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 are almost constantly discussing disruptive, 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/capabilities that promise more growth, better profits, greater efficiency, greater employee satisfaction, etc. However, so far, few of these solutions have succeeded in bringing about large-scale change to revenue-generating roles in the industry.
Technology has largely been developed to increase efficiency and, if adopted correctly, will achieve something; however, individuals who need to use technology or input data to enhance insights to improve efficiency often achieve little. 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 provides actionable insights for organic growth opportunities for customers and operators. Arguably the first of its kind, it provides revenue-generating roles in the insurance value chain with a tangible “What’s in it for me?” giving them not more data, but action. opinion.
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 homogeneous mix of clients that can provide cross-sell and up-sell opportunities to the acquired institution. With GenAI, acquired agencies can be compared across geographies, acquisitions, etc., to identify customers with similar profiles but different insurance solutions, providing producers with substantive insights to revisit their customers’ insurance plans, And open up greater organic growth opportunities powered by insights into where to act.
- Submission preparation and client portfolio quality checks: For brokers and/or agents without a national practice group or specialized industry group, insureds in industries outside of their core strike areas often face challenges in asking the right questions to understand risk and match coverage. GenAI can significantly reduce the time it takes to determine adequate coverage and prepare for submission.Specifically, the technology can help prompt brokers/agents based on their knowledge of the insured, the industry the insured is in, the industry the insured is in, and the types of questions they should ask about the insured company relative to other companies. risk profile, and the content provided in 3RD In addition, GenAI can act as a “spot check” to identify potentially overlooked upsell or cross-sell opportunities and support E&O mitigation. Historically, portfolio coverage and the quality of subsequent submissions will be subject to the absolute discretion of the producer and account team handling the account. With GenAI, brokers and/or agents can leverage years of knowledge and experience to ask the right questions, serving as a quality check, cross-sell and up-sell tool.
- Smart placement: 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. Notably, as customers’ risk profiles change almost constantly, so do carriers’ risk appetite, making finding the best allocations for agents and brokers challenging. Powered by GenAI, agents and brokers can compare carrier stated preferences, clients’ risk and policy recommendations, and financial contract details for agency or broker-generated submission summaries. This provides the account team with placement recommendations that are in the best interests of the client and the institution or broker, while reducing time spent on marketing, both in finding the best markets and in avoiding markets that do not 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 the specific risk management actions provided by the agent or broker, and these fees are typically “less than” billable. In theory, GenAI as a feature could ingest a customer contract, evaluate the fee-for-service agreements therein, and build a summary that could then be made available to employees servicing the account on a tool like Internal Knowledge Exchange. This knowledge management solution can provide employees with specific guidance at the time as needed on what fees should be charged based on contractual obligations, providing revenue growth opportunities for agencies and brokers with unknown, uncollected receivables.
- Quickly customize customer-specific marketing materials: Historically, if a broker or broker wanted to expand a non-core capability (e.g., digital marketing), they would hire or lease that capability to get the right expertise and the right return on effort. While this works, it leads to scaling GenAI type solutions provide a solution to this as they allow an agent or broker to scalably access non-core functionality (such as digital marketing) for a fraction of the investment investment and cost, and for example, GenAI output can be quickly customized, enabling institutions and brokers to generate industry-specific materials for middle market clients (e.g., we cover X% of the market and Z amount of peers) without having to create one-offs in a timely manner Sales Materials.
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 of our broker/agency clients to take three key next steps when evaluating the use of this technology in their own workflows:
- Follow the subset of data: Leveraging GenAI requires some data to be highly reliable in order 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 the data set before scaling. Doing so will provide private computing models with a data set that can be used to provide value to the enterprise before scaling up data hygiene efforts.
- Prioritize pilot use cases: As with many emerging technologies, the value delivered by executing use cases is being tested. Brokers and agencies should assess what the potential high-value use cases are and then create pilots to test the value of these areas through feedback loops between development and development teams. The revenue generation team makes necessary adjustments and changes.
- How to assess governance and adoption: As we have 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 how the technology is likely to become the first of its kind. The first one has a significant impact on the revenue and organic growth of the revenue-generating team 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 the underwriting and claims implications for carriers and MGAs.please contact Heather Sullivan or Bob Besio If you would like to discuss further.
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