AI in customer service: How businesses are moving from queries to resolution

A customer asking for a refund does not necessarily need a conversation with a chatbot. They need to know whether the refund is eligible, where the request stands and when the money will arrive. An automated response that repeats the refund policy without checking the transaction leaves the actual problem untouched. That distinction is becoming more consequential as businesses expand the role of AI in customer service.

According to Salesforce's 2025 State of Service report, Indian service professionals estimated that AI handled 30% of customer-service cases at the time of the survey. They expected that share to reach 50% by 2027. The figures point to a growing role for automation, but the business value depends on what these systems can accomplish beyond answering questions.

From answering queries to resolving cases

Consider the refund request. An AI system can identify the customer's intent, retrieve the relevant policy, and check the status of an order. If the request meets predefined conditions, it can initiate the next step in the workflow. If the transaction requires an exception or additional approval, the case can move to a human agent with the relevant details already assembled.


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The same approach applies to order changes, appointment rescheduling, and routine account queries. AI can connect information that would otherwise require an employee to search across several screens, then trigger actions within approved business rules. The distinction is between automating a response and automating a service process. The first can reduce the time spent answering a query. The second can reduce the effort required to resolve it.

The human handoff still matters

Not every customer problem fits a standard workflow. A disputed charge, a failed delivery involving several parties or a complaint from a frustrated customer may require judgement, negotiation or an exception to policy.

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AI can still contribute by identifying the issue, summarising previous interactions, retrieving relevant records, and suggesting possible next steps. The human agent enters the conversation with context rather than asking the customer to repeat the entire story. Research published in the Quarterly Journal of Economics in 2025 examined AI assistance among 5,172 customer-support agents. It found an average 15% increase in issues resolved per hour, with effects varying across workers. The findings suggest that AI can improve service productivity by supporting employees as well as handling routine interactions independently. For service leaders, this makes escalation design as important as automation itself. Systems need clear boundaries around what they can resolve, when approval is required and when a human should take over.

Measure the resolution, not just the response

A chatbot's response time offers an incomplete picture of performance. Businesses also need to understand whether the issue was resolved, whether the customer had to contact support again and whether the case was routed correctly. Accuracy matters too: A fast answer based on the wrong policy can create more work and undermine trust. The most useful deployments connect customer conversations with reliable business data and the systems needed to act on it. They also give service teams a way to identify failed resolutions and improve the underlying workflow.

The Best Use of AI in Customer Service category at the ET AI Awards 2027 recognises organisations applying AI to customer service in ways that create meaningful business and customer outcomes. For businesses putting these systems into practice, the strongest case rests on what changed for the customer, the service team and the organisation.

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