GenAI has taken the world by storm. You cannot attend an industry conference, participate in an industry meeting, or plan for the future without GenAI joining the discussion. As an industry, we have near-constant discussions about disruptions, evolving market factors – often beyond our control (e.g., consumer expectations, capital market impacts, continued mergers and acquisitions) – and the optimal solution to these factors. This includes using the latest assets/tools/skills that promise more growth, better margins, higher efficiency, higher employee satisfaction, etc. However, few of these solutions have succeeded in creating massive change for the revenue-generating roles in the industry… until now.
Technology was largely designed to increase efficiency and when used correctly there are small successes; However, the people who have to use the technology or enter the data that provides the insights to drive efficiency are often the ones who get little or no benefit from the solution. At its core, GenAI has increased the accessibility of insights and has the potential to be the first technology to be widely adopted by revenue-generating roles due to its ability to provide actionable insights into organic growth opportunities among customers and carriers. It is arguably the first of its kind to ask a tangible question: “What’s in it for me?” to the revenue-generating roles within the insurance value chain by providing them with insights to act rather than data.
There are five key use cases that we believe illustrate the promise of GenAI for brokers and agents:
- Actionable “Customers Like You” Analysis: In brokerage businesses that have grown largely through the combination of acquisitions, it is often difficult to identify comparable client portfolios that can provide cross-selling and up-selling opportunities to the acquired agencies. GenAI can be used to compare acquired agencies’ books of business across geographic regions, acquisitions, etc. to identify customers who have similar profiles but different insurance solutions. This opens up essential insights for producers to rethink insurance programs for their customers and opens up greater organic growth opportunities based on insights into where they need to act.
- Submission preparation and quality assurance of the customer portfolio: For brokers and/or agents who do not have national practice groups or specialized industry teams, insureds in industries outside of their core attack zone often present a challenge in asking the right questions to understand exposure and match coverage. The effort required to determine appropriate coverage and prepare submissions can be dramatically reduced using GenAI. In particular, this technology can help provide the broker/agent with guidance on the types of questions to ask based on what is known about the insured, the industry in which the insured operates, the risk profile of the insured’s business compared to others, and the information available in 3approx Data sources of the parties. In addition, GenAI can act as a “spot check” to identify potentially missed up-sell or cross-sell opportunities and help mitigate E&O. In the past, the quality of portfolio coverage and subsequent submission was at the sole discretion of the producer and the account team servicing the account. With GenAI, a broker and/or broker has years of knowledge and experience on the right questions at their disposal, acting as a QA and cross-selling and up-selling tool.
- Smart Placements: Risk placement decisions for each customer are largely made by account managers and producers based on the level of relationship with a carrier/insurer and the carrier’s known or perceived appetite for a customer’s given risk portfolio. While the wealth of knowledge gained from years of brokerage experience is remarkable, the changing risk appetite of carriers due to near-constant changes in clients’ risk profiles makes it difficult for agencies and brokers to find the optimal brokerage. With GenAI’s support, agencies and brokers can compare a carrier’s stated appetite, the client’s risks and policy recommendations, and the financial contract details for the agency or broker to create a submission summary. This provides the account team with placement recommendations that are in the best interests of the client and the agency or broker, while reducing the time spent on marketing, both in terms of finding optimal markets and avoiding markets where risk would not be acceptable.
- Avoiding loss of sales: Because clients choose advisory fees rather than commissions, fees that are not order-specific but are assigned to specific risk management measures taken by the agency or broker are often “undercharged.” GenAI as a feature could theoretically capture customer contracts, evaluate the paid service agreements within them, and create a summary that can then be made available in an internal knowledge exchange-like tool to employees servicing the account. This knowledge management solution could provide specific guidance to the employee when needed on what fees should be charged based on contractual obligations and provide a revenue growth opportunity for agencies and brokers that have unknown, uncollected accounts receivable.
- Custom marketing materials in a snap: In the past, when an agent or broker wanted to expand a non-core skill (e.g. digital marketing), they either hired or rented the skill to get the right expertise and return on investment. While this worked, it resulted in an expansion of SG&A costs that could not be closely linked to growth. GenAI-type solutions provide a solution to this problem by giving an agent or broker scalable access to non-core functions (e.g. digital marketing) for a fraction of the investment and cost and a potentially better outcome. For example, GenAI output can be quickly adjusted to enable agencies and brokers to generate industry-specific material for mid-market clients (e.g. we cover
While the use cases we have outlined are in the prototyping phase, they show what the near future might look like when humans and machines come together for the benefit of revenue-generating activities. There are three key actions we encourage all of our broker/agent clients to take next as they evaluate the use of this technology in their own workflows:
- Focus on a subset of the data: To use GenAI, some of the data must be extremely reliable in order to generate actionable insights. A common misconception is that in order to take advantage of the benefits of GenAI, it must be all of an agent or broker’s data. However, the reality is: start small, execute and then expand. Identify the data elements that are most important to the desired insights and establish data management and cleansing strategies to improve this dataset before expansion. This provides the private computing models with a data set to work with, adding value to the company before expanding data hygiene efforts.
- Prioritize pilot use cases: As with many new technologies, the value created by executing use cases is tested. Brokers and agents should evaluate the potential high-value use cases and then create pilots to test the value in those areas with a feedback loop between the development team and the revenue-generating teams for necessary tweaks and changes.
- Evaluate how to govern and introduce: As we’ve discussed, the insurance industry has been slower to adopt new technologies. Therefore, brokers and agents should be prepared to invest in the change management and adoption strategies necessary to demonstrate that this technology may be the first of its kind to have a materially positive impact on revenue and organic growth of revenue-generating teams.
While this blog post is intended to provide a non-exhaustive overview of how GenAI could impact distribution, we have many more thoughts and ideas on this topic, including implications for underwriting and claims settlement for both carriers and MGAs. Please reach out Heather Sullivan or Bob Besio if you would like to discuss further.
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