In recent conversations, Brian Poppe at Mutual of Omaha highlighted AI adoption in four stages – a transition from AI being a fun tool that employees use to a final stage where AI-driven processes are integrated for seamless workflows. And certainly directionally, this appears to be correct – AI use cases have historically been focused on addressing specific inefficiencies and then scaling, which will eventually lead to AI driven processes.
While there is an acknowledgment that AI is a rocket that will move insurance in a truly transformative way, carriers are treating AI as if you are putting a new engine in an old car: it performs better, but it is still driven the same way.
AI-driven processes raise an entirely different question – do legacy operating models make sense for insurance carriers in the age of AI? And the answer is – no, they do not.
When we think of an operating model, definitionally we should think of the way in which people, processes, technology, and capabilities are arranged within an organization to deliver value to customers. The evolution of AI will be AI assisting with a task to developing a process that is driven by AI. But operating models are derived from the assumption that processes are human-driven.
Consider the underwriting process and how the evolution of technology has changed it. Historically, you needed many underwriters to carefully review applications, assess the risk, and then provide a quote on pricing against that risk. RPA reduced the manual effort and increased productivity, but the process remained the same. Then automation and enhanced underwriting (e.g., algorithmic, usage-based, simplified, etc.) were implemented to provide faster underwriting, but the organization did not necessarily change to reflect these changes. Instead, carriers have viewed this from the lens of capacity and workforce management.
But if you were building a new insurance carrier today, would you structure underwriting in the same way that it is today? Most likely, no. And as AI evolves over time, you would certainly design a different operating model.
In other words, processes designed around AI and technology would require a quite different organization than human-driven processes. The more carriers lean into AI-driven processes, the more the legacy operating model makes little sense.
The Legacy Trap: Why Current Models Are Not Changing
If we accept that AI-integrated processes are directionally where the insurance industry is headed, then the question is why haven't carriers designed new models? There are several reasons why organizations are not evolving:
1. Organizational Resistance: AI-driven processes come with an uncomfortable question – what is the role of a human in this new environment? Most assume that it means that AI is "coming for their role," and to some extent, they may be correct. But that assumption hinges on two beliefs – that all capabilities can be automated and that all automated capabilities no longer require people. Neither of these beliefs is true.
2. Lack of Success With AI: There is an often-cited statistic that 95% of all AI projects do not make it from pilot to tangible, measurable ROI. This suggests that although carriers are investing in AI and understand its capabilities, they are not finding success at scale, delaying transition to AI-driven processes and capabilities. While this suggests that the AI-driven process may not be as close as some believe, it would be incorrect to dismiss it as hype. Executives and insurance leaders only need to be directionally right, and innovation in the space should be balanced with an AI strategy on what to invest in and how to prioritize.
3. Unproven Models: Insurance carriers are conservative – an op model built on new technology is a significant risk and has not aligned with traditional automation strategies. Typically, a process is automated and then resources are reallocated or modified once the investment has generated ROI. But there is evidence of carriers operating in dual environments with new operating models, in what some have called a "two highway" approach – a legacy environment for in-force business coupled with a new environment for new products. A new target operating model does not need to be an enterprise effort initially – it may be useful to design a different model in a specific business unit to run in parallel to assess strengths and weaknesses before eventually scaling it.
Building AI-Native Op Models: A Practical Framework
If carriers accept that integrated AI processes creating new workflows is the future, then part of the planning effort must be an exercise developing a new target operating model. As carriers seek assistance with developing these models, there are five key principles that will lead to the greatest chance of success:
1. Realize Directionality Is More Important Than Timing: Carriers do not need to know exactly when a transformation will occur, they only need to think in terms of where AI is moving directionally. Consider various capabilities in insurance. Operational support of the insurance model is likely headed toward significant automation of processes, while sales and marketing are likely to remain less automated in the future. From an operating model perspective, that likely means that AI driven processes will push workflow in the back office (think of new business submission or policy administration), while in the sales capability, you are more likely making the agent/advisor/broker more efficient (e.g., next best actions, generating marketing material with existing pre-approved templates).
2. Ignore Biases and Existing Requirements: One of the most difficult aspects of designing a new operating model in general is getting stakeholders to leave "the way it has always been done" at the door. Remember that this is a white space exercise and should be framed as such. For example, policy servicing should initially be thought of in terms of desired customer/agent experience, not how that service is delivered. When framed appropriately, carriers can focus on what they want to achieve and then assess how they would achieve it.
3. Understand the Hard Lines: For some carriers, there are hard rules that they will not consider. For example, risk appetite in underwriting may make some AI-driven processes impossible, or there may be a decision to create a large case workflow that is human driven to provide white-glove treatment for a particular agent class. Understanding enterprise non-negotiables upfront eliminates downstream decision-making on the op model.
4. Embrace Uncertainty: Carriers must understand they are blazing a new path forward. There is no cookie-cutter approach to a new operating model. While there are proven approaches, the result is that you may no longer have a clear benchmark. AI is introducing uncertainty and the only thing that we know is that it will transform the way that insurance carriers operate. The introduction of AI-driven processes will inevitably create a feedback workflow connecting actuarial product design, underwriting, and claims to create real-time adjustments to initial assumptions. The long-term consequences are unknown, but carriers still have to develop these capabilities to compete in the market.
5. Iterate, Iterate, Iterate: While there is directional design, understand that operating models evolve as new data is presented. While there are assumptions that sales (particularly personal lines) will continue to be driven through agents and brokers, significant change in customer dynamics or technology could change these assumptions. Additionally, end-state operating models make assumptions on where technology will be, not where technology is today. That may mean an agile approach to op model development.
The process of developing these operating models will not be instant. But carriers must begin the process of reassessing how they are organized to meet client needs in the age of AI. Digitally native carriers like MGT Insurance (organization built around AI stack to support small businesses) and Ethos (organization built around underwriting that can be done in five minutes) are already further along in this journey than legacy insurers, and the consequences may mean bloated organizations, reduced profitability, and an inability to compete in the marketplace, particularly in price sensitive markets. Embracing AI while ignoring op model transformation is only delaying the inevitable. As AI evolves, what assumptions in your op model might need rethinking?
