Contact centers are shifting from experimenting with AI to scaling it, but legacy performance metrics and knowledge management bottlenecks are complicating the path to realizing promised labor cost savings and customer experience improvements.
- Legacy contact center metrics increasingly misaligned with AI-driven outcomes.
- Knowledge management identified as key constraint in AI success.
- Outcome-based scoring and process redesign critical for operator gains.
Market signal
The enterprise technology market is witnessing a transition of AI in contact centers from pilot projects to broader operational use. Gartner’s projection that conversational AI could reduce contact center labor costs by $80 billion this year highlights significant commercial momentum. Vendors such as Cisco and Five9 are launching AI-driven virtual agents aimed at cutting costs and improving customer workflows within cloud contact center environments.
Despite growing investment, the technology’s effectiveness is under scrutiny as traditional contact center performance indicators—most notably average handle time and first call resolution—fail to adequately reflect the customer experience or issue resolution quality. The market signal thus signals a critical need for more sophisticated measurement frameworks that align with AI’s impact on real outcomes, rather than speed or containment alone.
Operator impact
For contact center operators, AI adoption introduces both opportunity and complexity. While labor cost reductions are a primary goal, the visibility of customer interactions means failures in AI performance can quickly damage brand reputation and erode trust. Operators must therefore ensure AI tools are backed by accurate, comprehensive knowledge management systems to prevent increased customer effort and unresolved issues.
Operators are also compelled to reconsider legacy workflows and governance practices. The interplay between AI agents and human representatives requires careful orchestration, with seamless handoffs being essential for maintaining service quality. Success depends less on automation volume and more on achieving consistent, improved customer and employee experiences through thoughtful integration and high data quality standards.
What to watch next
The evolution of performance metrics in contact centers will be a critical area to monitor, especially how operators and vendors develop and adopt outcome-based scoring methods. These metrics will better reflect AI’s ability to resolve customer issues and enhance satisfaction beyond superficial speed improvements.
Additionally, advances in knowledge management technologies and integration capabilities will play a decisive role. Operators should watch for innovations that facilitate richer data quality, real-time knowledge updates, and process redesign approaches enabling more effective AI-human collaboration. Progress in these areas will likely differentiate leading contact center implementations and influence broader AI-driven customer experience strategies.