How insurance firms can approach AI like today’s digital leaders
More than a year and a half after ChatGPT was released to the public, the insurance space is well aware of the predominant use cases for AI and machine learning. And, while SaaS companies have been racing to develop tools and technology for those primary use cases, little is being discussed about how insurers should think about emerging technology so that future implementations don’t fall flat.
Many legacy companies in the financial services sector are finding innovative use cases for generative AI to be few and far between. Leaders appear to be in their wait-and-see era, pondering the practical impacts AI will have on the space after it’s had a chance to mature among insurtech startups or adjacent industries.
For the past several years, AI has been primarily deployed in every sector as an efficiency booster. The term “doing more with less” persists, especially in margin-strapped environments like insurance.
Still, more than three in four insurance CIOs (76%) ranked excelling in customer experience (CX) as their top priority going into 2024, followed by generating revenue (68%) and boosting margins (65%), according to a Gartner study.
For companies that want to adopt AI in insurance effectively, the aim is to break away from wait-and-see tactics and siloed investments in back-office technology and focus on greater speed, personalization and quality in their digital customer experiences.
What is an AI-powered customer experience?
A well-architected, AI-powered customer experience has three ingredients: robust knowledge about the end customer, highly usable digital user interfaces and a lot of math.
Customer knowledge
Insurers can only truly deploy AI for CX when they have a solid, growing understanding of their customers. In the age of advanced AI, both qualitative and quantitative data are essential for developing great digital customer experiences.
While data collection has gotten much more sophisticated and granular over time, to truly understand the needs, motivations and psychographics of your customers — those traits that ultimately help you know why someone would choose your insurance policies over a competitor’s — you need to speak with users face to face and construct qualitative audience personas.
At the same time, you need to ensure that the data you collect is ready for AI. This includes, at a minimum, enhancing your data’s availability and improving how that data is organized. Qualitative user insights and personas will serve as a foundation to fine-tune your growing understanding of your customers through data collection and utilization.
Digital user interfaces
You can’t build AI-powered customer experiences without considering where digital applications will play a role. Almost all U.S. consumers have access to internet-connected devices, and they expect to use those smartphones, tablets and computers to complete essential activities like connecting with their agents, renewing their policies, updating their coverages and more.
And while it is possible to deploy AI to make traditional means of communication more effective and personalized — think robo-generated emails or text messages from an agent persona — more policyholders want and need access to account-based applications that enable end-to-end self-service capabilities across every policy they hold.
Like many financial services companies, insurers have traditionally relied on licensing customer account portal technology from third parties.
While this can be an affordable way for insurers to get a customer portal off the ground, these white-labeled portals are not designed with your specific users’ needs in mind. Many also live on legacy codebases that make it difficult, if not impossible, to integrate AI into the customer experience.
The insurers that can profit most from the current AI revolution own their digital custom experiences and can make the updates they need to best serve their customers now.
Math
It’s helpful to define artificial intelligence as the act of applying math to large data sets. Although highly generic, this definition is meant to help AI skeptics soften their attitude toward the technology in a way that can prompt productive action.
As it applies to customer experience, AI can be helpful in predicting a customer’s activity based on signals generated by the customer’s use of a mobile app or call to a support center, for example. The technology can then recommend actions or content and even fulfill those tasks with few — if any — humans in the loop.
Customer experiences of the future will require deep expertise across these three areas should firms wish to remain competitive over the long haul.
Three tracks for implementing AI across your business
Companies have three major approaches to consider with emerging technologies, mainly: Is it better to partner, license or build?
Partner: Borrow innovation from technology-first innovators
Insurtech startups have received tremendous investment as of late. Ernst and Young found that investments in insurtech startups doubled between 2020 and 2021, with most of the funding going to property and casualty-focused companies.
Insurers would do well to partner with, and even invest in, insurtech startups that fit their organizational profile and have a chance to create novel growth opportunities.
License: Take advantage of emerging tech in your current solutions
Many insurance companies leverage industry- or function-specific technology from large tech firms that have rapidly adapted AI to their product use cases.
Salesforce, for instance, recently launched Einstein Copilot, a generative AI conversational assistant for Salesforce users. For firms that feel most comfortable with a wait-and-see approach to tech adoption, this path is often preferred as it doesn’t usually require complex implementations, leverages data that already exists in prevailing platforms and can, in some cases, be customized with relatively minimal effort.
Build: Create novel solutions to enhance competitive advantage
It’s likely your company will encounter a time when the use case you want to execute is not achievable through partnering or licensing. In such cases, you will need to consider building your own solution.
Building a bespoke AI solution for your business is complex, but the benefits of building over buying are numerous. Most importantly, novel AI solutions built specially for your agency create unique, sustainable competitive advantages.
While building custom is not risk-free, you control those risks in ways that are otherwise outsourced in the other two approaches.
How Wipfli can help
Wipfli is ready to help you embrace AI with our comprehensive AI services. Our industry-experienced team focuses on your users and ROI, providing you with guidance at every stage, from identifying and preparing for AI opportunities to implementation and education. Contact us today to learn more about how we can help you leverage AI to achieve your goals.
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