In the insurance sector, the customer contact center is likely where AI technology has the maximum potential for cost efficiency and customer satisfaction. That said, AI in insurance faces the most hurdles, ranging from technical debt (from legacy systems) to regulatory oversight.

While 41% of insurance carriers are actively deploying AI across core business areas, only 20% regard their AI implementation as having reached maturity level or operating at enterprise scale. 60% expect AI to significantly transform their business models within the next 1-3 years. To achieve this outcome, insurance carriers must look to reduce the execution gap while investing in contact center AI solutions.

Here’s a look at the various challenges in deploying AI tools for customer service in the insurance domain.

1. Legacy systems and fragmented data
   45% of insurance carriers cite lack of data readiness as their biggest challenge to widescale AI deployment.
   Most of the insurance contact centers operate on legacy systems that keep customer data “siloed” and isolated from other business functions. AI systems in contact centers fail to produce accurate, relevant results when trained on siloed, fragmented data. 
2. Automating crisis scenarios
   A consumer requesting a change of address or a duplicate ID card can be managed through a routine servicing call using an AI-enabled chatbot. However, during a catastrophic situation (like a hurricane surge), consumers file their first notice of loss (FNOL), which involves higher stakes.
   The problem occurs when carriers deploy generic, conversational bots to handle both routine inquiries and FNOLs. Any mishandling can damage the customer’s brand trust and directly impact customer retention.
3. Regulatory compliance
   Behind operational risks (41%), insurance leaders rate data privacy (22%) and regulatory risk (15%) as the major obstacles to AI adoption.
   Among the highly regulated domains, insurance demands that regulatory authorities have clear audit trails of business decisions (for instance, claim rejection or policy premium increase). Without proper governance, AI prompts can result in “hallucination” or outdated outputs from models.
4. Unclear ROI
   66% of insurance carriers plan to increase their AI spending, but only 13% are highly confident of measuring their AI ROI. When it comes to insurance contact centers, most carriers approach AI only as a cost-cutting tool rather than for value generation.
   This is why insurance executives fail to measure the monetary benefits of using AI in their contact centers.

How carriers can overcome these challenges

To be successful in AI implementation, insurance carriers must execute the following solutions:

• Decouple routine service calls and transactions from FNOL and catastrophe workflows.
• Implement human-in-the-loop (or combine AI agents with human agents) to improve call resolution speed and efficiency.
• Establish a structured AI model governance path to approve and change customer-facing scripts quickly.
• Close the loop in insurance claims automation to ensure real-time data from contact centers flows directly to the underwriting process.

Conclusion

An AI-powered customer engagement platform can deliver customer-centric benefits in insurance contact centers, but success depends on addressing friction points like fragmented data, regulatory compliance, and workflow design.

Is your AI strategy failing to account for reality? Our latest eBook, “The Service Moment Insurers Cannot Automate Incorrectly: A Diagnostic for Claims and Contact Center Leaders,” introduces six operational benchmarks designed to test whether your service center is truly ready to scale. We guide you through the “honest” questions—like “who is allowed to change the AI script?”—that identify if your operation can handle a crisis or just a normal Tuesday.

Are your current contact center AI solutions hitting a wall? Download the diagnostic now to benchmark your preparedness and connect with our insurance domain specialists.