3 Months After AI in the Contact Center — Results from Real Operations Data

We analyze three months of performance data after deploying an AI-powered contact center solution.

Background

In December 2024, Company A, a mid-sized Korean retail enterprise, was facing a dual challenge: declining customer service quality and rising agent turnover. The numbers told the story — an average of 2,400 daily inquiries, a mean wait time of 4 minutes and 32 seconds, and 1,800 cases handled per agent per month were pushing the system to its limits.

RIFT partnered with Company A to design an AI-powered contact center solution, which went live in January 2025.

3-Month Performance Data

Metric	Before	After 3 Months	Change
Average Wait Time	4 min 32 sec	1 min 48 sec	-60%
First-Contact Resolution Rate	54%	78%	+44%
Customer Satisfaction (CSAT)	3.2/5.0	4.1/5.0	+28%
Cases per Agent per Month	1,800	2,350	+31%
Agent Turnover Rate	8.2%/month	3.1%/month	-62%

What We Didn't Expect

The biggest gains came from an unexpected area. While cost reduction was the primary goal, the most significant change turned out to be agent satisfaction.

By having AI pre-classify repetitive inquiries and generate draft responses, agents were able to focus on complex cases. This led to improved job satisfaction, which in turn resulted in a 62% reduction in turnover.

On the other hand, during the first two weeks, AI response quality fell short of expectations, with the manual intervention rate reaching 40%. After data tuning and prompt optimization, the system stabilized by week four, and the current manual intervention rate stands at 12%.

Key Takeaways

The first 2–4 weeks after AI deployment must be designated as a stabilization period.

The real results come from augmenting agents' capabilities, not replacing them.

Beyond quantitative metrics, collecting weekly agent feedback was the key driver of quality improvement.

Next Steps

Company A plans to extend AI coverage to voice-based support channels during Q2. RIFT is preparing a pilot targeting voice recognition accuracy of 95% or higher.