Most CX leaders can describe what AI-driven customer service is supposed to look like: faster resolutions, fewer repetitive interactions landing on agents, a support operation that scales without headcount growing at the same rate. What’s harder to articulate is where an organization actually is in their journey to being AI-driven. They might have implemented certain AI capabilities and successfully automated a number of tasks that their reps used to perform manually. But the experience still feels reactive and limited. Many companies face this challenge, implementing AI but never becoming AI-driven. In fact, more than 80% of AI projects fail to reach meaningful production deployment, roughly twice the failure rate of non-AI technology projects. The root causes are consistent: poor data quality, use cases that aren’t tied to business outcomes, and infrastructure that can’t support the next stage. In this guide, we’ll examine these issues and introduce a four-stage CX AI Maturity Model that exists to solve for them.
