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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.