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 Standardized proposals position insurance companies as outdated in an era where personalization has become standard across industries.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.

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.