Generative AI is changing the game in customer operations—but traditional KPIs like CSAT, AHT, and deflection only tell part of the story. This blog outlines the four critical metric categories that leading organizations are using to measure real GenAI impact: Resolution Rate, Conversation Quality, Time to Resolution, and Containment Rate.

Are You Making Business-Critical Decisions with Just 2% of Customer Interaction Data? Enterprise contact centers handle millions of customer interactions each year, yet most rely on manual quality assurance (QA) processes that analyze only 2% of calls, chats, and messages. This leaves 98% of interactions completely unexamined, creating hidden compliance risks, missed revenue opportunities, and agent performance gaps.

Contact centers serve as the frontline of patient experience, handling everything from appointment scheduling to critical care coordination. Yet, many healthcare call centers struggle with inefficiencies, compliance risks, and operational bottlenecks—all of which impact patient satisfaction and organizational costs

Are You Making Business-Critical Decisions with Just 2% of Customer Interaction Data? Enterprise contact centers handle millions of customer interactions each year, yet most rely on manual quality assurance (QA) processes that analyze only 2% of calls, chats, and messages. This leaves 98% of interactions completely unexamined, creating hidden compliance risks, missed revenue opportunities, and agent performance gaps.

Contact centers serve as the frontline of patient experience, handling everything from appointment scheduling to critical care coordination. Yet, many healthcare call centers struggle with inefficiencies, compliance risks, and operational bottlenecks—all of which impact patient satisfaction and organizational costs

Are You Making Business-Critical Decisions with Just 2% of Customer Interaction Data? Enterprise contact centers handle millions of customer interactions each year, yet most rely on manual quality assurance (QA) processes that analyze only 2% of calls, chats, and messages. This leaves 98% of interactions completely unexamined, creating hidden compliance risks, missed revenue opportunities, and agent performance gaps.

As enterprises race to harness the potential of Generative AI, the spotlight has often been on Large Language Models (LLMs). Yet, an equally transformative shift is happening behind the scenes—one that challenges conventional approaches and rewrites the playbook for AI adoption.

As enterprises race to harness the potential of Generative AI, the spotlight has often been on Large Language Models (LLMs). Yet, an equally transformative shift is happening behind the scenes—one that challenges conventional approaches and rewrites the playbook for AI adoption.