An AI-First Approach to Customer Service

Making conversational artificial intelligence a first line in customer interaction saves time and frees human agents to focus on more involved tasks.

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When the pandemic began, insurance providers were caught by surprise, like many of us. Operationalizing digital transformation efforts within contact centers was necessary to support remote staff as well as to respond to a rise in digital-first customer behavior.

Across each iteration of the contact center, customer service agents have worked diligently to provide stakeholders with efficient and accurate information, depending on legacy systems in support of their efforts. While many have begun or are well on their way to enhanced service capabilities, an elevated level of activity has forced customer service departments to make a series of critical decisions.

Moreover, in times of financial uncertainty, departments must prioritize technologies and the challenges they resolve in order of urgency. Sourcing solutions that are not only immediately beneficial but also support the long-term growth of an organization is key. A service approach that prioritizes leveraging technology, and placing conversational artificial intelligence as a first line in customer interaction, saves time, frees human agents to focus on more involved tasks and creates a path for scalable growth.

The Methodology

Many insurance institutions may be skeptical of the capabilities of a virtual agent. Insurance is a naturally complex space to operate in, with not only high amounts of regulation but also customer needs varying on an individual level. Yet some of the leading insurers offer the most customization, and, with advancements in natural language understanding and AI, today’s virtual agents are much more capable than their predecessors.

A virtual agent serving as the first line is able to supplement, not take over, the efforts of human employees. If a query needs to be handled by a human, virtual agents are able to route customers to the appropriate place, maintaining the continuity of the initial chat to bring contact center staff up to speed quickly. Striking this balance between virtual and human agents allows conversational AI (CAI) to handle more frequent queries. In the insurance contact center, as responsibilities are offloaded onto the virtual agent, employees are free to address other aspects of their roles and can play a valuable part in expansion of a VA’s capabilities. Built-in intents enable a chatbot to get up to speed when implemented in insurance use cases, and AI trainers can ensure virtual agents are delivering consistent, high-quality experiences that maintain the personal touch for each customer.

See also: How to Simplify Customer Experience

Accelerating Claims Cycles

Claims are the backbone of any insurance agency. Accelerating claims cycles through conversational AI simplifies the process, improving resolution speed by allowing enhanced self-service. Modern virtual agents can be integrated into existing systems to aid customers in updating their account information, purchasing new plans and even filing a claim. While these may seem like mundane tasks for a technologically  advanced system powered by AI to be handling, they can often be some of the most administratively heavy for a live service rep to handle. With less of a burden from repetitive processes, live agents can dedicate their time to improving resolution speed of the claims that have already been processed as well as other top issues in terms of complexity.

This powerful integration with existing legacy platforms ensures that all customer information is accurately maintained across the organization, addressing data silos that add unnecessary complexity to the claims process. Equally important, 24/7 virtual agent availability makes it possible for firms to be available in a time of need and extend critical service capabilities to meet the needs of  the round-the-clock customer.

The Automation Opportunity

The benefits of automation go beyond enabling efficiency, accuracy and reducing workloads within customer service centers. Conversational AI opens the doors for nurturing employee skills and expands the opportunities for training. Virtual agents require a level of oversight, with AI trainers optimizing conversation flows, and leveraging the data compiled by CAI to improve internal processes. Adopting virtual agents can create new paths for call center staff.

Insurance customer service centers have also been able to implement internal virtual agents with resolution rates as high as 97%, as seen in’s engagement with leading Nordic insurance provider Tryg. An internal virtual agent serves as a live resource for call-center employees to quickly glean accurate organizational information without disrupting customer calls. Conversational AI platforms provide some of the most scalable functionality of any solution, incorporating paths for expansion for service, support and sales at any size. 

Incorporating conversational AI as a frontline response to customer service extends the service capabilities of an institution and lays the groundwork for continued expansion. Typically, the assumption is that virtual agents are in some way reducing the need for human employees, but the most effective use of conversational AI is one that strikes a balance between the two. Accelerating claims cycles presents yet another opportunity to improve operations.

Today’s virtual agents are capable of being trained in many verticals, with some solutions coming with out-of-the-box intents and libraries focused on insurance. Ultimately the path ahead for insurance customer service is one that expands on the investments in technology already made. Working in disparate systems, or addressing one problem at a time is not an approach that will establish sustainable growth.

Bill Schwaab

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Bill Schwaab

Bill Schwaab is the VP of North America at

He is focused on growing the North American presence, with an emphasis on the financial services, banking, insurance and e-commerce sectors. He brings with him more than 15 years of experience in conversational AI, machine learning and data analytics and a track record of helping mid-sized to large enterprises scale through the use of AI.


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