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3 minute read
Brendan McLoughlin, President of e123, is participating in an executive education course at the Massachusetts Institute of Technology on Artificial Intelligence (AI) and its implications for business strategy. This is the fourth in a series of blog posts where he shares the insights he is gaining and how they apply to health insurance distribution.
Artificial Intelligence (AI) holds immense promise for Life and Health Insurance carriers and marketers, offering the potential to revolutionize operations and drive business growth. However, effective implementation of AI solutions is often challenging and requires careful consideration of business strategy and capability. In this post, we'll explore how insurance distribution organizations can prepare themselves for AI adoption by following key guidelines outlined by the Boston Consulting Group (BCG) in their 2022 article “Five Rules for Fixing AI in Business”.
Rule 1: Align AI Applications with Business Objectives
BCG emphasizes the importance of ensuring that AI applications align with desired business outcomes. This involves a clear understanding and discussion of organizational strategic goals. Simply put, collaboration with stakeholders who understand the business challenges and are aligned by the same strategic goals is essential for building AI systems that address meaningful business needs.
For insurance distribution organizations, increasing lifetime value (LTV) and decreasing customer acquisition costs (CAC) Are crucial strategic goal. This goal can be summarized as optimizing the ratio LTV/CAC. By focusing on increasing LTV and reducing CAC, insurance companies can gain a sustainable advantage in a competitive market.
Rule 2: Harness External Data for Enhanced Insights
While internal data provides valuable insights, BCG highlights the significance of leveraging external data to amplify the business impact of AI. Numerous companies erroneously presume that internally generated data alone suffices to fuel machine-learning models. However, the inclusion of external data yields significant benefits. While gathering comprehensive, precise, and pertinent external data for machine learning demands considerable effort, it also furnishes a distinct competitive edge. This is because it enables decision-making grounded in the dynamic shifts occurring within the environment.
For insurance carriers and marketers, external data sources such as demographic data, healthcare trends, and regulatory information can offer valuable insights into customer behavior and market dynamics. The appropriate external data sources will vary by application, from NIPR to the Census Bureau, but it is worth the effort. Combining external and internal data sources into AI models will improve decision-making and provide a meaningful competitive edge.
Rule 3: Break Down Complexity for Effective Implementation
Given that AI is immensely powerful, it is tempting to view machine learning as a massive special project that must overlay all business activities. However, BCG suggests that successful AI implementations involve breaking down this huge potential into smaller, actionable parts to conquer complexity. It is important to maintain the discipline that each AI project should result in a measurable outcome that helps achieve a strategic goal.
In the realm of insurance distribution this approach translates to focusing on specific business problems such as:
- Better product/member matching, which would increase LTV
- Smarter targeting of marketing spend, which would decrease CAC
- Modifying commission plans by time of year, geography or product mix, which would decrease CAC
- Early identification of agent compliance issues, which can reduce CAC, increase LTV and prevent major hardships
Rule 4: Drive Business Decisions with Machine Learning
The effectiveness of AI should be measured by its ability to facilitate concrete business decisions and drive incremental value. Implementing AI alone is not sufficient; the goal should be tangible, measurable strategic impact. Machine learning, in and of itself, therefore, is often not enough. Optimization algorithms play a crucial role in leveraging AI knowledge to suggest decisions aligned with business objectives. Collaboration between technology experts and business stakeholders is essential for integrating AI capabilities into processes effectively and obtaining measurable outcomes. By combining domain expertise with AI capabilities, insurance marketers can make informed decisions that optimize performance and drive growth.
For insurance marketers, this means collaboration between technology and the distribution experts, including internal staff and external partners such as FMOs, MGA, agencies, and even individual agents. If the goal of your insurance organization is to optimize LTV/CAC, it is important that the role of every player in the value chain is understood and taken into account.
Rule 5: Focus on Future Utility, Not Just Accuracy
BCG emphasizes the importance of focusing on the usefulness of AI outcomes rather than solely on today’s accuracy. Nowhere is this more true than in insurance distribution. With constant change driven by regulation, healthcare advances, competitive moves and shifting value chain dynamics, Insurance distribution organizations should prioritize building AI systems that provide actionable insights relevant to today and future contexts and challenges. By anticipating future needs and focusing on the practical utility of AI solutions, companies can maximize the impact of machine learning on business performance.
Conclusion
Implementing AI in insurance distribution organizations requires a collaborative approach that integrates technology expertise with business knowledge. By following BCG's guidelines and aligning AI initiatives with broader strategic goals, companies can harness the full potential of AI to achieve meaningful objectives that create competitive advantage and drive business success. By embracing AI as a strategic enabler, insurance distribution organizations can stay ahead of the curve and unlock new opportunities for growth and innovation in the dynamic insurance market.
Want to learn more about the future of AI in insurance distribution? Get in touch here. For prior posts in this series, click below:
