This article delves into the application of systems using multiple AI "agents" and into human-machine collaboration in industry, aiming to reveal to readers without a technical background how they can lead us to a tech-driven, AI-powered work world.

Think about the writing process, typically divided according to people's work habits into: gathering information, drafting, revising and finalizing. What would happen if artificial intelligence were introduced?

The seemingly linear and unidirectional writing task could be entirely generated by AI, although the content generated by AI might not meet our expectations or standards. Alternatively, we could have AI assist us in analyzing and handling complex topics, collecting, filtering and summarizing useful information from multiple sources, embodying human-machine collaboration. Further, if the content of an article is rich, covering profound discussions and extensive knowledge, assigning each step of the writing process to agents with different expertise and requiring them to cooperate with each other would undoubtedly produce superior outcomes. That is the approach of multi-agent systems.

Early AI capabilities were limited, and it was more of a "human-centered" auxiliary tool. As machine capabilities improve, the nature of human work changes, and the "machine-centered" human-machine collaboration model becomes possible, continually evolving the boundaries between humans and machines.

In this new work model, AI is not just a tool or service; it has become a collaborative partner for humans, facing challenges and solving problems together. 

For example, in the healthcare field, AI is assisting doctors in analyzing cases and developing treatment plans, and playing a role in personalized medical services. In the creative and design industry, AI's involvement provides new sources of inspiration and creative tools for artists and designers, pushing innovation while challenging traditional creative processes.

Everyone is a participant and beneficiary of this transformation. Multi-agent systems and human-machine collaboration are changing our ways of working, reshaping our behaviors and habits. In the relationship between humans and AI, we can act as observers overseeing AI's work from a god's-eye view, or actively participate in the multi-agent system, cooperating with AI as part of the process. As consumers, understanding how AI affects the provision of products and services can help us make more informed choices. As AI technology spreads, many jobs will evolve, and understanding these technologies helps us adapt to future work environments and seize new career opportunities.

I. The Future of Human-Machine Collaboration and Multi-Agent Systems

As AI technology rapidly advances, we are entering a new era assisted by intelligent systems and robots. Multi-agent systems and human-machine collaboration not only showcase technological innovation but also directly affect our work and lifestyle, signaling that cooperation between humans and machines will become the norm.

1.1 What is an Agent?

Computer scientists and AI researchers Stuart Russell and Peter Norvig in their book "Artificial Intelligence: A Modern Approach" define an agent as "anything that can be considered to perceive its environment through sensors and acts upon that environment through effectors." This definition applies to humans, robots, software programs and artificial intelligence.

With the rollout of large language models like GPT-4, agent scenarios have seen explosive growth. Below is an illustration by Yohei Nakajima showing the interaction between agents and a large model (GPT-4):

Agents act like the "decision engines" for large models, communicating with GPT-4 to complete high-difficulty tasks such as data analysis, brainstorming, resource management, language translation and task organization, prioritizing tasks needed to achieve user-defined goals.

1.2 Defining a New Work Paradigm Through Human-Machine Collaboration

Under the human-machine collaboration model, AI is no longer just a tool for executing commands but a working partner for humans, together solving problems and completing tasks. This cooperation significantly enhances work efficiency and frees humans from repetitive or high-risk tasks, allowing more involvement in creativity and emotional intelligence tasks. Subsequent demands for new skills, including AI technology knowledge, data analysis capabilities and communication coordination skills for cooperating with AI, will be key to future workplace success.

In the past, most people only needed to understand the surface functions of the tools or technologies they used enough to apply them in daily life and work, without delving into how they work. For instance, when using the Windows operating system, users are more concerned with how it can help them complete tasks or enjoy entertainment, rather than understanding its internal mechanisms. However, this traditional approach to technology understanding, particularly in the field of artificial intelligence and in the context of multi-agent systems, may prove insufficient because in future human-machine collaborations, everyone's role might resemble those of agents in a multi-agent system, necessitating a deeper understanding of technology.

When the nature and organizational form of work shift toward models similar to multi-agent systems, where previously human-centered processes are deconstructed and reconstructed according to machine capabilities, each person plays a role similar to that in a multi-agent system, focusing on their expertise and tasks while collaborating with others or AI assistants to achieve larger tasks and goals.

McKinsey's report "Insurance 2030: The Impact of AI on the Future of Insurance" predicts that by 2030, AI and related technologies will have a significant impact on distribution, underwriting, pricing and claims. However, these changes are not just imaginary stories, nor do they have to wait six years; they are realities happening today.

These transformations indicate that AI is reshaping every aspect of insurance sales at an unprecedented speed, making it smarter, more efficient and user-friendly. As technology continues to advance and its applications deepen, we expect AI to play an increasingly important role in the development of various sectors of the insurance industry, creating more value for businesses and customers.

1.3 AI-Driven Work Environments and Changes in Human Performance

People's attitudes towards artificial intelligence are at two extremes: One end is denial (believing AI cannot replace my job), and the other is panic (believing AI will definitely replace my job).

In AI-driven work environments, human performance will undergo multiple changes:

• Enhancing Specialization and Efficiency

In work models centered on human-machine collaboration and multi-agent systems, the repetitive, simple parts are replaced by AI, allowing individuals more opportunity to focus on their areas of expertise. The efficient collaboration between humans and machines not only accelerates the process of resolving user issues but also optimizes the efficiency and effectiveness of task completion. This cooperation symbolizes the future trend of work—achieving higher productivity through technological power.

• Stimulating the Desire to Learn and Develop

Whether with telesales or planning consultants, when individuals realize that they are playing a role similar to that in a multi-agent system in their work, their motivation to learn relevant technological knowledge also increases. This realization pushes individuals to understand their roles in the team, how to collaborate with AI more effectively and thus enhance personal skills and the overall work effect of the team.

• Developing Cross-Domain Skills

Future work requires not only deep professional knowledge but also cross-domain skills, including technological knowledge, human-machine communication and coordination abilities, and an acceptance of innovative thinking. The enhancement of personal cognition and awareness, in turn, helps the iterative progress of AI capabilities, which is key to successful collaboration in a multi-agent system and is also the foundation for individuals to adapt to future work changes.

• Promoting Personalized Career Paths

As standardization and digitalization progress, everyone's job becomes more personalized and modularized. Those who cannot adapt choose to leave, while those who accept the challenge have more opportunities to tailor their career paths based on their interests, expertise and goals. This change not only enhances individual job satisfaction but also provides a broad space for personal long-term development.

• Improving Understanding and Application of AI Technology

For most people with non-technical backgrounds, a basic understanding of AI technology becomes particularly important in the new AI-driven work environment. Inquiry into "why" gradually becomes a norm in the workplace. This is not only about individual career development but also about how to effectively position oneself and maximize value in the future work world.

II. Multi-Agent Systems (MAS)

Large language models (LLMs) provide AI agents that excel in different fields, and multi-agent systems bring them together to cooperatively accomplish set goals. Below, we introduce what multi-agents are and how they differ from single agents, role specialization and content generation.

2.1 What is Multi-Agent?

Imagine a scenario where a team composed of members with different expertise works together to complete a complex project. In a multi-agent system, the team members are replaced by AI agents, each playing a specific role and responsible for specific tasks. This type of collaborative mechanism allows multi-agent systems to effectively solve complex problems that are difficult for a single intelligent entity to handle.

Multi-agent systems excel at handling complex, dynamic tasks that require coordination of multiple participants' actions, relying on their expertise, flexibility and creativity to gain an advantage. For applications of multi-agent systems in smart home management, supply chain optimization and personalized services, see the last section.

However, the construction of multi-agent systems faces many challenges, such as effective coordination between agents, managing system complexity and the illusions of AI and inadequate understanding of vertical professional fields. Only when these issues are effectively addressed can multi-agent systems function properly.

2.2 Differences From Single-Agent Systems

The comparison between single-agent systems and multi-agent systems goes beyond their distinct technological architectures; they also differ in complexity, interaction modes and the strategies for completing tasks through cooperation or competition:

• Simplicity and Focus of Single-Agent Systems

Single-agent systems are characterized by their straightforward nature, making them ideal for executing clear, relatively fixed tasks within a controlled environment. In these systems, a single intelligent entity can make decisions and perform tasks independently. For example, a chatbot providing responses based on user inputs, or a personal assistant software managing schedules and reminders, are typical applications of single-agent systems.

• Complex Interactions and Cooperation in Multi-Agent Systems

In contrast, multi-agent systems consist of multiple intelligent entities that can be functionally similar homogenous bodies or functionally different heterogeneous bodies. They operate in parallel within the same environment, achieving common or individual goals through cooperation, competition or independent actions. The core is the coordinated actions between agents, involving complex communication, negotiation and learning other agents' behaviors.

• Collective Intelligence of Multi-Agent Systems

Multi-agent systems are not merely about stringing together single agents with different functions. Their complexity mainly arises from the dynamic interaction rules and cooperative strategies between agents, and how they adjust themselves and make decisions based on external environments and the statuses of other agents. Similar to human collective decision-making, multi-agents exhibit characteristics of swarm intelligence, where the system's overall performance exceeds the simple sum of individual agents' capabilities.

Designing and implementing multi-agent systems require consideration of complex factors such as communication protocols, decision mechanisms, and cooperation and competition strategies, all of which go far beyond what is involved in single-agent systems. The application of multi-agent systems has driven deep development in AI technology and provided new perspectives and methods for solving real-world complex problems.

2.3 Differences with Role Specialization

Agents can possess unique skills and "personalities"; we can define their capabilities through online resources or uploaded document knowledge. Building an efficient, scalable and stable multi-agent system is not easy—it requires clearly defining each agent's role and responsibilities during the system design phase, which is crucial for ensuring the system works collaboratively to achieve the intended goals.

Suppose we are launching a product. At this point, a multi-agent system can be employed, assigning a role and function to each agent:

• The Importance of Role Specialization

In a multi-agent system, clearly defining role specialization helps delineate the scope of responsibilities within the system, allowing each agent to perform optimally in its area of expertise. This specialization not only prevents redundant work but also enhances overall work efficiency. The clear demarcation of roles and responsibilities among agents facilitates their smooth collaboration, efficient information exchange and decision-making negotiation, thereby optimizing the overall system performance.

• Collaborative Efficiency and System Scalability

Clear role definition not only enhances collaborative efficiency but also increases system scalability. When faced with new demands or functional adjustments, the system can adapt flexibly to changes without needing a complete system overhaul, by introducing new agent roles or modifying the responsibilities of existing agents. Although this flexibility is crucial for coping with the ever-changing environment and demands, without effective interaction and coordination among agents, simply having roles and responsibilities will not enable a multi-agent system to function effectively. In the next section, we will discuss how agents can interact effectively.

• Implementing a Multi-Agent System

Defining roles provides the basic framework for the system, but implementing a well-functioning multi-agent system requires additional steps:

• Specific Implementation of Agents: Each role must be realized through an AI agent, involving the programming, training and configuring of agents' behaviors and response strategies.
• Interaction and Coordination Mechanisms: Effective interaction and coordination mechanisms among agents are key to a multi-agent system's capabilities, ensuring agents can share information, make collective decisions and cooperate on tasks.
• Environment and Context: Multi-agent systems typically operate in a specific environment. Defining the environment and how agents perceive and affect it is an essential part of system design.

• Integration and Testing: The success of the system depends on the effective integration and comprehensive testing of all parts, ensuring that agents can work together and operate as expected.

  Building a multi-agent system capable of solving complex tasks involves in-depth planning and technical implementation, and a profound understanding of the system's goals and the modes of cooperation among agents is necessary.

2.4 Differences With Content Generation

ChatGPT interacts with users primarily based on a word-by-word generation mode. As a large language model (such as the GPT series), it generates responses based on the information provided by users, the context of the question and the training data of the large model. This means that in the process, there are not multiple independent agents responsible for different parts of the answer or different stages of processing, but a unified model is responsible for understanding the question and generating the entire answer.

Regardless of whether the user uses a conversational prompt or a structured prompt, the large model, due to its ability to coherently understand the input question and generate corresponding answers, performs well in handling single interactions. However, this method's limitation lies in its heavy reliance on the capabilities of a single model and the breadth of its training data, unlike the specialization and flexibility of multi-agent systems in specific tasks.

In the introduction of this article, we used the writing process to illustrate the concept of multi-agent systems. Below, we continue this analogy to further explain the differences between a single word-by-word generation model and a multi-agent system. Multi-agent systems assign different tasks to specialized agents, not only improving the professionalism and efficiency of writing but also enhancing flexibility, adaptability and creativity, ultimately achieving high-quality writing outcomes.

• Specialized Processing

In a multi-agent system, each agent is responsible for a specific task within the writing process. For instance, some agents focus on gathering information from specific fields, while others are responsible for structuring the draft or refining the style and language. This clear division of labor ensures that each part of the writing process meets higher professional standards and depth.

• Parallel Processing

One of the major advantages of multi-agent systems over single models is their capability for parallel processing. Multiple agents work simultaneously, much like having several assistants who help the author collect information from various sources or edit different parts of the article at the same time, significantly speeding up the overall writing process.

• Flexibility and Adaptability

Another highlight of multi-agent systems is their outstanding flexibility and adaptability. The real-time collaboration between agents can quickly respond to changes in the article's needs, whether adjusting strategies based on initial feedback or optimizing information collection and editing methods during the writing process.

• Enhanced Creativity

Different agents bring a diversity of perspectives and backgrounds to the article, enriching the content and creativity. This input from various intelligent entities not only expands the boundaries of thought but also makes the content more innovative and appealing.

• Quality Control

While generative AI sometimes produces hallucinations, we can establish specialized agents within a multi-agent system responsible for quality control of the article, including fact-checking, grammar and spelling checks, and ensuring the coherence of the content. This collaborative mechanism can effectively enhance the overall quality of the article.

Thus, compared with single word-by-word generation models, multi-agent systems offer comprehensive advantages—specialized processing, high efficiency, flexibility, enhanced creativity and quality control—providing robust support for complex writing tasks and offering a more refined and high-quality solution. This not only marks the progress in writing technology but also opens up possibilities for future content creation.

2.5 Effective Interaction and Coordination Between Agents

In a multi-agent system, ensuring effective interaction and coordination between agents is crucial. The latest paper, "AIOS: LLM Agent Operating System," proposes that in the AIOS framework, agent communication is achieved through a shared memory and storage system. Other methods such as tool invocation and context management are also explored, and interested readers may refer to the paper for more details.

Let's focus on several core technologies and concepts, such as communication protocols, negotiation mechanisms and learning algorithms.

• Communication Protocols

Imagine if each agent were a country; communication protocols would be like the language and etiquette between diplomats. They specify the format, timing and manner in which agents exchange information, ensuring that messages are sent and received correctly. Just as people need translations to communicate in different languages, communication protocols help different agents understand each other's "languages."

• Negotiation Mechanisms

Negotiation mechanisms are methods used in multi-agent systems to resolve conflicts and reach consensus. Like in a team project where members need to discuss and decide on the best course of action, agents also need some way to "discuss" and decide which action fits best with the current goals. This can be implemented through various algorithms such as voting, auctions or rule-based decision processes.

• Learning Algorithms

Learning algorithms allow agents to improve their behavior based on experience. This functionality is similar to how humans learn to ride a bicycle: we might fall initially, but through repeated attempts and adjustments, we eventually learn to balance. In a multi-agent system, agents can "learn" the best ways to interact by observing feedback from the environment and the behavior of other agents, thus improving the system's overall performance over time.

• Simple Application Scenarios

Imagine we are organizing a large party and need to coordinate multiple teams (agents): catering, music, venue setup, etc. Each team has its specific tasks, but they must communicate and coordinate with other teams to ensure the party goes smoothly. This requires a good "communication protocol" to ensure accurate information transmission; a "negotiation mechanism" to resolve any situations where, for example, the venue setup team needs more time, affecting the catering team's preparations; and a "learning algorithm" that allows each team to learn from each event how to cooperate more effectively.

III. Case Studies of Human-Machine Collaboration and Multi-Agent Systems

Below are some examples of applications of human-machine collaboration:

Medical Diagnostic Collaboration

IBM Watson Health, in partnership with several global hospitals such as the Memorial Sloan Kettering Cancer Center in New York, uses its cognitive computing capabilities to analyze clinical data and provide precise diagnostic suggestions and personalized cancer treatment plans. This showcases the tremendous potential of artificial intelligence in enhancing the quality of medical services and accelerating the diagnostic process.

Creative Design Assistance

Adobe Sensei, Adobe's AI and machine learning platform, is integrated into software such as Photoshop and Illustrator, providing intelligent editing features like automatic image cropping and facial recognition adjustments. This helps designers work more efficiently, demonstrating AI's role in advancing the creative industry.

Here are some examples of multi-agent systems applications:

Intelligent Traffic Management

Singapore's smart management of urban traffic through its Intelligent Transport System (ITS) allows the city to monitor and adjust traffic flow in real time, effectively reducing congestion and enhancing road usage efficiency. This demonstrates the capacity of multi-agent systems to improve public service efficiency.

Supply Chain Automation

Walmart, one of the world's largest retailers, uses AI and machine learning algorithms to optimize its supply chain management. Through the Retail Link system, Walmart automatically tracks inventory levels, predicts demand and ensures timely product restocking, showcasing the application value of multi-agent systems in commercial operations.

Smart Home Systems

Google's Nest smart home product line, including the Nest Learning Thermostat and Nest Protect smoke alarm, learns users' living habits to automatically adjust home temperatures and monitor safety, creating an efficient and comfortable home environment. This highlights the potential of multi-agent systems to enhance the quality of life.

Through these specific examples, whether in the fields of healthcare, creative design, city management, retail logistics or home living, we observe that both human-machine collaboration and multi-agent systems are playing an increasingly important role. These technologies not only enhance the efficiency of work and life but also provide higher quality and more personalized services. As technology continues to evolve, we anticipate even more innovative applications to emerge in the future.

IV. Conclusion

As we step into a new era shaped by AI technology, multi-agent systems and human-machine collaboration are not only hot topics in the field of technology but are also beginning to influence our ways of working, living habits and even our thinking. Steve Jobs once said, "Technology is nothing. What's important is that you have faith in people, that they're basically good and smart, and if you give them tools, they'll do wonderful things with them." Multi-agent systems and human-machine collaboration are precisely such tools, giving us unprecedented capabilities to solve complex problems, enhance efficiency and stimulate innovation.

Through this article, we have witnessed the application of these technologies across various sectors. They have made services more personalized, decisions more intelligent and creativity unleashed. However, facing the new challenges brought by artificial intelligence, we need new perspectives, new ways of thinking and interdisciplinary collaboration to fully harness the potential of multi-agent systems and human-machine collaboration.

As technology continues to evolve and its applications expand, we have good reason to believe that multi-agent systems and human-machine collaboration will open doors to innovation in more fields. In this process, every one of us is a participant, and our curiosity, creativity and desire for knowledge will be the driving forces that propel this intelligent revolution forward.

This article is not only a description of current technological trends but also an expression of hope for the boundless possibilities of the future. Let us join hands with AI, dance on the edge of intelligence, explore the yet-to-be-discovered wonders and together create a world that is smarter, more efficient and harmonious.

Adam Denninger leads Capgemini’s global strategy and product management for the insurance industry and manages its relationships with the insurance technology ecosystem. He has 20-plus years of experience creating and delivering solutions at the intersection of business and technology.

Insurance Thought Leadership

As you look ahead to 2025, what are the key trends we should expect to see play out? 

Adam Denninger

Common themes emerge across all insurance sectors. One of the biggest trends we're seeing is that customers are no longer tolerating complex, convoluted experiences in either sales or service. This applies to policy owners, members, agents, and distribution partners alike. Insurance companies are responding with significant investments to address these pain points, particularly through experience improvements, service enhancements, and contact center optimization – all powered by gen AI and new tools to increase efficiency.

Another major trend, particularly in the P&C space, focuses on better understanding and pricing risk. For instance, how do you differentiate between two 18-year-old male drivers – one excellent and one poor – using available insurance data? The same applies to property insurance, where two houses near each other on Florida's coast might represent vastly different risk profiles. This trend has led to substantial investment in underwriting workbenches, especially in traditionally tech-resistant markets like London.

Similar developments are occurring in life insurance, where there's a push toward fluidless underwriting. After a decade of discussion, companies are finally implementing better, faster, and less expensive underwriting processes while maintaining or improving accuracy.

The third major pillar of investment centers on cost optimization. Companies are exploring various approaches to reduce operational costs, whether through legacy system replacement, cloud platform migration, Gen AI implementation, or enhanced self-service capabilities.

Looking ahead, I expect to see step changes in the industry's overall technology capabilities. While the benefits of current investments may take one to three years to materialize, the scale and scope of approved projects suggest meaningful transformation is on the horizon.

Insurance Thought Leadership

How is generative AI currently improving insurance operations, and what developments do you see beyond basic efficiency gains?

Adam Denninger

I see several key developments happening with generative AI in insurance. One of its most powerful capabilities is synthesizing disparate sources of unstructured data – including reports, police documents, and news articles – into concise, fact-based summaries. This functionality is being integrated into both sales and claims processes. 

Working with Google, I've seen this in action through their new claims handling tool that some companies are implementing. While a human claims handler remains involved, the AI handles the analysis of backend documents, synthesis, and initial recommendations. This is truly game-changing, though companies still need to get comfortable with the accuracy and keep human oversight during these early stages. 

The efficiency gains in claims handling will be astounding, especially considering the cost of claims handlers. We're seeing similar developments on the underwriting side. For example, at Capgemini, we've developed a tool that can analyze unstructured public data from financial reports and news articles to create actuarially viable and defensible rating factors for D&O insurance – something previously unheard of. 

On the customer experience front, generative AI is transforming contact centers. The technology enables natural conversations where AI agents can gather information organically to populate quote information. Instead of dealing with structured IVR systems with rigid questions, customers can have fluid conversations. For instance, a customer might casually mention needing auto insurance for their 18-year-old son's first car, and the AI can extract and process that information while maintaining a natural dialogue.

What's revolutionary is that the computer can have human-like conversations and extract relevant information without requiring exact scripts or button pushing. This capability allows insurance companies to completely reimagine their contact center operating model. While these solutions are still being developed and tested, I believe they represent a significant advancement.

Insurance Thought Leadership

What companies are successfully implementing chatbot technology using large language models, and how close are we to seeing widespread practical applications?

Adam Denninger

Currently, no company has successfully implemented this technology in production at a high level. The main challenge, particularly for insurance companies, lies in integrating these new capabilities into existing legacy systems. This integration is incredibly complex and requires years of investment to implement in a production environment. While I can't name specific companies due to NDAs, I know several tier-one organizations are actively investing in these solutions. 

The technology is definitely coming. Initial iterations of these solutions will likely begin appearing in the market within the next six months, allowing people to start experiencing the technology firsthand. I anticipate widespread adoption and implementation from major carriers in production environments within two years.

The transformation from traditional scripted chatbots to these more sophisticated, conversational AI systems represents a leap forward.

Insurance Thought Leadership

And reducing customer service expenses through automation actually improves customer satisfaction.

Adam Denninger

It's definitely a win-win situation. While people are great, they do make mistakes, and not every customer service representative can know everything about the company. In contrast, a chatbot can be programmed with comprehensive knowledge about the company and provide consistent, accurate answers within its designated scope. 

Let me use Google as an example. Their claims-driven capabilities, which are part of an ecosystem of claims tools they're developing with our customers, deliver more accurate upfront answers in a fraction of the time compared with traditional methods. This isn't just a chatbot – it's a comprehensive claims management solution. 

The end result is faster, more accurate service that benefits everyone involved. Who wouldn't prefer getting their claims settled more quickly and accurately?

Insurance Thought Leadership

How far can we get with incremental innovation, and when do we need to think about complete system redesign?

Adam Denninger

When carriers come to us wanting to leverage new technology and transform their business operations, I typically recommend a specific strategy: Keep their existing legacy ecosystem and processes intact while building a completely new greenfield solution.

This approach allows companies to retain all their valuable assets – their product expertise, operational knowledge, regulatory understanding, industry experience, and distribution networks – while running them on legacy systems. They can then selectively transfer what they need to a new platform, creating a best-in-class solution. 

In my experience, this is unquestionably the most effective and efficient approach. However, it's highly customer-specific whether organizations choose to pursue this path. The hesitation is understandable – building an entirely new operating model and platform represents one of the highest-risk IT and operational programs possible. You're essentially betting the company that this new platform will be superior going forward. 

While this approach carries higher risk, it's demonstrably the most efficient and effective way to achieve transformation. The companies that do choose this path tend to be true market leaders who are willing to take that calculated risk for potentially greater rewards.

Insurance Thought Leadership

Do you know the origin story of Target? It fits that model. Back in the 1980s, when I was in the Wall Street Journal’s Chicago bureau, we covered a relatively quiet company called Dayton Hudson. Like many department stores at the time, they had experimented with a heavy-discount approach, but they did it as a greenfield, largely because of the dynamics among the five brothers who had inherited leadership of Dayton Hudson from their grandfather, who founded it. The four oldest brothers didn’t think discount retailing would work. The youngest did and set up Target in 1962, with the condition that his brothers would stay out of the way. Target grew so fast that it eventually subsumed the rest of the business, which was renamed Target in 2000.

Adam Denninger

From my perspective, a greenfield is always the right way to go. But I also fully understand people's hesitancy given sort of the risk of doing that. You need the right leadership to pull that off. So probably that brother was a fantastic corporate leader, is what I would say!

Insurance Thought Leadership

What might a successful greenfield operation look like for a top five P&C insurer in five years?

Adam Denninger

The operation will fundamentally transform the entire policy lifecycle by drastically reducing operating costs and improving response speed across sales, service, and claims processes. Everything will be faster, better, and easier, with significantly lower costs due to AI support. The system will enable maximum self-service capabilities, available anytime, anywhere, through any channel, benefiting both distribution partners and insurers. 

The real beauty lies in the data integration. All interactions, policy sales, servicing, quotes, and third-party data will feed into a single core system for the insurance company. This creates incredible power for cross-selling, underwriting outcomes, pricing optimization, marketing effectiveness, and claims handling. It enables a virtuous circle of continuous improvement that's impossible with current systems. 

The reason insurance companies can't achieve this today isn't due to lack of desire or knowledge – it's because their existing systems literally can't communicate with each other. The current infrastructure is an incredibly complex morass that prevents meaningful progress. While you might achieve incremental improvements like a 5% reduction in claims handling time, true step-change transformation isn't possible with legacy systems. 

What we're going to see is carriers finally operating the way we all want them to – efficiently, responsively, and driven by data. It will start with a few pioneering companies, and their success will force others to follow suit. This transformation isn't just a possibility – it's an inevitability. The only question is when companies will make the leap.

Insurance Thought Leadership

How significant is the potential for insurance companies to shift toward a Predict & Prevent model, particularly in using data to help prevent accidents and losses?

Adam Denninger

The model has enormous potential, with the main limitation being our ability to deliver data to the right place at the right time. We already have access to extensive data about traffic accidents, risky roads, and dangerous drivers. On the property side, we know which types of houses carry more risk based on building code history. The challenge lies in creating effective mechanisms to share this information with individuals who need it most. The technology already exists -- our phones can alert us to traffic jams, for instance. The next step is getting this valuable data out of its current silos and into public-facing systems. 

Currently, insurance companies are still focused on more fundamental challenges, like updating their pricing models more efficiently. Many companies are struggling with the fact that it takes up to five years to roll out new pricing models, which is unsustainable in today's market. 

However, the Predict & Prevent approach is universally recognized as valuable. The key is developing systems that can make data usable and accessible, such as mobile apps that can provide real-time location-based alerts and warnings. Once we overcome these implementation challenges, this model will only continue to improve and expand.

Within the next five to 10 years, I expect consumers will regularly receive location-based notifications from major tech companies like Apple and Google through their mapping services. These notifications will likely alert you to available hybrid parking spots and other location-specific information. Insurance companies will also leverage this technology advancement. Once they have the capability to effectively analyze policyholder data, they'll be able to provide customized notifications about specific risks and maintenance requirements based on the type of property being insured.

Insurance Thought Leadership

Thanks, Adam. I really appreciate your taking the time.

For those of us who have worked in this industry for a decade and longer, when you honestly assess how claims handling has evolved, you would fairly conclude that while certain aspects have improved – some even impressively – the fundamental model, process, service and financial outcomes have essentially remained unchanged or marginally improved. 

When comparing insurance claim modernization with other financial and consumer services, the shortcomings become even more obvious. Yet the environment in which claims occur and are resolved has changed significantly.

The reasons for this lack of breakthrough are many and complex, and playing catch up in real time has not been easy – but it is possible, and mandatory.

Evolution vs. Transformation

Claims evolved from paper in the 1980s to computerization in the 1990s, followed by mobile technology by the late 1990s and into the new millennium. So-called transformation from legacy, often home-grown, core CMS (claim management systems) to modernized systems was the most recent, costly and arduous of undertakings. 

While many large carriers began tackling policy management and CMS upgrades 10 or more years ago, some are now just finishing or making those changes. Carriers find themselves “upgraded” but now need to bridge integration and digitization gaps to fully realize massive system investments often encumbered by budget and technology prioritization fatigue. Disappointment is expressed throughout as gains in system maintenance and ease of modification fall short of high expectations. In other words, transformational change is not yet completed despite time and tremendous capital expenditures. 

This is not a vote for turning back, as such legacy system upgrades were overdue by decades.

The core process of claims intake, assignment and the remaining linear claim steps is unchanged throughout the industry. Claims handling is considered part art, part science, difficult to untangle and requiring apprenticeship learning and development that takes years. Litigation risks and fiduciary responsibilities are real but often amplified as barriers to change. Visions of STP (straight-through-processing) have been muted because of loss cost containment priorities, and cloud migration no longer appears to be the next silver bullet, as the landscape is changing.

Interdependency among vendors, providers and other ecosystem friends and foes has grown and become more sophisticated. Many are sizeable, global companies making their own technology investments and are constantly vying for a larger piece of the pie. The reliance on such entities has never been higher when it comes to repairing, restoring and managing a multitude of moving parts in today’s claim response whether individual or catastrophic scenarios. The insurtech wave over the last 10 years has only added to these players that are now posturing with AI in the forefront, regardless of depth and validation. 

The stakes are high as premiums become unaffordable and both claim operational productivity and indemnity control are leaned on to offer relief. Either way, the horizon is a next frontier of incomplete digitization, limited integration and a fragmented provider/vendor space while AI is unfolding rapidly. 

See also: The Need for Excellence in Claims Handling

Claims Needs More Attention, Respect and Resources

It is counter-intuitive that the claims operation, which consumes the majority of a carrier’s total income, receives the minority of investment in dollars and technology. And much of that is for maintenance, not development. Because claims is (wrongly) perceived as little more than a major cost center, it can be underappreciated in C-suites and in other operating areas. In tough times, it is the first to incur layoffs. 

While we often hear about how the claim is “the moment of truth” in insurance, it has until recently been mostly lip service emphasizing indemnification control while requiring intensive people resources to overcome numerous process workarounds. Carriers have recognized that the critical importance of customer retention is heavily tied to claims satisfaction and must rely on people assets to fill various process holes. Yet what is really needed to make true progress is real transformation in philosophy and operating models.

Tinkering vs. Transformation

The industry has embraced numerous third-party information technology solutions, but they have almost all been “point solutions” that address only single functions within the broader end-to-end claim process. True transformation will require a bold, holistic mindset, not just modifications and “band aids.” Integrations, AI and marketplace models are part of the solution. Solution providers have a similar responsibility to help unlock inefficiencies among repair, replacement and restoration following a loss and remain uncoordinated or lack incentives to partner.

True Transformation: Predict and Prevent vs. Respond and Mitigate

Underpinning true claims transformation will be a new paradigm built around a new “predict and prevent” operating model vs. the legacy approach of “respond and mitigate.” Connected sensor and data-enabling technologies and business models are widely available and should be pursued aggressively. This transformation will unlock innovative insurance product models. Increasing cyber, data and climate risks are just a few that call for greater loss detection and avoidance.

The New and Different Automotive, Repair and Consumer Landscape

The insurance industry needs to stop operating in ways that worked in yesterday’s automotive and consumer environment. Driver assistance and electric vehicles have forever changed the auto physical damage and collision repair landscape. Consolidation of the collision repair industry, backed by well-funded private equity investors, has shifted the balance of power between them and auto insurers. Auto manufacturers have begun disrupting the auto insurance and collision repair process through branded insurance and certified repair programs requiring the use of their parts. Consumers are shopping and switching auto insurance at record rates in the face of relentless premium increases. The insurance industry’s image is at an all-time low, including a highly negative view of their claims adjudication practices.  

The Silver Tsunami

The insurance claims adjuster workforce has been shrinking and now tops the list of vanishing industry jobs. Several factors are driving this shrinkage, including fewer auto claims, cost cutting, and aging out, all of which is expected to continue. This reality makes it incumbent on carriers to transform the claims process by leveraging technology and implement effective upskilling programs for the surviving workforce.  

Too Many Solutions – Too Few Integrations

The insurance information technology landscape is proliferated with numerous startups as well as legacy providers, most of which offer closed, proprietary solutions and only a fraction of which are integrated with carrier claims systems. And almost all of these solutions address only a small subset of the claim process. 

Shiny Things Distraction

The insurance industry has suffered from the same irrational attraction to “shiny objects” that consumers display, and the costs have been high. Research, testing, meetings, IT resources, pilots, staff time have all contributed, and unwarranted opportunity costs too frequently result in abandonment. Carriers need to identify their challenges and then review potential solutions rather than the other way around. 

New Technology Potential

It is surprising to consider how much new claims technology has been developed, tested and effectively validated by a small number of carriers but has not been more widely explored or adopted by the wider industry. Consolidation and attrition among this segment may be expected and could address this gap in time. 

Critical Leadership

Real transformation by large, complex companies is not possible without the clearly articulated encouragement from leadership including CEOs and the rest of the C-suite. These leaders will need to convince their stakeholders of the existential need for transformation and be cheerleaders for the required innovation.    

See also: Why Do Insurance Claims Take So Long? 

Legislative/Legal Refocus 

The insurance industry has historically been quick to point to regulators as impeding their ability to innovate. While sometimes true, this claim is exaggerated, and carriers need to learn how to navigate these challenges. Confronting the threats of legal system abuse, social inflation and consumer dissatisfaction can become a common cause for these constituents.

The time is now, conditions are ripe, the solutions are at hand and the future of the industry awaits.

After the chaotic first three weeks of the new Trump administration, it's impossible to know what the long-term effects will be of the tariffs, the rescinded tariffs, the threatened tariffs and the president's broad endorsement of "tariff" as "the most beautiful word in the English language." But some of the effects on the insurance industry are becoming clear, especially on auto insurance — and they aren't good. 

This week, I'll tell you what I can about what happens next. I'll also take a look at the absence of insurance ads on the Super Bowl broadcast, which probably changes nothing but which I choose to take as a possible sign of a new seriousness by insurers to educate consumers on their needs for coverage and as a shift away from just pounding brand names into people's heads. Finally, I'll dig into a smart piece that provides a way to think about implementing generative AI, as "digital workers."

Let's start with the tariffs.

Auto insurance premiums have pretty much caught up with the surge in prices for new and used cars and for replacement costs that came out of COVID. It disrupted supply chains and made many parts scarce. The lack of cars on the roads led to increasingly reckless driving, which has been slow to wane even though driving has for years been back to pre-pandemic levels. Distracted driving seems to keep getting worse, too. The spread of technologies such as driver-assist has made repairs more expensive, as has the increasing market share of electric vehicles. 

But just when you thought it was safe to get back in the water, here come the tariffs. Even the scaled-back version of Trump's tariffs, which cover only aluminum and steel, could add $1,500 to the price of a new car, according to this MSN article. The reason: The average car contains about 1,000 pounds of steel, at a cost of $6,000 to $7,000. Add a 25% tariff, and that steel costs $1,500 or so more. 

As the article notes, car makers could shift suppliers to avoid the tariffs, but the higher cost of imports creates a pricing umbrella, and other suppliers will surely take the opportunity to raise prices. 

If Trump follows through on the 25% tariffs he's threatened to apply to all goods from Canada and Mexico — before backing off in the face of a 600-point drop in the Dow Jones Industrial Average — the costs of cars and replacement parts would soar even more. The Wall Street Journal says the boost could be $3,000 per car. MSN goes further, putting the price increase at $6,250 for a $25,000 car. 

Even if Trump never follows through on the tariff threats, "The damage has been done," according to a Georgetown University professor quoted in the WSJ. “We are going to have tremendous uncertainty looming over the industry for the foreseeable future…If I’m an insurance company, I’m pretty dour at the moment, and certainly highly motivated to pass on my risk in the form of higher premiums to consumers.”

That sounds right to me. I think we've reentered an inflationary phase for cars because of the tariffs and threatened tariffs. The only question is how bad it will be and how long it will last. 

Now to the Super Bowl ads.

I suspect the reason for the absence of Mayhem, Flo, the GEICO gecko, etc. was the (smart) decision not to get caught up in a wave of ads by startups you'd never heard of and by established brands that somehow decided that having facial hair take on a life of its own would sell chips and pizza bites. (My younger daughter thinks both companies must have made the same sort of ill-advised prompt with a generative AI in a search for a novel idea.)

But I hold out hope for a more meaningful reason. I keep thinking the insurance industry will take seriously its complaints about a protection gap and will focus on educating consumers about the need for life insurance, flood insurance, etc. I keep thinking the industry will move toward the future of insurance — the Predict & Prevent model — and will help people think about how to harden their properties and communities against wildfire and other threats. 

I've been thinking this for a long time. Here are articles I published as far back as 2016 and 2014 that complain about the reliance on ads vs. substance. 

I won't even try to claim that the bang-your-name-into-people's-heads approach isn't effective marketing. I'm sure it is. But I'll keep hoping that the insurance industry will find a way to focus consumer attention on the really important long-term issues.

On to "digital workers."

You've read a lot recently about AI agents — including articles I've published at ITL — but I still commend to your attention a recent piece by my old friend and colleague John Sviokla, because it provides a useful way to think about agents, as well as a great example.

While AI often seems esoteric and vaguely threatening, John describes how AI personas can be created and simply put into the staffing pool, as any human would be. Hire the AI or don't; the choice is yours. 

His great example is OneDigital, which helps meet work-life needs for more than 100,000 employers and which has created 10 digital workers, with about a dozen in the pipeline. 

“'Frank' is an expert in sales effectiveness and strategizing on growing the services that OneDigital provides to existing clients," John writes. “'Yesper' was recently added to help managers mentor and support their team members while maintaining OneDigital’s innovation and client-first culture, as OneDigital has grown significantly over the last few years. There are many others, including 'Ben,' an expert at everything to do with lowering healthcare costs for OneDigital’s clients."

I remain cautious about AI agents, because I'm not ready to let them act on my behalf, but I can imagine having digital workers who report to me and who I supervise, perhaps rather closely, at least until they prove themselves. And the AIs available to us are improving so fast that we should be planning for the next round, and the one after that.

Cheers,

Paul

Life insurers play an essential role in protecting the financial well-being of people by providing products that mitigate financial risks and aid in retirement planning. Traditionally, insurance companies have been "second movers," slower in adopting technology due to their long-term focus and desire to clearly understand the risks and rewards of change.

New capital investments in technology must meet internal benchmarks for opportunity cost and overcome the high barriers to adopting unproven innovations. Many insurers interact with customers through third parties or digital platforms, providing another barrier to decisiveness. This cautious approach has led some to view the life insurance industry as ripe for disruption, especially with the rise of fintech and insurtech in the late 2000s. 

There have been numerous historical examples of insurers whose early adoption of transformative technologies — such as advances in computing (MetLife, Franklin Life, and PacLife's UNIVAC mainframes, 1954), the internet (Progressive's auto-insurance.com, 1995), and development of derivative financial instruments (Keyport Life Insurance's KeyIndex FIA, 1995) — gave them a competitive edge.

Artificial intelligence (AI), and in particular generative AI, is just such a new technology that will drive growth in embedded value for those companies that can harness it.

While all the flavors of AI are interesting, we believe the value of machine learning to insurers is already well-understood. Instead, we plan to focus on how insurers will create value from generative AI, such as large language models (LLMs) and similar models and capabilities. For simplicity, we refer to this as "AI" throughout.

See also: AI Bias in Life & Annuities Insurance

Generative AI capabilities for insurers

The following features and uses of generative AI are key to insurers:

Generalist capabilities: AI has broad capabilities and potential reasoning abilities across a diverse array of tasks, with the ability to become further specialized in particular domains or task areas through additional fine-tuning. This flexibility allows insurers to deploy AI across multiple functions, reducing the need for multiple specialized systems.

Human-computer interface: AI provides a new way to interact with computers. Spoken or written colloquial language can "prompt" a unique response from a machine. This more stochastic approach is a departure from the traditional interface humans have with computers: entering commands to receive outputs determined through deterministic algorithms. These more intuitive and natural interactions can lead to improved experience, efficiency and satisfaction.

Multimodal content generation: AI can produce substantial written, programmatic, pictographic, video and audio responses based on simple requests. This core capability drives higher productivity for knowledge workers and can allow insurers to scale this generation with fewer resources.

Personalization and customer experience: AI can correlate general information (from its training data) with specific information from end-user input and customer data to create personalized writing and responses. This stands to elevate the customer experience when compared with deterministic chatbots or static knowledge bases, as people typically respond better to more personal interactions.

Automation and improving efficiency: AI can automate routine and repetitive tasks, which reduces the scope for human error and increases efficiency. Even in instances where the accuracy is not high enough to completely entrust to AI, it can expedite tasks by performing a first pass that a human can improve upon.

Facilitating innovation and creative thinking: AI can accelerate innovation in companies by assisting with scenario analysis, suggesting solutions to complex problems, and providing predictions and challenges for the feasibility of new ventures.

Optimizing and expanding AI in insurance 

We examine the role that AI will have in the insurance space across the following dimensions:

Optimizing and improving current capabilities

Generative AI stands to provide widespread efficiencies across current operations. The mechanisms for these improvements rely on taking tasks that are currently done by people (usually working with computers) and inserting generative AI into the workflow. One example of this is using generative AI to summarize existing documents, or to take notes on a call. Microsoft has incorporated this feature ("Copilot") into the Office Suite already. Insurers can leverage these capabilities by creating generative AI chatbots (for example, through a "CompanyGPT" interface) and search assistants that reference company document repositories to support querying of complex documents such as contracts, policy forms and product specifications. Envision a new company employee in the marketing division asking an AI bot detailed questions about a specific annuity policy. While this may not fully replace detailed reading of the source documents and conversations with knowledgeable colleagues, using an AI chatbot can reduce the amount of human time spent searching for the right document or chunk of text embedded within a document.

By improving the efficiency of current capabilities, AI assistants will lower the cost for activities such as searching for information or providing additional teaching or training to employees. As the costs for these activities drop, we expect to see insurers develop employees who are functionally more knowledgeable because they have access to more information through efficient search assistants.

Expanding abilities available from generative AI

We recognize that generative AI will also expand the opportunities available to insurers. The mechanism of this expansion is using new AI capabilities that did not exist before LLMs and generative AI: machines producing content, collating ideas and concepts with less time and effort than people do. AI will serve as an assistant to the marketer (Holloway, 2024), to produce copy and images in the company's own style, at first as a way for marketers to deliver their visual ideas to professional artists, but eventually producing content that is ready for publication.

Specific to insurer operations, generative AI may help the insurer fraud division detect signs of fraud activities in real-time. Currently, call center employees need to balance providing good customer service while also watching for red flags but, with an AI assistant, these duties could be bifurcated between the human and the machine. For example, an AI assistant could listen to voice calls between customers and administrators and be on the lookout for social engineering tactics, unusual requests or suspicious communication, while the person on the line focuses on the customer's needs. With access to a repository of call transcripts, the AI could also detect repeated scripts used in organized fraud attempts or similar schemes. The AI could keep the service representative in the loop regarding any potential fraud issues and suggest countermeasures, including alerting an organization's fraud specialists for intervention. Of course, fraudsters already employ generative AI in creative ways, such as voice cloning (Bethea, 2024), in this "arms race."

See also: AI in Insurance: 2025 Predictions

Challenges to AI adoption in insurance

Insurers must be strategic in deploying AI in their organizations: adopting these technologies comes at a financial and resource cost. Despite that, we believe that failing to adopt AI in various ways produces an opportunity cost that will eventually put most insurers at a strategic and competitive disadvantage, as widespread adoption of AI will lead to, for example, better customer service, higher worker productivity and lower fraud.

Envision learning of the first spreadsheet technology (say in the late '70s or early '80s) and believing: this will put company actuaries and accountants out of a job, it will cost a ton of money, and the company puts itself at risk by relying too much on machines to do the work we have done successfully as humans thus far.

Business—and insurers—are at the next major technology inflection point, and (despite what actuaries say) it's bigger than the spreadsheet.

Insurers must devise a clear strategy for AI, considering opportunities that make sense for their core business, and consider taking calculated risks on others.

Building and buying

Innovating at the cutting edge of any new technology is a high-risk, high-reward gambit. Early innovators that succeed can reap outsized returns and durable advantages. However, the unpredictable nature of technological innovation also means that many early bets will be misplaced and thus result in wasted resources.

The prospect of building new AI tools and processes within an insurance company will be tempting. One approach is to build the new technologies in-house: to hire new experts and integrate new workflows. After all, if there are major new opportunities available, then why not invest and earn the returns of that investment?

Another strategy is to be a more passive fast follower. Let other companies take the early bets and risk their capital and only be reactive, seeing which bets pay off and only implementing those proven ideas, but necessarily with a time lag. With this approach there is less overall risk of overinvesting in unproven technology, but it will reduce the value of any competitive advantage and, indeed, if the first movers are truly successful, it could put your company at a significant disadvantage playing catch-up.

Another uncertainty looming over both options is that it is still unclear to what degree in-house customized solutions will be more useful than off-the-shelf third-party tools, if at all. It is possible that the products of generalist providers such as ChatGPT will suffice, or that successful specialist vendors will emerge in the insurance space. Going back to the spreadsheet example, no insurance companies would have benefited by creating their own customized spreadsheet software, as major software developers such as Apple, IBM and Microsoft offered superior, general solutions. Moreover, few companies were significantly harmed by adopting spreadsheets at a slower pace than their competitors. Yet while both those things are true, any insurer that resisted the technology entirely would have found itself eventually defunct. Similarly, as major business technology players continue to pour billions of dollars into developing AI solutions, keeping an in-house solution current with external developments may prove to be a Sisyphean task.

The decision will likely come down to company culture. Those companies for which such disruption is more welcome, and that are willing to test "tried-and-true" methods against new models, will find themselves faster adopters of this new technology. They will be the companies most willing to hire external guides for their AI build, and to hire a new team themselves. If the company or CEO has been rewarded in the past for quick adoption of new technology, they will be ready for this next paradigm shift.

Regulation and compliance

In addition to our focus on key strategic areas, this series will also dedicate an installment to compliance and regulatory considerations. The National Association of Insurance Commissioners (NAIC) has issued the 2023 model bulletin "The Use of Artificial Intelligence Systems in Insurance" (NAIC, 2023). Insurers, primarily through their legal and compliance teams, have adopted internal policies on the use of AI at work, and have begun to articulate their positions regarding the use of AI for business operations. This area of practice is constantly evolving with the new tools and techniques that generative AI brings us and it will continue to evolve as the cost of new intelligence continues to decline.

Overreliance on AI

Even the most techno-optimistic insurance companies will realize that placing too much reliance on AI presents new risks:

• Relying too heavily on machines for tasks traditionally done by people will atrophy our human connections to the business. The more we cede tasks and decisions to models, machines and electronic agents, the less we are in control of the core operations of the business, and we risk losing culture and the raison d'etre of the venture. Employees might not take kindly to the prospect of AI-generated performance reviews, for example.
• There is value to the institutional memory that we generate through our daily interactions, tied together over decades to produce quality products and experiences for our customers. Allowing machines to do more tasks may bring efficiencies to our customers—through better experiences and lower prices—but this could come at the cost of not developing sufficient human intelligence about the customer experience.

In the end, the balance between reliance on people and machines will be a company decision, dependent on culture and economics and leadership, as well as external factors such as competition and larger business trends.

Reliability

Knowing when and how to rely on the output of AI will become a harder task as AI becomes more prevalent. AI in its current form is prone to hallucination. However, though often less discussed in the same terms, humans also are prone to such "hallucinations"—our own human frailty can manifest in diverse ways such as misunderstandings and overconfidence.

Underwriters will try to override decisions where the AI suggests accepting profitable business. Claims might get flagged as fraudulent when the model is noticing rare but unconcerning behavior.

The future of work will involve the complementary hybridization of work between people and AI. The employees best able to navigate this future will have the openness to let the technology automate things when it can, and then further use it augment their judgment when it can't. We must retain the healthy skepticism to understand that, just because AI determined an answer, it is not automatically correct.

Work force transformation

Any adoption of AI to perform tasks currently done by people will transform the way a company thinks about doing work. To start with, we envision AI completing or assisting with certain tasks done by managers in much less time. These tasks include rudimentary work, such as taking notes and writing memos, but also include more cerebral tasks such as assigning work (or suggesting assignments), brainstorming ideas on new ventures and outlining project workflows.

This requires an "upskilling" for current employees to adopt AI in their own roles—in a compliant manner—to improve their own efficiency. A large adoption of AI will no doubt lead to additional business gains: with extra time, skilled and thoughtful employees have more opportunity to contribute to other business ventures.

Most roles will be transformed not only by "upskilling" to incorporate AI into their current processes, but also by changing the nature of those roles altogether. We envision actuarial and underwriting roles, for instance, being rewritten over time to incorporate new parameters of risk from new data sources and newly available analyses. Just as the advent of spreadsheets within analytical professions revolutionized what could be done in those positions (leading to new positions, new products and more consumer choice), the new AI capabilities could produce new roles that were previously unfathomable.

See also: 4 Golden Opportunities With AI

The imperative of trust in the age of AI

The insurance industry's greatest asset is trust: consumer trust that the company will be there to pay claims when it promised it would, to alleviate financial distress in a time of need. With lifetime contracts, this trust must be held "forever," for as long as customers live. However we move forward with adopting AI, we must ensure that we retain and enhance consumers' trust.

In noninsurance settings we already see the online landscape muddied with "AI slop," or the vast array of AI-created content that proliferates on social media and in email and more.

In insurance, we must present the case for AI use to our customers: how we reach comfort with "black box" algorithms, or models that seem to lack explainability. We must be vigilant to recognize the potential for biased outcomes to emerge from our models, which are inevitably based on data and methods that can never be completely free of bias. Finally, customers will rightly have concerns about the use of their personal data in our AI models. We must address these concerns with transparency and understanding as an industry.

But in addition to defensive trust fortification, insurers should consider the variety of ways that AI allows for a better customer experience, thereby expanding trust. As an example, insurers that use AI to successfully anticipate their customer needs and behaviors may create a bond that is hard to generate when seeing every policyholder as simply "part of a pool" of risks. Insurance companies can foster longer-term relationships with consumers through proactive engagement, sending personal, relevant communications when people will most welcome them (e.g., at the time of life events like purchase of a new house).

In that regard, some companies will use AI to their competitive advantage, to strengthen trust among their customers.

This paper is the first in a series that will explore strategic areas where life insurers may deploy artificial intelligence (AI): in underwriting, customer experience, risk assessment, fraud detection, and operational efficiency. We will also publish an installment dedicated to regulation and compliance considerations in the use of AI.

The world today is burning, drowning and crumbling in ways that no one can ignore. The tragic scenes in Los Angeles in January were just the latest proof. As temperatures rise and weather patterns grow more violent, the question is no longer whether disaster will strike but when – and how hard. 

Economic losses from natural catastrophes reached $350 billion last year. And in this new age of climate uncertainty, insurers find themselves at a crossroads: adapt or collapse. The storms will come, but human and financial losses need not rise with the seas.

In California, wildfires now rage with such frequency and ferocity that several major insurance companies have simply quit. In less than a year, from summer 2023 to early 2024, eight insurers stopped writing new home policies in the state, claiming that wildfire risks made business untenable. 

Flood-prone areas are facing the same grim exodus of insurers, leaving thousands uninsured and vulnerable. This is the so-called "protection gap" – an inoffensive term that in reality means homes left unprotected, lives potentially left in ruins.

To close this gap, insurers must shift from paying compensation for damage to actively predicting and preventing it from happening. The existing model must ascend to the next level. The climate crisis has made nature too fierce, predictable only in its unpredictability. "Acts of God" have become commonplace. Now, the cost of reaction and response are just too high. Hence the need to predict and prevent.

Consider this: Every euro spent on disaster prevention saves between five and seven euros in recovery costs. This was a finding of the European Investment Bank; it describes a sound investment by any standard you choose. The question is why more insurers are not doing it. 

One answer lies in the sheer complexity of modern risks. Unlike hurricanes or earthquakes, so-called "peak perils," which tend to devastate large areas, wildfires and floods often strike with pinpoint precision. One house burns while its neighbor remains untouched. This confounds traditional risk models, which rely on broad statistical averages.

Technology presents a path through the fog. Geospatial technology, in particular – mainly satellite data, made intelligible with artificial intelligence and geoanalytics – can now monitor the planet in astonishing detail. Insurers can track weather patterns in real time, pinpointing the precise location of emerging risks. They can find out not just that a fire is coming, but exactly which valley it will sweep through and which homes lie in its path.

This is no longer science fiction. Some forward-thinking insurers are already using such tools. If a wildfire looms, they alert clients to take defensive action – clearing brush, installing fire-resistant materials or even hiring private firefighting teams. The same principle applies to global commerce. "Moving assets" – ships carrying vital goods – can be tracked minute by minute. If political unrest, piracy or a typhoon threatens a key shipping lane, insurers can warn businesses to reroute their cargo, avoiding catastrophe before it strikes.

Yet, for all its promise, this predictive approach to insurance faces resistance. It demands that insurers rise to the height of a major challenge, becoming highly active, data-driven risk analysts and managers, working closely alongside clients to reduce exposure before disaster hits. It means investing in historical datasets, sophisticated geospatial technology and human expertise. It means rethinking what insurance is or, if you prefer, "completing" insurance, realizing more of its potential to make human settlements robust.

Critics will say that such efforts are costly, that they will cut into profits. Or they'll say that the technology isn't there yet, that it can't possibly face up to the task we're facing. This is too pessimistic. 

Insurance emerged as a means to manage the uncertainties of a dangerous world, to save people from having to lean on neighbors and loved ones to pick up the pieces after catastrophe. It has evolved and evolved and evolved again as the world has changed. History, after all, is ruthless to those who won't adapt. 

Today, the dangers facing us have multiplied and intensified. So must the industry's capabilities. It isn't easy, but it is simple. That evolution, a genuine leap forward for this essential, even vital sector, can help to keep people safe.

We don't need to overstate the point, but the stakes are high. If insurers retreat from the riskiest places, the protection gap will widen into a chasm. Whole regions could become uninsurable, their economies shattered. The alternative is a world where insurers, equipped with expertise and the most advanced available technology, serve not just as financial backstops, as essential as those are, but as frontline defenders against the crises of the future.

The climate crisis is no longer a far-off threat to our communities. It is here, reshaping lives and landscapes with brutal indifference. Insurers have a choice: predict and prevent, rise to the occasion, enlarge its important social role as a guarantor of community resilience, or retreat. The storms will rage regardless. But how much we lose – both in lives and livelihoods – depends on what we do now.

My DeepSeek prompt: 

You're a management consultant who writes in the clean, edgy style of Hunter S. Thompson. Write a 500-word essay on generative AI in property and casualty insurance. Consider where we are in the context of OpenAI's five phases of AI: chat, reasoning, agent, innovation, organizations. Include specific use cases from insurance companies such as Progressive and Allstate. Consider the $450 billion in AI infrastructure investment made by Big Tech in 2025 alone. Consider the human and corporate reluctance to use AI to replace people, but also consider that humans hate jobs staring at screens all day moving tokens around.

See also: How AI Is Changing Insurance

DeepSeek’s output (unedited):

The Gonzo Frontier of Generative AI in Property and Casualty Insurance: A Wild Ride Through the Five Phases of OpenAI’s Vision

The property and casualty insurance industry is a beast—a bloated, bureaucratic leviathan that thrives on risk assessment, claims processing, and the occasional existential crisis. It’s a world where humans stare at screens all day, moving tokens of data like croupiers in a casino where the house always wins. But now, generative AI has crashed the party, and it’s not here to play nice. It’s here to rewrite the rules, disrupt the flow, and maybe—just maybe—set the whole damn table on fire.

Let’s start with OpenAI’s five phases of AI: chat, reasoning, agent, innovation, and organizations. Right now, the insurance industry is straddling the first three phases like a drunk cowboy on a mechanical bull. Chatbots are already ubiquitous—Progressive’s “Flo” has gone digital, answering customer queries with the kind of relentless cheer that only a machine could muster. Allstate’s “Digital Virtual Assistants” are handling claims inquiries, freeing up human agents to do… well, whatever it is humans do when machines take over the grunt work. But this is just the warm-up act.

The reasoning phase is where things get interesting. Generative AI isn’t just regurgitating pre-programmed responses anymore; it’s starting to think. Sort of. Allstate is using AI to analyze accident photos and estimate repair costs, a task that once required a human adjuster with a keen eye and a tolerance for tedium. Progressive is leveraging AI to predict risk with terrifying accuracy, crunching data on driving habits, weather patterns, and even social media activity to set premiums. It’s not perfect, but it’s getting there—fast.

Then there’s the agent phase, where AI starts to act autonomously. Imagine a world where your insurance policy updates itself in real time based on your behavior. Drive like a maniac? Your premium spikes instantly. Install a smart home security system? Your rates drop before you can say “Big Brother.” This isn’t science fiction; it’s the logical next step in a world where AI is increasingly calling the shots.

See also: How AI Will Transform Insurance in 2025

But let’s not kid ourselves. The real game-changer is the innovation phase, where AI doesn’t just optimize existing processes—it invents new ones. Picture this: generative AI designs entirely new insurance products tailored to niche markets. Pet insurance for crypto bros? Done. Pay-as-you-go coverage for gig economy workers? Easy. The possibilities are endless, limited only by the imagination of the algorithms.

And then there’s the organizations phase, where AI doesn’t just assist humans—it replaces them. This is where the rubber meets the road, and where the industry’s reluctance to fully embrace AI becomes glaringly obvious. Humans hate change, especially when it threatens their livelihoods. But let’s be real: humans also hate jobs that involve staring at screens all day, moving tokens around like digital Sisyphuses. AI can do that faster, cheaper, and without complaining about the coffee.

The $450 billion Big Tech poured into AI infrastructure in 2025 wasn’t just a flex—it was a signal flare. The future is here, and it’s powered by algorithms. The question isn’t whether AI will transform property and casualty insurance; it’s whether the industry will embrace the change or get dragged kicking and screaming into the brave new world. One thing’s for sure: the ride is going to be wild. Buckle up.

Author’s note: I started typing the prompt at 8:01 and submitted the article at 8:09.

The financial services sector currently accounts for about 10% of all deals and is experiencing positive momentum. Throughout 2023, the industry has maintained a consistent level of deal activity and has demonstrated year-over-year growth in both deal value and deal count. There is still a pressing need for organizations to improve their divestiture processes – in both selection and execution.

See also: 3 Key Workplace Challenges Reshaping Business

Complexities of Divesting Entangled Entities

When looking at a business through the lens of a divestiture, an asset can often be thought of in one of two ways: Either it is largely independent from the parent company, or it is dependent on infrastructure owned by the parent. The latter scenario, commonly referred to as "entangled," presents additional complexities regarding how to effectively separate during a divestiture.

"Entangled" is an umbrella term that can describe many relationships between the parent and the division being sold. These two parties might be connected by the labor they employ or by the infrastructure they use. The entanglements that are easiest to manage are relationships with third parties where the master agreement sits with the parent company but the purchase orders or statements of work specific to the entity being divested are straightforward. On the other end, time-intensive and difficult-to-manage entities are tethered to their parent companies by IT applications and infrastructure. In these cases, program management resources that can coordinate between different functions and subject matter experts within each function are crucial.

The degree of entanglement also affects compliance considerations that must be made when selling an asset that has previously been dependent on the parent company. These considerations will hinge on the size of the deal and the degree to which the industry in question is regulated. For insurance specifically, parent companies need to inform numerous regulatory organizations at both federal and state levels, while also completing an approval process for some aspects of the divestiture. Regardless of the industry of the seller, the parent company also must be compliant with employment laws when managing workforce reductions or as employees are moved to the new owner.

Practical Considerations

1. Planning for the known and the unknown

It is important to establish a structure through which the divestiture will be managed. This management structure can include things like a governance hierarchy, a suggested schedule for when different groups should meet, the responsibilities of different required roles and a framework for making critical decisions. It is also important to ensure this management structure is flexible. The proper governance and meeting cadence that exists six months from the close of the transaction may no longer be appropriate when launch is only one month away. Having this flexibility will allow for the divestiture to maintain momentum throughout the process while also being able to respond to unforeseen complexities.

An effective way to facilitate this flexibility is to employ a program management team. The team will serve as the glue that holds things together, connecting different stakeholders with each other at the appropriate times and planning ahead for critical milestones. This team works within parameters of the governance structure to ensure quality, timelines and efficient decision-making. As the needs of the divestiture change, the program management team will adapt accordingly and keep the plan current. It is important for this team to communicate clearly and to keep the broader team motivated during periods of intense work.

2. Establishing effective communication

A divestiture represents a time of uncertainty for employees of the company. When a deal is newly announced, employees will question the longevity of their jobs. How a company communicates with its employees during this stressful period will greatly affect employees' trust and engagement. As a successful divestiture is contingent on retaining key talent and continued engagement, communication to assuage employees' fears will go a long way to stabilize the situation and enable the success of the new organization.

There are different ways a company can effectively communicate with its employees. First, a central communications team should be established to own, develop and manage all communication activities and messaging. Key communication milestones such as deal announcements and employee town halls should be set and prepared in advance. Employee messaging should be as transparent and factual as possible and foresee questions that employees may have at the top of their minds. Less formal communication activities may also be deployed – such as smaller meetings hosted by department heads – however, it is important to ensure that the messaging delivered across all these meetings is consistent and vetted by the central communications team.

See also: How Business Rules Engines Can Slash Time to Market

3. Retaining critical talent to limit business disruptions

Successful separations are predicated on retaining critical talent and staffing key roles with the right personnel. Separations signal times of change and uncertainty for personnel for various reasons, such as cultural misalignment with the acquiring company, changes in leadership and disruption in ways of working (relocations, job function changes, etc.), which often result in a marked increase in attrition rates. High turnover at the outset can affect company operations directly and indirectly – by demoralizing and placing extra burden on remaining employees.

There are, however, ways to limit talent disruption and impact on business operations. One way is the creation of a talent retention program, in which critical personnel are identified in advance and offered incentives to stay with the company. Incentives can be monetary (stock options, bonuses) and non-monetary (developmental opportunities). Incentives can also be set up to align with transition timing as the impact of the divestitures may sometimes be felt for years down the line. Encouraging employee engagement during the transition is another effective way to improve employee satisfaction, which reduces turnover and loss of productivity. Employees want to feel valued and heard in company matters, and engaging employee feedback is one way to achieve that.

4. Mitigating Stranded Costs

"Stranded cost" is a term commonly used to describe a component of the parent company organizational structure that was previously supporting the divested entity but did not transfer as part of the transaction. Stranded costs can come in the form of people, third-party contracts, IT infrastructure or any other piece of the overall cost structure. These costs differ from other costs incurred by the parent company in that they no longer generate any value. Because of the entangled nature of many divested entities, planning for stranded costs, or even the recognition that they exist, can often be overlooked. This is a misstep that can be mitigated, but it requires the identification of potential problem areas upfront.

As an example, IT is a function that is often difficult to unwind and so creates stranded costs. This is in part driven by the contractual landscape that supports an IT function, as day-to-day activities are typically driven by a consortium of third-party agreements that may include outsourced resource support and hardware and software licenses. These agreements are often not in sync with the timeline of the divestiture, so the parent company may be responsible for paying for goods or services that it no longer uses once the transaction is complete. Timely planning and communication with the respective vendors can help to amend contracts and minimize any residual obligations. This process takes a considerable amount of time and effort, and the involvement required also scales in relation to how entangled the entity is and how many agreements it has.

In sum, companies in the process of divestiture can approach unraveling entangled assets thoughtfully by putting some guardrails in place. Establishing a structure with teams appointed to manage programs and talent retention, ensuring proper timing for all changes and putting existing employees' morale first are some ways companies can retain value and support their business through a transformation.

Have you noticed that all life insurance carriers ask the same basic questions on their applications?

"Who is the owner of the policy?"

"Who is the beneficiary of the policy?"

"Do you have a history of cancer?"

"Do you have a history of heart disease?" et cetera.

The problem is, while they all ask the same basic questions, none are identical, and all have enough differences that a producer can't simply ask the questions once and populate the forms. This causes a great deal of redundant keying and rework by the producer, and confusion with the customer. This is particularly true when a client wants multiple policies and must endure multiple instances of carriers' electronic applications.

With this frustration in mind, ACORD pulled together stakeholders from across the life insurance value chain to develop a first-of-its-kind standard life application that could be delivered in a digital format. This new application includes all reflexive questions, is pre-cleared for use in 48 jurisdictions, and will enable improved producer and customer experience.

Many have asked for one standard life application, and due to the various industry factors and trends below, the standardized life application has finally become a reality.

See also: A New Era in Life Insurance Underwriting

Consumer Frustration With Repetitive Online Applications

Historically, there has been a gap between the digital experience expectations of consumers and what the life and annuity (L&A) industry has provided during the life insurance application process.

Imagine a typical agent-assisted sale with today's digital application, requiring an investment of about 30 to 40 minutes of the client's time. The client completes the application online, but a meaningful percentage of cases today are "issued other than applied." The natural inclination is to apply for coverage with another carrier to try for a better risk classification and lower price.

The customer is expected to begin the application again, requiring another 30 to 40 minutes. In the customer's eyes, the questions are the same and have already been answered. Why couldn't the answers be saved and used by the next carrier? This is possible with a standard life application.

NIGO Delays for Producers

Now imagine an agent-assisted sale with today's paper application, which is frequently seen in the large case market, which is known for sophisticated policy ownership (e.g., trust-owned policies, business-owned policies, etc.) and often requires multiple policies.

The producer first typically completes a client intake form (abbreviated medical history) to perform field underwriting to determine the risk class to quote. When the carrier, product type, and amount are determined, the producer needs to complete each paper application, then proofread them before having the client sign them.

Even with rigorous proofreading, there is a significant chance a key question is missed, and the policy is NIGO (Not In Good Order), causing delays in underwriting. This translates into a delay for the carrier earning premium, a delay in coverage for the client, and a delay in payment of the agent's commission.

If carriers adopt the standard life application, both online and paper applications will be dramatically improved. Application/enrollment vendors can configure their platforms to ask the questions once, then populate the carrier's electronic applications when coverage is selected. The redundant field underwriting questions can be merged with the application's medical history questions so they only need to be asked once, and NIGO rates with paper applications will be reduced because the producers won't be forced to familiarize themselves with every detail of every carrier's forms.

Pre-Cleared by the Interstate Insurance Compact

The new standardized life application is pre-cleared for use in 48 jurisdictions by the Interstate Insurance Compact. The compact implemented electronic product filing in 2007 to review and approve carrier submission of product application forms for its member states, bringing uniformity to the industry, and keeping forms maintained and updated across jurisdictions. The compact has been involved with the creation of the ACORD Standard Life Insurance Application since the beginning and strongly supports adoption.

The standardized application was created with bracketed questions that carriers can decide whether to include or exclude. Reflexive questions were also included to reduce the number of supplemental forms. Carriers that choose to use the standard application will automatically be moved to the Compact's Expedited Review Process Queue. Upon compact approval, the carrier can personalize the form to fit their branding and documentation guidelines. This shortens the form's approval time for a carrier's application from weeks to days.

See also: Removing Pain Points for Life Insurance Actuaries

A Competitive Advantage for Carriers

Carriers recognized that the question structure of a standard life application can improve data quality. The standard life application's questions were designed to incorporate behavioral science principles and industry best practices to improve response accuracy and reduce bias. They were also designed to maximize the amount of structured data and minimize free text. This will ensure maximum automation in underwriting and reduce manual interpretation on some carriers' medical and exam forms.

The reflexive questions were designed to ask only information necessary to continue underwriting. The reflexive questions refrain from asking questions the client likely won't know (or may misstate), like tumor stage and grade.

In addition, the standard application forms packet contains a full litany of avocation questions rather than just scuba and aviation. It includes motor sports racing, powerboat racing, rock climbing, back country skiing, and many others, and asks reflexive questions relevant for many of the reinsurance underwriting manuals to determine the appropriate risk classification.

ACORD has already completed the mapping of the initial data structures from the standard life application and reflexive questions to the ACORD Next-Generation Digital Standards (NGDS) JSON formats for easy manipulation and traceability.

The Future of Applying for Life Insurance

The new standardized life application will change how consumers, distributors, and carriers work through the life insurance value chain. The application offers a solution to the convoluted, time-consuming, and frustrating process of applying for life insurance policies and makes it easy to match consumers' digital expectations in 2025.

The benefits of a standardized application are threefold: Consumers avoid confusion and misrepresentation on their applications; distributors reduce NIGO delays and improve efficiency when completing multiple applications; and carriers can reduce form approval times and implementation costs with their enrollment system vendors.

A new study reports that U.S. homes will lost 6% of their value in the next 30 years, or a collective $1.47 trillion (that's trillion, with a "t"), because of climate change and the staggering premiums that insurers will have to charge to cover the growing risks.

The study by First Street, a group that focuses on climate threats to housing, says the home insurance premium amounted to about 8% of the mortgage payment not long ago. Today, the average is closer to 20%. And First Street says premiums will rise an average of 29% by 2055, not counting inflation, to catch up with the risks that aren't currently priced into premiums (partly because of resistance from regulators) and to account for the growing threats from climate change. Some locales may see premiums double or quadruple, soaring so high they become unaffordable.

Unless....

... insurers find a way to work with homeowners, regulators and other government officials to reduce the risks. Saving homeowners some significant portion of $1.47 trillion would be a huge service to humanity, while being immensely profitable for insurers. 

And I'm not sure I've ever seen the media so sympathetic to insurers, on any subject, so now would be a great time to start. 

The report by First Street says insurance currently underprices climate risk for 39 million properties across the continental U.S. That's 27% of the total. 

And First Street projects that as premiums soar to match the growing risks, the decline in housing values will just be the beginning of the disruption. It talks about climate change migrants — people who move to escape its effects. First Street says there will be 5 million such climate migrants this year alone and 55 million by 2055. Among other things, First Street predicts that the migration will slow the growth of the Sun Belt. (It also singles out Sacramento, which I live near, as one of the cities that will be hit hardest by rising insurance premiums. Hmmm.) 

A lot will happen between here and 2055, so the effects are hardly set in stone. But the report lays out in stark detail the opportunities — and challenges — that homeowners insurance presents for aggressive, forward-thinking insurers.

The way the report has been covered, along with recent stories about insurance, also shows remarkable sympathy for insurers, caught between frightening climate scenarios and regulators wanting to protect consumers from too-rapid disruption. We're all used to seeing complaints about heartless insurers canceling policies and not paying claims, so the different tone has been refreshing. 

Here is an essay in the New York Times, described as a joint effort between ProPublica and the Times, that presents the First Street data as requiring price increases by insurers. The same with this piece in USA Today, which sneaks in a little brag about how long and extensively the publication has been writing about First Street, in particular, and climate's effect on home ownership, in general. Here is another piece in the Times, this one laying out the oh-so-very-long timeline for people trying to rebuild after the recent fires in the Los Angeles area, dramatizing the pain of restoration and, thus, the importance of prevention. Here is a piece sympathetic to insurers in the Washington Post. I could go on. 

I wrote just three weeks ago about what I think next steps for insurers should be, as they try to help policy holders harden their properties and prevent damage from future natural disasters, so I'll merely summarize here:

• Engage with regulators and government officials at all levels and use the shock from the LA fires as a way to get to the tough conversations that need to be had about pricing risk properly and about how to persuade policyholders to change risky behaviors. These conversations must go way beyond the 22% increase in homeowners premiums that State Farm just filed for in California.
• Educate those who have lost properties on how to build back stronger and encourage them to do so.
• Try to get information on climate risks to potential buyers or builders of homes long before they're about to sign on the dotted line. Zillow has taken steps in that direction. The First Street study is another example. There must be many more.
• Help communities develop and stress test plans to better protect themselves.

As much as we all hope the LA fires will mark a turning point in the thinking about climate risks for homeowners, we also know that we have so very far to go. But the First Street report provides some dire forecasts that can serve as sort of anti-targets, as outcomes to avoid. 

Let's keep building momentum.

Cheers,

Paul