IOV LABSThe Deflection Dividend: The Economics of AI Customer-Service Automation for Korean SMBs
Abstract
Enterprise call-center automation is widely reported, yet the segment with the most leverage, Korean small and medium businesses that can least afford dedicated agents, is the least quantified. We build a transparent, reproducible savings model from just two public 2026 benchmarks: the fully-loaded cost of a Korean customer-service agent and the deflection rate of AI support. A regular agent costs about 37.5M KRW a year fully loaded, 1.2 to 1.25 times gross salary, of which salary is only eighty percent. At a deliberately conservative deflection rate of 55 percent, below the 65 to 80 percent band that structured intents benchmark at, a three-agent team saves about 62M KRW a year (1.65 FTE) and a five-agent team over 100M. The saving is linear in the deflection rate, so the whole result hinges on that single number, which we set low because a 45 percent deflection can mean only 14 percent true resolution. Net of a plausible subscription the return is 17 to 52 times. For the smallest shops, with no agent to remove, the dividend is recovered owner time, about 29 hours a month, not headcount. We present this as an upper-bound model, not a measurement, disclose that the author runs a product in this category, and specify the single one-month measurement that would turn the model into evidence.
Keywords
- AI customer service
- deflection rate
- SMB
- unit economics
- call center
- labor cost
- automation
- Korea
- reproducibility