AI is no longer a future promise – it’s a present reality reshaping the insurance industry. This new Majesco report explores how insurers can move beyond pilots to enterprise-wide transformation. Discover practical uses cases, proven financial outcomes, and strategies that unlock efficiency, reduce costs, and elevate customer experiences. This report is your roadmap to harnessing GenAI and Agentic AI to create lasting competitive advantage. Download now to see how insurers are turning innovation into measurable impact.

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Sponsored by ITL Partner: Majesco

Smarter life underwriting through patented automation

In an industry where precision and speed are paramount, underwriting has long been a balancing act between risk accuracy and operational efficiency. But with the expansion of alitheia, a rapid risk assessment platform from Munich Re Life US, carriers now have more flexibility than ever to automate without compromise.

In a landscape where underwriting must evolve to meet rising consumer expectations and the drive toward automation, alitheia stands as a beacon of what’s possible when data science, machine learning, and Munich Re’s deep risk expertise converge.

Modular flexibility meets strategic integration

alitheia offers a modular platform that supports a wide range of risk assessment capabilities. Carriers can adopt it as a full end-to-end solution or integrate specific components – such as predictive models, configurable rules engines, and underwriter workbenches – into their existing systems.

Carriers have seen instant offer rates climb to 50%, tripling industry averages. And with decisions made within 48 hours in 75% of cases, the platform is setting new benchmarks for underwriting speed and accuracy. [1]

Patented innovations that power smarter automation

Backing these capabilities are newly awarded U.S. patents for proprietary methods that optimize the sequence of underwriting requirements and interpret free-form text using advanced natural language processing (NLP). These innovations help carriers streamline workflows, reduce costs, and accelerate decision-making, while maintaining rigorous risk standards.

From bee stings to breakthroughs: Natural language processing that understands context

One of the most compelling aspects of alitheia is its ability to parse and contextualize free-text responses in insurance applications. In traditional systems, benign entries like “I stubbed my toe” could trigger manual reviews, slowing down the process. alitheia’s natural language processing engine maps such responses to a medical ontology, enabling instant decisions for non-medically significant disclosures.

This capability can increase the number of cases processed instantly by an additional 3-5%, reducing reliance on full underwriting and improving applicant experience. [1]

John Hancock puts alitheia to work

John Hancock’s collaboration with Munich Re demonstrates how alitheia can be tailored to a carrier’s unique underwriting philosophy. By leveraging automated EHR assessments and proprietary models, John Hancock is focused on enhancing the buying experience for consumers and agents, as well as greater efficiency for their underwriters, with the goal of delivering faster turnaround times. [2]

Learn more about modernizing with alitheia

Discover how alitheia’s patented technology and modular architecture can support life insurance underwriting. Whether a comprehensive solution or targeted enhancements are needed, alitheia delivers the flexibility and power to help carriers achieve their automation goals.

Learn More

Sponsored by ITL Partner: Munich Re

[1] Based on carrier experience. Results may vary.

[2] John Hancock Disclosures:
Policy issuance is not guaranteed as any life insurance purchase is subject to completion of an application, including health questions, and underwriting approval. John Hancock may obtain additional information, including medical records, to evaluate the application for insurance; and after the policy is issued, to identify any misrepresentation in the application.
Insurance products are issued by: John Hancock Life Insurance Company (U.S.A.), Boston, MA 02116 (not licensed in New York) and John Hancock Life Insurance Company of New York, Valhalla, NY 10595. MLINY091525144-1

As classes start this fall, parents across the country are surprised to see some school districts are doubling down on their cellphone policies. Similarly, other organizations that serve youth—such as camps, houses of worship and recreation programs—also are implementing stricter cellphone bans in an attempt to help young people focus.

While insurance agents and brokers shouldn't be involved in making these kinds of decisions for organizations, they can advise their clients as to potential risks inherent in a blanket cellphone ban. Certain issues might pose a challenge.

Phone damage

Schools and other organizations have become creative in how they keep kids away from their phones, from designated storage areas such as classroom caddies to requiring phones be kept in their lockers.

Damaging or losing phones is a very tangible risk—and a policy gap—when organizations elect to take them away from the students for a short time. A new smartphone can cost anywhere from $500-$1,200. Multiply that by dozens of students' phones lost or damaged in a given month, and it can be a budget breaker.

Of course, not all phone storage methods are equal. Asking students to dump their phones in a common caddy at the beginning of class or an event can easily lead to cracked screens, spills and students accidentally taking home the wrong phone. One potential way to manage that risk is to designate a specific, numbered spot for each teen's phone.

Medical situations

Young people who have diabetes or other chronic health conditions may use their phones to monitor their blood sugar levels, food intake or other important information. If an organization attempts to take their phone away from them, it could not only cause a potential health emergency but also could lead to a costly lawsuit because of negligence.

Any policy banning cellphones must make an exception for medical situations. Otherwise, the organization may be exposed to major liability if an incident occurs.

Emergencies

One nuanced risk area—especially in schools—is how cellphone access plays out during emergencies. On the one hand, having a cellphone can be incredibly helpful. Teens may use their devices to report an incident, contact emergency services or communicate with loved ones. In some cases, student cellphone calls during active shooter situations have made the difference between life and death.

However, cellphones can also be harmful in emergency scenarios. Widespread use during a crisis may:

• Spread misinformation and panic, especially through social media.
• Jam cellphone towers, potentially disrupting communication for emergency responders.
• Accelerate the arrival of parents and community members, which can complicate evacuation or lockdown procedures.

Organizations must weigh these risks carefully. A well-crafted policy should strike a balance, ensuring teens can access their phones when truly needed, while minimizing the potential for unintended consequences.

The DONUT approach

Risk management of a cellphone ban all ties back to policy. If an organization wants to safeguard itself against potential insurance claims or complaints, it should work with its attorney to develop a solid policy to back it up.

It can be helpful to use the acronym DONUT as a guiding principle:

• D – Development – The first step in developing a clear policy is taking the time to include all stakeholders who will be affected. It's important to frame the potential new policy in a positive way, showing how it can ultimately increase test scores and decrease bullying.
• O – Opportunity – Organizations need to give all people an opportunity to offer input into the intricacies of the policy. This includes both the people who will be enforcing the ban (such as staff members or volunteer leaders) and the teenagers themselves.
• N – Notice – It's always a good idea to give the young people of an organization proper notice about a cellphone ban—through a medium they actually use. In other words, group texts and social media posts are more likely to get through to their intended audience than paper letters and emails.
• U – Uniformity – The teens of an organization shouldn't be the only ones whose cellphone use is limited. How would it look, for example, if a camp forbade cellphones, but the camp counselors were always on their phones. Adults should be setting a good example by putting away their devices, as well.
• T – Timeliness – Once the cellphone ban goes into effect, enforcement should be consistent and timely. Staff members need to implement consequences at the time of the infraction—not a day or two later.

Depending on an organization's previous rules regarding cellphones, making changes could trigger some culture shock. Therefore, it may be best to try a phased approach. For example, rather than requiring all youth to set their phones aside at the start of an activity, class or camp, organizations may instead ask that they keep the phones on "silent." They're more likely to get buy-in if they ask for small changes at the beginning.

While cellphones can create risk surrounding teenagers, so, too, can cellphone policies. That's why it's important that organizations perform enough research ahead of time to anticipate potential problems.

Recent announcements of two new robotaxi services in the U.S., together with a robotaxi-related surge in Tesla stock, seem to be raising lots of questions about how quickly autonomous vehicles (AVs) will become a major factor on our streets and about what they will mean for auto insurers.

Once you understand the "stack," to use the Silicon Valley term for the layers of hardware, software and operational service that make up a robotaxi service, the implications start to become clear.

So let's have a look. 

The reason for the recent surge of interest in robotaxis stems in part from last week's announcements by Amazon and May Mobility. Amazon said its toasterlike Zoox AVs will start operating in Las Vegas, while May Mobility said it will start offering rides in Atlanta in its autonomous vans, which will be offered to those who hail a ride through Lyft. 

In addition, Tesla fans are out in force again, boosting the stock with claims such as that the company will be able to remove safety drivers from its robotaxis imminently and can thus have thousands of the vehicles operating fully autonomously by the end of the year. 

As backdrop, there was a highly publicized report from Goldman Sachs in June that says fully autonomous robotaxis will be generating $5 billion of revenue in the U.S. by 2030. That's a lot of robotaxi rides.

I'd urge some caution on the pace. Zoox is just offering rides between five points in Las Vegas. May Mobility is operating only in a section of Atlanta, with what the CEO calls “a few hands’ worth of vehicles” and with safety drivers behind the wheel. Tesla is operating perhaps three dozen cars in Austin, Texas, all with safety drivers, and, while CEO Elon Musk said earlier this year that millions of autonomous Teslas will be on the road by the second half of next year, his projections have been wildly overoptimistic for a decade now. 

Still, robotaxis seem to finally be on a clear glide path toward widespread adoption. 

Google's Waymo says it has 2,000 AVs on the road in Phoenix, San Francisco, Los Angeles, Austin, and Atlanta, completing hundreds of thousands of paid rides per week. Waymo plans to keep steadily increasing the number of cities it serves. Wired magazine says that, "in China, WeRide, Baidu’s Apollo Go, and Pony.ai are all running robotaxis in multiple cities; WeRide has started operations in Abu Dhabi, too." Regulators in Beijing and Shenzhen are allowing the robotaxis to operate without safety drivers, and Abu Dhabi encourages AVs in special zones. So, while Europe is moving somewhat slowly, there is plenty of international competition to keep up the pressure for improvements in the technology and for deployment. 

What does this all mean for auto insurance?

Let's look at the stack. It's dividing up into the hardware (the car), the software (the artificial intelligence that operates the car) and the operator (the company that will keep the cars recharged, positioned so they can get to passengers quickly, cleaned, and so on). 

Insurers could have a role at any of those tiers but will likely have little to do with the software. In any case, any insurance will be commercial. Personal insurance won't be a factor for the simple reason that people don't need to insure their driving if they aren't driving. 

The hardware

For a time, General Motors tried to take on all three layers in the stack. It made the cars that used the AI developed by its Cruise subsidiary, which also operated a robotaxi service. But GM shut Cruise down last year and is focusing on incorporating its AI into existing lines of vehicles. Tesla is also trying to handle all three layers, while offering insurance, to boot, but as I've said several times now (including here), I don't think Tesla can pull it off. Musk is just using cameras as sensors, while others are using radar and LIDAR, as well. Musk does have better access to certain kinds of data than other robotaxi companies because he has cameras in every car and has so many Teslas on the road, but I don't see how having better maps can cover for the lack of real-time data from sensors that are more sophisticated than cameras. Tesla may eventually have usable technology, but Musk is years behind Waymo, and I think he'll stay there. 

If Musk does succeed with his grandest vision, he would introduce an opportunity for personal auto insurance in the robotaxi world. That's because he has said individual owners will be able to upgrade their software with Tesla's latest AI capabilities and make their cars available as part of a robotaxi fleet that Tesla would coordinate. Owners would presumably be responsible for the upkeep of their cars, so they might buy insurance to cover their liability.

But if I'm right that Tesla will be a minor player in robotaxis for the foreseeable future, then the hardware is a separate layer. It splits into two pieces: the manufacturing and the ownership. 

The role of insurance in manufacturing will be what it's always been. Car makers will have to worry about product liability and could purchase insurance, but the behemoths that do that sort of work will likely self-insure. 

Ownership could be a different question. Just about nobody wants to own assets these days. Everybody wants to be an asset-light company like AirBnB and not have to commit the hundreds of billions of dollars that robotaxis will cost. So it's not clear yet who will own the vehicles. If, as I suspect, huge companies — perhaps formed just for the purpose — own the cars, they'll likely self-insure. If not, there could be opportunities for commercial insurers.

The software

The AI is where the magic happens, and the work is so expensive that it is either being done by huge companies now or by startups that will surely be acquired by massive companies. So, yes, this layer will surely be self-insured. There's room for commercial insurance on, say, theft of business secrets but not for personal auto insurers.

The operators

Who will own this layer isn't quite clear yet. Waymo, for instance, is operating its own network at the moment, and that makes total sense at this stage of the technology. Waymo needs to have people in-house available when one of its vehicles runs into a problem, both to smooth over issues for paying customers and to learn where problems are, so the underlying AI can continually be improved. But Waymo and others won't always have to own the whole operating process.

Yes, Waymo will need to always be running the AI in its cars, but that doesn't mean it will have to handle the dispatching, the recharging, the cleaning and so on, and there could be opportunities for insurance there. 

I suspect the dispatching will be handled by big companies. Uber and Lyft have been doing a nice job of positioning themselves as partners to AV companies. Google could also muscle its way into dispatching through its maps — if you're asking about a destination, Google could easily offer you a ride. So there could be an interesting battle here (perhaps with antitrust implications for Google), but I don't think it will matter to insurers because the winners will self-insure against any customer issues.

The rest of the operations, though, could create opportunities for commercial insurers. The cleaning and recharging could well be handled by smaller companies, perhaps different ones in different cities, and they could well want to lay off some of the risk of dealing with customers, who can have any number of hard-to-predict problems. 

But, again, no luck for personal auto insurance.

As I've said, there are plenty of reasons to think that some of the claims about robotaxis are hyperbole, but if Goldman Sachs is even close to right that robotaxis will be generating $5 billion in revenue by 2030, vs. my back-of-the-envelope calculation of maybe $100 million this year, then we're on an exponential curve. And as all those companies that didn't come to grips with the pace of Moore's law have learned over the past few decades, exponential change can sneak up on you really fast. It's always wise to be thinking ahead.

Cheers,

Paul 

P.S. If you really want to think ahead, imagine what cities will look like if there is the sort of takeover by robotaxis envisioned in this article at Vox: "A self-driving car traffic jam is coming for US cities." 

The underlying changes will be deceptively simple. Parking garages, which take up as much as 40% of the space in some cities, will pretty much disappear. So will curbside parking. But the number of cars on the road will increase drastically. 

When you go from there, though, there could be all sorts of effects. Maybe apartment buildings replace the parking garages, and the additional housing makes cities less expensive and draws more people. Maybe having even more people in cities facilitates time in the office and leads to more office buildings, too. Maybe restaurants and other small businesses benefit greatly from increased foot traffic... or maybe they don't, because robotaxis are taking people straight to their destinations, so they aren't walking and window shopping. And maybe robotaxis make it easier to live in suburbs and exurbs, so city populations actually diminish and people work at home more.  

The good news is that cities won't change nearly as fast as the car fleet does. It takes a lot longer to tear down and replace a parking garage or rip up city streets than it does to beam a software update to a car. 

But I still find this kind of thing interesting to ponder and thought you might, too. 

Life insurance quotes typically feel like form letters. That's because most insurers still use off-the-shelf policy administration software that prioritizes core functions like policy issuance, billing, and claims processing. The proposal generation component receives minimal attention during system development, so it fails to address individual customer circumstances and preferences.

Here's the difference between a traditional proposal and the sort made possible by today's AI:

Traditional Proposal: 

"Dear Customer, thank you for considering ABC Life Insurance. We are pleased to offer you a life insurance plan with standard coverage options at competitive rates."

AI-powered Proposal: 

"Dear Sarah, we understand your priority is protecting your family's future while keeping monthly expenses manageable. Based on your age, health profile, and financial goals, we've designed a tailored plan that secures your children's education and mortgage while giving you flexibility for future needs."

When potential policyholders receive standardized proposals, a significant disconnect emerges between customer expectations and actual delivery. Standardized proposals position insurance companies as outdated in an era where personalization has become standard across industries. Trust erodes when initial quotes fail to acknowledge a customer's specific financial situation or coverage needs.

That's why insurance companies should prioritize implementing life insurance policy administration software that generates personalized proposals and quotes.

Smart Proposal Generators in Policy Administration Systems

AI-powered proposal generators in policy administration systems mark a new era in insurance technology. These advanced platforms leverage artificial intelligence and automation capabilities to streamline personalized proposal creation for customers, unlike their traditional counterparts.

AI proposal generators consist of four essential components that work together to streamline quote distribution:

• Automated Data Capture – These generators enable life insurance policy administration software to pull insured details directly from multiple sources, including applications, CRM records, and third-party databases. This eliminates manual data entry and ensures information accuracy across all proposal documents.
• Instant Formatting Capabilities – AI maintains brand standards while following carrier-specific requirements. Templates automatically adjust based on product types and regulatory guidelines without compromising visual consistency.
• Real-Time Validation – Built-in validation mechanisms flag missing information before client presentation. This prevents incomplete proposals from reaching potential policyholders and reduces back-and-forth communication.
• Comparison Views – The system analyzes pricing and coverage differences across multiple carriers, presenting unified comparisons that help customers make informed decisions.

Insurance companies benefit significantly from implementing life insurance policy administration systems equipped with AI-powered proposal generator systems. Quote processing time drops from minutes to seconds when multiple carrier quotes require conversion into client-ready proposals. These capabilities enable underwriters and insurance managers to focus on advisory services instead of administrative tasks.

These systems eliminate repetitive data entry tasks while ensuring faster customer responses. When clients receive accurate proposals quickly, insurer professionalism increases, and trust builds more effectively throughout the customer relationship.

How AI Proposal Generators Modernize Proposal and Quote Distribution

AI-powered proposal generators in life insurance policy administration systems work as smart workflow engines that change how insurers deliver tailored quotes. These tools create a simplified path from customer data intake to final delivery.

1. Customer Details Intake and Data Preparation: AI systems collect and normalize client information automatically from multiple sources, including forms, emails, and CRM records. This first step removes manual data entry and reduces errors while creating clean, well-laid-out information for processing.
2. Product Configuration and Pricing: The AI models analyze customer data against available products and calculate premiums using dynamic pricing models. The system creates accurate quotes based on business logic and pricing structures quickly. Complex scenarios like volume pricing or custom tiers are handled seamlessly.
3. Content Generation and Personalization: The AI models enable life insurance policy administration systems to create targeted proposals by adding relevant sections based on the client's type and requirements. Product features, case studies, and testimonials are recommended by the system while brand consistency stays intact across all documents.
4. Compliance and Risk Controls: Built-in compliance features ensure all proposals generated by life insurance policy administration software meet regulatory standards and internal guidelines. The AI models confirm eligibility criteria and verify that proposals follow jurisdiction-specific requirements before moving forward.
5. Workflow Automation, Approvals, and Omnichannel Distribution: The system routes proposals through approval workflows, tracks engagement metrics, and delivers documents through email, messaging platforms, or client portals. This complete automation supports scalable proposal generation operations while life insurers retain control of quality. A recent insurtech survey cites that the worldwide insurance automation investment market is projected to touch 1.3 billion USD by 2031.

Key Challenges Resolved by AI Proposal Generators in Policy Administration Systems

Modern technology has solved many problems that come with manual proposal generation. AI-powered life insurance policy software tackles these age-old challenges head-on.

1. Accelerated Proposal Creation: Traditional proposal development requires weeks of coordination between underwriters, agents, and administrative staff. AI-powered systems complete this entire process within seconds. Insurance agents can eliminate intensive proposal data compilation tasks and paperwork. Teams can focus on client consultation and relationship building rather than administrative processing. The speed improvement proves substantial when measured against manual methods. Where traditional systems require multiple touchpoints and approval cycles, AI handles data analysis, pricing calculations, and document generation simultaneously.
2. Enhanced Accuracy Through Automated Validation: Human errors frequently occur during manual calculation processes and data interpretation stages. AI systems perform continuous data cross-checks and pattern recognition to identify discrepancies before proposal finalization. The technology flags inconsistencies in premium calculations and coverage recommendations that manual review might overlook. Automated validation mechanisms ensure that customer data aligns with product eligibility criteria and pricing models. This eliminates calculation errors that damage insurer credibility and customer trust.
3. Streamlined Compliance Management: Regulatory requirements vary across jurisdictions and product lines, creating compliance complexity for insurance companies. AI-powered policy administration systems automatically verify regulatory standards and internal guidelines during proposal generation. The technology recognizes compliance obligations specific to different markets without relying on manual checklists or institutional knowledge. Built-in compliance features ensure proposals meet all necessary regulatory standards before client presentation. This eliminates the risk of legal penalties and extensive compliance evaluations.
4. Scalable Operations Management: Massive enrollment periods and periodical fluctuations challenge traditional proposal generation processes. AI systems process multiple applications simultaneously without requiring additional staffing resources. Insurance companies handle proposal volume spikes efficiently while maintaining consistent service quality. The technology scales proposal generation capacity based on actual demand rather than fixed resource allocation. This enables insurers to respond to market opportunities without operational constraints.

Final Words

AI-driven proposal generators are transforming policy administration systems used by insurance firms. These tools solve long-standing problems with traditional systems and streamline generic, time-consuming processes into customized workflows. Insurance companies can now offer tailored quotes that match each customer's specific needs instead of generic proposals.

The benefits go well beyond customization. Agents used to spend hours creating proposals manually, which led to errors and inconsistencies. These intelligent systems now handle everything automatically - from collecting data to delivering the final product - while meeting accuracy and compliance standards. This change lets insurance professionals build better client relationships instead of getting bogged down with paperwork.

The insurance sector is at a turning point. Once defined by legacy systems, complex actuarial models and decades-old policy structures, the industry now sits on the cusp of transformation powered by artificial intelligence (AI), including its subsets, generative AI (GenAI) and agentic AI.

According to EY, nearly 99% of insurers are either already investing in GenAI or exploring it due to its expected productivity, cost and revenue benefits, while KPMG highlights that 81% of insurance CEOs now list GenAI as a top investment priority despite economic uncertainty.

In this reality, we're seeing insurers move beyond proofs of concept into enterprise-scale adoption, unlocking outcomes across cost optimization, customer engagement and productivity. However, to truly embrace AI and its benefits, insurers need to rethink their approach to the operating model.

In this article, we'll explore why a product-aligned operating model is essential for scaling AI, where AI delivers tangible outcomes and the reinvention of the software development lifecycle (SDLC) with the ultimate goal of building long-term agility and growth.

From projects to products: How operating models are changing

Historically, change in insurance was delivered through projects. Teams formed temporarily around a scope and budget, handed off work across functions and disbanded at "go-live." Ownership was fragmented: Business wrote requirements, IT built, operations supported and data sat apart. That model optimized for completion, not continuous outcomes, and every new initiative restarted the learning curve.

Today, leading insurers organize around enduring products, including claims intake, quoting, billing, fraud detection and agent experience, which are each owned by a cross-functional team spanning business, data, engineering, design and risk. These product teams run on backlogs and objectives and key results (OKRs), ship frequently and treat AI, data and controls as integral. The shift concentrates accountability, shortens decision time and turns change into a repeatable capability.

The benefits are material. Product-aligned models reduce handoffs, embed governance where work happens and scale AI consistently across lines of business. They improve cycle time and quality, make investment transparent and help talent focus on customer and agent outcomes instead of internal coordination. For AI specifically, this model unites infrastructure, data and process expertise under clear ownership, giving organizations the trust, agility and repeatability required to move beyond pilots to production at scale.

The lesson here is that technology transformation must be matched by operating model transformation. Traditional structures, designed for incremental change, can't fully harness the potential of AI. That is why HCLTech's research found that 88% of surveyed businesses are moving toward product-aligned operating models.

Culture plays a decisive role. Those who embrace AI along with an operating model and cultural transformation will emerge as winners.

Where AI is delivering tangible outcomes

Insurance is inherently data-driven. From decades-long life policies to property and casualty (P&C) lines dependent on climate, location and risk data, the industry generates vast amounts of structured and unstructured information. Historically underused, this data is now being unlocked by GenAI, which can connect directly to disparate sources and derive insights without extensive re-engineering. What was once too expensive to modernize has suddenly become viable, enabling insurers to transform legacy systems, streamline claims and fraud detection and create new growth opportunities.

In this environment, there are three areas that stand out where insurers are realizing measurable value today:

1. Driving productivity and reducing costs

AI-powered platforms are streamlining IT operations, the software development lifecycle, QA and testing. Productivity improvements range from 12–15% up to 40–45%. For example, AI-assisted testing and code generation have cut cycle times significantly.

2. Enhancing customer and agent experiences

Whether in contact centers, claims processing or agent interactions, AI is reimagining engagement. Automation is not just about efficiency; it's about building more intuitive, personalized journeys.

3. Empowering the workforce with AI assistants

Digital assistants for underwriters, claims analysts and agents are emerging as powerful tools. Rather than replacing human expertise, these AI co-pilots augment decision-making with real-time insights and recommendations.

These outcomes are why 65% of insurers expect AI to deliver revenue lifts of over 10%, while 52% anticipate cost savings.

Moving from experimentation to scale

For several years, insurers explored AI through proofs of concept. That period of over-experimentation is now giving way to a new phase: implementing AI at scale to deliver enterprise-wide impact.

Scaling AI, however, is not just a technical challenge; it is an organizational one. Insurers must start by establishing a clear value realization framework. Without a baseline, it is impossible to track benefits such as cost savings, productivity gains or customer experience improvements.

Equally important is organizational change management. AI alters workflows, including how underwriters assess risk, how claims are processed and how customer service agents interact with policyholders.

In underwriting, for instance, AI is already enabling faster, more accurate risk assessment and reducing time-to-quote. Similarly, in group insurance, AI-driven automation is streamlining the quoting process, cutting cycle times and improving pricing accuracy. Unless employees are engaged and supported through such changes, adoption falters.

Responsible AI must also be embedded from the outset. Governance frameworks, regulatory monitoring, bias mitigation and continuing risk assessment are critical in a sector where trust is paramount.

Success will hinge on culture. Organizations that treat AI as an isolated initiative risk marginalizing its potential. By contrast, those that democratize AI by placing tools in the hands of underwriters, claims handlers and IT engineers foster adoption at scale.

Redefining the software development and IT operations lifecycle

One of the less visible but highly important areas where AI is transforming insurance is the end-to-end software development lifecycle (SDLC). While many organizations deploy point solutions for specific stages, the real opportunity lies in orchestrating AI across the entire lifecycle.

Consider the chain reaction: Inaccurate requirements gathering leads to flawed code; flawed code creates more defects in testing; weak testing allows problems into production. From demand capture and code generation through QA and release, embedding AI throughout the lifecycle enables insurers to improve quality, reduce cycle times and lower costs.

Similar benefits extend into IT operations, where insurers are moving away from traditional machine learning models toward agent-based automation. These adaptive systems empower administrators to build agents that can "skill themselves on the fly," creating resilience in run environments.

Building long-term agility and growth

AI is no longer a futuristic ambition. Instead, it is a present-day competitive differentiator. It enables insurers to cut costs, accelerate modernization, elevate customer and agent experiences and empower employees with intelligent tools.

But success will depend on more than technology. It requires clear value frameworks, responsible governance, cultural adoption and new operating models. With KPMG finding that 62% of insurance CEOs citing talent gaps as a barrier to growth, investing in people is also crucial. Here, AI should be seen as a partner to human expertise, not a replacement.

The winners in insurance will be those who seize this turning point to not only re-engineer processes but also reimagine possibilities. AI is not just reshaping the industry; it is redefining its future.

In October, most of us enrolled in Obamacare, or Affordable Care Act (ACA), plans on the federal marketplace or on state exchanges will receive our 2026 health plan renewals. And those of us who are getting an enhanced federal subsidy, or eAPTC (enhanced advance premium tax credit), with incomes over 400% of the federal poverty level (FPL), are in for a big shock: Our subsidies will be gone, leaving us to pay the full cost of our health insurance – with premiums jumping as much as 75% or more, in some cases much more.

For a quick background: Before 2022, mid- to higher-income earners paid the full cost of their health insurance, with no federal tax subsidy, but the American Rescue Plan Act (ARPA) was signed into law in 2021, expanding subsidies to millions of mid- to higher-income earners (this was extended in 2022 through the Inflation Reduction Act (IRA)). But this will end on Dec. 31, 2025, without congressional action, forcing people to pay the full, higher premiums.

As of this writing, if you earn more than $62,600 per year ($84,600 for a couple, or $128,600 for a family of four), you will lose your subsidy starting Jan. 1, 2026. For example, a 55-year-old single Milwaukeean today with a health plan costing $298 per month would pay $657.¹ A family of four paying only $518 per month would see a monthly bill of $1,786.¹

And this isn't even taking into account the overall increases in health plan premiums coming next year, estimated to be around 8%.

The bomb is about to drop, and most people don't even know it.

According to a recent poll by the Kaiser Family Foundation (KFF), a health policy research firm, only 7% of us are very aware of the coming subsidy loss, with 21% knowing "some," 33% knowing "a little," and 40% knowing "nothing at all" about it.

The Congressional Budget Office (CBO) estimates that as many as 4.2 million people will lose coverage if the enhanced subsidies aren't extended. Many will either drop their coverage or seek other options.

The impact of the loss of enhanced subsidies will vary by state, with average monthly premiums hitting some people as much as 300% or more.

Most congressional members are aware of the coming surge, and it has been hotly debated in the House and Senate. The recently passed "O3B" (One Big Beautiful Bill), did not include a provision to extend the enhanced subsidies. There have been some legislative proposals to address this issue, in the meantime. Back in January, senators Tammy Baldwin (D-Wis.) and Jeanne Shaheen (D-N.H.) introduced their Health Care Affordability Act to make the enhanced subsidies permanent. Republicans – who are split on extending the enhanced subsidies – and others have explored alternative reforms to the ACA. These alternatives include offering block grants to the states, encouraging health savings accounts (HSAs), and promoting alternative plans (short-term medical plans, hospital indemnity plans, etc.). As of this writing, however, there is currently no bill on the table in Congress.

Some states, aware of the coming shortfall, have explored solutions to mitigate what some see as a coming crisis. These range from tweaking state insurance programs to adding spending initiatives to fill the gap. But the states see the gap as too large for a state to fill sans action from the federal government.

There are some who think that we should scrap the ACA altogether, while others are calling for additional expansion of the ACA and Medicaid. But for millions of middle- to higher-income Americans facing the prospect of highly unaffordable health insurance premiums just a few months away, it isn't theoretical or philosophical or something to be debated and discussed. It's reality. People will face tough decisions of where to cut to make up for their substantially higher health insurance premiums. Some will simply drop their insurance and go without. Others will opt for limited insurance coverage that won't fully protect them.

But for those in Congress who can still do something to protect those who are facing the loss, there is time to act. A word to the wise for our political leaders: In an election year, before you are hit with the tsunami of millions of angry constituents who will demand immediate action, work with your colleagues on your side and across the aisle and find a way to extend the enhanced subsidies that you put in place just a few years ago to help them avoid a catastrophe.

The rising complexity and costs of catastrophes in the U.S. are challenging homeowners carriers, hampering profitability and driving up insurance premiums. In some high-risk regions, a weaker insurance market, in combination with economic and demographic impacts of catastrophes, may be a drag on a region or state's credit quality.

As insurers come to terms with the new realities of today's natural catastrophes, they are raising prices and — in worst cases — abandoning certain markets altogether, adding to the cost of living and limiting home-price appreciation. These factors can influence where people choose to live, possibly steering them away from the more exposed regions.

The impact on a region's housing market and economic activity can be a significant factor in determining municipal credit quality, as seen in Louisiana. Texas offers an example of how a state facing a regular slate of challenging weather events can still have a vibrant economy and draw new residents.

Homeowners Line: A Cautionary Tale

The homeowners' insurance market offers insight into the cost pressures that catastrophe risk currently poses to housing markets. Conning's 2024 Homeowners Crisis Focus Study shows how the shift in disaster profiles—particularly the rise in "secondary perils" such as severe convective storms and wildfires—has rendered many existing models and pricing mechanisms obsolete. Despite rising direct premiums written (DPW), carriers continue to struggle with profitability, and largely due to the growing impact of catastrophes. Further complications arise as restrictive regulatory environments in some high-risk states limit pricing options for carriers. In 2023, this led to a series of credit downgrades for some carriers.

In response, some insurers have raised premiums, restricted eligibility, and even exited high-risk markets altogether. For some affected states, these measures have contributed to higher costs of living and declining homeownership rates.

The health of housing markets and property insurance stability directly influences municipal credit quality, as these factors are critical to real estate activity and property tax revenues. And as we note in our 2025 State of the States credit report, governments would be wise to monitor these fundamentals to better understand their exposure to potential credit pressures.

Housing: Municipal Credit Indicator

The U.S. housing market remains a foundational source of financial stability for state and local governments. It drives tax revenue and reflects broader economic and demographic trends, such as population growth — all important factors in assessing municipal credit. In catastrophe-exposed regions, greater risk may suppress House Price Index (HPI) growth, which, in turn, can lead to outmigration and lower property tax collections. Hawaii, Florida, and Colorado fall into the group with the greatest exposure and weakest home price growth. Meanwhile, Kansas, Mississippi, and Alabama also face high risk, but their home prices have held up better.

While states may be insulated in part by revenue diversification, municipalities are more directly affected: Varying by issuer and year, property taxes can account for up to 61% of total revenues for local governments. Counties and cities that rely more heavily on property tax collections face increasing fiscal uncertainty as development and housing markets stagnate or decline.

Given the potential impact on credit quality, Conning highlights investing in infrastructure, diversifying taxes, and building disaster reserves to address emerging concerns, particularly for high-risk regions and states.

A Tale of Two (High-Exposure) States

Texas

Texas is regularly exposed to natural catastrophes yet maintains a strong credit outlook. The state has suffered various types of perils (e.g., Hurricane Harvey in 2017, Winter Storm Uri in 2021), has a catastrophe-losses-per-capita rate well above the U.S. median, and has HPI growth well below the U.S. mean.

Despite these challenges, Texas's credit outlook remains relatively positive for several reasons. It has one of the nation's most competitive tax structures, high GDP and population growth, and reserves that have remained above average during the past several years. These factors may buffer the rising risk of catastrophes, helping Texas and its municipalities sustain their creditworthiness.

Louisiana

Louisiana stands out as the most climate-exposed state in the nation, consistently ranking at the top for catastrophe risk and losses. Between 1980 and 2024, Louisiana's total estimated damages from billion-dollar catastrophes were approximately $300 billion— 31% of which accumulated in the past five years.

This high exposure has triggered a feedback loop preventing credit quality growth. The reaction of homeowners' carriers has also forced many to rely on state-backed insurance, further straining public finances. Meanwhile, the need for infrastructure recovery is adding budget pressure, but, unlike Texas, Louisiana lacks the flexibility to absorb the impact: Louisiana had one of the weakest HPI and population growth performances in our 2025 State of the States report, partially responsible for its overall last-place ranking.

Best Medicine: A Healthy State Economy

In regions that experience high catastrophe risk, the struggles of homeowners insurance carriers and uncertainty in housing markets may signal broader fiscal challenges for states and municipalities. Risk mitigation efforts are becoming increasingly important, particularly as potential changes to federal disaster response programs—such as proposals to restructure the Federal Emergency Management Agency (FEMA)—introduce additional uncertainty around future recovery support.

In one attempt, Louisiana lawmakers in April introduced a bill aimed at reducing homeowners' insurance costs by establishing a catastrophe reinsurance fund, although it is without a pledge of the state's full faith and credit. The Reinsurance Association of America suggests the program may struggle due to a lack of diversification and a high concentration of risk.

Ultimately, while catastrophe exposure poses significant challenges for homeowners, insurers, and municipal credit quality, the contrasting experiences of Texas and Louisiana highlight that fiscal strength and risk management are critical to maintaining credit stability amid escalating natural disaster costs.

Footnotes

1 Source: ©2025 Conning, Inc., "2024 Homeowners' Crisis Focus Study"

2 Source: ©2025 Pew Research Center, Jeff Chapman. "How Local Governments Raise Their Tax Dollars." Pew Research Center, Washington, D.C. (July 27, 2021). https://www.pew.org/en/research-and-analysis/data-visualizations/2021/how-local-governments-raise-their-tax-dollars, accessed on August 14, 2025.

3 Source: ©2025 Tax Foundation: https://taxfoundation.org/research/all/state/2025-state-tax-competitiveness-index/

4 Source: ©2025 Conning, Inc., "2025 State of the States"

5 Source: ©2025 NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2025). https://www.ncei.noaa.gov/access/billions/, DOI: 10.25921/stkw-7w73

6 Source: Steve Hallo. "Industry Opposes Louisiana Bill to Create State-Backed Reinsurance Program." AM Best, Baton Rouge, Louisiana. (April 25, 2025). Industry Opposes Louisiana Bill to Create State-Backed Reinsurance Program, accessed August 18, 2025.

The idea of property and a neighbor is easy.

The idea of digital and cyber and a neighbor is hard.

The first set is visible. The second is invisible. One exists in real space. One is ethereal.

Property - it’s houses, cars, trucks, machines, buildings, businesses, infrastructure, products, and more. Real things with real people and real-world locations - a very physical world with names, addresses, and contact information. Things you can see. Things you can touch. Very relatable parts of any real neighborhood things. (Save intellectual property for another time).

Digital and cyber are not that. They exist in an e-world where everything is e-real. 

Every memory storage location and every processing chip can be thought of as having their own names, addresses, and contact information, but only in an e-real way. On the internet, there are IP addresses. The physical device could literally be anywhere, but in compute logic, it’s all just a bunch of slashes and dots away from any other device any-e-where as one address can route and link to others.  

A home or building sits on land or a lot all uniquely assigned mutually exclusive coordinates. Data scattered among redundant arrays of independent disks do have their own addresses with 1’s and 0’s, but these can be overwritten or even erased as well as copied and stored in multiple places which may move around. Similarly other storage mechanisms, including cloud storage, are also in play. Everywhere your data move may be considered another neighborhood, and if their prior instance is not scrubbed, that ghost trace is a latent neighbor that you didn’t know you didn’t know about.  

These addressable endpoints also include situational features - like operating system, software version, patch sequence, and other options that reflect what is going on at each endpoint. As with an open window on a rainy day, you would have wanted it closed if you knew what was going to come through it. The stakes are higher when an interloper is looking for open doors and windows, or as digital/cyber relates, ports and vulnerabilities. 

Just as a house has doors and windows that can be locked or left open, each digital address (IP address) has services and software that can be secured or exposed. But even locked doors can have weak locks or hidden flaws—some locks are easy to pick, and some windows can be forced open. Similarly, even protected digital services can have vulnerabilities that skilled attackers can exploit.  Sometimes an indirect approach is easier, like posing as technician to a call center representative to open a door unwittingly.

We are becoming more comfortable with the concept that digital equals information, digital equals data, and digital channels are ways of interacting with these. 

We are in a transition to the mindset that everything now is data…. Desktop and remote is how we imagine and represent the people, places, and things in any real neighborhood.

But we are just at the threshold of understanding that these representations stored in the ether of the internet are living in invisible cyber neighborhoods.

We can think of a cyber neighborhood where every computer core or memory storage device in a chip, circuit board slot, machine, server, rack, network, sub-network, datacenter, platform, cloud, cloud region, etc. is like a real-world rooftop address geolocation or even a rally point like a pin drop or a WhatThreeWords Earth pixel. 

The programmers, administrators, hackers, programs, bots, code, communications, protocols, APIs, and AI agents are neighbors under those rooftops and around those locations. 

Some compute environments are like an owned and occupied home by the same person for decades, others are like a rent-by-time-slice hoteling office, and some are like a dark alley or underpass with shady dealings and no identity required. 

(Read also: “No one can hear an AI scream in cyberspace…” from ITL.)

The reality of “bad neighbors” in the real world and “bad neighbors” in cyberspace is stirring the insurance world and the risk marketplaces. 

There has been a sector rotation in cyber criminal appetite turned toward P&C this year, and an unfortunate horizontal weakness is currently in active exploit with a popular CRM system product. Whether targeted or opportunistic, the e-safety of the insurance neighborhood cannot be taken for granted. 

The idea of a safe neighborhood or a dangerous one can transfer between real and e-real constructs.  Safety as an index can be ephemeral when exposed to a threat and quickly remediated, or it can be structural and lie undetected while exploited at scale, a false presumption of safety. When, not if, hidden exploits are uncovered, the assessment and remediation processes cycle anew.

The risk of the e-world is constant and global. This is unlike real world perils like watching the track of a hurricane, which is seasonal and geospatially proximate.

Primacy and recency of cyber threats are the constant reminders of what is less imagined - our digital neighbors in our digital neighborhoods are in a continuous state of invisible digital churn. Any time we share any digital resource, there are others sharing it, too.

While there may be some examples of isolated computing with no connections, communications, hosting, integrations, or application programming interfaces, the most common enterprise IT situation is multiple core systems interacting on premise and intra/inter cloud resources with vendors, third parties, and partners.

It is difficult to delve into the wildness of internet cyber situations; some are inherent, while others are sporadic. Some are software- or hardware-related that appear accidental with incidental vulnerabilities, and others are thoughtfully crafted exploits by human ingenuity, now adding AI capabilities.

Regardless of the nature of the cyber risks, the level of connectedness and the risk across connections may vary user by user, company by company, machine by machine, software by software, interface by interface, network by network, platform by platform and cloud by cloud.

Like people and businesses occupying houses and buildings in the physical world at literal addresses using a variety of names and aliases, the digital world can be seen in a similar fashion.

Company computer infrastructures and their cyber vulnerabilities span a spectrum of more fully controlled with more uniform homogenous cyber risk (walled garden and locked down with dedicated security and engineering) to widely distributed with dynamic heterogeneous cyber risk (hosted on multiple platforms with multiple networks with different management systems and software and haphazard oversight of many participant digital neighborhoods and denizens (people, businesses, robots, and AI agents, etc.).

From a moated castle to a flea bag hotel the risk of both the infrastructure and the neighboring occupancy is an analogy of the consistency or inconsistency of cyber risk, which will vary over time. A bad actor can get into a castle but then be confronted and mitigated. But a bad digital neighborhood leaves more at risk more of the time.

What is invisible to the eye is the infrastructure connectedness of extended digital networks. Many castles working together may tunnel to each other. Many discount motels may do the same. Throw in a crime-ridden abandoned building drug den and you get a deteriorating sense of what could be out there - invisibly except for digital means.

So... a long wind-up.

Extending the analogy just a bit further, some digital means look at all the doors and windows of all the spaces known to belong to a company or to be transacted by the company and another. But these approaches don’t include all the adjacent and proximate spaces to those. These are “glass partly full” covering approaches that combine strength and efficiency but lack comprehensiveness. 

The concept of watching and recording hundreds of millions of internet domains and billions of interactions between them and archiving those observations across a decade and more seem too large for assessing any single company’s risk. But someone has done it, for a different business reason than cyber assessment. Now comes the serendipitous epoch of cross purpose innovation - re-purposing an existing asset for a new use case.

The fabric of a connected, internet-wide data infrastructure permits the rollup of sub-networks, networks, domains, and “ultimate domain,” which tie information across the digital world into a form where it can be linked to legal entities. This is where cyber risk at each digital rooftop can be assessed and aggregated to a building, block, tract, region, and so on to score the whole of the risk as an algorithm of consistency over each of its parts. These parts can be associated logically to the legal entity level and a new understanding of cyber risk can be attributed, aggregated, and accumulated like never before.

This capability to assess organizational risk across complex and otherwise invisible connections is novel and useful. As cyber threats change over time, and legal/digital entities also change over time, the continuing dynamic assessment adapts and creates information to act on.

Turning data into decisions and actions makes this process valuable. And that value can be achieved by incorporating these data, analytics, or both, into modern digital and cyber analyses and risk management and monitoring solutions. Using multi-level risk scoring that can count and analyze the number and severity of vulnerabilities at each level will let you see not just where the problems are but how serious they are.

Ensembles of data and analytics most always deliver more robust solutions.

The future of insurance isn't about specialists wrestling with complex core systems. It's about insurance teams conversing with the core as naturally as they talk to each other, thereby reducing the cost of change, accelerating time to market, and creating more space to focus on customers.

AI is often framed as a threat to jobs. In reality, its greatest potential lies in freeing people to focus on high-value work while intelligent systems handle complexity, coordination, and routine tasks. Few industries stand to benefit more from this shift than insurance.

Like businesses in many sectors, insurers understand that AI is key to reducing costs, accelerating service, and driving smarter decisions. But what many are discovering are the limits of simply layering AI models onto legacy systems. The real breakthrough won't come from adding more AI. It will come from deploying it differently.

This is where agentic AI becomes truly disruptive. When embedded in a modern, cloud-native, API-first architecture, agentic AI enables insurers to move beyond today's bolt-on chatbots and narrow automation. Instead, they can create what I call the Conversational Core — a platform where intelligent agents orchestrate workflows across policy, claims, billing, and distribution, and business users leverage the system freely, engaging with it in natural language.

The Power of Agentic AI in Insurance

Agentic AI, where intelligent agents collaborate across systems to enable automation of complex, high-volume tasks, marks a step change in organizational effectiveness. By orchestrating across workflows, teams, and channels in real time, agentic AI can unlock new levels of automation, efficiency, and service. But only if supported by modern architecture.

Today, the majority of AI implementations in insurance are limited to chatbots — useful proxies for human-led conversations that answer basic questions or route requests. Helpful, but narrow. They make existing processes more efficient, yet fail to fundamentally change how the business operates.

Agentic AI is different. Embedded directly into the core, intelligent agents don't replace judgment. Instead, they take on the high-volume, complex tasks that slow people down, while humans stay in control. They can be applied across the full insurance lifecycle to handle what I consider to be low-hanging fruit:

• Smart quoting and file intake.
• Census and enrollment automation.
• Intelligent OCR for documents.
• Billing reconciliation.
• Risk assessment and fraud detection.
• Case and work automation.

In each case, agentic AI augments human workflows, reduces errors, speeds up admin-intensive tasks, and improves accuracy.

Beyond these foundations, more advanced functions are emerging, from collecting all the information required for underwriting to adjudicating complex claims, where AI agents can monitor events, suggest next actions, and execute workflows under human oversight.

The real driver here isn't automation for its own sake but orchestration: enabling insurers to coordinate decisions and processes across modules, channels and partners. While the most advanced scenarios are still developing, the foundational use cases are already within reach. Yet, in practice, few insurers have taken the leap.

From Bolt-On AI to the Conversational Core

Much of what's called AI in insurance is still machine learning: algorithms optimized for narrow tasks. Generative models are beginning to appear, but the real breakthrough will come when intelligent agents combine ML's predictive strengths with GenAI's orchestration power and insurers can interact with them conversationally across the core. Crucially, this must be embedded at the core, not bolted on at the edges. This isn't about evolving previous features, it's about creating new opportunities.

To unlock this potential, GenAI must become a native part of the operating core: acting on real-time data, triggering workflows, and collaborating with humans where it matters most. When the platform is enabled as an agentic AI framework, every service can be orchestrated by intelligent agents.

Rather than tweak existing processes, this approach establishes a new operating norm for insurance: Configure-Test-Deploy. What is standard in digital-native industries like Amazon, Uber, and Netflix now becomes possible in insurance and accessible to business users through natural conversation.

As with the platforms run by the digital giants, delivering this requires a MACH-based, cloud-native, API-first, AI-native, and data-ready architecture. With these foundations, agents can securely connect to any module, retrieve and act on real-time, enriched, contextual data, and coordinate decisions across the entire value chain.

What's more, when the platform is natively enabled as an agentic AI framework, insurers and partners can build and integrate their own intelligent agents. These aren't limited to single functions. They can span underwriting, claims, billing, policy servicing, and distribution in one coordinated flow. These agents draw on enterprise data from across the platform, execute tasks through secure application programming interfaces (APIs) and event-driven interactions and provide results to business users conversationally.

Critically, governance is built into the fabric of the platform. Intelligent agents acting across underwriting, claims, billing, policy servicing, and distribution not only operate more efficiently but also safely, compliantly, and transparently with auditability and human oversight at every step.

This is the essence of the Conversational Core. Not bolt-on features, not incremental chatbot upgrades, but a new operating model for insurance where intelligence is embedded at the heart of the core and insurers no longer operate their systems, they converse with them.

The Legacy Roadblock to Intelligent Insurance

The challenge for most insurers is structural. Their core platforms were never designed for an AI-enabled world. Many are still powered by monolithic systems that don't support native integration of GenAI and lack the openness needed for intelligent agents to interact with data across the business. Instead, AI is bolted onto isolated processes while data remains siloed, inaccessible and out of sync.

Monolithic systems are like walled castles: secure in their time but closed, rigid, and costly to maintain. Modern business requires open cities that are connected, adaptable, and designed for constant exchange.

This rigidity has two consequences. First, every attempt to introduce AI becomes a bolt-on, limiting its impact to narrow use cases. Second, the cost and complexity of change skyrocket. Even simple improvements can take months or years. For AI agents that need to orchestrate across underwriting, claims, billing, and servicing in real time, these constraints are a structural blocker.

In short, legacy systems don't just slow insurers down. They prevent them from unlocking the very technologies that could help them compete in a digital-first, data-driven market.

Building the Foundations for Intelligent Insurance

The shift away from monolithic architectures is not new. Across industries, enterprises have already embraced cloud-native, modular, API-first platforms with AI-ready data fabrics because they enable agility, cost efficiency, and continuous innovation. The same principles that transformed digital leaders in e-commerce and beyond now provide the blueprint for insurers ready to take the next step with agentic AI.

Let's be clear. Agentic AI isn't just another technology trend. It is the enabler of something bigger: the Conversational Core. A fundamental shift in how insurers configure, operate, and orchestrate their businesses to innovate, and serve their customers. The real question is not whether it will become part of the industry landscape, but how quickly insurers can create the foundations to take advantage of it. Those who act now will be the first to turn automation into orchestration, insight into action, and insurance into a truly intelligent enterprise.