Posts in category: Modern Insurance Trends & Developments

Artificial intelligence is moving beyond the stage of being a tool that people use.

It is increasingly becoming a system that can reason, coordinate, create, test, learn, and act.

This shift is particularly significant for insurance, an industry built around information, rules, decisions, documentation, and complex workflows. As generative AI evolves into more autonomous, agentic systems, insurers are beginning to reconsider not only what technology can do, but how technology itself should be built and integrated into the enterprise.

One emerging concept captures this transition: the Binary Big Bang.

It describes a defining moment in the evolution of AI and software development, where autonomous systems begin challenging long-standing assumptions about how digital products are created, how much they cost to build, and who—or what—participates in their development.

The implications for insurance could be substantial.

Breaking Through the Natural-Language Barrier

Foundation models changed the relationship between people and software by making natural language a powerful interface for interacting with technology.

Instead of translating an idea into highly structured instructions, people can increasingly describe what they want in ordinary language and allow AI to interpret, develop, and refine the underlying solution.

This dramatically expands the possibilities for software development.

For insurers, generative AI is therefore more than another layer of automation.

AI models and agents are becoming potential components of the enterprise itself, with applications spanning customer service, underwriting, claims, risk assessment, product development, and operational management.

The opportunity is not simply to automate today’s processes.

It is to rethink the processes themselves.

Insurance executives can begin building what might be described as a cognitive digital brain—an interconnected environment in which data, AI models, workflows, organizational knowledge, and autonomous agents work together.

The value comes from the connections between these components.

From AI Assistants to AI Agents

The next stage of this evolution is agentic AI.

AI agents are designed to pursue goals, reason through problems, use external tools and information, make decisions, and take actions with varying degrees of autonomy.

For insurers, this opens the possibility of distributing parts of the technology development lifecycle across specialized AI agents.

A requirement-management agent, for example, could interpret business needs, organize priorities, track progress, and ensure that development remains aligned with defined objectives.

A code-development agent could translate requirements into structured software components while maintaining traceability between business needs and technical implementation.

A testing agent could simulate different user scenarios, identify potential issues, and repeatedly test applications throughout development.

A deployment and support agent could assist with releasing applications into production and identifying or resolving environment-specific issues after launch.

Instead of software development being a linear sequence of human-led activities, it could become a coordinated ecosystem of specialized digital workers.

That has the potential to change both the speed and economics of building technology.

Three Forces Reshaping Insurance Technology

As AI becomes increasingly embedded into technology environments, three interconnected forces are emerging: abundance, abstraction, and autonomy.

1. Abundance: More Technology, Faster

Legacy technology remains a major challenge for insurers.

Maintaining aging systems can be expensive, while modernization efforts often require significant time, specialized skills, and investment.

AI could change the economics of this equation.

Generative AI can accelerate software development, help interpret legacy code, identify technical debt, generate documentation, and support the migration of older applications into modern environments.

The result could be a greater capacity to build and improve digital systems without relying entirely on traditional development models.

Research indicates that 78% of insurance executives believe AI agents will reinvent how their organizations build digital systems.

The demand for this additional capacity is also clear. If software engineering resources were unlimited, 62% of executives would prioritize launching new products and services, while the same proportion would prioritize adding new features to existing offerings.

AI-driven development could help narrow that gap.

2. Abstraction: Making Complexity Easier to Navigate

Insurance contains enormous amounts of complexity.

Underwriting decisions, claims processes, policy rules, customer interactions, regulatory requirements, and internal workflows all involve multiple layers of information.

Generative AI can help make that complexity more manageable.

Instead of forcing employees to navigate numerous systems and information sources independently, AI can summarize information, surface relevant insights, provide recommendations, and create more intuitive interfaces.

In underwriting and claims, AI can support decision-making by bringing together relevant information at the right moment.

In customer service, agentic systems can use customer context to create more personalized interactions.

The technology essentially becomes a layer of abstraction between people and underlying complexity.

Employees do not necessarily need to understand every technical detail behind a system to use its capabilities effectively.

3. Autonomy: Moving From Assistance to Action

The most significant change may be the transition from AI that assists people to AI that can perform defined activities independently.

Autonomous systems can increasingly analyze information, make decisions within established parameters, execute workflows, and respond to changing conditions.

This does not mean removing humans from the equation.

Instead, it creates the possibility of designing workflows in which technology handles predictable, information-intensive activities while people remain responsible for oversight, judgment, exceptions, and strategic decisions.

As data becomes more integrated, insurers could potentially encode business processes, institutional knowledge, rules, and workflows into interconnected AI environments.

The result is an operating model that can respond dynamically rather than simply following rigid sequences of instructions.

AI Turns Data Into a Working Asset

Insurance has never suffered from a lack of data.

The challenge has often been making that data accessible, understandable, and useful at the moment a decision needs to be made.

AI can help change that.

Modern AI systems can identify patterns, connect information from different sources, surface previously overlooked relationships, and deliver relevant information to employees when it matters.

This can influence virtually every stage of the insurance technology lifecycle.

AI can support:

• Generating documentation, use cases, data dictionaries, and user stories
• Configuring information for modern technology platforms
• Rewriting legacy applications for newer technology environments
• Reconsidering requirements earlier in the development process
• Creating comprehensive test cases before a new application is built
• Connecting business requirements more directly with technical implementation

This creates a different development philosophy.

Instead of waiting until the end of a technology project to test whether the solution meets business needs, AI can help validate assumptions much earlier.

That can reduce rework, accelerate development, and improve the connection between technology and business outcomes.

The New Generation of AI-Powered Underwriting

Underwriting provides a particularly clear example of how these capabilities can come together.

AI-powered underwriting systems can analyze submissions, identify missing information, assess whether a risk fits established criteria, and surface insights that help underwriters make decisions.

The potential value is not simply speed.

It is the ability to process larger volumes of information consistently while giving skilled professionals better context for complex decisions.

Similar approaches are emerging in reinsurance, where AI assistants can monitor information from a broad range of sources, synthesize relevant developments, and provide underwriters with a more current view of potential risks.

As these systems mature, the underwriting process could become less dependent on manually searching for information and more focused on interpreting insights and exercising professional judgment.

The human role does not disappear.

It becomes more concentrated around the decisions where expertise matters most.

A New Architecture for Insurance

The Binary Big Bang represents more than another stage in the technology cycle.

It points toward a different way of building and operating insurance businesses.

Software may become easier to create. Digital capabilities may become more abundant. Complex processes may become easier to navigate. And autonomous systems may increasingly perform work that previously required significant human intervention.

But the real transformation comes from combining these capabilities.

An insurer’s competitive advantage may increasingly depend on how effectively it connects AI, data, people, workflows, and institutional knowledge into a coherent digital environment.

That requires more than adding AI tools to existing systems.

It requires rethinking the architecture of the business itself.

From Automation to Reinvention

The most important question is no longer simply:

“What can AI automate?”

A more consequential question is:

“What could insurance become if technology could build, understand, and operate parts of the business alongside people?”

That is the deeper significance of the Binary Big Bang.

AI is moving from the edges of insurance technology toward its core. As autonomous agents become more capable, insurers have an opportunity to redesign how products are built, risks are evaluated, claims are processed, customers are served, and decisions are made.

The organizations that embrace this shift will not simply have faster technology.

They could have a fundamentally different way of working.

The next chapter of insurance technology may not be about adding more software. It may be about creating software that can increasingly build, understand, and improve itself.

The insurance industry is entering a period defined by uncertainty. Geopolitical tensions, changing economic conditions, evolving customer expectations, technological disruption, and shifting affordability are reshaping how insurers think about risk and growth.

Volatility itself is not necessarily the defining challenge. The bigger question is how insurers respond to it.

The organizations preparing for the next phase are looking beyond short-term reactions. They are strengthening their digital foundations, redesigning operating models, and applying artificial intelligence where it can produce measurable improvements—from faster decisions and lower operating costs to more consistent customer experiences.

The future of insurance will not simply be about adopting more technology. It will be about changing how the business works.

Here are five developments that could shape the industry’s next chapter.

1. Insurers May Become Architects of Longer, Healthier Lives

Longevity is more than a retirement-financing issue.

As people live longer, they may face a combination of financial uncertainty, changing health needs, potential chronic conditions, increasing care requirements, and the possibility of losing independence.

These risks do not fit neatly into separate insurance categories.

Retirement savings, health coverage, protection, long-term care, and financial planning can all influence the experience of aging. Yet insurance products have traditionally been organized around separate business lines.

The opportunity is to think more holistically.

Future-facing insurers may increasingly develop solutions that connect financial security, health resilience, protection, and independence across different stages of life.

Technology can make this approach more practical. Cloud platforms, connected data, and AI-driven personalization could allow insurers to provide more continuous guidance instead of relying primarily on occasional transactions.

This could include:

• More integrated financial, protection, and health solutions
• Personalized guidance delivered at sustainable cost
• Tools that encourage better savings and coverage decisions
• Connected ecosystems spanning insurance, healthcare, wealth, and care services
• Digital experiences designed around life stages rather than individual products

The deeper shift is from simply managing insurance policies to helping customers navigate increasingly complex and longer lives.

2. AI Could Connect Intent, Workflow, and Execution

AI is moving beyond isolated automation.

The next stage is about connecting what people want to accomplish with the processes and technology required to make it happen.

Instead of employees navigating multiple systems and manually coordinating every step, AI-enabled environments could allow users to describe an objective and have technology assemble portions of the workflow.

For insurers, this could affect underwriting, claims, customer service, policy administration, and other parts of the value chain.

To make this practical, organizations may need an AI workbench—a governed environment containing reusable tools, workflows, data connections, controls, and templates for developing and supervising AI-enabled work.

Several capabilities will become increasingly important:

Intent-led work: Business users can describe desired outcomes in natural language while AI helps construct appropriate workflows.

Human oversight: People remain responsible for high-impact decisions through approval thresholds, exception handling, escalation procedures, and audit trails.

Context-rich data: AI needs access to relevant customer, policy, claims, risk, and interaction information rather than isolated data fields.

Connected ecosystems: External technology and service providers can contribute specialized capabilities while performance, quality, and customer outcomes remain measurable.

Business and technology alignment: Business teams and technology teams work more closely so AI-enabled processes can evolve without sacrificing governance.

The competitive distinction may eventually be less about who has AI and more about who can deploy it repeatedly, safely, and at scale.

3. AI Agents Could Reshape Insurance Distribution

The way people make purchasing decisions is changing.

Consumers are becoming increasingly comfortable using AI to research products, compare alternatives, understand complex choices, and receive recommendations.

Insurance is particularly suited to this shift because it can be complicated, highly personalized, and difficult to compare.

Instead of visiting multiple websites or navigating lengthy product journeys, customers could increasingly rely on AI agents to help define their needs, compare options, apply preferences, and potentially initiate transactions.

This does not necessarily eliminate insurers or human advisors.

Instead, it could change where influence occurs.

The companies that gain visibility may increasingly be those whose products, pricing, eligibility rules, and coverage details can be clearly interpreted by AI systems.

That creates new requirements for transparency.

Insurance products may need to be structured so that important information can be understood by both people and machines, with clear pricing, coverage explanations, limitations, and decision logic.

In an AI-mediated marketplace, being easy to understand could become an important part of being easy to choose.

4. Core Platforms Could Become Innovation Foundations

Traditional insurance platforms have provided consistency, control, and standardization. But systems designed around yesterday’s processes can also make change slower and more expensive.

That tension is becoming increasingly important as insurers seek faster product development, personalization, and AI-enabled operations.

The emerging alternative is a more modular architecture—one built from reusable capabilities, connected data, APIs, events, and orchestration layers.

Rather than rebuilding the core whenever a product or customer journey changes, insurers could create flexible layers around the core that allow individual capabilities to evolve independently.

Several changes may become particularly significant:

Sovereign and controlled AI: Organizations may seek greater control over how critical AI capabilities are deployed, governed, and integrated into their technology environments.

Cloud-native architecture: Cloud adoption becomes less about simply moving existing systems and more about creating modular, continuously evolving technology.

Packaged operational services: Certain processes may increasingly be delivered as standardized capabilities or outcomes rather than large technology projects.

Real-time data: Data could shift from retrospective reporting toward active decision-making in areas such as pricing, claims triage, risk assessment, and customer engagement.

AI-enabled workspaces: Underwriters, claims professionals, and service teams may increasingly work in environments where people, data, and AI tools operate together.

The goal is not technology for its own sake.

The real measure of modernization will be whether insurers can introduce products, change processes, and respond to customers faster without sacrificing control.

5. Embedded Insurance Could Become a Core Growth Channel

Insurance is increasingly appearing inside the journeys where customers are already making decisions.

Instead of asking customers to stop what they are doing and search separately for coverage, embedded models can place relevant protection directly into a transaction or workflow.

This could include:

• Product protection during online checkout
• Warranty and shipping-related coverage
• Insurance within automotive purchasing and mobility journeys
• Protection integrated into home and smart-home ecosystems
• Coverage offered within travel and ticketing experiences
• Event-linked or usage-based protection

The appeal is straightforward: insurance becomes part of an existing decision rather than another task customers must complete separately.

For insurers, however, successful embedded distribution requires more than creating partnerships.

Products need to be easy to integrate. APIs need to work reliably. Partner onboarding needs to be efficient. Offers need to be flexible enough to fit different customer journeys while remaining simple enough to understand.

The strongest opportunities may emerge where insurance solves a clear problem at precisely the moment that problem becomes relevant.

A New Insurance Economy Is Taking Shape

The insurance industry has traditionally relied heavily on people, complex technology environments, established distribution networks, and large operational structures.

That model is beginning to change.

AI can alter the economics of individual processes. Modern data infrastructure can make decisions faster and more connected. Modular technology can make innovation less dependent on large-scale system changes. Embedded distribution can move insurance closer to the moments when customers actually make decisions.

Together, these developments point toward a broader transformation.

The insurers preparing for the next decade may not simply be the organizations with the newest technology. They may be the ones that successfully connect digital foundations, intelligent operations, flexible products, and relevant distribution into one coherent operating model.

The central challenge is therefore not predicting exactly what the future will look like.

It is building an organization flexible enough to adapt as that future continues to change.

Insurance has always been built around managing uncertainty. The next challenge is learning how to innovate within it.

The insurance workforce is approaching a turning point.

A significant share of insurance professionals is expected to reach retirement age by 2030, while generative AI and increasingly autonomous systems are rapidly changing how work gets done. Together, these forces are creating a workforce challenge unlike anything the industry has faced before.

AI could help insurers address productivity gaps, improve decision-making, and redesign many everyday processes. But technology alone will not solve the talent challenge.

The insurers best positioned to benefit will be those that can attract new talent, develop existing employees, and give their people the skills needed to work effectively alongside increasingly capable AI systems.

AI Transformation Starts With People

The insurance industry is particularly well positioned for AI adoption because much of its work involves language, information, analysis, documentation, and data.

At the same time, most new enterprise data is unstructured, appearing in documents, correspondence, conversations, images, reports, and other formats that traditional systems can struggle to process efficiently.

Generative AI changes that equation.

Its ability to interpret and work with unstructured information creates opportunities across underwriting, claims, customer service, sales, risk management, and many other functions.

But realizing that potential requires more than deploying new tools.

Employees understand the practical realities of insurance processes better than anyone. Their knowledge is essential for identifying where AI can create value, where human judgment must remain central, and how roles should evolve.

This makes the human element of AI transformation a strategic priority.

The challenge is that many insurance leaders are already concerned that skills shortages could prevent their organizations from capturing the full value of generative AI.

Preparing the workforce, therefore, should not be treated as a secondary initiative.

It should be part of the transformation strategy from the beginning.

1. Replace Uncertainty With Transparency

AI may be capable of performing an increasing number of tasks, but it does not eliminate the need for human judgment, creativity, critical thinking, empathy, or relationship-building.

Employees need to understand that distinction.

Research shows that many insurance workers are concerned about the effects of AI on stress, workload, and job security. These concerns cannot simply be dismissed. They need to be addressed through clear communication and meaningful involvement in the transformation process.

One of the most important messages insurers can communicate is that AI does not necessarily mean replacing people.

In many roles, it means changing how people spend their time.

Only a relatively small proportion of tasks across some insurance roles are expected to become fully automated, while many others are likely to remain unchanged or become augmented by technology.

That distinction is important.

Consider underwriting. Skilled underwriters are already in short supply, yet a substantial portion of their working time can be consumed by administrative and information-gathering activities.

Generative AI and autonomous systems could help collect and analyze information, summarize documents, identify patterns, and surface relevant insights.

The underwriter can then spend more time on what technology cannot easily replicate: evaluating complex risks, applying judgment, engaging with stakeholders, and making nuanced decisions.

The same principle applies to customer service.

AI-powered systems can handle routine questions and straightforward requests, allowing human representatives to concentrate on complicated cases and deeper customer relationships.

The objective is not simply to automate work.

It is to redesign work around the strengths of both humans and machines.

When employees understand this vision and have a voice in shaping it, AI is more likely to be viewed as an enabler rather than a threat.

2. Reskill at Speed and Make Learning Continuous

The skills required in insurance are changing quickly.

Organizations that continue relying on yesterday’s capabilities may find themselves struggling to capture tomorrow’s opportunities.

The appetite for learning is already there. A large majority of workers express interest in developing generative AI skills, yet relatively few insurers are currently reskilling employees at the scale required.

That creates a significant opportunity.

Reskilling should not be treated as a one-time training program. It should become part of everyday work.

Effective learning strategies can combine digital courses, workshops, practical exercises, mentoring, peer learning, certifications, and hands-on experimentation.

The emphasis should also be on practical application.

Insurance professionals already know how to work with structured information. Generative AI can help extend those capabilities into the vast world of unstructured data, allowing employees to work more efficiently with documents, correspondence, reports, and other complex information.

External partnerships can strengthen this effort.

Collaboration with universities, technology providers, professional organizations, and specialist training institutions can provide access to emerging knowledge and new learning methods.

But formal training is only part of the equation.

A strong learning culture also requires recognition.

Employees who develop new capabilities should be encouraged and rewarded. Progress can be made more engaging through challenges, peer communities, recognition programs, and other approaches that make learning feel like an ongoing professional journey rather than an additional obligation.

The ultimate goal is to make learning part of the flow of work.

As AI evolves, employees will need opportunities to continuously refresh their skills—and AI systems themselves will also need to evolve through ongoing monitoring, learning, and governance.

3. Rethink How Insurance Attracts Talent

The insurance talent challenge extends beyond reskilling existing employees.

The industry must also become more competitive in attracting new generations of workers.

This is particularly important for roles involving engineering, cybersecurity, data, software, analytics, and AI, where insurance competes with almost every other major industry for talent.

Younger workers have historically shown relatively low interest in insurance careers, while demographic changes are increasing the gap between the number of people leaving the industry and those entering it.

The response starts with a stronger employee value proposition.

Insurance can offer something that many technology-driven industries cannot: meaningful impact at enormous scale.

The industry helps individuals manage uncertainty, supports businesses through disruption, enables economic activity, and contributes to the resilience of communities.

That purpose should be made visible.

At the same time, insurance needs to demonstrate that it is not defined solely by legacy processes. Innovation, AI, data, digital transformation, cybersecurity, and emerging technologies are becoming increasingly important parts of the industry’s future.

A compelling employee proposition should connect these two ideas:

purpose and possibility.

Once that proposition is clear, recruitment strategies can become more targeted.

Insurers can work more closely with universities and educational institutions that specialize in technology and data-related disciplines, develop early-career pathways, encourage employee referrals, and engage graduates, apprentices, and other emerging professionals.

Recruitment can also become more personalized.

Generative AI and agentic systems can help tailor communications, accelerate administrative processes, improve candidate matching, and create a smoother experience for applicants.

But insurers should look beyond traditional talent pools as well.

There are many overlooked groups—including caregivers, veterans, career changers, and other professionals—who may possess highly transferable skills such as communication, problem-solving, resilience, organization, and relationship management.

The future workforce may be broader than traditional recruitment models suggest.

From Technology Transformation to Cultural Transformation

AI adoption is often described as a technology challenge.

For insurance, it is equally a people and culture challenge.

Organizations need to understand how roles will change, identify emerging skills gaps, create relevant development pathways, and determine which capabilities should be developed internally and which may need to be sourced externally.

Workforce data can help leaders understand where those gaps exist.

Competitive intelligence can also help insurers benchmark talent requirements, compensation, skills, and career opportunities against the broader market.

This allows recruitment and retention strategies to evolve alongside the industry itself.

But perhaps the biggest shift is cultural.

An organization cannot become AI-enabled simply by purchasing AI tools.

Employees need the confidence to experiment with them. Leaders need to create space for learning. Teams need to understand how responsibilities are changing. And governance needs to ensure that new systems are used responsibly.

The insurance workforce of the future will therefore require more than technical fluency.

It will require curiosity, adaptability, judgment, collaboration, and a willingness to continuously learn.

Building a Workforce Ready for What Comes Next

The convergence of demographic change and generative AI presents insurance with both a challenge and an opportunity.

The industry could face a growing shortage of experienced professionals at precisely the moment when technology is changing the nature of their work.

But these forces can also accelerate a long-overdue reinvention of the workforce.

The insurers that prepare effectively will not simply ask, “What can AI automate?”

They will ask:

“What could people achieve if AI handled more of the work around them?”

That shift in perspective changes everything.

It moves the conversation from replacement to augmentation, from training to continuous learning, and from recruiting for yesterday’s roles to building capabilities for tomorrow’s business.

AI may transform the tools of insurance.

People will determine what that transformation becomes.

The life and annuity industry experienced a period of exceptional momentum between 2022 and 2024. Strong sales, improving margins, and substantial capital flows created favorable conditions for insurers and encouraged continued investment across the sector.

But markets rarely stand still.

As conditions began changing, questions emerged about whether the strategies that worked during the recent growth cycle would remain effective in a more constrained environment. Lower interest rates, evolving customer expectations, regulatory pressure, technological change, and shifting distribution models are creating a different set of challenges.

For life and annuity executives, the next phase may require less focus on repeating the successes of the past and more attention to building businesses that can adapt to what comes next.

Here are eight strategic areas worth watching.

1. Rethink the Architecture of Insurance Products

The interest-rate environment can have a significant influence on the economics of life and annuity products.

When yields are attractive, relatively straightforward products may be easier to design and price competitively. When rates decline, however, insurers may have less room to offer compelling returns while maintaining sustainable economics.

That makes product architecture increasingly important.

Rather than focusing exclusively on individual products, insurers can explore solutions designed around broader retirement needs—including income stability, flexibility, liquidity, longevity protection, and growth potential.

The opportunity lies in creating products that work together as part of a larger financial strategy rather than treating each offering as an isolated transaction.

2. Build Connected Product Ecosystems

Customers rarely think about their financial lives in product categories.

They think about retirement income, savings, financial flexibility, and long-term security.

Insurers can respond by developing interconnected product ecosystems that address different stages and needs throughout a customer’s financial journey.

For example, growth-oriented products could potentially be combined with solutions designed to provide guaranteed income or liquidity. The value comes not simply from having several products available, but from making them easier to understand, combine, and manage.

Achieving this requires more than product development. It may also require integrated technology, consistent customer experiences, better advisor tools, and systems capable of connecting different parts of the insurance portfolio.

3. Move AI From Experiment to Infrastructure

Artificial intelligence is rapidly moving beyond pilot programs and isolated experiments.

Across the insurance value chain, AI can support underwriting, claims, customer service, distribution, operations, compliance, and product development. Generative AI is expanding what employees and advisors can accomplish, while more autonomous forms of AI could eventually perform multi-step tasks with limited human intervention.

But technology alone does not create transformation.

Insurers seeking meaningful value from AI may need to redesign processes, improve data foundations, establish appropriate governance, and prepare employees for new ways of working.

The question is increasingly shifting from “Where can we use AI?” to “How should the business be redesigned around what AI makes possible?”

4. Look Beyond Investment Performance

Investment expertise remains important, but long-term differentiation may depend on much more than investment performance.

Product innovation, actuarial capabilities, distribution, customer experience, technology, and operational efficiency can all influence an insurer’s ability to compete.

AI and automation may also create opportunities to rethink the underlying cost structure of the business.

The insurers that combine financial expertise with operational and technological capabilities may be better positioned to adapt as market conditions change.

5. Treat Regulation as Part of the Strategy

Regulatory expectations continue to evolve alongside changes in ownership structures, risk profiles, technology, and market practices.

Instead of treating compliance as a separate function that reacts to new requirements, insurers can integrate risk management into broader transformation efforts.

Modern stress-testing capabilities, stronger data infrastructure, automated monitoring, and AI-supported compliance tools can help organizations identify potential issues earlier and respond more efficiently.

A proactive approach can turn regulatory readiness into part of a company’s operating model rather than simply another layer of oversight.

6. Make Distribution More Focused

The insurance distribution landscape is becoming increasingly diverse.

Independent advisors, traditional agents, financial institutions, digital channels, and other distribution models can have very different needs and customer relationships.

Trying to serve every segment in exactly the same way may make it difficult to create meaningful differentiation.

A more focused strategy could involve developing specialized tools, experiences, and support for specific distribution channels.

For example, advisors may benefit from technology that helps analyze customer portfolios and develop personalized proposals, while other distribution networks may require different forms of training, technology, or sales support.

7. Orchestrate Capabilities Instead of Building Everything

Insurance transformation does not necessarily require every capability to be developed internally.

As technology evolves quickly, strategic partnerships can provide access to specialized expertise, platforms, data, and innovation without requiring insurers to build every solution from scratch.

The challenge is finding the right balance between internal capabilities and external partnerships.

Successful orchestration means knowing which capabilities are strategically important to own, which can be sourced externally, and how different technologies and partners can work together within a coherent operating model.

8. Reconsider the Mass-Market Opportunity

One of the industry’s biggest opportunities may also be one of its most difficult challenges: making sophisticated financial solutions more accessible to people with modest assets.

Large portions of the population approach retirement without sufficient financial preparation. Traditional advisory models may not always be economically practical for every customer segment.

Technology could change that equation.

AI-powered tools may help automate research, personalize education, simplify complex financial concepts, and support advisors serving a broader customer base.

The objective is not necessarily to replace human advice, but to make expertise more scalable and potentially more accessible.

Preparing for a Different Insurance Cycle

The next phase of the life and annuity industry may look very different from the conditions that supported the rapid growth of recent years.

If interest rates remain constrained, insurers will need to think differently about product design. If customers expect more personalized experiences, distribution models may need to evolve. If AI continues advancing rapidly, operating models and workforce skills will have to change alongside it.

The central question is therefore not simply how to maintain growth in a favorable market.

It is how to build an organization capable of competing when the market is no longer favorable.

That means connecting product innovation with distribution, technology with operations, and investment expertise with customer needs. It also means treating AI, regulation, demographic change, and retirement readiness not as separate trends, but as interconnected forces shaping the industry’s future.

The next chapter of life and annuity may not be defined by another boom. It may be defined by how effectively insurers adapt when the rules of the market change.

For decades, robots have largely existed within carefully controlled environments. They assembled products, transported materials, performed repetitive tasks, and followed precisely defined instructions.

That model is beginning to change.

The combination of large language models, advanced AI reasoning, and increasingly capable physical hardware is creating a new class of machines: generalist robots that can interpret instructions, understand their surroundings, adapt to changing situations, and perform a much broader range of activities.

In other words, AI is beginning to move beyond the screen.

It is gaining a physical presence.

For the insurance industry, this development represents more than another technological milestone. As robots become capable of interacting with people, property, workplaces, healthcare environments, and infrastructure, they will also interact with risk in entirely new ways.

The opportunity is significant. So are the questions.

From Programmed Machines to Generalist Robots

Traditional robots have typically been designed for a specific purpose. A robotic arm might repeatedly perform the same manufacturing task, while an automated vehicle might follow a predetermined route.

These systems can be highly effective, but their flexibility is limited.

Generalist robots represent a different approach.

Powered by increasingly sophisticated AI models, they can potentially interpret natural-language instructions, recognize objects, understand spatial relationships, respond to environmental changes, and determine how to complete unfamiliar tasks.

Imagine telling a robot to retrieve a particular item from another room. Instead of requiring a sequence of pre-programmed commands, the robot could interpret the request, locate the object, navigate its surroundings, avoid obstacles, and return with the item.

This ability to combine perception, reasoning, and physical action opens the door to a much wider range of applications.

Consider an autonomous mobility assistant operating in a busy public environment. It could potentially navigate around people, identify obstacles, respond to verbal instructions, and help someone reach a particular destination.

The machine is no longer simply executing a predefined task.

It is interpreting the world around it.

That shift has profound implications for insurance.

When Physical AI Becomes Part of the Risk Landscape

Every new capability creates a corresponding set of questions for risk professionals.

What happens when an autonomous machine makes an incorrect decision?

Who is responsible when someone is injured?

Does liability rest with the owner, manufacturer, software developer, operator, technology provider, or another party?

And how should responsibility be determined when several systems contribute to a single decision?

These questions are familiar from other areas of automation and autonomous technology, but physical AI introduces additional layers of complexity.

A robot operating in the real world can affect people and property directly. It may encounter situations its developers did not anticipate, interact with systems it was never specifically designed to work with, or respond to circumstances that fall outside its original training data.

As robots become more capable, insurers will need to understand not only what these systems are designed to do, but also how they behave when conditions change.

1. Robots Could Transform Risk Assessment and Claims

One of the clearest opportunities lies in property inspection, risk assessment, and claims management.

Generalist robots could potentially enter environments that are dangerous, inaccessible, or impractical for people.

After a natural disaster, for example, autonomous machines could enter damaged buildings, inspect infrastructure, capture images and video, and collect information without immediately exposing human assessors to hazardous conditions.

On construction sites, robots could monitor working environments and identify potential safety issues. In industrial settings, they could inspect equipment or hard-to-reach areas.

Even wearable robotic systems such as exoskeletons could support professionals performing physically demanding inspections or claims assessments.

The result could be faster assessments, richer evidence, and reduced exposure to dangerous environments.

But there is another dimension to consider.

When Machines Start Finding Patterns

Advanced AI systems can identify patterns that humans may overlook. That capability can be extremely valuable when assessing the cause or severity of a loss.

A robot could potentially combine visual information, environmental conditions, historical data, and contextual signals to form a view of what happened.

But insurers should not assume that an AI-generated conclusion is automatically correct.

Machine-learning systems can identify relationships that are difficult for humans to explain. They can also develop unexpected behaviors when exposed to new data or when multiple AI systems interact.

Research into phenomena such as unexpected or indirect learning in AI systems illustrates just how difficult it can be to understand every behavior emerging from complex models.

For insurers, this creates an important principle:

More data does not automatically mean better decisions.

The data generated by physical AI could eventually influence claims, underwriting, risk models, pricing, and product design. Strong governance, validation, human oversight, and clear accountability will therefore become increasingly important.

2. A New Workforce, and a New Workers’ Compensation Question

The impact of robotics will not stop with insurance companies themselves.

The businesses insurers cover are also likely to become increasingly automated.

Factories, warehouses, construction sites, logistics operations, healthcare facilities, and other workplaces may gradually integrate more autonomous machines into everyday operations.

This could reduce certain types of workplace risk while creating entirely new ones.

Robots might monitor working environments, detect unsafe conditions, or perform dangerous tasks that would otherwise expose employees to injury.

But what happens when an autonomous machine makes a mistake?

Traditional workers’ compensation and liability frameworks are built around human activity and relatively understandable chains of responsibility. Physical AI can introduce much more complicated relationships between employee, employer, machine, manufacturer, software provider, and operator.

As robots become more autonomous, insurers may need to reconsider how workplace risks are classified, monitored, and transferred.

The question may no longer simply be:

“Who was operating the machine?”

It may become:

“Who designed, trained, deployed, maintained, supervised, and ultimately controlled the machine’s behavior?”

That distinction could have significant implications for future insurance products and coverage structures.

3. An Aging Population Could Accelerate Robotic Care

Another major opportunity—and challenge—lies in healthcare and long-term care.

Longer life expectancy and changing demographic patterns are placing pressure on care systems in many parts of the world. At the same time, many healthcare organizations face shortages of skilled workers.

Robotic assistants could potentially help address some of these challenges.

Machines may eventually support patients with mobility, transportation, medication reminders, household tasks, monitoring, or other activities of daily living.

For families and care providers, this could offer valuable additional support.

But care environments involve some of the most vulnerable people in society, making risk management especially important.

What happens if a robotic assistant incorrectly interprets an instruction? What if a patient falls while being supported by a machine? What happens when a system encounters a situation it was never trained to handle?

These are not simply technical questions.

They are questions of responsibility, safety, accountability, and trust.

Insurance will have an important role to play in understanding these emerging risks as robotic systems become more integrated into care environments.

Cybersecurity Becomes Physical Risk Management

The more connected robots become, the more important cybersecurity becomes.

A compromised digital system can already cause significant financial and operational damage. A compromised physical system could potentially create consequences in the real world.

Imagine a connected machine responsible for moving people, inspecting infrastructure, assisting patients, or operating within an industrial environment.

A cybersecurity vulnerability could potentially become a physical safety issue.

This creates a convergence between cyber risk and physical risk.

Insurers will therefore need to consider questions such as:

• How securely are robots connected to external systems?
• Who can access their software and data?
• How are updates and patches managed?
• What happens if connectivity is interrupted?
• How quickly can a compromised system be isolated?
• Who is responsible for monitoring autonomous behavior?
• How is evidence preserved after an incident?

Cybersecurity can no longer be treated solely as an IT concern when software has the ability to control physical machines.

Responsible AI Moves Into the Physical World

AI governance becomes even more important when algorithms can directly affect people and environments.

Transparency, fairness, explainability, accountability, privacy, and human oversight are already central considerations for responsible AI.

Physical robots add another layer: real-world consequences.

An incorrect recommendation in a digital environment may require correction. An incorrect physical action could potentially result in injury or property damage.

That means insurers and the businesses deploying these technologies will need to think carefully about how AI systems are tested before deployment and monitored afterward.

Responsible AI should not be treated as a compliance exercise performed at the end of a technology project.

It needs to become part of the entire lifecycle—from design and training through deployment, monitoring, incident response, and continuous improvement.

Insurance Will Need to Insure the Transition, Not Just the Technology

The emergence of generalist robots creates an unusual situation for insurers.

They will not simply be insuring robots.

They will be insuring the new ecosystems created around them.

Manufacturers, software developers, operators, businesses, healthcare providers, infrastructure owners, technology platforms, and consumers may all become connected through increasingly autonomous systems.

This could create new forms of liability, new cyber exposures, new workers’ compensation considerations, new property risks, and potentially entirely new insurance products.

At the same time, the data generated by robots could improve the industry’s ability to understand existing risks.

The challenge will be finding the balance between using that information to improve decision-making and recognizing the uncertainty that comes with systems whose behavior may not always be completely predictable.

Preparing for a World of Physical Copilots

The emergence of generalist robots marks a significant shift in the relationship between humans and technology.

AI is no longer confined to applications that read, write, analyze, or recommend.

Increasingly, it can see, move, interact, and act.

For insurance, that means the future of robotics cannot be viewed solely as a technology story. It is simultaneously a story about liability, cybersecurity, workplace safety, healthcare, claims, underwriting, risk modeling, and customer protection.

The most important question may not be whether robots will become more capable.

It is how society, businesses, regulators, and insurers will adapt when machines become active participants in the physical world.

The opportunities are substantial: safer inspections, faster claims, better risk intelligence, additional support for workers and caregivers, and entirely new ways of managing complex environments.

But every new capability also introduces new uncertainty.

The insurers best prepared for this next chapter will need to do more than understand what robots can do today. They will need to continuously evaluate what these systems are learning, how they behave in unfamiliar situations, and where responsibility sits when something goes wrong.

When AI gets a body, risk gets a new dimension.

And insurance will be one of the industries responsible for understanding it.

For more than a decade, insurers have been examining how technology is changing underwriting.

Yet one challenge has remained remarkably persistent: underwriters spend too much time doing work that is not actually underwriting.

Across the industry, professionals have traditionally devoted a significant portion of their working day to activities such as collecting information, checking documents, entering data, coordinating administrative tasks, and navigating multiple systems.

Recent research suggests that this is beginning to change.

The improvement may have been gradual so far, but the expectations surrounding artificial intelligence and automation are anything but incremental.

For the first time, many insurance executives appear to believe that technology could fundamentally reshape how underwriting is performed—and do so at a much faster pace than previous waves of innovation.

The Difference Between Another Technology Wave and a Real Shift

Insurance has experienced its share of technological revolutions.

Knowledge-management systems promised easier access to information. The Internet of Things introduced new sources of real-time data. Advanced analytics gave insurers increasingly sophisticated ways to identify patterns and assess risk.

Each became part of the broader insurance technology landscape.

But none completely redefined the underwriter’s role.

AI may be different.

The combination of generative AI, automation, advanced data ingestion, natural-language processing, and increasingly intelligent decision-support tools has the potential to address one of underwriting’s most persistent problems: the amount of time spent assembling and processing information instead of applying expertise to risk.

That distinction is important.

The goal is not simply to make existing underwriting faster.

It is to rethink what the underwriter should actually be doing.

Automation Could Change the Equation

Recent executive research points toward a significant reduction in the amount of time underwriters may spend on non-core activities as AI and automation mature.

Across different insurance segments, executives increasingly expect these technologies to have a meaningful impact on underwriting.

The change is already underway.

Over the past several years, insurers have experimented with AI in areas such as data collection, information synthesis, risk analysis, and underwriting support.

Not every experiment has delivered the expected results. But the broader direction is becoming clearer: AI is increasingly being viewed as a practical tool for removing friction from underwriting rather than simply an experimental technology.

Several workforce expectations illustrate the scale of the change:

• 81% of surveyed underwriting executives expect AI and generative AI to create new roles to a large or very large extent.
• 65% believe their workforce will require additional skills as AI becomes more deeply integrated into underwriting.
• 42% expect they may need access to external talent pools to fully capture the technology’s potential.

These figures point to an important conclusion.

The AI transformation of underwriting is not only a technology story.

It is a workforce story.

The Rise of the AI-Augmented Underwriter

The underwriter of the future is unlikely to be replaced by a machine.

Instead, the role may increasingly become a collaboration between human expertise and machine capabilities.

AI can already support many activities that traditionally consume substantial amounts of underwriting time.

Natural-language systems can interpret requests from customers and brokers, identify relevant information, and route inquiries toward appropriate workflows.

Automated data ingestion can collect and organize information from multiple sources.

Pattern-recognition models can identify relationships and anomalies that might otherwise require significant manual investigation.

Decision-support tools can help assess straightforward cases, while automated workflows can coordinate multiple steps within a single process.

The result is a potential shift in the division of labor.

Machines handle more of the information-heavy work. Humans spend more time on judgment, relationships, exceptions, and complex risk decisions.

That does not make underwriting less important.

It makes the human contribution different.

From Data Collectors to Risk Decision-Makers

Consider how much of an underwriter’s expertise can be buried beneath administrative work.

A professional may have years of experience assessing complex risks, yet much of the working day can still be consumed by finding documents, reconciling information, entering data, requesting missing details, and moving between systems.

AI has the potential to absorb more of these activities.

Instead of beginning every assessment with a blank screen and a collection of fragmented information, an underwriter could increasingly begin with an AI-generated view of the risk, supported by relevant data, identified patterns, and suggested next steps.

The human then becomes the critical layer of judgment.

They can challenge assumptions, investigate unusual circumstances, apply contextual knowledge, communicate with brokers or customers, and make decisions where automated systems are less reliable.

This is not the disappearance of underwriting.

It is the reinvention of underwriting work.

Three Priorities for an AI-Enabled Underwriting Future

Technology alone will not deliver this transformation.

Insurers will need to rethink strategy, talent, workflows, and organizational culture at the same time.

1. Build an AI-Led Strategy

AI initiatives should not exist as disconnected experiments.

Insurers need a clear strategy for how AI will operate within their broader technology environment, supported by a strong digital foundation.

As AI systems become increasingly agentic, the opportunity becomes even broader.

Instead of simply using AI to answer questions or summarize information, underwriters may eventually be able to delegate individual workflow tasks to specialized AI agents.

An agent could gather information, another could organize documents, another could compare relevant risk factors, and another could prepare a preliminary assessment.

The underwriter remains responsible for the overall decision while AI coordinates more of the surrounding work.

2. Reimagine Talent and Workflow

Introducing AI without redesigning the underlying workflow can limit its value.

Insurers should consider how work should be divided between people and machines, which skills will become more important, and where human expertise will deliver the greatest value.

A skills-based approach can help organizations identify emerging capabilities, retrain existing employees, and prepare teams for new responsibilities.

At the same time, AI adoption needs to be connected to broader process redesign.

Simply adding an AI tool to an inefficient workflow does not create an efficient workflow.

The process itself may need to change.

Responsible AI principles should also be embedded throughout this transition, particularly when automated systems influence important underwriting decisions.

3. Create a Culture of Experimentation

AI is developing too quickly for organizations to rely entirely on traditional top-down innovation models.

Employees working closest to underwriting processes often have the clearest understanding of where technology could remove unnecessary effort.

Giving teams room to experiment can reveal valuable use cases that may not emerge from a centralized technology strategy.

The objective is not uncontrolled experimentation.

It is structured curiosity: allowing employees to test new capabilities while maintaining appropriate safeguards around core decisions, data, security, and risk.

The organizations that learn fastest may be those that create enough freedom to experiment without losing control of the decisions that matter most.

The Underwriter Is Not Disappearing

Technology has repeatedly changed the tools underwriters use.

AI may change the work itself.

But that does not mean human expertise becomes less valuable.

In a more automated environment, underwriters may spend less time collecting information and more time interpreting it. Less time navigating administrative processes and more time evaluating complex risks. Less time performing repetitive tasks and more time exercising judgment.

The central question is therefore not:

“Will AI replace the underwriter?”

A more useful question is:

“What could an underwriter accomplish if AI handled more of the work surrounding the decision?”

That question opens a much broader vision for the future.

From Automation to Augmentation

The next chapter of underwriting is unlikely to be defined by technology alone.

It will be defined by how effectively insurers combine human judgment, intelligent automation, data, and increasingly capable AI systems.

Previous technology waves changed individual parts of underwriting.

The current generation has the potential to connect those parts into something much more integrated.

If insurers build the right digital foundations, rethink workflows, invest in new skills, and encourage responsible experimentation, AI could help move underwriting away from administrative complexity and toward what it does best: understanding risk and making informed decisions.

The future underwriter may not be less human.

They may simply have a much more capable machine working beside them.

Global trade is becoming harder to predict.

Changes in tariffs, supply chains, inflation, interest rates, consumer spending, and geopolitical relationships can quickly move from one part of the economy to another. For businesses, this means that traditional approaches to planning, pricing, sourcing, and risk management may no longer be enough.

Insurance is deeply connected to these changes.

When economic conditions shift, the impact can appear across the entire insurance value chain—from customer demand and premium volumes to claims costs, investment returns, operating expenses, and risk appetite.

Some economic scenarios suggest that trade disruptions could contribute to higher inflation while putting downward pressure on global economic growth. Higher interest rates can also create challenges for insurers managing the relationship between assets and liabilities, while changes in investment yields can affect earnings.

At the household level, these pressures can translate into higher everyday costs and reduced disposable income.

For insurers, the consequences can be significant.

Life and property-and-casualty businesses may face softer demand as consumers and companies become more cautious about spending. At the same time, insurers may encounter shrinking risk pools, greater pressure on premiums, rising claims severity, and increased volatility in financial results.

Yet uncertainty does not only create risk.

It can also expose opportunities to rethink how insurance companies operate.

The organizations that strengthen their ability to adapt may be better positioned not only to absorb disruption, but to find new sources of growth within it.

Resilience Is More Than Surviving Disruption

Resilience is often described as the ability to withstand a shock.

For insurers, that definition is no longer sufficient.

Modern resilience means being able to absorb disruption, adapt quickly, continue delivering value, and emerge from uncertainty with stronger capabilities than before.

This distinction matters.

A company that simply survives a difficult period may return to where it was before. A resilient organization can use disruption as a reason to improve its operating model, technology, workforce, customer relationships, and strategic position.

Research across industries has repeatedly linked stronger organizational resilience with better performance during periods of significant stress.

For insurers facing an increasingly unpredictable environment, resilience should therefore become an enterprise-wide capability rather than a collection of isolated initiatives.

Four dimensions are particularly important.

1. Operational Resilience: Make the Business More Adaptable

Insurers are facing simultaneous pressure from rising operating costs, increasing competition, changing customer expectations, new purchasing behaviors, and evolving risk patterns.

Simply cutting costs may provide short-term relief, but sustainable resilience requires structural improvement.

Modern technology, automation, data, and AI can help insurers redesign processes and create more efficient operating models.

The most effective approach is unlikely to be human versus machine.

It will be human plus machine.

Automation can handle repetitive processes, AI can analyze large volumes of information, and employees can apply judgment, experience, and context where they matter most.

Operational resilience also extends beyond internal processes.

Supply chains, procurement, sourcing, technology providers, and distribution networks all need to be considered. Organizations can explore new sourcing models, shared capabilities, specialized service networks, and more flexible operating structures to improve efficiency and access expertise.

Distribution itself is also changing.

Embedded insurance, for example, allows coverage to be offered directly through platforms customers already use, such as travel, retail, or digital services.

The broader lesson is simple: resilience can come from redesigning how insurance is delivered, not merely from reducing what it costs.

2. Commercial Resilience: Rethink Pricing and Growth

Economic uncertainty creates a difficult commercial balancing act.

Insurers need to determine which rising costs they can absorb, which need to be reflected in pricing, and how those decisions will affect demand.

This becomes particularly challenging when claims costs are already increasing and customers are becoming more sensitive to price.

A purely transactional approach may not be enough.

Insurers can look for opportunities to better understand customer needs and develop products around actual behaviors, preferences, and changing circumstances.

Behavior-based offerings, flexible coverage structures, personalized services, and new distribution models can create opportunities to remain relevant even when customers are under financial pressure.

Growth may also require a different perspective on partnerships, investments, and acquisitions.

In slower economic conditions, disciplined strategic choices can help insurers strengthen capabilities while preparing for the next phase of growth.

3. Technology Resilience: Build a Stronger Digital Foundation

Technology has become central to insurance resilience, but the goal should not be to accumulate more technology.

It should be to build a digital environment that is secure, adaptable, and capable of supporting continuous innovation.

Three capabilities are particularly important:

Cybersecurity.
As insurers become more connected, their exposure to cyber threats increases. Strong security controls, monitoring, governance, and response capabilities need to be embedded into the technology environment.

AI and automation.
AI can help improve productivity, identify emerging risks, analyze customer interactions, and support faster decision-making. Increasingly autonomous AI systems may also monitor information in real time and trigger appropriate workflows.

Data foundations.
AI is only as useful as the data surrounding it. Simplified cloud environments, reliable data pipelines, strong model governance, and connected technology architectures can provide the foundation required for intelligent decision-making.

The objective is a digital core that can evolve as technology evolves.

A resilient technology strategy should allow insurers to adopt new capabilities without having to rebuild the organization every time a new innovation emerges.

4. People Resilience: Invest in the Workforce Behind the Transformation

Technology cannot create resilience on its own.

People remain responsible for interpreting information, challenging assumptions, managing relationships, making complex decisions, and turning new technology into practical business outcomes.

This makes talent strategy just as important as technology strategy.

Insurers need to think differently about how they attract, develop, and retain people.

Continuous learning, flexible career paths, digital skills, and opportunities to work with emerging technologies can help make insurance careers more attractive to a new generation of professionals.

This is particularly important as experienced employees retire and organizations face the loss of institutional knowledge.

AI can also contribute to workforce development.

It can help identify skills gaps, recommend learning opportunities, and reduce the time employees spend on repetitive work.

For example, an underwriter supported by AI may spend less time gathering and organizing information and more time evaluating complex risks.

As technology changes traditional apprenticeship models, insurers may also need to look beyond conventional talent pipelines and access specialized expertise from outside the organization.

The workforce of the future may be defined less by tenure and more by adaptability.

Resilience Should Act Like a Trampoline, Not a Cushion

There is an important difference between absorbing disruption and using disruption as a catalyst.

A cushion softens a fall.

A trampoline absorbs impact and creates upward momentum.

That is a useful way to think about organizational resilience.

The goal is not simply to make a company strong enough to withstand difficult conditions. It is to build an organization capable of learning from disruption, adapting its response, and emerging with new capabilities.

That requires resilience to be treated as a connected strategy.

Operational efficiency cannot be separated from technology. Technology cannot be separated from talent. Commercial strategy cannot be separated from customer behavior. And risk management cannot be separated from the broader economic environment.

These elements increasingly influence one another.

Turning Uncertainty Into Strategic Momentum

The global economic environment is likely to remain complex.

Trade relationships can change. Costs can move unexpectedly. Customer behavior can shift. Technology can introduce new opportunities and new risks at the same time.

Insurers cannot eliminate this uncertainty.

They can, however, become better prepared to respond to it.

That means moving beyond short-term reactions and building capabilities that remain useful across multiple scenarios.

The most resilient insurers will not necessarily be those that predict every disruption correctly.

They will be those capable of responding quickly when the prediction is wrong.

Ultimately, resilience is not a defensive strategy.

It is a growth capability.

In an unpredictable market, the ability to adapt may become one of the most valuable assets an insurer can build.

Natural catastrophes are becoming harder to treat as occasional disruptions.

Floods, wildfires, storms, earthquakes, and other climate-related events are placing increasing pressure on communities, businesses, governments, and insurers. In the first half of 2025 alone, global insured catastrophe losses reached an estimated $84 billion, putting the year on track to become another in a growing run of years with losses exceeding $100 billion.

For insurers, this is more than a claims-volume problem.

It represents a fundamental shift in the underlying risk environment.

As the frequency, severity, and unpredictability of catastrophic events evolve, insurers are being forced to reconsider not only how they price and manage risk, but also how they support customers before, during, and after a loss.

The traditional insurance model has largely been built around a simple sequence:

Risk occurs → damage happens → claim is submitted → insurer pays.

That model is increasingly being challenged.

The emerging opportunity is to create something more proactive:

Understand the risk → help prevent the loss → respond quickly → support recovery.

When a Claim Becomes a Moment of Truth

Few interactions between an insurer and its customer are as emotionally significant as a catastrophe claim.

When a home is damaged by flooding or fire, a business is forced to close, or a family suddenly loses access to essential belongings, customers are not simply evaluating a financial transaction.

They are asking whether the organization they trusted will actually be there when they need it.

This makes claims a defining moment for the insurance brand.

A slow response, unclear communication, or complicated settlement process can turn an already difficult situation into a deeply frustrating experience. In an era of social media and immediate communication, those experiences can also quickly become public.

As a result, claims quality is increasingly a brand issue.

Despite significant investment in digital transformation and AI, improvements in several customer-experience measures have remained relatively modest in recent years. Some insurers have seen progress in customer satisfaction, but broader measures such as loyalty, effort, and long-term relationship value continue to present challenges.

The message is clear: improving the claims experience is not simply about operational efficiency.

It is about building lasting trust.

From Paying for Losses to Helping Prevent Them

One of the most important changes taking place across insurance is a shift from a payout mindset to a protection mindset.

Historically, insurers have primarily responded after an event occurred. Increasingly, technology allows them to intervene earlier.

Connected devices, predictive analytics, generative AI, agentic AI, satellite information, environmental data, and other technologies can help identify potential risks before they become costly claims.

Imagine a connected property where a system detects an electrical hazard before it triggers a fire.

Or a building where sensors identify a water leak before significant structural damage occurs.

Or a community where predictive models identify increasing wildfire risk and trigger preventative measures before flames reach vulnerable properties.

In each case, the insurer is doing more than preparing to pay a claim.

It is helping reduce the probability or severity of the loss itself.

That represents a fundamental change in the role insurance can play.

Three Ways the Claims Model Is Changing

1. From Reactive Claims to Proactive Protection

The first opportunity is to identify and address risks before they become losses.

IoT devices can continuously monitor properties and equipment. AI can analyze large volumes of information to detect unusual patterns. Predictive models can help identify emerging risks.

Together, these technologies can support earlier intervention.

For insurers, that may mean fewer severe claims and more efficient operations.

For customers, it can mean something even more valuable: avoiding the loss altogether.

The future of claims may therefore begin before a claim exists.

2. From Transactional Service to Customer Experience

Technology should not make the claims journey more complicated simply because the underlying systems are becoming more sophisticated.

Customers generally want the opposite: fewer steps, clearer communication, faster answers, and greater visibility into what happens next.

AI can help by summarizing complex claim information, identifying missing actions, routing cases, supporting employees, and giving customers more timely updates.

But technology is only part of the equation.

The best digital claims experiences should combine speed with empathy, automation with human judgment, and efficiency with transparency.

The goal is not to remove people from the claims journey.

It is to remove unnecessary friction so people can focus on the moments where human interaction matters most.

3. From Catastrophe Response to Catastrophe Resilience

Traditional catastrophe models have primarily focused on estimating potential losses.

That remains important, but the scale and complexity of emerging risks are encouraging insurers to think more broadly about resilience.

Instead of asking only:

“How much could this event cost?”

insurers can increasingly ask:

“What can we do to reduce the damage before the event occurs?”

This could include preventative property measures, automated alerts, environmental monitoring, physical risk mitigation, rapid-response services, and partnerships with organizations capable of acting on the ground.

Resilience can therefore become more than a pricing consideration.

It can become a source of product innovation.

Data Could Change the Way Insurers See Risk

The increasing availability of real-time and historical data is another major driver of this shift.

Property sensors, connected devices, satellite imagery, weather information, claims histories, customer interactions, and other data sources can provide insurers with a much richer picture of risk.

But data alone is not the answer.

The real value comes from turning information into timely action.

An insurer that knows a property is at elevated risk but cannot communicate with the customer or initiate preventative support has only partially solved the problem.

The future model will require stronger connections between data, prediction, decision-making, and action.

This is where AI agents and automated workflows could become particularly important.

Instead of simply identifying a risk, intelligent systems could potentially help initiate the next appropriate step—whether that means notifying a customer, escalating a case, coordinating an inspection, or supporting a claims professional.

Catastrophe Risk Is Also a Test of Operational Resilience

As catastrophe events become more frequent or severe, insurers may face sudden surges in claims volumes.

Thousands of customers may need assistance at the same time.

Traditional manual processes can struggle under these conditions.

Automation and AI can help insurers scale certain activities more effectively, from initial claims intake and document processing to case summaries, customer communications, and workflow management.

This can allow human teams to focus on complex cases while technology handles more repetitive tasks.

However, resilience also requires preparation.

Systems need to be tested under pressure. Data needs to remain accessible. Communication channels need to function during disruption. Employees need clear processes. Customers need reliable information.

A resilient claims operation is therefore not simply one that processes claims quickly.

It is one that can continue functioning when demand suddenly exceeds normal capacity.

Innovation Needs to Be Measured by More Than Technology

The insurance industry has been investing heavily in innovation, and evidence suggests that many initiatives are producing meaningful results.

Research has found that a large majority of innovation programs achieve or exceed their expected financial outcomes. Even more report progress against non-financial objectives such as customer engagement, satisfaction, brand strength, and employee experience.

This matters because the value of innovation cannot always be captured in a single financial metric.

A successful claims transformation may reduce expenses.

But it may also shorten customer wait times, improve employee productivity, strengthen communication, reduce preventable losses, and help customers recover more quickly.

Those outcomes are interconnected.

The New Claims Equation

The changing catastrophe landscape is forcing insurers to reconsider what a successful claims experience looks like.

It is no longer enough to simply calculate the loss accurately and issue the appropriate payment.

Customers increasingly expect insurers to help them understand risk, prevent avoidable damage, respond quickly when something happens, and guide them through recovery.

That requires a different model of insurance.

One built around prevention as well as compensation, prediction as well as reaction, and relationships as well as transactions.

The insurers that adapt successfully will not necessarily be those with the most technology.

They will be those that connect technology to a clear purpose: helping customers experience less disruption, recover faster, and feel supported when uncertainty becomes reality.

As catastrophe risk continues to evolve, insurance has an opportunity to become more than a financial safety net.

It can become part of the resilience system itself.

The future of claims is not simply about paying faster. It is about preventing more, responding smarter, and helping people recover with greater confidence.

Artificial intelligence has the potential to fundamentally change how health insurance claims are managed. It can help insurers process information faster, identify patterns earlier, reduce administrative friction, and create more consistent experiences for policyholders.

But technology alone does not create transformation.

Successful modernization requires insurers to rethink how work is performed, prepare people for new ways of working, and redesign the digital environments where decisions are made.

A useful framework is to think of AI-led modernization through three connected priorities: Reimagining the work, Reshaping the workforce, and Redesigning the workbench.

Together, these principles can help insurers build claims operations that are more agile, resilient, transparent, and capable of delivering measurable value at scale.

1. Reimagine the Work

The first step is to rethink the work itself rather than simply automate existing processes.

Put Data at the Center

Health claims generate and depend on enormous amounts of information. Bringing relevant data together—from claims records and medical documentation to healthcare-provider information—can give insurers a more complete view of each case.

Better-connected data can support more informed decisions throughout the claims journey, while also helping customers and healthcare professionals understand what is happening and what comes next.

The goal is not simply to collect more data. It is to make the right information available at the right moment.

Change the Operating Model, Not Just the Technology

Installing an AI solution without changing the underlying process can limit its impact.

Claims modernization may require insurers to rethink workflows, responsibilities, decision points, escalation procedures, and the way teams interact with technology.

AI should therefore be viewed as an opportunity to redesign operations—not simply as another software layer added to an existing system.

Start With Focused Opportunities

Large-scale transformation does not have to begin everywhere at once.

Targeted pilots can help insurers test new capabilities in specific processes, teams, or customer journeys while establishing measurable outcomes.

Potential starting points could include digital claims submission, automated document processing, intelligent claims assessment, or expanded automation for straightforward cases.

Early successes can demonstrate value, uncover practical challenges, and provide lessons for broader implementation.

2. Reshape the Workforce

AI may automate portions of claims work, but people remain essential.

The future claims workforce will increasingly combine human judgment with machine-generated insights and recommendations.

Keep Humans in the Loop

Human oversight is particularly important when decisions are complex, sensitive, or outside the patterns an AI system has been trained to recognize.

Claims involving unusual medical documentation, eligibility questions, potential fraud, or other edge cases may require experienced professionals to review and challenge automated recommendations.

Human feedback can also help improve AI systems over time.

The objective is not to remove people from the process. It is to give them better tools and focus their expertise where it creates the most value.

Make Change Management Part of the Transformation

Even highly capable technology can fail to deliver its potential if employees do not understand how to use it.

Claims professionals may need new skills, including working effectively with AI tools, writing precise prompts, interpreting model outputs, and making controlled adjustments to digital workflows.

Training should therefore be considered part of the implementation itself rather than something added after the technology is deployed.

Build Employee Ownership

Successful transformation requires more than technical approval.

The people who actually perform claims work understand the practical challenges that systems need to solve. Involving employees early through workshops, process-design sessions, and feedback loops can reveal opportunities that may not be visible from a technology perspective alone.

When employees understand the purpose of a new system and have a role in shaping it, adoption becomes more practical and meaningful.

3. Redesign the Workbench

The final piece is the environment in which claims professionals work.

Modernization requires more than choosing an AI model. It requires an architecture that allows data, applications, people, and AI capabilities to work together.

Choose Technology Around the Business Need

Insurers have increasingly more technology choices, from integrated platforms to specialized solutions.

The right approach will depend on the organization’s existing architecture, strategy, data environment, risk requirements, and long-term goals.

Modular architectures can allow insurers to combine specialized capabilities rather than relying on one system to solve every problem.

APIs, cloud infrastructure, and effective ecosystem integration can make these components easier to connect and evolve.

Strong vendor management is equally important as insurers become more dependent on external technology providers.

Combine AI With Traditional Analytics

New AI capabilities should not replace proven analytical techniques simply because they are newer.

Historical claims data, comparable cases, healthcare trends, and established analytical models can provide valuable context for identifying unusual patterns, potential overpayments, underpayments, or suspicious activity.

The opportunity lies in combining these capabilities rather than relying exclusively on rigid rules or treating every claim in exactly the same way.

Treat Data Migration and Testing as Critical Work

AI systems are only as reliable as the data and processes supporting them.

Moving data from legacy systems into a new environment requires careful planning, clear ownership, extensive validation, and rigorous testing.

Testing with real-world transactional data can help insurers evaluate whether models perform accurately across different cases and identify potential issues involving fairness, transparency, explainability, or consistency.

Responsible AI should be built into the modernization process from the beginning.

Control the Scope

Ambitious technology programs can quickly become complicated.

Generative AI and other emerging technologies create new possibilities, which can make it tempting to expand a project before its original objectives have been achieved.

Defining a clear baseline scope, agreeing on measurable outcomes, and establishing decision-making responsibilities can help prevent unnecessary complexity.

A disciplined implementation does not limit innovation. It creates the conditions for innovation to scale.

Build a Digital Core That Can Grow

Ultimately, insurers need an architecture that allows successful experiments to become repeatable capabilities.

A strong digital core can connect data, applications, AI tools, workflows, and governance mechanisms across the organization.

Instead of maintaining isolated AI pilots, insurers can build reusable components that support multiple claims processes and business areas.

This can reduce duplicated investment, improve consistency, strengthen oversight, and make future innovation easier to implement.

The A.R.T. of Modern Health Claims

AI-led claims modernization can be viewed through three connected goals:

AI-powered — using intelligent technology to improve decisions, automate appropriate work, and uncover useful insights.

Resilient — building operations and technology that can adapt to changing volumes, requirements, risks, and customer expectations.

Trusted — ensuring that AI-supported decisions remain transparent, explainable, responsible, and subject to appropriate human oversight.

The three elements reinforce one another.

AI without resilience can create fragile systems. Resilience without trust can undermine adoption. And trusted technology without meaningful modernization may fail to deliver sufficient value.

From Individual Pilots to Enterprise Transformation

The insurance industry is already moving toward greater automation, digitization, and workflow modernization. Organizations that successfully connect these capabilities can potentially improve claims efficiency while creating smoother experiences for customers and business partners.

However, there is no universal blueprint for modernizing health claims.

Every insurer operates within a different combination of legacy technology, regulatory requirements, workforce capabilities, data quality, customer expectations, and business priorities.

The most effective transformation therefore begins with context.

Rather than asking simply, “How can we add AI to claims?”, insurers should ask:

“How should claims work differently when AI, data, people, and technology are designed to operate together?”

That question shifts modernization from a technology project to an operating-model transformation.

And that is where the larger opportunity lies: not simply processing claims faster, but creating a health claims experience that is more intelligent, adaptable, human-centered, and ready for what comes next.