Contact center AI deployments are failing not because the technology is inadequate, but because organizations are building solutions without first defining the problems they're solving. The root cause sits upstream of implementation: teams chase agentic AI as a strategic imperative rather than a targeted response to a specific business challenge, then measure success against metrics that obscure rather than illuminate performance. Containment rate—the industry's default KPI—tells you whether a customer got an answer, not whether that answer actually resolved their issue or created value. This distinction matters enormously. When CX leaders conflate containment with resolution, they're optimizing for the wrong outcome, which explains why significant AI investments often fail to move the needle on revenue, churn, or customer satisfaction. The question facing teams already running Agentforce, Salesforce Service Cloud AI, or similar platforms is whether they've audited their deployment against a clearly articulated problem statement, or whether they've simply accepted the vendor's definition of success.
The path forward requires a fundamental shift in how contact centers approach AI governance. Static deployments—systems trained once and frozen in place—depreciate rapidly as customer behaviour, product offerings, and competitive dynamics evolve. Self-learning systems that continuously adapt to real interaction data eliminate the need for perpetual manual annotation cycles and keep AI assets aligned with current business conditions. More critically, CX leaders need to expand their measurement framework beyond containment to capture resolution quality, upsell effectiveness, and direct contribution to revenue growth. This reframing transforms AI from a cost-reduction play into a revenue-optimization engine, which changes both how you justify investment and how you allocate resources post-deployment. For teams operating on constrained budgets, the implication is clear: faster time-to-value and deployment-ready data pipelines aren't nice-to-haves—they're prerequisites for justifying the spend in the first place.
In this CX Today discussion, host and Associate Editor Rhys Fisher sits down with Claudio Rodrigues, Chief Product Officer at Omilia, to cut through the AI hype and get real about why so many contact centers are underperforming despite significant investment. If your AI deployment isn’t delive