to quickly and accurately assess claims. MDM enables:

• Rapid identification of impacted clients and regions by providing consolidated, accurate policyholder and coverage data.
• Quicker mobilization of resources by automating claims prioritization based on a unified view of risk exposure and client profiles.

6. Regulatory Reporting Accuracy

Insurance companies are subject to rigorous regulatory requirements across multiple jurisdictions (e.g., Solvency II, IFRS). MDM ensures that insurers can meet these standards by:

• Unifying risk, policy, claims, and financial data across departments, ensuring consistency in the data used for regulatory reporting.
• Automating the generation of accurate, real-time reports, reducing the risk of non-compliance and costly penalties.

MDM also enables better management of personally identifiable information (PII), ensuring compliance with global data privacy regulations like GDPR, by applying consistent security and privacy controls across borders.

7. Data Standardization for Global Operations

Insurance companies with global operations face the challenge of managing data from diverse regions, each with its own standards and formats. MDM helps:

• Standardize data from various countries and lines of business, aligning it with a global data model.
• Simplify the integration of data from new acquisitions or partners, allowing seamless data consolidation across regions without affecting business decisions.

This standardization enhances global operational efficiency and ensures that data discrepancies do not hinder the decision-making process.

See also: Insurance Faces Growing Natural Disaster Challenges

8. Fraud Detection and Prevention

Fragmented and inconsistent data create vulnerabilities for fraud in the insurance industry. MDM helps mitigate this risk by:

• Creating a unified view of customers and claims, making it easier to identify and prevent duplicate or fraudulent claims.
• Cross-referencing claims data across multiple jurisdictions, enabling insurers to detect patterns indicative of fraud, such as repeated claims under different aliases or policy numbers.

Conclusion

MDM transformation is more than just a technology upgrade; it’s a strategic initiative that can drive substantial business growth and operational efficiencies for insurance companies. By creating a single source of truth across data silos, MDM empowers insurers to enhance risk management, regulatory compliance, fraud prevention, and customer service. As insurance companies continue to embrace data-driven decision-making, MDM will play an increasingly pivotal role in ensuring long-term success in an increasingly competitive marketplace.