Source: SiliconANGLE
As AI customer service agents move from pilots to production, companies are discovering that the real bottleneck isn't the technology itself but keeping internal knowledge systems current and accurate enough to power reliable responses. The gap between AI performance metrics (which look good in controlled settings) and actual business outcomes (customer satisfaction, resolution rates, repeat contacts) reveals that enterprises have underinvested in data governance, creating a credibility crisis for AI deployments that promised immediate cost cuts. The question has shifted from "can we build it?" to "can we maintain it?"—a harder, less venture-fundable problem that favors companies with disciplined knowledge operations over those betting on breakthrough algorithms.