Every carrier is in a scramble to achieve gains through AI. 2025 investment in AI for insurance was estimated to be over $10 billion, primarily driven by carriers. Yet only 7% have managed to exit the pilot phase. AI investment is usually limited or fragmented and not designed for scale.
Carriers know they need to invest in AI, but their investment thesis must change. Executives must be strategic, but what framework can carriers use to prioritize AI investment?
Carriers should ask themselves two key questions:
- Should investment be focused on bolstering strengths or addressing weaknesses?
- Does the investment require you to take the stairs or allow you to ride the escalator or elevator?
Strengths vs. Weaknesses
Over the years, there has been an evolution in how insurance carriers have viewed managing the value chain. Carriers shifted from owning the end-to-end insurance value chain, to outsourcing the non-core capabilities, to outsourcing their weaknesses and focusing on their strengths.
AI provides insurance leaders the opportunity to rethink this position. For example, claims administration, which may have been outsourced to TPAs in the past, could potentially be brought back in house, due to both cost and operational capability considerations. But the investment from carriers is limited and must be prioritized.
Leveraging AI to address weaknesses can be appealing, particularly if the weaknesses were driven by human capital considerations. Improved spans of control, greater capacity, and overall process efficiency are all benefits of AI. But consider where AI success is most likely:
- Quality data
- Platforms and technology that can integrate with AI
- Employees with the right skills and talent
- Strong processes
- Culture that is open to change
Those are more likely to be strengths for carriers, not weaknesses. For that reason, it is generally better in the short term for insurance carriers to invest their capital into their own strengths and gain even stronger competitive advantages in the market. These AI use cases will likely have greater success, and that success will compel the organization to further invest in the business.
There is another reason that carriers may seek to focus on their strengths. AI is not unique to any particular carrier – TPAs, recordkeepers, and other insurance stakeholders are also making AI investments into their capabilities. If a carrier is outsourcing key functions, then the answer may be to re-evaluate the initial vendor selection to determine who is the best provider in the age of AI. For example, a TPA may have been the right solution five years ago for claims administration, but TPA capabilities have evolved to the point where a carrier may find different leaders and select a different vendor.
While focusing investment on existing strengths may be the preferred strategy in the short term, not every carrier will have the luxury to ignore weaknesses. For longer-term investments, how should carriers think about AI investment?
Stairs, Escalators, and Elevators
Every carrier will find themselves in one of three circumstances:
- The carrier does a lot of things well but is not a leader in any particular area or capability
- The carrier has a critical weakness that must be addressed (e.g., underwriting/suitability KPIs and metrics)
- The carrier identifies a trend that all carriers must address in the future (e.g., hyper-personalization of products)
If AI is being used to address any of the above, then insurance carriers will need to consider the investment required to address these trends, both now and in the future. That investment comes in both cost and time.
To address each of these areas, leaders should consider whether they need to take the stairs, if they can ride an escalator, or if they can take an elevator to where they want to be.
- Taking The Stairs: Some AI investments will require significant upfront work to maximize the potential return and achieve scale. For example, attempting to leverage AI at scale to improve underwriting requires data, clear processes, and the right workforce for the most complex cases. In these situations, there are no shortcuts – carriers have to make the investment.
- Riding The Escalator: Some carriers may be able to identify opportunities where outside vendors can provide an accelerant – you will still move faster if you make the investment, but you can see improvement even with small action. One example is next-best-action tools in the sales and distribution space. Vendor tools can still provide significant cross-sale and upsell potential, even without carrier data, but leaders in the space combine their unique data points with the vendor's tools to achieve best-in-class outcomes.
- Ride The Elevator: In rare cases, there may be AI tools that allow carriers to go from wherever they are today to industry par or even leading capabilities. One possible example is AI tools leveraged for call deflection in call center environments – leveraging these tools can be done with minimal existing capabilities for significant impact. True elevators can be helpful, but eventually, they become table stakes and no longer a differentiator.
The Path Forward
This framework gives carriers a way of reimagining their AI investment – instead of AI committees that simply approve or deny use cases, carriers should be formulating strategies that chart a path on where they want to be and the requirements to get there, then aggressively drive scalable solutions. To do that, carriers have three key considerations.
- Identify Your Competitive Advantages – AI investment should begin with assessing value chain strengths. For simplicity, there are five key potential areas: product development, sales/distribution, underwriting/suitability, customer service, and claims. Carriers should identify the areas where they are strongest and determine how AI investment can strengthen their foothold. For example, a carrier that has strong product pricing and development capabilities may want to leverage AI to bolster their capabilities in the market. Buttressing existing capabilities, particularly if leading in the space requires carriers to take the stairs, may cement industry leaders.
- Determine The Market Requirements – After investing in their strengths, carriers should determine what it takes to win the market. This will be heavily product-specific, but there are broader trends that are applicable across the industry. For example, carriers should continuously benchmark key cycle times, volumes, and processes against the industry. Wherever they are behind, carriers should leverage AI to focus on achieving industry parity. Carriers need to be mindful – AI is useful, but only when it is placed on optimal processes. This means that carriers need to ensure that they have taken the stairs where necessary, and that may mean investment in areas that do not have the same appeal as direct AI investment.
- Develop A Perspective On The Future – After addressing strengths and winning in the market, carriers should focus on longer-term trends and how they develop capabilities to meet the emerging trends. For example, hyper-personalization of products is a growing area of focus. Consumer demand for products that are targeted at their cost point and needs is not going anywhere. From an AI investment perspective, carriers will need to determine how AI helps them address this trend – is it more dynamic product pricing based on consumer needs? AI being leveraged to process increased sensory data to provide more dynamic premium pricing? Or behavioral suggestions to reduce costs or provide additional coverage based on consumer actions? Carriers' bets on directionality will influence what investment is necessary, and when.
Carriers can use this framework in 2026 and beyond to structure their AI investment in ways that will be most immediately significant. But maximizing that investment means understanding where to place their bets and determining what opportunities exist to leverage accelerants and vendor solutions to achieve those results, while also recognizing where carriers are unable to avoid the foundational work to support AI solutions at scale.
