What insurers can do to ensure high-functioning AI-based CX solutions
Avoid mistakes and eliminate waste in AI-based CX implementations by following these basic best practices for optimal performance
In previous articles, we discussed how AI-based technology is reshaping the future of customer experience (CX) in the insurance industry. Current trends indicate its increased acceptance in the sector based on customer expectations for a retail-like experience in all transactions. We shared some use cases, provided a view into how CX technologies can be integrated seamlessly with existing infrastructure, and highlighted five major touchpoints where AI-based CX can improve outcomes. These included:
- Underwriting and risk management
- Sales and policy management
- Claims processing
- Empowering agents and brokers
- Employee training
In this article, we will discuss the best practices for embedding AI-based CX into your insurance domain, including some of the challenges you can expect going forward. We will touch on the importance of data security and privacy involved in CX initiatives, and close with how to measure success for continuous improvement over time.
Start with an implementation roadmap
Implementing a workable, continually improving, AI-based CX functionality involves a series of strategic steps that ensures the technology enhances customer service across the enterprise. Our recommended implementation roadmap starts with a thorough assessment of your existing IT infrastructure and leads to the final deployment and refinement of the capability:
- Identify use cases and assess infrastructure You won’t know where you’re going unless you set a destination. Begin by determining specific areas in your organization where AI-based CX can add value. This may include areas of customer service, claims processing or, for example, sales and marketing, providing personalized insurance recommendations. Once you understand where and how CX can improve your performance, assess what it will take to acquire the capability. Evaluate your existing data and technological infrastructure. Can it support a successful integration? How will the implementation affect collateral systems? By taking the time to ensure compatibility with your current environment, you will minimize the time, cost, and effort it will take to achieve your goals.
- Establish a robust data foundation and train the capability AI-based CX relies heavily on robust data and continual training of your capability to deliver the desired results. You will find relevant, insurance-specific data all around and within your organization. Collect and classify glossaries, FAQs, and complex queries that can help train your CX bots in understanding and responding to industry-specific terminology and customer needs. As you proceed, keep data security and privacy concerns top of mind. Implement security measures by encrypting sensitive information to ensure compliance with privacy regulations, such as DGPR and CCPA, to protect client data during interactions. High-quality, accessible, secure data will form a strong base for any successful CX program.
- Test, deploy, and continuously improve Nothing will undo your efforts faster than a CX bot that performs poorly. Test your bot’s performance rigorously before going live. Continue monitoring its performance to ensure accuracy and relevance in responses, using feedback loops for ongoing refinement. As you deploy the capability across company platforms, such as the company website or customer service portals, be sure to customize responses to reflect your brand’s tone and offerings. A well-trained CX capability will help differentiate your brand in the marketplace. Lastly, establish a continuous improvement protocol, incorporating new data, expert insights, and customer feedback, to enhance your capabilities and service quality over time.
Stay the course using best practices
AI-based virtual agents can and will make a positive impact on your organization’s CX, lowering cost-to-serve, improving customer satisfaction, and adding incrementally to your bottom line – if used appropriately. To ensure maximum effectiveness, we recommend the following best practices for implementing AI-powered conversational agents in your customer service environment:
- Customize the conversational AI model: Train your conversational AI model with industry-specific data to ensure accurate understanding and responses to customer queries.
- Continuously update the model: Insurance policies and processes evolve over time. Regularly update your model with the latest information to provide the most relevant, current, and accurate responses to customers.
- Provide human oversight: While conversational AI can handle routine queries, it is crucial to have human oversight to handle complex or sensitive customer interactions. This ensures that customers receive the best possible care and support.
- Collect customer feedback: Regularly collect feedback from customers regarding their conversational AI experience. This feedback can help identify areas for improvement and enhance your overall customer experience.
Be aware of the limitations
In addition to the benefits you can expect to see from a well-conceived, well-executed conversational AI model, there are also a number of potential concerns and limitations that should be addressed as you proceed. One concern is the potential for biased responses. conversational AI models learn from the data they are trained on, and if the training data contains biases, it can inadvertently produce biased responses. Insurance companies must be vigilant to ensure that the training data is diverse and representative.
Another limitation is conversational AI’s inability to handle complex legal or regulatory questions. Insurance policies are governed by intricate legal frameworks, and conversational AI may not always be equipped to provide accurate legal advice. In such cases, it is essential to direct customers to legal experts or provide alternative support channels.
As well, while AI-based solutions can automate many tasks, they cannot completely replace human employees. There will always be tasks that require the unique skills and intelligence of human beings. Therefore, always strive for a balance between automation and human touch when applying chatbots and other AI-based solutions.
AI for added security
The importance of a proactive approach to data security and privacy in the deployment of AI-based solutions cannot be overstated. In addition to encrypting any data used to establish or improve your AI-based CX program, it also a best practice to aggregate and anonymize your data to safeguard against unauthorized access and remain in compliance with industry standards.
Also consider how AI can add value to your cybersecurity strategy. Horizontally, AI can be used to detect anomalies and fraudulent patterns as they emerge in real-time, cutting breaches at the source. With regular security updates and comprehensive audit logs, it can prevent unauthorized access, while monitoring system activities. It can also automate routine compliance processes, such as know your customer (KYC) and anti-money laundering (AML), helping your organization comply with HIPAA laws, as well as other applicable state privacy mandates.
Measure outcomes for better outcomes
To ensure the ongoing effectiveness of your AI-based CX program, it is essential that you continually measure its performance, identifying areas to improve ROI.
Customer satisfaction is one of the most obvious areas to assess. Collect feedback from policyholders who have interacted with the system by using surveys, ratings and feedback forms to gather insights.
Keep a close watch over the accuracy of your chatbot’s responses, comparing them to pre-defined correct answers or expert judgments. Measure the percentage of correct responses provided by the conversational AI agent to evaluate its overall accuracy. Identify any common errors or misconceptions and prioritize fine-tuning accordingly.
Monitor the response times of your conversational AI agent to ensure timely and efficient customer support. Measure average response times from the moment a query is received to the delivery of the response. Aim for fast response times to maintain customer satisfaction and minimize wait times.
Finally, track the number of customer inquiries resolved by conversational AI per pre-determined timelines. This task completion rate will provide valuable insights into what needs to change to relieve bottlenecks and improve customer support processes.
Prepare your organization for a successful AI launch
Following a deliberate AI-CX implementation roadmap, with an eye on concerns and a full awareness of best practices, is something any insurance firm can and should do to remain competitive in today’s fast-paced, consumer-centric environment.
If you would like to explore your fitness to implement an AI-based CX solution, or speak with one of our expert consultants on the topic, please visit EXLservice.com.