Customers don’t want to talk to bots. Can AI drive better CX through operations?
Artificial intelligence carries enormous potential for innovation—but the way we talk about it today remains surprisingly narrow. The narrative focuses almost exclusively on customer experience understood in the most literal sense: faster chat responses, 24/7 chatbots, intelligent forms. We focus on solutions that are relatively easy to implement and visible to the customer.
Meanwhile, market data paints a more cautious picture. Customers don’t want to interact with machines in situations that require trust. According to Gartner’s report “4 Customer Insights to Improve the Service Experience,” as many as 88% of consumers have serious concerns about AI in customer service, and 64% would prefer that companies not use it there at all.
Key Insights: How AI in Insurance Operations Improves Customer Experience
- AI in insurance operations enhances customer experience by automating claims processing, underwriting and service workflows.
- Machine learning models analyze customer data in real time to personalize policies, pricing and communication.
- AI-powered chatbots and virtual assistants increase service availability while reducing response times and operational costs.
- Automation of claims handling reduces errors, accelerates payouts and improves transparency for policyholders.
- By integrating AI into core insurance systems, companies improve efficiency while delivering faster and more consistent customer interactions.
Does this mean the end of AI in the insurance industry?
While artificial intelligence hasn’t yet earned customers’ trust on the front lines, its true value emerges in areas that have long resisted automation. The customer experience is the result of an entire chain of actions within the insurance company—including those invisible, operational ones. This is where technology can successfully support complex, costly, time-consuming, and error-prone processes.
AI can meaningfully improve service quality, even if the customer never interacts with it directly. For the company, it translates into tangible outcomes: shorter processing times, lower costs, higher quality, and better margins.
AI agents: the answer to the lack of time
For years, insurance specialists have struggled with a growing volume of cases and time pressure. This is where AI agents come into play—not to replace human decision-making, but to automate tedious, manual tasks and streamline daily work.
Artificial intelligence analyzes documents, identifies inconsistencies, highlights data gaps, and prepares recommendations. The expert doesn’t start from a blank page—they receive a structured summary that serves as the basis for the final decision.
Examples:
- Underwriting: Instead of manually reviewing documentation, the underwriter receives a ready analysis of the case, including missing documents, procedural comments, and a recommendation for next steps—such as applying the right rating model or clauses.
- Claims handling: The system aggregates data from the policy, the claim report, and product documents (T&Cs, procedures, etc.), analyzes them along with photographic evidence, and identifies discrepancies—e.g., the declared car color doesn’t match the photos. The adjuster receives an analytical summary, red flags, and a decision recommendation tailored to the current claim status.
Business impact:
- Reduced time spent on administrative work and decision preparation
- Fewer human errors and oversights
- Standardized recommendations while preserving expert judgment
- Shorter customer wait times and greater process predictability
Reclaimed time and fewer errors
A large portion of work in insurance companies consists of administrative activities. AI automates process elements that were previously beyond the reach of traditional workflow systems. Thanks to its ability to interpret unstructured documents—such as descriptions, reports, or broker queries—it can take over repetitive tasks.
Examples:
- Group policy applications: The AI agent checks document completeness, flags missing items, and generates a report. Instead of several days of email exchanges, the client immediately receives a precise list of missing documents.
- Broker slips: AI reads non-standard files, identifies client data, coverage scope, insurance sums, and information about assets and locations, then converts them into a structured database ready for use in the quotation system.
Business impact:
- Significant reduction in document processing time
- Less manual work and lower risk of errors
- Faster case progression through subsequent process stages
- Better utilization of employee expertise
- A more efficient document flow visible to customers—fewer corrections, fewer inquiries, faster service
Decisions in the broader customer context
In insurance, decisions are often made in reference to a single case—a policy application, a claim, or a broker inquiry. This point-in-time approach may work technically, but it often overlooks the broader relationship with the customer.
An AI agent allows this context to be included. By analyzing data dispersed across multiple systems—policy, claims, CRM—it can build a complete view of the customer relationship: what products they hold, their claims history, payment regularity, premium levels, and overall importance within the insurer’s portfolio.
This changes decision-making:
- The underwriter no longer assesses only a single risk but sees the customer as a whole.
- The claims adjuster reviews a case but also learns that the customer has been loyal and valuable to the portfolio for years.
- The collections team may treat a client who has generated high premiums and pays regularly differently from one with marginal importance, poor payment history, and high loss ratios.
The result is more consistent, predictable decisions and a long-term relationship. Customers feel treated as partners, while the company gains better portfolio control and reduces the risk of losing valuable contracts.
Investing in AI and CX that delivers measurable returns
Back-office work translates directly into front-end outcomes. AI doesn’t need to be visible to the customer to enhance their experience. It’s enough that it improves processes that slow down operations and distract experts from value-adding tasks. It’s estimated that 30–70% of specialists’ time is currently consumed by administrative activities that bring no value to the customer.
The key is to start with the most time-consuming and inefficient areas—keeping in mind that over time, AI will find applications even in places that seem unlikely today. It’s an investment that pays off twice: through lower operational costs and a better customer experience.
This article was originally published (in Polish) in “Gazeta Ubezpieczeniowa,” issue 42/2025, on October 20, 2025. The electronic version is available here: https://gu.com.pl/gazeta-ubezpieczeniowa-nr-42-2025
