GenAI has taken the world by storm. You can’t attend industry conferences, participate in industry conferences, or plan for the future without GenAI being part of the discussion. As an industry, we have an almost constant discussion about disruptive, ever-changing market factors—often beyond our control (e.g., consumer expectations, capital market impacts, ongoing mergers and acquisitions)—and the best ways to address them. This includes using the latest assets/tools/features that promise greater growth, higher profits, greater efficiency, greater employee satisfaction, and more. However, to date, few of these solutions have succeeded in creating large-scale change for revenue-generating roles in the industry.
Technology has been developed largely to improve efficiency and, if adopted correctly, can achieve something; however, individuals who need to use the technology or input data to enhance insights to improve efficiency often gain little to no benefit from the solution. In essence, GenAI increases the accessibility of insights and has the potential to be the first technology to be widely adopted by revenue-generating roles as it can provide customers and operators with actionable insights into organic growth opportunities. Arguably, it’s the first product of its kind to offer a tangible “What’s in it for me?” Revenue-generating roles in the insurance value chain provide them not with more data, but with actionable insights.
We believe there are five key use cases that illustrate GenAI’s promise to brokers and agents:
- Actionable “customers like you” analytics: In brokerages that have grown primarily through mergers and acquisitions, it is often difficult to identify a homogenous mix of clients that can provide cross-sell and up-sell opportunities to the acquired institution. With GenAI, acquired institutions’ business books can be compared across geographies, acquisitions, and more to identify customers with similar profiles but different insurance solutions, providing producers with substantive insights to revisit their customers’ insurance programs and open up greater organic growth opportunities through actionable insights.
- Submission preparation and client portfolio QA: For brokers and/or agents who do not have national practice teams or specialized industry teams, insureds in industries outside of core strike areas often face challenges in asking the right questions to understand risks and match coverage. The amount of work required to determine adequate coverage and prepare submissions can be significantly reduced with GenAI. Specifically, the technology can help prompt the broker/agent to ask the types of questions based on their knowledge of the insured, the industry in which the insured operates, the risk profile of the insured company compared to other companies, and the following 3 scenarios:RD Source of party data. Additionally, GenAI can act as a “spot check” to identify upsell or cross-sell opportunities that may have been overlooked and support E&O mitigation. Historically, the quality of portfolio coverage and subsequent submissions would be entirely at the discretion of the producer and the account team handling the account. With GenAI, brokers and/or agents have years of knowledge and experience in asking the right questions at their fingertips, serving as a quality check, cross-sell and up-sell tool.
- Smart placements: Risk allocation decisions for each customer are primarily driven by account managers and producers based on the level of relationship with the carrier/underwriter and the known or perceived carrier preference for the customer’s given risk profile. Despite the wealth of knowledge we have gained over many years of placement experience, finding the best placements for agencies and brokers can be challenging as clients’ risk profiles are almost constantly changing and operators’ risk appetite is constantly changing. Powered by GenAI, agencies and brokers can compare carriers’ stated needs, clients’ risk and policy recommendations, and agency or broker financial contract details to generate submission summaries. This provides account teams with placement recommendations that are in the best interest of both the client and the agency or broker, while reducing marketing time, both in terms of finding the best markets and avoiding markets that don’t accept risk.
- Avoid loss of revenue: When a client chooses advisory fees instead of commissions, these fees are not specific to the retainer but are attributable to specific risk management actions provided by the agency or broker, and these fees will generally be “less than” billable. GenAI, as a feature, could theoretically take a customer contract, evaluate the fee-for-service agreements therein, and build a summary that could then be made available to an internal knowledge exchange-like tool for employees servicing the account. This knowledge management solution provides revenue growth opportunities for agencies and brokers with unknown, uncollected receivables by providing employees with specific guidance when needed on what fees should be charged based on contractual obligations.
- Quickly customize client-specific marketing materials: Historically, if an agent or broker wanted to expand a non-core capability (such as digital marketing), they would hire or lease that capability to get the right expertise and the right return on effort. While this approach worked, it resulted in amplification of selling, administrative, and administrative expenses (SG&A) that were not closely tied to growth. GenAI-type solutions provide a solution for this, as they allow an agent or broker to scalably access non-core functionality (such as digital marketing) for a fraction of the investment and cost, and potentially achieve better results. For example, GenAI output can be quickly customized, allowing agencies and brokers to generate industry-specific materials for middle market clients (e.g., we cover X% of the market and Z number of peers) without the need to create one-time sales collateral in a timely manner.
While the use cases we mapped out are in the prototyping stage, they do paint a picture of what it will look like in the near future when humans and machines meet for revenue-generating activities. We encourage all broker/agent clients to take three key next steps when evaluating the use of this technology in their own workflows:
- Focus on subsets of data: Leveraging GenAI requires some highly reliable data to generate usable insights. A common misconception is that it must be all the data of an agent or broker to take advantage of GenAI, but the reality is to start small, execute, and then scale. Identify the data elements that are most critical to the insights you want and establish a data governance and cleansing strategy to improve that data set before scaling. Doing so will provide private computing models with a data set that can be used, providing value to the enterprise before scaling up data hygiene efforts.
- Prioritize pilot use cases: As with many emerging technologies, the value provided by executing use cases is being tested. Brokers and agents should assess what the potential high-value use cases are, then create pilots to test the value in these areas and establish feedback loops between development and revenue teams to make necessary adjustments and changes.
- Assess how to govern and adopt: As we’ve discussed, insurance as an industry is slow to adopt new technologies, so brokers and agents should be prepared to invest in the change management and adoption strategies necessary to demonstrate that this technology is likely to be the first of its kind to significantly impact revenue and organic growth for revenue-generating teams in a positive way.
While this blog post is intended to be a non-exhaustive look at how GenAI will impact distribution, we have additional thoughts and ideas on the matter, including implications for underwriting and claims for carriers and MGAs. Please contact Heather Sullivan or Bob Besio If you would like to discuss further.
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