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.

My old friend and colleague Chunka Mui recently posted a thoughtful essay on how companies should start thinking about the next phases of the generative AI revolution — which is where the profound changes will happen. 

By now, just about every company is experimenting with gen AI, and many even have gone beyond pilots and into production. But, drawing on Doug Engelbart's classic thinking about businesses' "A, B and C processes," Chunka lays out the need to go beyond AI's usage in what Englelbart would call A processes — those that a company uses to operate every day. Companies need to institute B processes, which are designed to improve those processes that run the business. And — here's the real payoff — companies need to design C processes to improve the B processes.

I know that can sound rather theoretical, but Chunka shows how it all gets practical very fast — and the approach has worked before. Drawing on Engelbart, among others, Chunka was the co-author of "Unleashing the Killer App: Digital Strategies for Market Dominance," which was a huge best-seller and a sort of bible for innovators in the internet boom of the late 1990s. Even after the bubble burst, the publisher of the Wall Street Journal lauded the insights in 2005 and labeled "Killer App" one of the five best books on business and the internet.

I'll give you the short version of Chunka's thinking and apply it to insurance, leading up to his seven questions that will help ensure that you're seeing the full potential for generative AI in your business.

To put insurance terms to the ideas in Chunka's piece, A-level processes are how agents and brokers sell, how underwriters price risk, how claims are settled and how customer service centers operate. We've all seen stories, and probably even personally experienced, how AI is being deployed at this level.

B-level processes improve those processes, and it's easy to see how gen AI can make the improvements happen even faster. The AI can, for instance, instantly spot patterns in responses to sales pitches to see what works and what doesn't work, including nuances such as time of day, day of the week, number of weeks or months before a renewal, proximity to a life event, etc. The AI can detect emotions that humans may miss as potential customers talk; the AI can then pass the information to agents, helping shape the conversations. And so on. The AI can also speed the process by which learnings are gathered, distilled and fed back to agents so the A-level processes improve and agents can be more effective next time. The same sorts of B-level improvements can happen in claims, underwriting, customer service and other parts of insurers' businesses. 

From what I've seen, many companies are at least starting to think about B processes. I've published quite a few pieces about, for instance, using AI to catch fraud, to have results from claims fed back to underwriters to improve their appraisal of risks, and to help underwriters both gather data more efficiently and to highlight changes in the policyholder's situation since the last policy was issued.

But I have yet to hear about much in the way of using AI to get to the next level, to the C processes. They're a bit harder to characterize but are crucial. As Chunka writes, "C-level work isn't merely about scaling incremental improvement—it enables organizations to question and redefine their very purpose. It allows not just better performance, but different futures. C-level improvements accelerate the rate and type of change—unlocking exponential leverage."

From the initial internet boom, I'd say Amazon is the best example of C-level thinking. It started out selling books and continually worked to sell books more efficiently — showing A-level and B-level processes — but was always driven by a C-level vision that founder Jeff Bezos referred to as "The Everything Store." He wanted to sell everything to everybody, even as he founded the company more than 30 years ago.

Amazon Prime was a direct outgrowth of that vision. Once Bezos started to host enough other businesses on the Amazon site, he saw he could lock in customers by offering them fast delivery based on an annual fee — getting them out of the habit of factoring shipping costs into every purchase. That lock-in then helped him attract more merchants, feeding a virtuous circle that continues to this day. 

AWS wasn't foreseeable back in the early days of Amazon but is the sort of happy accident that can happen when you set out for a C-level reinvention rather than just a B-level continual improvement. Bezos saw that many merchants needed help operating their sites, so he started a cloud service — and being in the business early let him see the huge demand before potential competitors and get a massive head start that has translated into a business that generated $108 billion of revenue last year, with an operating margin north of 35%.

For insurers, I could see a C-level approach to gen AI facilitating the move toward a Predict & Prevent model, beyond today's repair-and-replace approach to risk and losses. Gen AI can gather information — even across the silos that bedevil insurers — and analyze it instantly, then send it to whomever needs to have it, in time to perhaps prevent a loss.  

A well-meaning recent attempt to get bad drivers to improve was based on a single communication to individuals with multiple moving violations, whose behavior was then monitored for the next six months. It won't shock you that driver behavior changed not at all. What we need is the sort of instant information that Nauto provides to truck fleet drivers about speeding, about tailgating, about drowsiness, about road conditions and accidents ahead, etc., based on AI analysis of images from cameras: one facing the road, one facing the driver. A C-level approach to innovation with gen AI can facilitate that sort of timely feedback — and not just for drivers. It can also help, for instance, the timely provision of information to utilities about faults in electric lines, as detected by Whisker Labs' Ting sensors in people's homes. A C-level use of gen AI could help communities monitor and encourage homeowners to harden their properties against wildfire, reducing the risks for everyone. And so on.

More generally, gen AI can be used to flesh out the sort of what-if scenarios that business leaders use to stretch their thinking and prepare for challenges and opportunities. Instead of just briefly entertaining the thought of a recession, of war spreading from Ukraine to other parts of Europe, or of even more remote possibilities, leaders can use gen AI to develop more elaborate scenarios and explore the complex interactions that may matter to a business but that are hard to see in a quick look. Even at huge companies that have planning departments, gen AI can help flesh out scenarios faster — gen AI could look at today's weak jobs numbers in the U.S. and speculate in detail on what it means for workers' comp enrollment, for employee-sponsored healthcare programs, for general economic growth, for Fed rate cuts and more.

"Killer App" explained the power of what-if analysis, in one of the many parts of the book that have stuck with me. Chunka said the invention of the electronic spreadsheet in the late 1970s led directly to the wave of mergers and acquisitions in the 1980s and 1990s. Why? While smart young financial analysts could always crunch numbers, they previously had to manually update every cell in a spreadsheet if an assumption changed. With the electronic spreadsheet, they could let their imaginations run wild.  They could just set an interest rate or a sales figure or cost savings or whatever and have the assumption ripple through a spreadsheet until the analysts got the sort of result from a potential merger or breakup that they wanted. Their bosses would then sell the idea to companies or aggressive investors — and watch the fees roll in. 

Chunka, boiling down his thoughts on the A, B, C processes, suggests these seven questions that you should ask yourself to make sure you get the full benefit from generative AI:

1. Are we using AI only to do the same work faster, or are we also using it to design entirely new ways of working?
2. What systems and processes do we have to spread AI-driven learning and improvement across the organization?
3. How are we actively identifying and challenging the assumptions baked into our current workflows, products, and business models?
4. Where could AI help us fundamentally reimagine our business model—not just optimize existing operations?
5. Who is accountable for leading and sustaining C-level improvement—and do they have the authority and resources to act?
6. How are we ensuring that AI adoption does not quietly encode and scale harmful biases, flawed assumptions, or misleading correlations?
7. Do we have the culture, skills, and adaptability to continually improve how we improve?

He writes, "The real prize is using AI to redesign the road itself—not just drive faster on the old one."

Cheers,

Paul

P.S. I've told my Engelbart story before, so I'll just reprise it briefly here.

In the late '90s, I attended a cocktail party at a friend's house in Silicon Valley and struck up a conversation with an older man, who expressed interest when I told him I edited a magazine for Diamond Management & Technology Consultants that focused on innovation through digital strategy. When he asked for an example of the sort of article I published, I told him I had just edited a piece on A, B and C processes. 

"But that's my idea," he said.

"That's Doug Engelbart's idea," I replied.

"And I'm Doug Engelbart," he said.

He was, too. Engelbart, one of the most celebrated of the pioneers of personal computing, lived next door to my friend. 

AI is revolutionizing the way insurers deal with underwriting and claims management. However, adoption still faces barriers that go beyond implementation. 

The most frequent blockers in adopting AI are not technological. Though insurers start AI projects with trusted vendors and a clear understanding of why the work is necessary, many stall. Our team has seen underwriters across commercial and specialty lines hesitate to rely on scores generated by AI. At the same time, claims teams worry about the possibility of using models to affect settlement decisions.

A Pilot That Stalled — And What Changed

I find it challenging to persuade underwriters to trust AI-generated recommendations. In a commercial P&C insurer pilot project, the AI model was ready in four weeks, but rollout stalled for several months because underwriters didn't trust scores without context. Adoption only took off after we explained how our AI advisor worked in real life. For this, we asked our partner to provide 4,000 historical data points, which we then used to train the AI model. Also, we did the following:

• Showed the top factors influencing AI scores
• Allowed underwriters to override AI outputs
• Offered to keep an audit trail of all recommendations and decisions
• Embedded AI results in the tools they already use

As a result, we've got a data-rich advisor that calculated triage, appetite, and winnability scores in a matter of minutes, but more importantly, a solution that underwriters trusted enough to start using. Such trust turned a pilot into a full-fledged software product, taking underwriting processing to a new level.

Transforming Manual Workflows Into Digital Journeys

In the case described, AI helped underwriters transform traditional, often outdated and manual, processes into an automated digital journey. Triage scores are calculated more accurately as the platform ensures that data is complete. Appetite matches submissions against preferred segments and considers the company's guidelines and rules. Winnability predicts the likelihood of winning the deal. All scores are calculated automatically, saving underwriters' time for final decision-making.

Overcoming Fears of AI Replacing Professionals

Another challenge is the fear that AI could replace underwriting and claims specialists. The key is to convince underwriters that AI is a helper rather than a rival. On a project that required a certain level of automation in claims, our solution was to integrate natural language processing to extract data from documents supporting claim submissions from customers. As a result, claim managers have 25% more time for complex cases requiring more attention and direct communication with clients.

Asking for feedback is also crucial. It allows you to discover when AI predictions and recommendations were right or wrong and use that information to refine the models. And when people see their feedback improve models, trust accelerates.

In measuring the impact of AI in underwriting and claims, it's not about providing ROI to leadership. It's more about building credibility, so the people who use AI believe it works.

From our experience, here's what works for measuring the impact of AI:

1. We measured the current state before AI was introduced (average triage time, claim cycle time, loss ratios, etc.)
2. Together with customers, we tracked the usage rate and override frequency
3. Our experts looked for early wins during one quarter to scale further

Success doesn't mean integrating complex algorithms only. It comes from addressing AI adoption challenges, delivering measurable results, and building solutions that insurers trust.

62% of HR professionals who participated in SHRM's 2025 study stated they operate beyond their normal capacity. Such heavy workloads can lead to burnout, hindering HR teams' ability to manage human resources efficiently, which can damage entire organizations.

Optimizing HR tasks with intelligent process automation (IPA) can help alleviate excessive workloads, prevent burnout, and improve efficacy. Unlike traditional process automation, which solely focuses on mechanizing structured, rule-based processes, AI-enabled IPA can be applied to a broader range of HR activities, including those requiring intelligence. Forward-thinking business leaders have already recognized the potential of IPA for enhancing various HR processes, with streamlining HR workflows with automation and AI being their top priority for the next one to two years.

Based on our experience in HRMS software development, here are key concepts of intelligent automation in HR, its common use cases, and implementation best practices.

Key technologies for intelligent automation in HR

● Robotic process automation (RPA)

Traditional RPA bots are already widely used in HR to automate repetitive, rule-based tasks, but they can't handle more complex and non-linear processes. Augmenting RPA bots with AI models enables them to process both structured and unstructured information, make informed decisions, and learn from data, which allows companies to streamline more time-consuming HR tasks.

● Smart assistants

Smart assistants use artificial intelligence capabilities to understand user queries formulated in natural language via voice or text and respond to them accordingly, from providing information upon request to performing actions across corporate systems, saving HR specialists' time.

● Personalization

Nearly 20% of employees surveyed by McKinsey in 2025 reported dissatisfaction with their employer, while 7% expressed a desire to leave their jobs, which can pose a risk of quiet quitting. Tailoring employee experiences to their unique needs and preferences is one way to make staff feel more valued and satisfied, which can improve retention.

However, providing personalized support services, career development opportunities, and wellness programs can be exhausting for HR teams already operating under heavy workload. Leveraging AI-enabled tools equipped with experience personalization capabilities is a way to address this challenge.

● HR data analytics

To identify employee skill gaps, detect turnover risks early, and make informed workforce management decisions, HR teams need to analyze large amounts of workforce-related data, which can be challenging due to its ever-increasing volumes. Automated AI-enabled data analysis tools can support HR teams in processing relevant information and generating data-based insights to accelerate and enhance HR decision-making.

Common uses for intelligent automation in HR

● Recruiting

Recruitment is often considered the most critical yet complex and time-consuming aspect of human resource management. Luckily, many recurring recruiting tasks can be streamlined with the help of intelligent process automation tools.

For example, Majid Al Futtaim, a Dubai-based retail and leisure company, leveraged a set of IPA technologies, including experience personalization and HR data analytics tools, to build a more efficient and smooth hiring process. Now, they use AI to automate candidate scheduling, personalize candidate communication, assess candidate fit for the company's culture, and even predict their likelihood of success in different roles. "We've reduced our time to hire by 30% with AI. AI has also driven a significant improvement in the quality of hires, ensuring that every new team member aligns with our culture and contributes to our vision," said Mai Elhosseiny, vice president of talent at Majid Al Futtaim.

● Onboarding employees

Onboarding newcomers is another time-consuming activity that IPA can optimize. A prime example is Santander, an Argentina-based financial services company that hires between 50 and 100 employees per month. Onboarding used to be performed manually and sequentially and required an average of six weeks per person. Santander automated this process with intelligent RPA bots, which can automatically inform relevant departments about new team members, set up employee accounts, and perform the necessary compliance checks for each new hire. As a result, onboarding was reduced to just two days.

● Supporting employee talent development

Talent development can create excessive workloads for HR teams. But AI-enabled IPA tools have already proven efficient for optimizing diverse aspects of talent development, from performance appraisal to internal talent acquisition and training personalization.

Kuehne+Nagel, a Switzerland-based logistics provider, intended to enhance visibility into career development opportunities for nearly 78,000 employees across 1,400 locations, streamline internal recruiting processes, and eventually stimulate internal mobility. To achieve these goals, the company decided to implement an AI-enabled internal talent marketplace. After employees fill in their profiles within the new system, AI algorithms automatically analyze their data, match it with open learning and job opportunities, and provide recommendations. The AI system also generates analytical insights for recruiters, which helps assess the company's current talent needs, evaluate talent gaps and strengths of employees, and identify internal candidates best-suited for specific jobs. The tool already helped the company increase conversion rate for internal candidates by nearly two times, while decreasing the time required to fill for internal requisitions by 20%.

● Handling employee queries

Companies can apply AI-enabled automation tools to handle various types of employee queries, including questions about benefits and training programs, time-off requests, and medical document submissions. 

Covestro, a German manufacturer of high-tech polymers, was looking to expedite the processing of sick leave certificates submitted by employees. Manual processing took an average of seven minutes, which was too time-consuming, given that HR teams received more than 500 certificates per week. The company deployed AI-powered RPA bots, which can classify submitted documents as sick leave certificates, extract necessary data, and then input it into employee profiles in the ERP system. As a result, Covestro saved 85% of the time HR teams previously spent on manual sick leave submissions processing.

Useful practices for implementing intelligent automation in HR

● Implement process intelligence tools

Automating the right processes is crucial, and AI-powered process intelligence tools can identify HR activities most suitable for automation.

These tools can provide process mining and task mining capabilities helping identify bottlenecks within workflows, visualize business process data for stakeholders involved in a project, and even predict the impact of automation on specific tasks.

● Start with a pilot automation project

Conducting a pilot IPA project allows companies to validate the feasibility of IPA without incurring expenses associated with a full-scale implementation, detect hidden automation pitfalls and hurdles early on, and lay a strong foundation for larger IPA initiatives.

For a pilot project, companies should select one or two HR-related processes and establish clear KPIs to measure the impact of automation. Companies need to carefully evaluate the project's success, collecting stakeholder feedback and analyzing lessons learned.

● Communicate IPA benefits to employees

According to Deloitte's 2025 report, companies are six times more likely to achieve a financial advantage from AI when employees feel they personally derive value. However, the same report reveals that 77% of companies do nothing to share the improvements AI can bring.

Managers spearheading IPA adoption should work closely with HR teams from the start of a project, articulating the improvements IPA can bring to their work and keeping them informed about the progress and impact of IPA.

Modern HR teams regularly struggle with excessive workload, which hurts their productivity. Implementing intelligent process automation to optimize a range of recurring HR tasks is a way to alleviate pressure and enhance the performance of human resources departments. 

As of 2025, more than 21 million applications for new small businesses have been filed in the U.S. But behind this growth lies a serious vulnerability: underinsurance. Just 13% of small business owners with insurance coverage feel fully prepared for risk.

This protection gap should be a wake-up call for insurers. Small and midsize enterprises (SMEs) are a rapidly growing segment, yet many remain underprotected, leaving them both vulnerable and underserved. For carriers, this is an opportunity to support a critical part of the economy while capturing growth.

But closing this gap demands a shift in strategy. Insurers must rethink how they engage small businesses, delivering solutions that are seamless, timely, and integrated into the systems SMEs already use to run their operations.

Insurers that act now can turn this unmet need into a growth engine and position themselves as trusted partners in an expanding market. Wait too long, and you risk a generation of business owners moving forward without you.

Why traditional models are failing SMEs

Consider a family-run coffee shop. For years, business has been steady and incident-free, so insurance feels optional — until a small electrical fire forces the café to close for three weeks. Without coverage, the owners pay out of pocket for repairs while losing revenue, and what once seemed like a "low-risk" business suddenly faces a financial crisis.

This story is not uncommon. Many SMEs put off purchasing insurance beyond the minimum required because the process feels inaccessible. Policy language is dense and full of jargon, leaving owners unsure of the difference between general liability, professional liability, and workers' comp. Nearly 70% of small businesses report struggling to understand coverage limits, leading to insufficient protection from the start.

Traditional distribution models compound the issue. Legacy carriers have established reputations that garner SME trust, but often rely on outdated, paper-heavy processes that feel inaccessible to busy business owners. Newer digital-first carriers offer sleek self-service platforms, yet many lack the credibility of heritage names. The result? SMEs are left without coverage that feels both reliable and convenient.

This disconnect doesn't stem from disinterest. In fact, 82% of small business owners say insurance coverage for their business is extremely or very important for their operations. The demand is real, but current products and channels don't meet the realities of SME size, budget, and needs.

Insurers have an opportunity to redesign coverage to reach businesses that traditional models have left behind. A digital-forward, personalized strategy will meet SMEs where they are by simplifying the path to purchase while building trust and long-term loyalty.

Three ways carriers can close the SME protection gap

SMEs remain underinsured because the insurance buying process often works against them. Policies are hard to compare, language is overly complex, and support isn't built for fast-moving businesses with limited resources.

Carriers that remove these friction points are better positioned to meet SME expectations and capture a largely underserved market. Here are three strategic moves that can help make it happen.

1. Get in on the ground floor of embedded insurance

Embedded insurance meets business owners at the exact moment they need it, delivered through the tools they already rely on, like accounting software, e-commerce checkouts, payroll platforms, and registration sites.

Rather than requiring a separate search or offline process, coverage options appear contextually, right where decisions are being made. This reduces friction and reframes insurance as a natural part of operations, making SMEs more likely to see its value and take action.

The opportunity is significant: Embedded insurance is projected to generate over $70 billion in gross written premiums by 2030. Investing now will position you at the center of how SMEs evaluate and manage risk.

2. Tailor communications to real business needs

Even when insurance is embedded at the right moment, the message still needs to resonate. Many SMEs don't know where to start when it comes to shopping for policies, and broad, generic messaging doesn't help. Businesses face industry-specific risks, so one-size-fits-all offerings leave owners unsure whether coverage really applies to them.

AI and data analytics are helping insurers change that. When connected to platforms that small businesses already use to manage finances, payroll, or HR, insurers can access real-time signals to tailor outreach based on how each business actually operates. A freelance graphic designer may benefit from professional liability coverage, while a growing food truck fleet is more concerned with commercial auto and workers' comp.

Personalization also helps business owners understand why coverage matters. When SMEs see that you understand their unique risks, insurance becomes less of a generic add-on and more of a practical safeguard for the business they've worked hard to build.

3. Balance digital tools with human connection

Even with embedded distribution making it easier to access coverage, trust is still earned through human connection. SMEs need the flexibility to start online and pivot to an advisor when questions or concerns arise.

Routine tasks like requesting certificates of insurance or updating information should be fast and self-service. But when it comes to claims or complex purchases, SMEs should have easy, immediate access to licensed advisors who can provide personalized, empathetic support.

Making human interaction a built-in feature strengthens SME confidence and drives long-term loyalty.

How to own the SME protection opportunity

Closing the protection gap is a win-win: Small businesses get the protection they need to weather setbacks, while insurers tap into one of the most dynamic and underserved markets.

To seize the moment, carriers must focus on embedded offerings that deliver personalized service. This ensures coverage aligns with the real, current needs of small businesses while remaining accessible and trustworthy.

SME growth shows no signs of slowing. Now is the time to rethink how protection is delivered, move beyond outdated models, and earn lasting trust from the entrepreneurs powering the next wave of economic expansion.

In an industry obsessed with innovation, one of our most under-leveraged opportunities lies in something as old-fashioned as human conversation.

Take long-term care planning (LTC). Despite decades of sobering statistics, consumer education, and product development, LTC Talk Avoidance Syndrome remains alive and well—not among consumers but among the very professionals meant to guide them.

It's a systemic issue. And for insurance executives and innovators, it's also a strategic opportunity.

Long-term care risk isn't a niche issue. According to the U.S. Administration for Community Living, nearly 70% of Americans over age 65 will need some form of long-term care, yet only a fraction are financially prepared. According to a LIMRA summary from late 2024, just 3% to 4% of adults over age 50 have some sort of insurance to mitigate the LTC expenses.

The need is obvious. So why aren't more clients protected?

Because far too often, the conversation never happens.

Financial professionals avoid the topic for fear of upsetting clients, getting bogged down in emotional resistance, or simply not feeling equipped. Clients avoid the topic because the implications are uncomfortable, the costs are intimidating, and the future is always "later."

This conversational gap isn't just bad for families. It's bad for business. Every missed LTC planning conversation is a missed opportunity to build trust, create loyalty, and provide meaningful risk management.

While many in our industry focus on performance, pricing, and product features, the most powerful differentiator may be something more human: emotional security.

Emotional security is what clients feel when they know they're protected, not just financially but personally. It's the trust that's built when a financial professional helps them face tough realities—and guides them through.

In today's commoditized landscape, emotional security has become the key to unlocking client loyalty, intergenerational planning continuity, and resilience in advisor-client relationships. It's what drives referrals, repeat business, and retention during volatile markets.

Yet most financial professionals aren't trained to offer it.

This is where insurance executives have an urgent and valuable role to play.

When we talk about innovation, we often default to digital tools, AI, or frictionless platforms. These are essential, of course. But we can't innovate our way around human fear, aging parents, or adult children caught off guard by caregiving.

We need to rethink our product development priorities.

Innovation must also mean designing products that make it easier for professionals to have difficult conversations, and easier for clients to say "yes" to planning. That's not just about simplicity. It's about psychological accessibility. It's about creating solutions that align with how people actually think, feel, and make decisions.

This kind of emotionally intelligent product design bridges the gap between protection and peace of mind. It's not just solving a financial problem—it's solving a behavioral one.

If emotional security is the goal, then LTC planning is its crucible. It's where we as an industry prove whether we're willing to lead people through life's most difficult transitions or let them face the issues alone.

This isn't just a distribution problem. It's a leadership opportunity.

• Are we equipping financial professionals with the tools and training to handle emotional resistance?
• Are we creating incentives that reward meaningful planning over quick wins?
• Are our products and messaging designed with emotional behavior in mind—or just actuarial logic?

LTC Talk Avoidance Syndrome doesn't just cost consumers. It costs us trust. It limits our growth. And it undermines the promise that our industry makes: to help people live with security, dignity, and confidence—no matter what life throws at them.

For those leading the insurance and annuity space, this is a moment to ask: How do we define innovation?

Yes, it's about technology. Yes, it's about efficiency. But it's also about empathy.

The future belongs to firms that recognize emotional fluency as a strategic asset, and emotional security as a deliverable, not just a byproduct.

This means:

• Creating solutions that address behavioral obstacles, not just financial gaps.
• Supporting financial professionals in building trust through emotionally intelligent planning.
• Embracing products that offer clients flexibility, security, and peace of mind in one package.

The LTC crisis is growing. The need for solutions is clear. What's missing isn't capability—it's courage.

Let's lead with both.

Nearly every article addressing medical inflation and its effect on property and casualty (P&C) insurance claims begins with a reference to the broader trend of lower overall inflation. While the cooling effect of inflation has notably benefited the prices of goods and commodities, healthcare services remain significantly affected. According to Peterson-KFF, in June 2024, the Consumer Price Index (CPI) for all urban consumers rose by 3.0% compared with the previous year, while medical care costs increased by 3.3%. When excluding healthcare services, the overall CPI growth was limited to 2.9%. This marked June 2024 as the first month since early 2021 where medical care prices had risen faster than the general inflation rate.

Additionally, over the span of the past two decades, the price of medical care and its subcomponents has escalated by 121%, compared with an 86% increase in the prices of all consumer goods and services. This disparity amounts to 35 percentage points. Notably, healthcare expenditures accounted for 18% of U.S. GDP in 2023, underscoring the growing significance of healthcare costs within the broader economic landscape. As such, the acceleration of healthcare inflation presents significant implications for workers' compensation and casualty bodily injury claims.

Here is a view of national health expenditures by year through 2023 as prepared by Peterson-KFF:

Factors Contributing to Medical Inflation

Several key factors contribute to the persistent rise in medical costs, including:

1. Aging Healthcare Workforce: The Association of American Medical Colleges projects that within the next decade, 40% of the U.S. physician workforce will be aged 65 or older. Simultaneously, a shortage of up to 3.2 million healthcare workers is expected by 2026. The reduced availability of healthcare professionals drives up wages for medical staff, in accordance with the principles of supply and demand. These increased provider costs inevitably flow through to patients, including those involved in P&C claims.

2. Increased Costs for Medical Equipment: The prices of medical equipment, parts, repairs and services are on the rise. Hospitals and medical facilities that rely on critical equipment face growing expenses. As with other cost increases, these higher expenses are ultimately passed down the line to payers, including insurance carriers.

3. Hospital Care Price Fluctuations: According to a report published by the National Council on Compensation Insurance (NCCI) in April 2025, although medical prices softened meaningfully in the first quarter on the combination of several trends, with physician care inflation price changes smaller than in 2024, over time, higher supply costs for equipment and supplies may also lead to higher prices in physician services, facilities, and long-term care. Hospital outpatient care prices saw a moderate growth of about 4% in the last quarter of 2024, followed by an even more moderate 3% increase in the first quarter of 2025. Inpatient care prices rose by approximately 3% in 2024.

4. Third-Party Bodily Injury Claims and Billing Irregularities: Third-party bodily injury claims are often reported late, with the demand package sometimes serving as the initial notice of a claim. These claims are frequently submitted using non-standardized forms, such as UB-04 or UB-92, which may lack necessary billing codes. This raises concerns about whether the charges submitted are inflated or exceed what is considered usual and customary for services rendered in a specific geographic area. In some cases, the answer to this question is affirmative.

5. Increase in Medical Providers and Services per Visit: According to the Workers’ Compensation Research Institute, the number of workers' compensation claims involving multiple healthcare providers has increased by 19% over the past five years. Additionally, the average number of services provided per medical visit has risen by 13% since 2017. The increased number of providers per claim, coupled with more services rendered per visit, results in higher costs per claim.

Implications for Workers' Compensation and Casualty Claims

The rise in medical inflation presents significant challenges for P&C carriers. To manage these challenges effectively, insurance providers must leverage insights from claims data. By using data-driven strategies, claims organizations can identify the most effective medical providers for each case, reduce medical costs, and improve claim outcomes for injured parties.

The adoption of advanced analytics tools will enable P&C carriers to navigate the complexities of rising healthcare costs. As healthcare inflation continues to outpace general inflation, carriers that use data-driven solutions will be better equipped to manage the financial pressures associated with rising medical expenses while ensuring that quality care is maintained for claimants.

By taking a data-informed approach, P&C insurance carriers can better position themselves to mitigate the financial impact of medical inflation, improve operational efficiency, and enhance overall claim outcomes.

As first published in WorkCompWire.

Renovation remains a defining trend in the U.S. housing market. In fact, nearly half of homeowners (48%) plan to renovate in 2025. Median budgets are climbing to around $24,000, while high-end projects often top $150,000. For high-net-worth homeowners, those numbers multiply — expansions, specialty rooms and luxury finishes are increasingly common.

Yet amid design plans and contractor negotiations, insurance is often overlooked. Homeowners should always notify their insurer before any renovation project begins. Failing to do so can result in higher deductibles, denied claims or policies that no longer fit the new risk profile. For affluent households, the stakes are especially high: The wrong coverage approach could mean hundreds of thousands in uncovered loss.

1. Unreported Exposure

Risk: Projects that cost more than 10% of a home's insured value, extend beyond a year or require moving out temporarily alter the home's risk. If insurers aren't informed, claims could be contested.

Best Practice: Notify your broker early. A simple litmus test: If you're moving out, disabling security systems or investing more than 10% of insured value, call your advisor. This allows for adjustments before the risk materializes.

2. Policy Reclassification and Deductible Shifts

Risk: Large-scale renovations can require a shift from a standard homeowner's policy to a builder's risk or course of construction policy. If overlooked, deductible surprises can surface. Some carriers apply a construction-related deductible many times larger than a typical homeowner's deductible.

Best Practice: Confirm with the insurer whether builder's risk coverage is required. These policies are designed for homes "in transition." Establishing them early prevents costly disputes if a fire, water loss or theft occurs mid-project.

3. Contractor and Subcontractor Liability

Risk: Renovations introduce third-party exposures. Hiring a contractor with inadequate general liability (GL) or workers' compensation (WC) coverage creates liability exposure. If a subcontractor is injured or damages property without proper insurance, a carrier may be left without recourse.

Best Practice: Require certificates of insurance from all contractors and subcontractors. For high-value properties, ensure GL limits are consistent with the replacement value of the home. Carriers frequently request this documentation and can help validate that coverage is adequate.

4. Underinsurance During and After Renovation

Risk: Renovations increase replacement costs. Without a coverage adjustment, reimbursement may only be for the pre-renovation value. Replacement costs surged more than 55% between 2020 and 2022, driven by inflation and supply chain challenges. If the homeowner's coverage hasn't kept pace, a catastrophic loss could leave the homeowner significantly underinsured.

Best Practice: Request periodic revaluation during and after construction. Policies with extended replacement-cost features or inflation guards can help, but they aren't substitutes for accurate dwelling limits. Insuring your home to value is critical after a renovation project.

5. Vacancy, Theft, and Fire Hazards

Risk: Many renovations involve temporary vacancy or disabled security systems, which dramatically change exposure. Standard homeowner's insurance often excludes theft or vandalism after 30 or 60 days of vacancy. Fire hazards from activities like sanding floors or rewiring electrical systems elevate risk.

Best Practice: Inform the carrier if living elsewhere during a project. Confirm that belongings in storage remain covered and that valuables such as artworks, if moved off premises, are stored in approved environments. Ask whether endorsements for theft of building materials, or a course of construction policy should be added while work is underway.

Closing Perspective

The numbers are clear: 98% of homeowner's insurance claims involve property damage, with average claim severity approaching $24,000 in higher-risk areas. For wealthy homeowners undertaking renovations, those costs can climb into six figures, so you need to make sure the proper coverages are in place prior to starting your project.

Renovation is a fundamental change to a home's risk profile. Treat it accordingly. By contacting the broker early, validating contractor coverage, adjusting limits during construction, and re-evaluating after completion, the insured is protecting both their property and their investment.

After weathering the initial shockwaves of the pandemic, employers thought they had seen the worst of healthcare cost volatility. They were wrong. What started as delayed screenings and deferred care in 2020 has morphed into a sustained surge in catastrophic claims that's pushing stop-loss insurance premiums to breaking points.

According to the Segal Group, medical stop-loss premiums increased an average of 9.7% for plans that maintained their deductibles – a figure that understates the pain many employers actually experienced.

The scale of these increases is a perfect storm of cost pressures that have been building since COVID-19. The delayed impact of missed cancer screenings during the pandemic is now showing up as advanced-stage diagnoses requiring expensive treatments. Meanwhile, specialty drug spending continues its relentless climb and will account for more than half of all drug spending this year, according to Mercer.

The industry is also seeing significant increases in costs associated with premature birth and neonatal care as medical advances allow healthcare providers to save babies who wouldn't have survived in previous decades. While these outcomes represent medical miracles, they come with substantial financial implications for employers and their stop-loss carriers.

Managing the unmanageable

This cost crisis is accelerating a shift toward self-funded arrangements that provide something fully insured plans cannot: visibility into where healthcare dollars are actually going. While employers can't stop specialty drug prices from rising or prevent the continuing impact of delayed screenings, claims data transparency has become a strategic necessity for managing these unavoidable pressures.

The difference comes down to who controls the data. In fully insured arrangements, carriers essentially own the claims information. When employers receive renewal quotes, they get limited visibility into what's driving their costs.

Self-funded arrangements with third-party administrators (TPAs) break open this black box. TPAs work for the employer, not the insurance carrier, and provide accurate, meaningful claims data on a regular basis. This transparency creates opportunities that simply don't exist in fully insured plans.

The symbiotic relationship between TPAs and stop-loss carriers amplifies this advantage. Stop-loss insurers require detailed claims data to assess risk and process payments, which means employers gain access to comprehensive information about their healthcare spending patterns. This visibility enables strategic decision-making about how to navigate an increasingly expensive environment.

Strategic cost management

Armed with detailed claims data, employers can move beyond simply absorbing premium increases to actively managing their healthcare costs. The transparency provides insights that enable targeted interventions and strategic adjustments to plan design.

Given the overall higher costs associated with providing health coverage to employees, employers must analyze their deductible levels and associated claims activity on an annual basis. The data helps employers evaluate critical questions: What impact would a high-dollar claim have on cash flow at a specific deductible amount? What is the risk tolerance for higher deductibles versus the cost savings from lower premiums?

Many employers are increasing their risk tolerance to manage stop-loss costs. A common approach involves raising deductibles, which increases the employer's financial exposure but significantly reduces stop-loss premiums. This strategy requires careful analysis of cash flow capacity and risk tolerance, but the premium savings can be substantial. The key is using claims data to make these decisions strategically rather than just reacting to price increases.

Claims transparency also enables employers to identify trends before they become expensive problems. For example, if data shows a high propensity for diabetes among employees, employers can implement targeted interventions like nutritional counseling or fitness programs. Early intervention costs far less than treating advanced diabetes complications.

Best practices for the new reality

Several strategies can help employers manage rising stop-loss costs while maintaining quality coverage. The foundation is comprehensive data analysis combined with plan management.

Wellness programs are one of the most effective cost-containment strategies. Simple initiatives like offering $100 incentives for cancer screenings or annual physicals can prevent much more expensive treatments down the road. Disease management programs can be particularly effective for common conditions like diabetes, where lifestyle interventions can dramatically reduce complications and costs.

Employers should also consider comprehensive preventive care programs that extend beyond basic screenings. On-site health screenings, flu shot clinics, and partnerships with local healthcare providers can catch health issues early when they're less expensive to treat.

Finally, employers should regularly benchmark their stop-loss coverage against market alternatives. The current environment of rapidly changing costs means that yesterday's optimal coverage structure may no longer be appropriate.

The transparency imperative

The combination of rising stop-loss costs and the transparency advantages of self-funded arrangements is creating a fundamental shift in how smart employers approach healthcare benefits. The tail from COVID is still there, and it remains very prominent. The delayed impact of missed preventive care will continue driving costs for years to come.

Employers that gain access to their claims data and use it strategically will have significant advantages over those operating in the dark. The transparency enabled by self-funded arrangements with TPAs and stop-loss coverage allows employers to take an active role in managing one of the largest expenses on their balance sheets.

Healthcare costs will continue rising, but employers can choose how they respond. For many, that path leads directly to self-funded arrangements that put claims data back where it belongs: in the hands of the employers who ultimately pay the bills.