NiCE has launched a new agentic AI innovation that improves service quality in customer interactions.
Having announced the innovation at Enterprise Connect, NiCE detailed how the solution uses companies’ existing interaction data from voice, chat, workflow, and digital channels to identify high-impact use cases and build production‑ready AI agents for deployment.
This innovation is designed to tackle practical challenges that companies face when trying to deliver AI at scale, aiming to close the gap between AI experimentation and product deployment.
Jeff Comstock, President, CX Product & Technology at NiCE, highlights that enterprises benefit more from a unified, AI native platform that uses real interaction data to deploy production ready AI agents and deliver measurable outcomes at scale.
“Enterprises don’t win by bolting AI point solutions onto their existing infrastructure. They win with one AI-native digital front door that orchestrates every interaction end-to-end,” he explained.
“NiCE strengthens that strategy by starting with real interaction data, quantifying the opportunity, and moving directly to production-ready AI agents. It helps organizations move quickly from AI experimentation to measurable outcomes at scale."
Why Enterprises Struggle to Scale AI in Customer Service
The innovation aims to address several barriers that prevent enterprises from deploying AI at scale.
Building AI agents can require manual identification from IT, data science, and operation teams, meaning organizations can spend months moving AI pilot projects into production systems.
As enterprise extraction data is scattered across digital channels, CRM systems, and workflow tools, integrating these sources requires significant engineering effort.
Companies also often struggle to identify the high-value automation opportunities due to the large, unstructured nature of interaction data.
Without automation analysis, organizations are forced to rely on manual reviews or limited analytics, making it difficult to identify tasks that can be automated or assisted by AI, such as common customer requests, repetitive workflows, or compliance checks.
Data silos can also occur when extensive amounts of customer interaction data go underused for training or generating AI agents due to enterprises collecting numerous communication platforms and CRM tools over time from different vendors, creating fragmented data environments.
AI deployment in customer interactions can also raise regulatory and operational risks, as traditional methods require extensive validation and oversight to protect customer data and brand reputation, slowing AI adoption.
How NiCE’s Platform Converts Interaction Data into Operational AI agents
NiCE’s agentic AI innovation automatically converts a company’s existing customer interaction data into fully functioning AI agents that can handle service tasks.
It analyses large volumes of interaction data such as voice calls, conversation transcripts, workflow logs, and digital messages to identify common customer intents and operational bottlenecks.