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.
From here, it can determine where automation could enhance outcomes, such as reducing service costs, improving customer satisfaction, or accelerating workflows.
The innovation platform uses this to automatically design and configure AI agents that have already been deployed in company environments, reducing the need for manual development and long testing cycles.
These AI agents are also able to understand customer intent, follow workflows, and execute tasks across systems.
This goal is to move enterprises from experimental AI pilots to large-scale deployment more quickly, increasing self-service resolution, reducing routine workloads for human agents, and shortening deployment cycles for new AI capabilities.
Robin Gareiss, CEO and Principal Analyst at Metrigy, argues that companies are no longer interested in experimental AI demonstrations, demanding AI that delivers measurable results.
“Organizations no longer want AI demos; they want provable results—and a unified platform can help get them there,” she said.
“In fact, Metrigy research shows that 82.4% of companies see value in a unified platform for CX and AI capabilities.
“By connecting enterprise data directly to deployment within a unified platform, NiCE’s closed-loop approach enables enterprises to scale AI with confidence.”
How Agentic AI Changes Contact Centre Operations
NiCE’s innovation launch signals a shift in how AI is introduced in CX operations, using that existing customer data to identify common needs and use already deployed AI agents to address them.
This reduces the need for long design cycles and manual scripting of chatbots and automation flows, allowing CX teams to move from experimentation to production deployments more quickly.
The announcement also changes how automation is perceived in contact centers, analyzing real interaction patterns rather than building individual bots for specific tasks to determine where automation will be most impactful.
For customer interactions, the innovation supports self-service resolution and reduces wait times in contact centres by taking on actions during conversations, such as customer verification, resolving service requests, and executing system updates.
AI agents created from interaction data can be trained on new conversations and refine their performance over time, creating a feedback loop in which customer behavior informs automation design and optimization.
