In the insurance industry, low client involvement is a recurring problem, especially when policies are held for a long time. Many policyholders have little contact with their insurer after the initial purchase, according to research from the Society of Actuaries. This leads to underutilized benefits and lost chances for retention or upselling.
This lack of continuous connection is exacerbated by antiquated, reactive customer service practices. Important touchpoints that may strengthen bonds and increase customer lifetime value are often ignored by insurers, who only communicate with clients when a claim is filed or a payment is late.
Insurance can benefit from implementing AI-driven solutions, which are already revolutionizing consumer engagement in other industries by fostering more meaningful, conversational experiences that go beyond conventional, transactional exchanges.
According to Interactions’ blog, companies can provide real-time, contextually aware, and customized responses across a variety of channels by combining sophisticated conversational AI with human-in-the-loop support.
By streamlining self-service and guaranteeing that every customer interaction is interesting and human-like, a hybrid strategy helps businesses lower expenses while increasing operational effectiveness, customer pleasure, and trust.
James Wood, EVP of Platforms and Applications at Interactions, and Abhii Parakh, Head of Customer Experience at Prudential Financial, will talk about how AI may be used to improve the insurance industry’s customer experience by providing proactive, individualized service while balancing risk and human oversight.
The relevance of use-case-driven AI design, the transition from reactive to proactive engagement, and the necessity of transparency and human-in-the-loop technologies to preserve trust are all highlighted in their discussion.
Two important takeaways for CX leaders from their discussion are examined in this article:
Changing client involvement from reactive to proactive: Building long-term trust with AI and moving beyond reactive support are facilitated by proactively advising clients on underutilized benefits based on their policy history.
Developing human-supervised outcome-driven agentic AI: Agentic AI should be designed to employ tools autonomously for results, but for difficult or high-risk jobs, humans should always be involved.
Changing Customer Engagement From Reactive To Proactive
Abhii starts up the conversation by considering how reactive systems will always exist in the life insurance industry because the firm must react to events like deaths. He does note, though, that contemporary life insurance offers more than just a death payout. Policies of today frequently provide estate planning, tax, and cash value accumulation benefits, so they assist clients in more ways than just being reactive during claims.
He then draws a crucial distinction: while being responsive and reactive is admirable, being proactive is what truly distinguishes a great business. According to him, businesses shouldn’t wait for clients to get in touch. They must foresee their requirements and offer proactive, beneficial information in order to serve them effectively:
“Decades ago, consultants who are no longer employed in the industry sold a lot of life insurance products. The majority of policyholders can’t recall what they bought or how their coverage operates. It’s okay, but not unusual, if you’re only responding to an allegation. A great customer experience involves proactively engaging policyholders with tailored advice based on their specific policy, how long they’ve had it, and their current needs by fusing human relationships with AI capabilities.
– Prudential Financial’s Head of Customer Experience, Abhii Parakh
Abhii notes that Prudential’s internal use of AI has been successful when talking about implementing new technology in the financial services industry. They have witnessed it operate precisely, without of hallucinations, and under stringent supervision. They go through stringent risk assessments and due diligence procedures because they operate in a highly regulated business. Thus far, the outcomes have demonstrated that AI can be dependable and productive when used internally.
But when it comes to applications that interact with customers, his team is moving more cautiously. They are proceeding cautiously but excitedly because this area is yet largely untested, particularly for operations like payment processing where errors could have dire repercussions. To avoid mistakes in these situations, they make sure that a person is kept informed.
James adds his team is similarly accustomed to operating within such boundaries and concurs with Abhii that risk assessment and compliance are crucial. He does stress, though, that the particular use case ultimately determines the degree of danger.
According to James, for instance, there is probably a low to medium risk if AI is used to find clients who have had a policy for a long time and could profit from a check-in or product recommendation. Outreach can be automated for this type of activity with little issue.
He concurs with Abhii, though, that employing a fully generative AI model could be dangerous in some higher-risk situations, such as processing payments. When managing sensitive financial data, such models may “hallucinate” numbers or violate compliance rules like PCI, which is a major problem.
Developing Human-Oversighted Outcome-Driven Agentic AI
Moving on to agentic AI, James notes that although many businesses today identify as such, the term has a specific meaning: it describes AI systems that are capable of using a variety of tools to solve problems and work toward results on their own, as opposed to merely producing responses.
According to him, generative AI will advance from passive, prompt-based systems to AI that can choose which tools to use, when to use them, and what input to utilize in order to do increasingly difficult, goal-driven tasks.
His team is already using some of the predictive AI capabilities available in the market for customer support, such payment automation. They are currently investigating ways to improve these systems’ intelligence and flexibility so they can deal with changes and make judgments quickly.
James points out that one of their initial use cases for agentic AI is to keep humans informed, utilizing it to proactively incorporate human oversight at the appropriate times rather than doing away with it.
In response, Abhii explains that although the term “agentic AI” is often used, he has not yet seen a multimodal, goal-oriented AI system that is genuinely autonomous and capable of acting on its own in an enterprise-grade environment using memory and context.
A large portion of what is being advertised as agentic AI still works more like classical AI or simple chatbots, which means that while it is useful, it cannot completely complete tasks without supervision.
Abhii connects his argument about agentic AI to the more general issue of how businesses are preparing for human interaction with AI systems. Humans continue to play a crucial role because these fully autonomous agents are still lacking, particularly in positions involving direct interaction with customers.
On the inside, though, there are indications of improvement. He gives early examples of “virtual employees”—AI systems that can work in marketing workflows and complete multi-step tasks like content submission and publication. These internal systems, which carry out useful tasks throughout workflows with little help, are beginning to resemble agentic AI:
The entire workflow is visible even while AI systems do tasks and communicate with other systems. Because of this transparency, human teams are able to keep an eye on the process, pause it, intervene, or make adjustments in real time. Even though we haven’t yet seen fully autonomous agentic systems in the workplace, there are new applications where people are being involved to maintain supervision and control as these technologies develop. In essence, human beings have historically performed some labor-intensive, non-customer-facing duties. In the future, agentic AI may be used to complete some of those laborious and manual activities. Teams may benefit from having more time to concentrate on developing human connections and other important tasks.
– Prudential Financial’s Head of Customer Experience, Abhii Parakh