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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.

For many patients, the healthcare journey can become complicated long before a claim is ever submitted.

Imagine a policyholder who begins experiencing severe abdominal pain but struggles to secure a timely appointment. What could have been a straightforward diagnosis develops into a much longer journey involving repeated examinations, additional tests, extended hospital stays, and increasingly complex treatment decisions.

Along the way, inadequate pain management or unclear discharge instructions can add another layer of frustration. And when the final insurance reimbursement does not align with expectations or medical expenses, dissatisfaction can extend beyond the claims process to the entire healthcare experience.

This scenario illustrates a broader challenge facing health insurers: a claim is rarely an isolated financial transaction. It is part of a much larger healthcare journey.

When the Claims Experience Becomes the Customer Experience

Health insurance claims sit at the intersection of healthcare, technology, finance, and customer service. When these elements are disconnected, policyholders can experience delays, uncertainty, inconsistent decisions, and unnecessary administrative effort.

Research has shown that a meaningful share of consumers remain dissatisfied with their health insurance claims experiences, with dissatisfaction particularly pronounced in parts of the Asia-Pacific region.

One contributing factor is the continued reliance on legacy claims environments and traditional cost-management approaches. Important information may exist across different systems, documents, providers, and historical records, but claims professionals may still need to manually piece those fragments together.

The result can be a decision-making process that is slower and less consistent than it needs to be.

The industry is beginning to move toward AI-enabled claims operations, but adoption remains uneven. Many insurers are experimenting with generative AI for claims intake and related activities, while a much smaller proportion have successfully scaled these capabilities across their organizations.

This gap matters.

The difference between experimenting with AI and redesigning claims around AI can be substantial.

Modernizing the Claims Platform Is Only the Beginning

For insurers seeking to improve both customer experience and operational performance, claims modernization needs to go beyond faster processing.

Accuracy matters. Speed matters. Explainability matters.

But so does empathy.

A policyholder dealing with illness does not experience a claim as a data point. They experience it as one part of an often stressful personal situation.

Modern claims platforms can help insurers connect information across the healthcare ecosystem, integrate data from multiple sources, and support more consistent decision-making. When combined with stronger collaboration between insurers, healthcare providers, technology partners, and distribution channels, modernization can create a more connected journey from diagnosis through treatment and reimbursement.

The objective is not simply to process claims more efficiently.

It is to create an environment where the right information reaches the right person at the right moment.

The Rise of AI Agents in Claims

Generative AI introduces another opportunity: moving from systems that simply analyze information toward systems that can actively support and coordinate parts of the claims workflow.

This is where agentic AI enters the picture.

AI agents can be designed to perform specific tasks, interpret information, interact with systems, and make recommendations with varying levels of human oversight.

A useful way to think about an AI-enabled claims environment is through two complementary roles: Super Agents and Utility Agents.

Super Agents: Orchestrating the Claims Journey

Super Agents can support broader stages of the claims process, bringing together multiple capabilities within a single workflow.

They may assist with:

• Digital claims intake
• Case summarization
• Information verification
• Claims assessment
• Adjudication support
• Fraud, waste, and abuse detection
• Communication and workflow coordination

Rather than forcing claims professionals to move between disconnected tools, these capabilities can be brought together into a more coherent experience.

Utility Agents: Supporting the Details

Utility Agents can focus on narrower, specialized tasks that feed information into the wider claims process.

For example, they can help extract information from documents, validate data, identify inconsistencies, surface relevant historical information, and provide actionable insights to claims assessors.

Together, these two layers can help create a claims environment where AI handles repetitive information-intensive work while people remain responsible for judgment, oversight, and complex decisions.

AI Does Not Always Require a Complete Technology Overhaul

One of the most important opportunities presented by modern AI is its ability to work with information trapped inside existing technology environments.

Legacy systems remain deeply embedded across the insurance industry, and replacing them entirely can be expensive, disruptive, and time-consuming.

AI models can potentially help insurers extract, summarize, organize, and synthesize information from existing systems, allowing organizations to unlock more value from historical data without immediately rebuilding every component of their technology architecture.

This does not eliminate the need for modernization.

Instead, it can create a bridge between today’s technology environment and a more intelligent future claims operating model.

Connecting the Healthcare Journey From Online to Offline

Claims modernization becomes even more powerful when it extends beyond the insurer’s internal processes.

Consider the earlier patient scenario.

If relevant information had been available earlier, if healthcare access had been better coordinated, and if treatment decisions had been supported by connected data, the patient’s journey might have looked very different.

This points toward a broader concept: connected customer healthcare.

The healthcare experience should not begin when a claim is filed. It begins when a person first notices a health concern.

Insurers can contribute to this journey by strengthening relationships across healthcare networks and creating easier connections between digital services and physical care.

Mobile platforms, healthcare-provider networks, digital appointment services, diagnostics, and claims information can work together to give policyholders a clearer path through the healthcare system.

Distribution partners can also play an important role by providing human support when customers need reassurance, explanation, or guidance.

Technology can improve efficiency, but human interaction remains an important part of an empathetic healthcare experience.

From Treatment to Prevention

The opportunity extends beyond managing illness.

Health insurers can increasingly support preventive care by connecting policyholders with wellness resources, screenings, diagnostics, health-management programs, and other services.

Digital platforms can make these services easier to access across different stages of life, while partnerships with healthcare providers can expand the range of available options.

Integrated data can add another layer of value.

When claims information, electronic health records, health assessments, wearable-device data, and broader health trends can be responsibly connected, insurers may gain a more comprehensive understanding of emerging needs.

For customers, this could mean more relevant health insights and earlier opportunities to address potential concerns.

For insurers, it can support more personalized services, better-informed products, and greater visibility into healthcare costs.

The long-term opportunity is therefore not simply to pay for healthcare after something happens, but to become part of a broader ecosystem that supports healthier decisions before problems become more complex.

Building a More Empathetic Claims Future

The evolution of AI in health insurance is about more than automation.

At its most meaningful, it represents an opportunity to rethink the relationship between insurers, healthcare providers, technology, and policyholders.

Modern platforms can connect fragmented information. AI agents can coordinate repetitive and data-intensive tasks. Human professionals can focus on judgment and empathy. Healthcare partnerships can connect digital services with real-world care. Preventive programs can shift attention from reacting to illness toward supporting healthier outcomes.

None of these changes should be treated as a universal blueprint.

Every insurer has a different technology landscape, operating model, workforce, customer base, regulatory environment, and strategic priority. The right approach will therefore depend on the context.

But the direction is becoming clearer.

The future claims experience may be less about submitting information, waiting for a decision, and navigating disconnected processes—and more about creating a continuous, connected journey in which information, technology, healthcare, and human support work together.

The real opportunity is not simply to make claims faster.

It is to make them smarter, clearer, more connected, and more human.

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.

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.

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.