In the complex world of insurance, underwriters and brokers play crucial roles — but they don't always see eye to eye. Brokers act as trusted advisors, helping clients find the right policies, while underwriters evaluate risks to keep coverage financially sound. Even though brokers and underwriters share an end goal, miscommunication and disconnected data often create friction between them.

What's the fix? Better data sharing and governance. 

Here's why the two factors are key to improving collaboration, building trust, and driving efficiency across the insurance industry.

The Role of Data Sharing

Underwriters and brokers can benefit from seamless, secure data sharing with enhanced risk assessment. For example, brokers can send underwriters detailed client data about applicable risks, such as operational metrics or a history of claims, enabling the underwriter to evaluate the risk with much greater precision. Underwriters can also share dynamic insights with brokers, so they can create custom policies that might better suit client needs. In general, better data sharing could reduce redundant communications and ad hoc, manual entry, streamlining the process of issuing policies and processing claims.

Data Governance

While secure data sharing is essential, the data itself must also be governed, not only to enable compliance and security, but also to improve the confidence of both parties in the integrity and authority of the data to be shared. In terms of compliance, mandates such as the California Consumer Privacy Act (CCPA) in the U.S. and the General Data Protection Regulation (GDPR) and Solvency II in Europe require especially strong data governance capabilities to align data with these regulatory requirements. Additionally, data governance can establish rules about which users can access which data, to protect sensitive client and business information from breaches and misuse. Finally, data governance maintains accuracy across shared platforms, reducing errors and improving decision-making. 

The Limitations of Current Technology

One would think that in this age of powerful data lake houses and other cloud platforms, data access, governance, and sharing would not be an issue. Although this is largely true, the gray areas of data lake houses — that is, the situations in which data lake houses alone cannot enable seamless collaboration — are becoming larger and larger.

Although data lake houses are considered capable of storing all data to support every need, they cannot store all data, and they will never be able to do so. During mergers and acquisitions (M&A), for example, the data resources of entire companies will be temporarily unavailable. And in the case of multi-cloud infrastructures, in which companies leverage the capabilities of different cloud providers, certain datasets or workloads will never be stored in the main, central lake house. Data-export restrictions, to comply with data privacy and other laws, are yet another reason why some data will always remain distributed.

From a collaboration perspective, when data is distributed, it is simply not immediately accessible, and therefore not governable, especially if it changes rapidly.

Even if a company did manage to keep all of its data in a data lake house, data lake houses have a few limitations with regard to collaboration. They lack universal-semantic-layer functionality, which means that some data within the lake house will not be immediately usable. Universal semantic layers automatically transform data from myriad applications and departmental silos into the form required by the end user. Similarly, data lake houses do not provide extensive search and discovery features with comprehensive access controls, presenting another obstacle to seamless underwriter-broker collaboration.

Logical Data Management: The Enabler

It is evident that underwriters and brokers need a solution — one that works either standalone or alongside data lake houses and other cloud platforms — and one that can connect disparate data sources and create a semantic layer above all of them, to enable seamless, secure, and governed data sharing, in real time.

One such solution is logical data management. This is a data management approach that operates differently from traditional, physically oriented data management approaches that rely on extract, transform, and load (ETL) processes. In contrast, logical data management platforms enable data management, including real-time access and governance, without first having to physically replicate data into a central repository. Organizations with data lake houses can easily implement logical data management platforms to include other cloud and on-premises data sources, even though the data may be geographically separated or otherwise in a functional silo.

Logical data management platforms enable insurers to create a unified view of all related data for brokers and underwriters. Leveraging APIs and open insurance standards, brokers and underwriters can use logical data management platforms to engage in seamless collaboration. AI-powered analytics can further enhance the potential of underwriter-broker collaboration, helping them to gain predictive insights in the realms of risk assessment and policy personalization.

The Missing Link

The entire insurance ecosystem benefits when underwriters and brokers can collaborate effectively and securely through the seamless sharing of well-governed data. For starters, policy issuance becomes faster, leading to shorter turnaround times, which helps improve client satisfaction and gives companies a competitive edge. With more accurate data, risk pricing becomes more precise, ensuring better profitability and fewer disputes. Plus, this collaboration enables the creation of client-focused solutions, offering policies tailored to specific needs and strengthening the relationship between brokers and their clients. On top of that, clear communication fosters trust and transparency and paves the way for long-term partnerships built on mutual respect.

As the industry continues to evolve, the need for seamless collaboration between brokers and underwriters is only going to grow. Embracing advanced data-sharing practices and strong governance helps bridge gaps and sets the groundwork for innovation, agility, and resilience in what's becoming an increasingly complex market.

Navigating the complex considerations affecting organizations and their third parties presents challenges for insurance professionals advising businesses on compliance matters. As external experts, insurance professionals can often provide key guidance on verification requirements that protect organizations, but this takes time. What begins as a simple call or email can often cascade into a series of lengthy exchanges — turning a straightforward inquiry into a time-consuming back-and-forth.

AI solutions are emerging as supportive resources that strengthen insurance knowledge, expertise and efficiency. What makes AI a game-changer isn't automation alone, nor is it replacing human expertise — it's how it equips those with insurance expertise, and those without, with intelligent insights to better understand what items are needed to achieve insurance compliance.

According to a recent survey, 90% of small business owners are unsure about the adequacy of their coverage. AI serves as an intelligent assistant, quickly surfacing important information and providing context when needed. This allows both insurance experts and non-insurance professionals alike to understand what's needed and why it matters, fostering alignment and transparency.

The impact includes faster verification, fewer coverage and requirement gaps left unaddressed, and faster time to compliance. As Gartner predicts a doubling in risk and compliance technology spending by 2027, companies recognize that AI solutions that enhance collaboration deliver the greatest returns.

In insurance compliance, AI provides benefits in three ways: quickly identifying emerging risks, providing deeper insights and analysis, and enabling informed decisions — all while reducing manual effort and enhancing accuracy.

AI provides intelligent risk insights

Insurance verification has long operated as a black-and-white checkbox: compliant or non-compliant. This binary approach frequently disrupts insurance professionals, who must answer repeated basic questions from clients and their third parties, taking time away from key advisory work.

AI can enhance the process by offering intelligent, real-time insights within existing workflows. The technology automatically screens uploaded certificates, instantly identifying non-compliant documentation and generating precise, tailored communications to insurance professionals in their preferred language and professional context.

By acting as both an intelligent flagging system and a nuanced translator, AI eliminates the time-consuming back-and-forth that typically delays compliance processes. Insurance professionals can now focus on strategic risk assessment, while the AI handles routine verification, communication, and alignment across different stakeholders.

For instance, the system can identify specific documentation needs. Instead of a simple status notification, an AI-powered platform can share what's needed in clear language, why it matters, and how to obtain it.

This clarity fosters an environment where insurance knowledge is seamlessly integrated into the process, creating alignment across all parties involved. With everyone operating from the same information, AI tools can streamline communication and reduce confusion.

The result is a more collaborative, transparent, and simplified process in which AI can handle routine inquiries. This allows professionals to trust that compliance is properly managed without the administrative headaches.

Meanwhile, third parties and their agents benefit from improved transparency, easy communications and automated notifications that demonstrate their compliance status, establish them as reliable vendors, and facilitate timely payments — all of which strengthen their business relationships.

AI centralizes compliance and enhances visibility

Compliance verification often involves multiple parties with different priorities and levels of insurance knowledge, which can create communication challenges and process inefficiencies.

For insurance professionals, AI transforms client advisory services through three key capabilities: providing real-time visibility into compliance status, identifying and clearly communicating specific documentation needs, and enabling automated, precise notifications to address emerging compliance gaps.

The transparency provided by these systems allows third parties to see precisely where they stand on compliance at any moment, enabling them and their insurance agents to take steps toward resolution. Organizations gain comprehensive visibility into compliance trends across their network, identifying patterns and opportunities for process improvement that might otherwise remain hidden in dispersed data.

AI's ability to analyze large volumes of compliance data also provides risk insights tailored to the appropriate industry context, flagging potential gaps in compliance that can be addressed by humans before they escalate into business disruptions. While AI can't yet fully interpret complex or conflicting information, these automated alerts help identify areas needing expert attention.

AI systems can provide instant responses to routine questions, highlight complex insurance industry terminology, and offer contextual guidance as end users navigate through the system, thereby enabling insurance professionals to dedicate their expertise to more nuanced and strategic case analyses. This creates efficiency while equipping end users with insurance knowledge, ensuring specialized expertise is applied where it adds the most value and creating a more streamlined experience for everyone involved. Requirements remain firmly in place, but the path to meeting them becomes clearer and more transparent.

AI delivers business impact

For insurance professionals, the business case for AI extends beyond helping clients achieve processing efficiency — it can enhance their own service delivery and advisory capabilities. AI creates value through three strategic dimensions: efficiency gains, speed to compliance, and relationship enhancement.

Time savings represent one of the most immediate benefits, as AI automates routine verification tasks and provides instant feedback. This acceleration removes bottlenecks that delay project starts, contract finalizations, and service initiations.

Coverage verification quality also improves. AI doesn't get distracted, tired, or rushed during busy renewal periods. Organizations typically see significant improvements in compliance rates when implementing AI-powered solutions. It's always helpful, ready, and insightful. It also flags renewals well in advance, giving everyone ample time to meet deadlines — eliminating those last-minute rush requests that disrupt workflows. This improvement represents real risk reduction through faster time to compliance and potential cost avoidance from unexpected claims that could arise from non-compliance.

Perhaps most valuable for insurance professionals is how AI can transform their client communications by automating timely, precise notifications across all parties. These systems ensure instantaneous, compliant updates that eliminate missed deadlines, reduce administrative stress, and keep insurance agents, businesses, and third parties seamlessly aligned — transforming potential communication chaos into a streamlined, proactive workflow.

Companies that adopt AI compliance tools can gain competitive advantages through faster onboarding, stronger protection, and more collaborative relationships. These advantages translate directly to bottom-line results through reduced administrative costs, lower risk exposure, and improved operational efficiency.

The future of insurance compliance

AI is transforming how risk insights are distributed and leveraged across the insurance compliance ecosystem. By providing relevant information exactly when it's needed, AI helps organizations, their third parties, and insurance professionals work together more effectively.

As these technologies continue to evolve, increasingly sophisticated applications will empower everyone involved in the compliance process — whether they have years of insurance expertise or are new to these requirements. These systems will enhance visibility across the compliance ecosystem, automate review and renewal workflows, and facilitate more transparent communication channels between all stakeholders.

With AI-powered compliance tools, insurance compliance can become even more efficient, accurate, and collaborative. Insurance compliance is evolving from a necessary process into a strategic advantage that strengthens business relationships while enhancing protection. The future belongs to companies that recognize compliance is about creating value, not just checking boxes.

Recent improvements in the dexterity of humanoid robots and of the AIs that control them have led to a surge of publicity about their prospects, not just for industrial uses but as possible helpers in the home. Elon Musk, never one to shy away from a bold prediction, says there will be more humanoid robots than people on Earth by 2040. 

So my ears perked up when I heard that a half-marathon in Beijing over the weekend would include a classification for two-legged robots, as a way of showing off all the progress Chinese scientists have made. The robots didn't do so well.

Of the 21 robots that were entered, one veered into a barrier right at the start and shattered, while throwing its human handler to the ground. Another's head fell off. Still another had smoke pour from its head, while one ran in the wrong direction at times, then sat down and declined to get up. 

All the robots took large amounts of human attention: changing batteries, spraying water on the robots to reduce overheating, etc. Many had to be tethered to controls held by a human, who ran (or, more often, walked) behind the robots. 

Only four of the robots finished in the allotted time of less than four hours, and the fastest took more than 2 1/2 times as long as the human winner, who clocked in at an hour and two minutes. 

None of that is to say that humanoid robots have no future. Enthusiasts liken the race to the Grand Challenge for autonomous vehicles held in 2001 that also produced embarrassing results but, 24 years later, has Waymo providing 200,000 fully driverless, paid rides each week in its robotaxis. 

But the race does suggest the need for a sober look at the hype about robots, to set expectations for the insurance industry over the next five to 10 years.

If you want to see for yourself what the race looked like, here is a video summary. (The broadcast cuts away after 50 seconds.)

For me, the upshot of the race, in keeping with other progress reports on AI and robotics, is that, no, humanoid robots won't outnumber humans in 15 years. Not even close. 

They will be especially scarce in homes, where they will accomplish little while costing as much as a car. (Musk says his Optimus robots will cost $20,000 to $30,000 when they become available next year — and he has a long history of overpromising.) I dislike doing dishes and laundry, vacuuming and dusting as much as the next person, but I'm not going to pay tens of thousands of dollars to avoid minor chores, especially when my Oura ring keeps telling me to get up and stretch my legs. And you want me to maintain the thing? The extent of my trouble-shooting consists of turning a device off and then turning it on again. 

Robots have much better prospects in manufacturing, where they are already a force and are helping workers' compensation carriers and employers keep reducing injuries and, thus, premiums. The robots don't look at all human, but they have automated an awful lot of the assembly in electronics factories and others. Amazon and others use robots to handle much of the grinding work in warehouses. 

Progress in manufacturing will continue, likely rapidly, because robots can benefit from improvements in AI while operating in a controlled environment, not having to worry about maneuvering in a small kitchen full of  little kids and a puppy.

Even in manufacturing, though, there are limitations. The Wall Street Journal reports, for instance, on how hard it's been for shoemakers like Nike to move work to automated factories in the U.S. and out of Vietnam and China. It turns out that the soft materials in the upper parts of shoes change consistency based on heat and humidity. Skilled human workers can adapt, but robots have trouble. Robots also struggle with the fact that no sole of a shoe is quite the same as any other. They have trouble, too, with the constant changes in shoe design; robots function best when they can finetune their handling of a task and then do it over and over and over and over. 

Those of us of a certain age long for Rosie, the maid in "The Jetsons." My daughters tell me the updated version is "Smart House," a Disney movie in which a boy tries to keep his father from dating by programming a house to be a surrogate mother. Or there's "Cassandra," a recent series about a family that moves into a decades-old smart home and reactivates its dormant AI assistant, who was once a human and who was transferred into an AI system. 

Whatever your hopes are for robotics in the long term, as you think about the prospects for the next five or 10 years, especially in the home, it's worth keeping in mind this image of the robot that crashed, shattered and threw its handler to the ground only a few feet past the starting line of the Beijing half-marathon:

Cheers,

Paul

When alleged errors or breaches of duties give rise to professional or management liability claims, renewal premium increases are likely to follow. Policyholders often push their brokers to remarket the account in pursuit of more competitive pricing. The question is: Should insurance programs be remarketed to avoid any post-loss premium increase? 

The answer is often "no" (as long as the carrier is acting in fairly good faith and the increase is reasonable). Doing so is often penny wise, pound foolish. 

Here's why:

If the carrier has tendered the claim, they are demonstrating good faith by doing so (particularly if it's a claim that falls in a gray area). The fact that they are willing to offer renewal terms is additional testament to that good faith. It's uncertain whether another carrier would have taken the same coverage stance or been more aggressive in disclaiming coverage. Brokers and policyholders are better off working with insurers that have demonstrated their willingness to stand by them. Additionally, if the client has built a long history with this particular insurer and coverage is replaced, the client is effectively beginning a new relationship.

Even if the carrier has only shown partial good faith, covering only a portion of the claim (while disputing coverage for a portion of what should be covered damages) it may still make sense to renew coverage. In such cases, brokers (and the insured's counsel) may wish to challenge the coverage decision. When making such challenges, policyholders are likely to encounter less abrasion when coverage is still with the insurer in question – those who elected to replace coverage immediately following a claim may encounter greater resistance.

It's important to maintain a good relationship with the insurers during the claims process. It's not that replacing coverage will necessarily change the insurer's coverage determination, but it could make the claims process and any coverage determinations for future related claims more contentious.

Replacing coverage also leaves open the possibility for errors. Strong directors and officers (D&O) programs are often built over time, and rounds of policy term negotiations. Any enhancements obtained will need to be carried over to a new carrier. Errors such as incorrectly applied retroactive dates, advanced prior and pending litigation dates, overly broad related claims clauses or specific matter exclusions, and unaccounted for subsidiaries, are just a few examples of very basic general errors that can occur when replacing coverage, all of which can have a crippling effect.

As a practical matter,  replacement can also have unintended coverage consequences. Take the following example: An insured maintains a D&O policy, in which the 2024-2025 term is with carrier "A". A claim is noticed to the D&O carrier during that term, and the carrier has agreed to tender coverage. Shortly afterward, the carrier provides a renewal with a 35% increase, which prompts the insured to replace coverage for the 2025-2026 term, issuing a new policy with carrier "B". Months into the new term, the organization receives a new, separate demand, which is tendered to the new carrier. However, the new carrier has determined the allegations are similar enough to the prior litigation, and per the policy's terms (which will very likely include a specific matter exclusion), the new carrier disclaims coverage because it is "related" to the initial claim the year before. Carrier "A," however, has determined that the two claims are not related, and also disclaims coverage. Such a situation sets the grounds for an obvious battle.

These are just some of the many considerations brokers and their insureds should consider prior to making premium-based decisions, which may be more harmful than beneficial. That being said, there are situations in which it may be prudent to consider another carrier, namely: if the carrier is perceived as being overly contentious with what should be a covered claim, if the renewal terms being offered are more restrictive, or if the renewal premium is unreasonable.

Enterprises are engaging with agentic AI in five distinct ways:

1. Agent-Open: Developers are building AI agents on open-source Agent Development Kits (ADKs) such as LangChain, LLamaIndex (Meta), Haystack (Deepset), and Transformers Agents (HuggingFace).
2. Agent-Closed: Developers are building AI agents on "big-software" ADKs from the likes of Microsoft, IBM, Salesforce, and SAP.
3. Data-Small: Data engineers are building data pipelines on which to train and inference proprietary AI agents using ADK-like tools from mainly Databricks and Snowflake. I call this "small" because only a small amount of enterprise data is typically fit for consumption by AI.
4. Data-Big: This approach makes major investments in ontologizing and unifying the full corpus of enterprise data to be consumable by AI at scale at some point. Some enterprises are attempting this work themselves; others are paying Palantir to do it with their Foundry platform. These are big, hairy engagements; think: SAP enterprise resource planning (ERP) of AI.
5. Expert Agents: The first four approaches are for building agents to streamline work and workflows for productivity and operational efficiency. An expert agent is, for example, the clinician in a clinical workflow, i.e., the cardiologist or nephrologist. (Yes, it's coming.) These expert agents are by their nature GPU-chip intensive, andm as NVIDIA makes the GPU chips powering 90% of AI, their CUDA, NeMo, and Clara tools are by far the most cost-effective option for building expert agents.

Enterprise leaders seem to be asking two questions, the first of which is, "Can we connect an agent--however it's built--to our core systems?"

Google has built--and open-sourced--over 600 connectors against the likes of Microsoft Office, Adobe Acrobat, Salesforce, Workday, and ServiceNow, enabling agents of any origin to understand the data models of these core enterprise systems. These connector models are trained to understand different data elements, so in a Salesforce customer relationship management (CRM) dataset, for example, the connector understands "What's an account?", "What's an opportunity?", "What's a product?" It also knows when to access data, maintaining permissions "seeing" only what it's authorized to see.

The takeaway: You don't need to use Big Software ADKs to build agents interacting with Big Software datasets.

The second question is, "Can we have agents of different origin on the same team? Will teams built on one ADK work with teams built on another?"

Anthropic's open-source Model Context Protocol (MCP) has rapidly become the industry standard for agent-to-tool, and agent-to-data integrations. For agent-to-agent communications, the standard is Agent Protocol (AP).

Recently, Google, in league with 50 technology and consulting partners, announced the new Agent-to-Agent (A2A) protocol. A2A offers significant upgrades over AP including enterprise-grade security by default, support for long-running and asynchronous tasks, modality-agnostic communication (AP is text only, A2A adds images, audio, video), and the biggie: vendor-neutral and framework-agnostic design. This reduces vendor lock-in and allows organizations to compose best-of-breed agent networks easily.

It looks like A2A, open-source and available by the end of the year, will become the industry standard for agent-to-agent communications working seamlessly with MCP on agent-to-tool, and agent-to-data interactions.

The takeaway: McKinsey pegs the current market for AI products and services at $85 billion, forecasting growth to a low expectation of $1.6 trillion and a high expectation of $4.7 trillion by 2040. That sets the over/under line at $3.2 trillion: Bet the over.

In the last half of 2024, there were over 300 announced M&A transactions in the insurance space, valued at more than $20 billion. While the number of deals was down due to economic uncertainty, persistent high interest rates, and regulatory scrutiny, deal value was higher than normal.

What's driving a substantial amount of insurance M&A activity?

The third-party administrator (TPA) market.

TPA investment and acquisitions are nothing new. But several factors are creating a hotter market, encouraging acquisitions now and over the next few years:

• Private Equity Demand: Private equity interest is driven by a desire to deploy capital and achieve greater returns through growth potential and operational efficiency.
• Top Line Growth: TPAs are embracing a growth-through-acquisition model – improve top-line growth through acquisition, then implement cost takeout initiatives to improve business unit margin.
• Market Consolidation: TPAs have to achieve critical size to effectively provide services across multiple lines of business and remain relevant in the market – consolidation is how larger TPAs remain relevant.

The result is clear – acquire or be acquired. As a TPA, your acquisition strategy is either to add services to provide multiple lines of business at scale or become focused on a particular niche and establish market dominance through expertise.

Where Do TPAs Go From Here?

In the midst of this acquisition rush, TPAs (and other interested parties) are always seeking the next big opportunity for growth. But not all growth opportunities are the same. TPAs need to be forward-thinking. Over the next decade, the best TPAs will:

1. Leverage automation, technology, and AI to significantly reduce both labor and non-labor costs to aggressively improve margin
2. Develop a customer-centric selling model through upsell and cross-sell opportunities, using multiple lines of business to meet evolving client needs and gain greater client penetration
3. Provide clients with expertise to solve increasingly complex challenges through services and claims administration
4. Use a target operating model that encourages integration of new acquisitions to achieve synergies and improve overall enterprise performance through shared services and enterprise corporate functions

No single acquisition will fit every TPA – a variety of factors will influence which particular acquisition makes sense to a given firm.

Instead, TPAs that are seeking to grow across multiple lines of business should focus on markets that are primed for expansion. While there are several opportunities, there are four growth markets that TPAs should strategically evaluate their desire and capabilities to serve:

1. Healthcare Claims and Administration:

The combination of increasing costs around healthcare administration, complexity of health claims, and shifting demographics suggest a growing, lucrative market for TPAs. But investors have more than just top-line revenue growth to focus on. AI and automation opportunities can significantly reduce medical errors, accelerate claims processes, and reduce costs by reducing manual effort and standardizing key processes. TPAs have an additional reason to enter this space – the growth of the market is not just driven by processing insurance claims. Employers seeking to self-insure and expanding healthcare networks (e.g., hospital networks) provides another customer base. There are a variety of services that can be provided – bill review, long-term care assistance, or mental health care. Future-minded TPAs will assess opportunities to leverage data and analytics insights to provide opportunities to reduce healthcare costs. PE firms may seek to purchase or develop a TPA to administer claims associated with wholly owned long-term facilities.

2. International Claims

The insurance market in emerging economies is expected to experience significant growth over the next five to 10 years, far outpacing growth in established Western markets. Consider that life insurance premiums are expected to grow by approximately 6% in countries such as China, India, and Latin America, compared with the standard 1-3% annual growth seen in the U.S. Property and casualty (P&C) is expected to follow a similar trend. Protection gaps need to be addressed, and a strengthening middle class will have disposable income to address them. Insurance providers may have an interest in entering those markets but will likely partner with claims administrators to support global markets. Growth in the market and the opportunity to leverage global shared services models to significantly reduce cost position TPAs to be critical as carriers expand. Strategically, global TPAs will need to consider regional strategies to navigate geopolitical risk (e.g., supply chain/tariff challenges and international sanctions against countries).

3. Cyber-Related Claims

Increasing frequency and severity of cyberattacks, such as ransomware, data breaches, and phishing, are driving demand for cyber insurance, particularly for businesses. Small and medium-sized businesses are becoming more aware of their vulnerabilities. TPAs have at least two types of services to focus on through acquisition. One opportunity is in the B2B space, where TPAs can provide claims adjudication and processing in support of businesses facing a variety of cyber-related issues. In that space, TPAs may focus on fraud detection services, particularly combating AI-enhanced threats. The second opportunity is for TPAs to focus on cyber insurance sold through an embedded insurance model. One example that will become increasingly common: Individuals who purchase software or AI tools will have the opportunity to buy basic cyber insurance at the point of sale, with the opportunity to enhance coverage for specific AI protection gaps (e.g., protection against intellectual property (IP) infringement tied to AI-driven operations).

4. Legal Claims Administration

Over the next decade, legal claims administration will present another frontier for TPAs seeking to grow. Specifically, class action lawsuits and mass torts will provide opportunities for TPAs to administer legal claims. Class action lawsuits will arise, particularly as data breaches and greater data connectivity will take center stage for businesses across all industries. And mass torts will continue to be more relevant – evolving legal theories are increasing mass tort possibilities, both through expansion of harmed parties and creative theories on liability. For example, public nuisance claims were used in opioid litigation and are being considered for climate change and data privacy litigation. The challenge for law firms is that they do not do well in handling settlement, tracking down claimants, and managing documents. TPAs that can leverage technology to simplify and support law firms will position themselves well in the market. For example, smart contracts could automate the distribution of settlement funds to class members. Once eligibility criteria are verified, the smart contract could release payments directly to claimants, reducing administrative costs and delays.

The Price of Admission

While each of these areas presents a significant growth opportunity for TPAs, there are barriers to entry.

• Regulatory and Compliance Challenges: Each of these services has stringent regulatory considerations – for example, international claims administration requires understanding jurisdiction-specific laws and regulations. TPAs need to have a plan for how they will satisfy the compliance obligations associated with any new business unit or line of business.
• Technology Integration: Most TPAs rely on proprietary systems for claims processing and data management. This forces TPAs to take one of two paths – allow newly acquired businesses to continue to run as-is, without integration, or attempt data and platform migrations. Organizations need a technology integration strategy as a part of their M&A.
• Market Competition and Valuation: In the current economic environment, high-interest rates and economic uncertainty make only the best deals viable. Increased competition drives up valuation, making deals less financially viable to many of the firms. Specialized TPAs, such as healthcare of cyber-focused TPAs, face valuation inflation risk.

TPAs attempting to grow will overcome these challenges through comprehensive strategy and a commitment to providing services to carry them into an evolving market. TPAs should use strong due diligence, explore partnerships, and evaluate lessons learned from competitors' acquisitions to give themselves the best chance for success.

Agentic AI represents a paradigm shift. It perceives information; understands the context and intent; and autonomously creates, modifies, and orchestrates workflows through contextual reasoning and continual learning. These AI agents enable enterprises to be perpetually adaptable to the dynamic needs of customer and market conditions. 

The true revolution emerges in human-AI collaboration and its ability to drive business transformation across industries.

What is Agentic AI?

An Agentic AI framework has the following key components:

• Model - To reason over goals, planning and generating responses
• Tools - To retrieve data and perform actions by invoking an application programming interface (API) or services
• Orchestration - To maintain the memory, state, tools, data acquired/retrieved etc.

Why the buzz?

Agents have been in existence in various forms such as robotic process automation (RPA), workflows etc., but their applications were limited to non-complex and rule-based tasks that lacked adaptability to dynamic needs and often require human intervention. This is where Agentic AI strikes a perfect symphony for knowledge and complex tasks. With autonomy at its core, Agentic AI moves from an assist role to business transformation.

In today's volatile, competitive and complex business environment, enterprises and businesses are looking to continually adapt. The recent advancements in AI, IoT, robotics, etc. together with the need to drive efficiency and agility, make Agentic AI suitable for various applications across industries. They range from horizontal services such as knowledge management, quality assurance, HR, finance, etc. to industry-vertical services such as underwriting/risk assessment, loan processing, market research, claim processing, fraud detection, clinical management, cyber security, customer servicing, supply chain management, self-driving cars etc.

Agentic AI is enabling a paradigm shift, and new business models are emerging. Enterprises that were focused on the software-as-a-service model are pivoting to service-as-a-software. Also, there is a rise in the number of Agentic AI frameworks such as AutoGen, LangChain, LangGraph, CrewAI, Agentspace etc. to realize this vision.

Four potential applications in industry

Below is a list of a few applications in the healthcare, insurance, manufacturing and technology services industries.

1. Drug discovery – It is a complex problem in the scientific community, which involves years of research, analysis, experimentation and collaboration to arrive at possible solutions such as drug discovery for COVID-19. The challenge is that solutions must adapt to dynamic needs, and new information may become available (such as new variants of COVID-19).

This complex biological problem requires an approach where it can be decomposed into manageable sub-tasks with specialized tools for targeted problem areas (specialized agents, digital twin, research databases, etc.). The process involves brainstorming of ideas (e.g.: brainstorming agent), extracting and synthesizing information from research databases (e.g. search agent), experimental tools such as genome sequencing, analyzing the results (e.g. analysis agent), reasoning the various outcomes simulated using digital twin via techniques such as chain-of-thought, graph-of-thought or tree-of-thought along with feedback loops for continuous learning.

2. Claims Management – It is the core of customer servicing in healthcare and insurance and involves complex process and workflows to determine eligibility, process large datasets such as electronic health records (EHR), X-rays, treatment procedures, diagnosis, recoveries, medical bills, etc. and payouts. For instance, in group benefits (such as disability), this time-consuming problem requires a human-in-the-loop approach to reduce the financial burden and accelerate recovery to participants.

The claim intake agent involves sensors, spatial data about the accident environment, visual language models to analyze the injury details; validation and fraud detection agent to process the claim -- spatial and image analysis, knowledge graph and digital twin to test the hypothesis space. Once a hypothesis is validated, a decision-making agent must weigh in on the job-specific impact, claimant's ability and timelines to recovery and accelerated payout via blockchain. The agent can further actuate its role as a recovery and support agent to continuously monitor the progress, adjust the payout based on progress and optimize recovery to improve overall experience with explainability.

3. Manufacturing - From controlling the flow of production lines to customizing products to making suggestions for improved product design, Agentic AI is likely to have multiple applications in smart manufacturing.

Data from sensors attached to machines, components, and other physical assets in factories and transportation can be analyzed by Agentic AI systems to predict wear-and-tear and production outages, avoiding unscheduled downtime and associated costs to manufacturers. German AI start-up Juna.ai deploys AI agents to run virtual factories, with the aim of maximizing productivity and quality while reducing energy consumption and carbon emissions. It even offers tailored specific goals, such as production agents and quality agents.

4. Technology Services – Enterprises need to be perpetually adaptable, which hinges on speed, quality and cost. Agentic AI will play a prominent role by emulating capabilities of:

• "Requirement analysis agent" such as creating user stories based on standards and template (LLM + RAG)
• "Design agent" to interpret the requirements and create a blueprint based on approved technology, architecture patterns, data flows/source to target mapping (e.g.: for data migration efforts) etc.
• "Data engineering agent" for automated data discovery, build ingestion pipelines leveraging appropriate connectors
• "Data quality agent" for AI/ML driven anomaly detection, de-duplication, self-healing/auto-correction (e.g.: using GIS data wrt location/address anomalies) in conjunction with various tools
• "Synthetic data generator" for test data generation
• Digital twin to create and test hypothesis via "test and learn" simulations, thereby improving productivity and efficiency of data and tech. services/roles.

The way forward

As with any technology advancements, fundamental principles must be applied, such as guardrails for ethics, values, empathy and to address potential bias; explainability and auditability to enable transparency; human-in-the-loop for oversight and decision-making; and accountability on critical areas such as healthcare, financial services etc., 

Human-AI collaboration are to be evaluated closely as this frontier of AI expands its horizons.

This article explores how the emerging "Vibe" paradigm—rooted in intuition, emotion, and seamless interaction—is redefining human-machine collaboration. Extending beyond development, we propose Vibe Insurance: an AI-native model that reduces friction, builds trust, and transforms transactional processes into empathetic, user-centric experiences. 

In a world shaped by generative AI, Vibe Insurance reimagines not just what technology can do but how it should feel. Unlike conventional insurtech solutions focused on automation or efficiency, Vibe Insurance centers on emotional resonance, trust-building, and fluid interactions—bridging the gap between digital precision and human warmth.

Figure 1: Andrej Karpathy's post on X

The literal meaning of "vibe" refers to a sense of atmosphere or feeling. When combined with terms like "coding," it forms the phrase "Vibe Coding"—a concept introduced by Andrej Karpathy earlier this year (Figure 1). While the term is gaining attention, its translation and interpretation in different languages remain fluid. Instead of focusing on a literal translation, many highlight its defining features: It is intuition-driven, free-form, and centered on creativity. As a result, it is sometimes referred to more descriptively as "intuitive coding," "freeform coding," or "spontaneous coding."

Some interpret Vibe Coding as a new development paradigm: Developers focus on application functionality and architecture design, while AI coding agents assist in writing the actual code. This interpretation highlights a new "human-machine collaboration" model, involving clear module division, precise prompt design, and iterative testing and refinement.

But "Vibe" isn't limited to coding. For example, when filling out online forms, traditional static, linear, and generic tools—such as dropdown menus, radio buttons, and one-way workflows—often feel tedious and frustrating, sometimes even causing users to abandon the process. The alternative is "Vibe Survey." Other extensions include Vibe Design and Vibe Marketing.

The concept of "Vibe" represents atmosphere, freedom, intuition, flexibility, and creativity. It emphasizes breaking away from mechanical interactions, striving for more natural and fluid experiences, dynamic process adjustments, and even the ability to perceive emotions. Compared with traditional methods, Vibe is faster, more efficient, and capable of meeting personalized needs while delivering a pleasant user experience.

People don't fill out forms to meet the demands of a company or individual; they do so because they have a need—whether for a job, a service, or a connection. This process is essentially about "matching." Surveys match potential customers with products, job applications match candidates with ideal roles, and event registrations match consumers with their preferred activities.

In this sense, Vibe is a mindset, a "human-centric" methodology. Its goal isn't to pursue speed but reducing unnecessary friction in user interface (UI) design, or human-AI coding collaboration, thereby enhancing user experience (UX) and achieving seamless integration between the virtual and real worlds.

Darren Yeo, in his article "The Hype and Risks of Vibe Coding," writes about Vibe and design: "For now, I'll keep those vibes in check and continue to treasure what remains valuable to me. Because at the end of the day, design isn't just about speed—it's about humanity." Indeed, the focus of evolution is never speed but "humanity."

Large language models (LLMs) are making this vision (Vibe Everything) a reality. With the right models and prompts, we can present content in a Vibe format to users. Achieving this isn't about retrofitting static products with AI features but rethinking the experience users desire when performing simple tasks like filling out forms.

This represents a natural evolution of interaction between AI-native products—those built with AI capabilities from the ground up—and users in the age of generative AI (GenAI). It discards rigid rules in favor of algorithm- and model-driven interactions, enabling dynamic workflows, multi-role collaboration, multimodal formats, and multi-channel touchpoints.

For example, if a user says, "Your interface is great, but the price is too high," the LLM can identify: "Positive: UI design; Negative: Price sensitivity," and respond with a thank-you message from the design team and a discount coupon. Prompts must define clear objectives (e.g., role + task instructions), and contextual memory ensures interaction consistency. LLMs are ideal for realizing Vibe interactions, transforming mechanical processes into warm conversations—whether in forms or code, natural language becomes the new human-machine interface.

However, challenges remain. For instance, freeform outputs may deviate from expectations, contextual memory limitations can disrupt interactions, and emotional/affective cognition is still underdeveloped. Other issues include reasoning for complex problems, latency, multimodal processing, security, and privacy. Despite these limitations, technologists are gradually improving these models through human-AI collaboration and fine-tuning for vertical-scenarios.

Emotional/affective cognition is essential to natural interactions and user engagement. However, current technology has significant gaps, including insufficient multimodal fusion for emotion recognition, poor contextual emotional coherence, and weak generalization across different cultures and individuals. In the Vibe interaction paradigm, user demand for anthropomorphic interactions (questioners) and the solutions provided by tech teams (solvers) forms a dynamic cycle, reshaping the foundation of human-machine collaboration.

This dynamic cycle resembles a "spiral causality diagram of demand-driven innovation." When people ask, "Why can't this be simpler/smarter?" (questioners), it exposes technological shortcomings. Engineers then develop tools to address them (solvers). As people enjoy the benefits of these innovations, they naturally ask, "Can it be even better?" This creates a self-reinforcing cycle of technological advancement. From touchscreen phones to voice assistants to emotion-aware devices, each breakthrough redefines how humans interact with technology.

Figure 2: The demand-innovation spiral in mobile phone technology

As shown in Figure 2, the evolution of mobile phones vividly illustrates the dialogue between human needs and technological innovation. From Motorola's "just make calls" brick phones to Nokia's "texting and cameras" feature phones, to the iPhone's "smart and connected" touchscreen revolution, each generation meets current demands while quietly paving the way for the next breakthrough. Today, as people expect devices to "understand emotions and show warmth," affective computing is opening a new chapter. While phones can't yet interpret frowns or voice tremors, they can infer needs from usage patterns. The best innovations, from functionality to emotional resonance, always respond to humanity's deepest desires.

Given the universality of Vibe as a methodology, what is "Vibe Insurance"? Unlike conventional insurtech solutions focused on automation or efficiency, it centers on emotional resonance, trust-building, and fluid interactions—bridging the gap between digital precision and human warmth. We believe that establishing a new paradigm of human-machine interaction in insurance—Vibe Insurance—requires combining emotional intelligence with dynamic workflow design, reshaping user trust and service experiences through AI-native interactions.

• For users, it reduces mechanical friction, enabling "seamless" experiences.
• For businesses, it rebuilds service value chains through emotional intelligence and trust quantification.
• For technology, it balances data-driven precision with human-centric warmth, achieving "algorithms with empathy."

Vibe isn't just a technological innovation but a mindset revolution—"intuition-driven, experience-first." When Vibe becomes the foundation of design, human-machine interactions will no longer be constrained by rigid rules but will evolve into creative, warm, and algorithm-driven exchanges. From Vibe Coding to Vibe Insurance, the core principle remains: "Reduce mechanical friction, let interactions flow naturally." Whether engineers collaborate with AI on code, users fill out dynamic forms, or policyholders engage in emotionally intelligent insurance planning, Vibe transforms cold processes into warm conversations.

The future of Vibe Everything hinges on balance. We must navigate the boundary between AI's "simulated emotions" and "avoiding overreliance." The ultimate goal of technology isn't to replace humans but to bridge the virtual and real worlds in a more humane way, using natural language as the universal interface. Vibe will redefine how we coexist with the digital world.

References and Notes:

1. Andrej Karpathy: "There's a new kind of coding I call 'vibe coding,' where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g., Cursor Composer w/Sonnet) are getting scarily good."

2. Cassius Kiani (April 1, 2025), Freeform Update: Why Vibe Surveys Beat Static Forms, https://every.to/source-code/freeform-update-why-vibe-surveys-beat-static-forms.

3. Darren Yeo (March 9, 2025), The Hype and Risks of Vibe Coding, https://medium.com/user-experience-design-1/the-hype-and-risks-of-vibe-coding-0d1e1ccd71d7.

4. ESCP Business School (Feb. 17, 2025), Artificial Intelligence and Emotional Intelligence: The New Frontier of Human-AI Synergy, https://escp.eu/de/news/artificial-intelligence-and-emotional-intelligence.

5. David E. Nye's "demand-innovation spiral," from Technology Matters: Questions to Live With. The core idea: "New technologies never emerge in a vacuum but respond to the flaws of existing ones—yet every solution becomes the incubator for new demands, creating a self-reinforcing cycle."

6. For explorations of affective computing in insurance, refer to LingXi Technology's articles:

• Emotional Intelligence Breakthrough: How Emotional Prompts Define Next-Gen Insurance Planning https://mp.weixin.qq.com/s/VTZ5S6hOlcRfWY75iSj3OQ.
• The Dual Faces of AI: Role-Playing and Emotion Recognition https://mp.weixin.qq.com/s/4dmkTNjcyUF3pwxsrjgxAw.
• The Paradox of AI Companionship: The Delicate Balance Between Emotional Support and Dependence https://mp.weixin.qq.com/s/loX_Yr3ItXgD0tq81uC7Gw.
• Experiment 23: AI and Trust—The Future of Insurance https://mp.weixin.qq.com/s/cLpa0BSKSkWeb3zlrsjqrQ.

Claims professionals habitually spend hours sifting through hundreds of pages of medical records for every single claim. Now, thanks to generative AI that sorts and flags key information up front, claims professionals can skip the document grind and focus on what matters: making smart calls and avoiding expensive slip-ups.

However, this miraculous time-saving efficiency isn't without its challenges. Along with the ability to rapidly process and extract meaning from vast collections of complex documents, many organizations have stumbled using AI for document processing by setting unrealistic expectations, leading to widespread disillusionment when the technology fails to deliver.

Three specific challenges directly affect the success of AI document systems: workforce adoption issues, compliance risks, and cost concerns.

First, workforce adoption issues arise when employees, without proper expectation-setting, experience immediate frustration. This causes them to conclude, "This isn't working," at the first sign of error, often resulting in abandoned projects before the AI system can demonstrate its value. Second, in highly regulated processes, errors can trigger significant legal and financial consequences that create substantial risk. Third, organizations frequently underestimate the operational costs of running sophisticated AI models at scale.

These challenges are particularly evident in highly regulated insurance processes that involve complex and lengthy documentation with significant compliance requirements but can be avoided with understanding of the technology's limitations and wise usage and mindful oversight of the programmed skillset.

Take Medicare Set-Asides (MSAs) managed by Medicare secondary payer compliance companies. MSAs are complex financial arrangements primarily used in workers' compensation and liability claims to allocate funds for future medical treatment. Handling MSAs demands analysis of extensive medical records, billing statements, physician recommendations, and prescription histories.

Claims professionals invest 15 to 20 hours manually reviewing an average of 300 to 500 pages of medical documentation per claim. Complex cases can often exceed 1,000 pages. This creates a large opportunity to leverage AI to help with the understanding and processing of data. However, mistakes can come at a significant cost, potentially resulting in rejected MSA submissions, delayed settlements, additional reserve requirements, and even long-term Medicare recovery actions against insurers or claimants who failed to properly protect Medicare's interests.

These potentially costly consequences make a thoughtful AI implementation essential for MSA processing. Success with AI for document processing occurs when it is used as a tool that enhances workflows. This is where intelligent document processing (IDP) systems demonstrate their potential, as they can combine AI with document management technologies to transform how complex, unstructured documents are handled.

By presenting AI as an enhancement to the claims professional's workflow rather than a replacement, a company is able to address both workforce adoption concerns and error risks simultaneously. The key is creating a system where claims professionals maintain decision-making authority while the AI handles the time-consuming organizational tasks. This integration is what makes document processing improvements possible.

Breaking down a typical MSA review process, roughly 30% to 40% of that time is spent on manual document organization and navigation. This includes sorting pages, identifying document types, and locating relevant information across hundreds of pages. The IDP system tackles these challenges by handling the initial heavy lifting. It digitizes and organizes documents, identifying important details automatically. Claims professionals can then work with this pre-organized data, significantly reducing the time spent on manual document sorting and navigation. The result is a structured foundation that allows claims professionals to navigate efficiently through what was once an overwhelming volume of information.

The most effective implementations of these systems incorporate human verification. Claims professionals begin with the AI-organized information, make refinements and corrections where needed, and then use this enhanced foundation to perform their specialized analysis. This verification step ensures accuracy while still capturing significant time savings. Once the claims professional confirms or corrects the AI's initial processing, the system can then perform more sophisticated tasks with the validated information.

For example, the AI system can identify and extract date references across hundreds of pages of documents, creating an initial chronological sequence. Rather than manually finding each date throughout hundreds of pages, claims professionals review the pre-assembled timeline to verify its accuracy and completeness. They can spot missing events, incorrect dates, or sequence errors by reviewing the overall pattern of care rather than hunting for individual date references page by page. Once the claims professional validates this timeline, correcting any errors they find, the system uses this confirmed data to generate a comprehensive chronological view of medical events.

This could also work with keyword flagging. The AI system can be programmed to identify critical terms such as "surgery" throughout the documentation, whether this is in images or PDFs. This is especially valuable because surgical procedures often represent significant costs that must be accounted for in MSA calculations. When the AI highlights these terms, claims professionals can navigate to relevant sections instead of manually sifting through them with the risk of overlooking something. When poor document quality causes the system to inadvertently miss important keywords, claims professionals can flag them, helping the system learn and improve.

This brings us to the challenge of managing operational costs. Sophisticated IDP systems address this by intelligently determining the appropriate level of AI processing needed for each document. Rather than routing everything through the most expensive large language models, these systems analyze document complexity, classification certainty, and business value. This analysis allows them to allocate computational resources optimally. Routine documents can be processed using lightweight models, while only complex or high-value documents require advanced generative AI capabilities.

This intelligent resource allocation creates significant cost savings without sacrificing performance. As claims professionals verify results and provide corrections for document misclassifications, missed medical events, procedure code errors, and ambiguous treatment dates, the system gradually improves its ability to assess document complexity and determine appropriate processing levels. Rather than creating additional verification work, the system focuses human attention only on elements with low confidence scores or high business impact.

By using this feedback to come up with better instructions, the system is able to learn from claims professional corrections to recognize similar documents in the future, becoming more efficient with each processed claim. This creates a positive cycle where accuracy increases while resource requirements decrease over time, addressing the operational cost challenge head-on.

This approach to implementing IDP systems provides solutions to the challenges related to workforce adoption issues, compliance risks, and cost concerns. It prevents employee frustration by positioning the claims professional as the decision-maker while the AI serves as a sophisticated but at times imperfect assistant. It maintains crucial quality controls to reduce legal and financial risks by keeping the responsibility directly with the claims professional. Through learning the type of intelligence that is needed, it also manages operational costs effectively over time.

This MSA case demonstrates how AI can enhance human judgment in document-intensive processes. Even when claims professionals must still review key documents, the value comes from making that review more structured and focused. By creating a feedback loop that continuously improves performance and managing computational resources intelligently, organizations can transform initial AI disappointment into sustainable success. This balanced approach delivers better outcomes for all stakeholders while avoiding the pitfalls that derail many AI initiatives.

"Best practices" are on the docket at every leadership offsite, conference panel and board-level meeting. But as currently acted on, best practices don't amount to much more than "doing a bit better."

A vague and watered-down expression can only result in equally vague and watered-down improvement measures: maybe a new dashboard, a revised call script, new key performance indicators (KPIs), a new customer relationship management (CRM) system. Sure, a dollar might be saved here or there. But what's needed is bold, lasting, transformative change.

To truly achieve "best practices," meaning evidence-based, scalable, and continuously refined over time, insurers would need to undergo a complete operational overhaul. The problem is, that kind of overhaul is an unappealing prospect—ripping out one system and trying to replace it with another can mean a slowdown in productivity and a focus on change management, rather than the work of actually doing insurance. Moreover, it can be controversial, disruptive, and highly visible—three things insurers tend to avoid, especially when margins are thin across many lines of business. Major change risks rattling investor confidence, unsettling personnel, and triggering concerns from customers and board members alike.

But how much of this resistance to fundamentally reexamine insurance operations is truly about protecting against disruption—and how much is a reluctance to think in new ways? The industry has long been defined by its conservatism, and that mindset continues to shape its decision-making. When younger professionals think of insurance, they picture fluorescent lights, thin cubicles, outdated software—nothing that relates, say, to an intuitive digital app. There's safety in legacy processes. "That's how we've always done it," the thinking goes. "It's worked so far. Our people are used to it. Why do things differently?"

But this needs to change. Introducing AI to areas of insurance that aren't yet using it can provide the necessary overhaul, one that fundamentally reimagines how agents do their work and how insurers collect, analyze, and apply data that will help them. 

Fortunately, this transformation no longer needs to be abrupt or alienating. Today's technology allows insurers to roll out change in a piecemeal, custom-tailored way—minimizing disruption while maximizing long-term impact.

The result won't just be checking a "best practices" box on paper. It will mean real operational improvements: higher margins, greater employee satisfaction, an easier time attracting younger talent amid a talent crisis, and higher customer satisfaction—all of which can help reframe the reputation of an industry long seen as inhuman and overly bureaucratic, especially in light of recent events.

Forget the Firm Handshake—Focus on the Data

First, data collection. Over the past decade, insurers have leaned on broad metrics like total premiums written, retention rates, and revenue per agent. But these numbers are too general to offer meaningful insight. They tell us what happened—but not why. Sure, we know this agent wrote this many policies in the past year. But are we any closer to understanding what actually drove that performance?

AI is often praised for its ability to zoom out—processing and connecting thousands of data points far beyond what the human mind can track. But what's underrated is its ability to zoom in. With the right inputs, AI can deconstruct the behavior of top producers, revealing the subtle habits that set them apart from their peers. It's not about collecting the most data—that just leads to a glut. It's about collecting the right data.

Traditional thinking still dominates how many executives explain producer success. They'll chalk it up to someone's alma mater, a trusty handshake, a family legacy in insurance—or fall back on vague clichés like "work ethic" or "wanting it more." The problem is, these explanations frame success as innate and unteachable. If top producers are simply born with it, then there's no hope—or strategy—for helping average producers improve.

Top producers' inner workings can be uncovered with the help of AI. It could be the speed and timing of their follow-ups. Or the exact phrasing they use to tailor pitch strategies to different clients. Or even their ability to strike the perfect balance between persistence and discretion. How a producer structures their day for maximum efficiency can also create ripple effects that lead to higher conversion rates.

Insurers need to strip away the mystique of high performance—not just to help average producers improve, but to show that success isn't luck or legacy; it's a learnable system. When producers can see the path, they're more likely to walk it—and more likely to stay.

Define "Best Practices," Not "Somewhat Better Practices"

Yet even if AI can generate accurate observations and build a data-driven template for the ideal agent, those insights won't translate into better performance if producers are still asked to use CRM systems they're reluctant to go into. This is the biggest bottleneck to achieving best practices: The systems meant to support agents are often the very ones holding them back.

The select few from the younger generation who are genuinely excited about working in insurance often become jaded quickly—usually thanks to the daily frustrations of using clunky CRM systems. All it takes is one lunch, one venting session with a friend in finance or tech, to realize how far behind their tools really are—and to start thinking about jumping ship.

These systems often demand additional work: manual data entry using clumsy interfaces with little to no integration with calendars or phones. Worse, the systems lack AI-driven insights—so agents are forced to treat every lead the same, regardless of how cold or warm it is. It's no wonder turnover rates among agents remain so high.

Incorporating AI into these systems isn't just a promising retention strategy for policyholders—it's a powerful one for agents, too. Success breeds success, so when they can instantly see what practices work, what paths to take to close a sale, they'll want to do more. It's human nature. In this case, advanced technology doesn't strip the job of its humanity—it restores it. It gives agents space to focus on what drew them to the field in the first place: building lasting, mutually beneficial relationships with clients.

But ease and humanity aren't the only reasons agents stay motivated and loyal to their agency. There's also a financial incentive. Modern AI-powered systems identify cross-selling and upselling opportunities that might otherwise go unnoticed, letting agents maximize their commissions.

Provide More Data Points to Underwriting

Just as performance analysis has historically relied on too few data points, underwriting has long been constrained by limited inputs—typically just credit scores and claim histories. But consumer behavior is evolving too quickly, and often unpredictably, for insurers to keep relying on such narrow datasets.

AI allows a far more diverse range of data points to be taken into account. It can factor in social media activity, purchasing behavior, and real-time insights from Internet of Things (IoT) devices. For example, telematics in vehicles enables insurers to monitor driving habits continuously, allowing for dynamic premium adjustments based on real-world behavior rather than outdated, static models.

For too long, insurers have been playing catch-up. Some lag times have shortened, but we should be aiming to eliminate the lag entirely. Any delay just measures how much margin is leaking. For the first time in history, we're within reach of truly real-time risk pricing.

Underwriters shouldn't fear an AI-driven overhaul of their sector. Just like producers, they didn't enter this industry to be buried in repetitive administrative work—only to be blamed for every oversight. With AI handling what it's best at, like fact-checking and surface-level analysis, underwriters can return to what they're best at: making high-level statistical judgments and strategic decisions.

Expect Regulatory Pressure From the States, Not the Federal Government

Even with the prospect of federal deregulation under the current administration, insurers shouldn't assume a more relaxed compliance environment. Several states, especially California, are already enacting stricter environmental regulations in response to escalating wildfire risk—putting pressure on insurers to offer broader coverage in high-risk zones. At the same time, backlash over prior authorization delays in health insurance is gaining political traction, with new legislation on the horizon. Insurers that move too slowly could face not just financial penalties but long-term reputational fallout.

AI can help insurers stay ahead of the U.S. regulatory maze by monitoring policy changes in real time, flagging discrepancies across states, and identifying inconsistencies in claims, contracts, and internal processes. In a market where compliance expectations differ across all 50 states, these capabilities are becoming indispensable—especially for regional carriers aiming to scale nationally without stumbling into regulatory blind spots.

Complying with anti-discrimination laws is another area where AI can make a real impact. But its value goes beyond just staying compliant—it creates fairer, more consistent decision-making that can help shift public perception. The insurance industry has faced long-standing scrutiny for biased practices, and AI—if used responsibly—can be a tool to change that narrative.

Know the Risks—But Don't Overstate Them

While AI holds real promise for improving sales, underwriting, and compliance, insurers shouldn't jump in without a clear plan. Regulators are becoming more cautious—and in some cases, more aggressive—about how AI is used. Without thoughtful implementation, AI may see the expected efficiencies undone by compliance issues.

Insurance is, by nature, a risk-averse industry—understandably so. The job, after all, is to anticipate consequences before they happen. But when it comes to AI, many insurers are overstating the risks in ways that aren't rational. The greater danger isn't adopting AI too soon—it's falling behind as AI becomes the standard across every other industry.