Knowledge Management Emerges as AI Contact Center Bottleneck

Knowledge management now limits AI contact center returns

Artificial intelligence deployments in customer service are leaving the experimental stage, and the constraint determining success has shifted from technology capability to knowledge management infrastructure. Contact centers present a high-stakes proving ground: they combine massive interaction volumes with substantial labor expenses and direct customer impact, making AI performance both measurable and consequential.

Gartner projects conversational AI will reduce contact center labor costs by $80 billion in 2026, driving a wave of voice agent launches and platform repositioning across the market. Cisco Systems has staked its strategy on AI agents spanning collaboration and customer workflows, while cloud contact center vendors race to deploy autonomous voice capabilities.

Why it matters

The shift from AI pilots to production deployments exposes a gap most enterprises underestimated: legacy metrics reward speed over problem resolution, and autonomous agents fail without rigorous data governance and workflow redesign. Companies that automate aggressively without addressing knowledge management will see customer trust erode faster than costs decline.

Legacy metrics break under AI workloads

Traditional contact center performance indicators are proving inadequate for AI-driven environments. Average handle time and first-call resolution have dominated the industry for decades, but these metrics optimize for speed rather than outcomes, according to Zeus Kerravala, principal analyst and founder of ZK Research.

A shorter call duration means nothing if the customer's issue remains unresolved. The transition from AI assistants to fully autonomous agents demands outcome-based measurement frameworks that track whether problems actually get solved, not just whether interactions conclude quickly.

Data quality and process redesign precede ROI

Vendors including Five9 have developed implementation playbooks and voice AI agents designed to accelerate deployment timelines, but the foundational work still falls to the customer organization. Bob Laliberte, principal analyst for networking and observability at theCUBE Research, emphasized that success requires practical attention to data quality, system integrations, and willingness to redesign established processes.

Knowledge management issues surface as the primary obstacle once the technology is in place. Organizations must address how information is structured, accessed, and maintained before autonomous agents can deliver consistent results.

Human-AI handoffs determine customer trust

The brands that will lead in AI-driven customer service are not those that automate the highest percentage of interactions. Instead, winners will be organizations that use AI to produce more consistent outcomes while maintaining both employee and customer trust, Kerravala noted.

The handoff between virtual agents and human representatives remains a critical design challenge. When autonomous systems fail, the quality of that transition directly affects customer effort, brand perception, and long-term loyalty.

The high visibility of contact center failures makes these environments unforgiving test beds. Poor AI interactions increase customer effort and damage trust in ways that are immediately apparent and difficult to repair.

These insights were shared by Laliberte and Kerravala during theCUBE's coverage of The AI ROI in Contact Center Summit, first reported by SiliconANGLE.