Focusing on the customer has resulted in increased sales and cross-sales, improved brand reputation and improved customer satisfaction and retention for insurance carriers. The use of generative artificial intelligence (AI), as well as large language models, low-code and no-code (LCNC) software and machine learning are increasingly a part of this new customer paradigm. These technologies have allowed carriers to refine, refocus and prioritize the customer experience (CX) more quickly and effectively. They have also had an impact on adviser/agent relationships where carriers have been able to improve working efficiencies across distribution channels.
The use of AI in CX initiatives will also affect the broader business objectives of carriers, such as revenue growth, market differentiation and long-term sustainability. AI speeds and simplifies time-consuming efforts like underwriting processes and claims processing. And it doesn’t end there. Other areas AI may be able to affect include:
- Web applications
- Order entry platform upgrades
- Digitized licensing and appointments
- Mobile personalization
- Business process reengineering
- Sales enablement programs
- Robotic process automation projects
- Business enablement optimization (paperless)
A steady move to digital, AI and the cloud, while gathering and analyzing existing data sets and running periodic testing of ideas and asking for feedback, is a prudent strategy for carriers looking to accelerate their digital transformation. This will help to identify risks, product misconceptions and process bottlenecks along the way, thus driving higher loyalty and customer satisfaction and advocacy. Understanding how data can give carriers information they need to make informed decisions is key, but AI won’t draw accurate conclusions on its own.
See also: 5 AI Trends You Can't Ignore in 2024
From a distribution viewpoint, advisers and agents have fully embraced the digital world. Many have actually become dependent on digital tools and see this trend continuing to improve customer engagement. They have a desire to do more for their clients and seek improvements in online applications, online illustrations and calculators, new business submission, tracking, performance reporting, policy delivery, transaction reporting and administration. The emphasis should be to collaborate with financial professionals to provide a simple but effective partner experience and build capabilities that help them to better engage with their clients, increase their productivity and enable new business opportunities.
Of course, distribution is still people-focused. For example, referrals are critical for new business, and many financial professionals depend on seminars and webinars. They use social media, email, television and radio to recruit for these events. Advisers and agents prefer these face-to-face meetings, either in their offices or in clients’ homes or online, to develop relationships. Can AI help here, too?
Digital tools proliferate, and most customers are tech-savvy. Smartphones, smart pads and tablets, smartwatches and other mobile devices are part of the modern landscape. AI may be able to help carriers fully benefit from them and keep up with advisers, agents and their customers. AI can be a market research resource for monitoring the sales cycle and determining what products are selling and why, and for mining customer data for insights on what their journey with a carrier’s brand is like. This research could be coupled with automated, AI-driven marketing programs that seek to engage customers, and their advisers/agents, and deliver more personalized solutions. Leveraging data, analytics and AI together could provide near-real-time contextualized insights to financial professionals that augment the personalized client experiences and improve cross-sell/up-sell.
Carriers have largely been compelled to explore this new technology. There wasn’t really an option. Celent, a research and advisory firm focused on technology for financial institutions globally, recently said the competitive gap established by early adopters could be persistent (due to a generative AI model’s inherent ability to learn and improve). So doing nothing comes with its own risk—and that risk exists across the board, from operational efficiency to customer engagement.
See also: 3 Key Uses for Generative AI
With all these potential improvements, risks have to be considered to balance enhancements with customer protections. AI has multiple challenges that need to be addressed to defend against reputational and brand risks. Among the concerns:
- data security
- data accuracy and misinformation (called hallucinations)
- privacy (such as in cyberattacks or misuse)
- functional limitations with creativity, ethics and common sense
- inherent bias or stereotypes
- copyright and consent issues
Don’t forget the human element!
AI is a tool to assist in, not define, outcomes. It offers numerous capabilities that can assist insurance carriers in gathering both distributor and customer insights and feedback, interpreting findings and even implementing engagement efforts. But insurance is still a people-first business, and carriers need to keep a balance between AI and the customer/adviser/agent that are at the center of their strategic initiatives. This balance allows for a refined and refocused customer experience.
