DATA MANAGEMENT
BY FRANK PALERMO, NEWROCKET
ILLUSTRATION PROVIDED BY ADOBE STOCK
CONTACT CENTER AI IS INHERITING A DATA CRISIS
AI has quickly become a defining investment priority for contact center leaders. Organizations are deploying virtual agents, automated quality management( AQM), real-time agent assistance, conversation intelligence, predictive routing, and generative call summaries.
The potential is significant. Contact centers produce enormous volumes of customer intelligence every day, including direct signals about customer intent, product issues, agent performance, operational friction, and emerging business risks.
The greatest obstacle to realizing that potential is rarely the model itself. The more persistent threat is the condition of the data surrounding it.
36 CONTACT CENTER PIPELINE
HERE’ S HOW TO MANAGE IT.
AI projects often expose years of fragmented systems, inconsistent definitions, unclear ownership, and weak governance practices.
A model can process information at tremendous speed, but it cannot independently repair the operational history embedded in that information. When the underlying records are incomplete, contradictory, or poorly governed, AI scales those weaknesses across the contact center.
That creates a difficult reality for leaders. The success of the next generation of contact center technology will depend heavily on data management decisions that many organizations have postponed for years.
CONTACT CENTER DATA WAS NEVER DESIGNED FOR AI
Most contact center data environments were developed incrementally.
• Telephony platforms were implemented to route and record calls.
• CRM systems were added to manage customer records and cases.
• Workforce management( WFM) platforms handled forecasting and scheduling.
• Quality management systems evaluated agent performance.