Why a data-first culture is key to unlocking value from AI in insurance

Data is the lifeblood of the modern insurance business. It is the central ingredient needed to drive underwriting processes, determine accurate pricing, manage claims, and drive customer engagement. Yet, despite the huge role it plays and the massive amount of data that is collected each day, most insurers struggle when it comes to accessing, analyzing, and driving business decisions from that data.

There are lots of reasons for this. In the health and life insurance space in particular, strict guardrails around data privacy and data security can make it difficult to access a complete picture of an individual patient experience across different care channels. Segmented business functions and different tools used for specific workflows often do not communicate with one another, creating data silos within a business. And the industry itself, which has grown through years of mergers, acquisitions, and technology transformation, has developed a piecemeal approach to technology. Today, multiple different systems and internal protocols must be navigated before it’s possible to see a complete, real-time picture of the member population.

Growth of AI Forces Conversation About Data

Meanwhile, the growth of AI-powered analytics, workflow management, and customer engagement tools has promised to revolutionize every aspect of the insurance business from underwriting to customer engagement. However, as many companies are finding out the hard way, there is a big leap to get to the promise of AI from the fractured data foundation inside many businesses. The fact is, even the world’s most powerful large language models (LLMs) are only as good as the data foundations on which they are built. So, unless insurers get their data houses in order, the real gains promised by AI will not materialize.

Over the course of our work together modernizing data architectures and integrating AI into a wide range of insurance workflows over the last several months, we’ve identified the four key elements of creating a data-first culture to support AI innovation.

• Leadership Buy-In: The first and most critical step to developing a successful data-first culture is support from the top. The process is going to require significant investment and important decisions about what gets prioritized, which legacy processes should be removed, and what the end goals of the new data infrastructure will be. That commitment must begin at the C-suite level. Leadership must prioritize data-driven strategies across all business functions.

• Cross-Functional Collaboration: It is also important to recognize that in a data intensive business such as insurance, a change made in one place will create ripple effects that reverberate throughout the entire enterprise. For that reason, data needs to be centralized, and leaders must encourage and incentivize collaboration between IT, data scientists, and business units to ensure data informs decision-making at every level.

• Data Literacy: Once data is centralized and accessible across multiple different business functions, it becomes important to educate employees, ensuring they understand how to read, interpret, and act on data insights. In our case, a key priority in our data modernization effort was to move our organization from reactive to proactive decision making based on data-driven insights. That’s more than just a philosophical shift; employees need to be trained in how to incorporate this type of information into their day-to-day workflows.

• Building a Center of Excellence to Drive the Project: Data modernization cannot be a side job. Organizations must invest in setting up a center of excellence or a dedicated team that will ensure the acquisition, ingestion, availability, accuracy, compliance, security, and availability of data to the rest of the organization.

The Data-Driven Value Proposition

There is a tendency when thinking about data modernization or AI-enablement efforts to compartmentalize them as the domain of the back-office tech team, or an ancillary part of the core business. Today, that is no longer the case. The companies that deliver superior levels of customer experience foster loyalty and brand advocacy, as well as drive increased efficiencies. This allows them to anticipate client needs, deliver faster claims processing, and offer highly personalized products. At their core, all these value propositions are driven by data.

Increasingly, an insurer’s ability to harness their data and use it to power better customer experiences will soon become the key differentiator separating world leaders from the rest of the pack.

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About the authors:
Munish Mahajan is senior vice president, data modernization at EXL and Diana Steinhoff is president and CEO at Renaissance Life and Health Insurance Company of America