TELUS Digital has outlined the operational requirements contact center leaders should evaluate when selecting an AI agent assist partner, marking a shift in enterprise contact centers from pilot curiosity to production requirement. The company's Fuel iX™ platform supports measurable gains including reduced average handle time (AHT), improved sales conversion, higher CSAT and NPS, reduced After-Call Work, and increased speed to proficiency.
Erin Walker, Global VP, CX AI, Business & Delivery at TELUS Digital, said: "AI agent assist technology works in production when three things are right: the platform integrates natively with the CRM and knowledge base so agents have full customer context in a single view; the latency is low enough that recommendations and next best actions land in real time before the agent moves on; and the system captures which recommendations agents accept, ignore, or modify, aggregating those insights across all agents so the deployment keeps getting sharper at scale."
What AI agent assist technology actually does
AI agent assist is enterprise software that provides real-time guidance to human agents during live customer interactions. The technology listens to or reads conversations across voice, chat, or email channels and surfaces relevant information directly in the agent's workspace, so agents don't have to search for it manually.
For customer support teams, this means the difference between an agent juggling multiple screens and one who sees the full customer context in a single view. The technology handles the search work while the agent handles the conversation.
Keeping AI agent assist sharp after go-live
Without a mechanism to capture which recommendations agents accept or modify, a deployment cannot learn from real usage, and quality can flatline after launch. A 2025 study tracking an enterprise AI assistant serving more than 30,000 employees found that performance degraded post-deployment as business context shifted. A structured feedback loop reversed the decline, lifting routing accuracy to 96% and cutting latency by 70% within three months.
Two organizational requirements follow. First, agent adoption: a feedback loop only generates a useful signal when agents use the tool consistently. That requires recognition tied to recommendation acceptance and onboarding that frames the tool as a career accelerant rather than an oversight mechanism. Second, ongoing engineering support: dedicated engineers must monitor recommendation quality and retrain on emerging signals, or the deployment hits a ceiling it cannot raise on its own.
How AI agent assist improves contact center productivity
Buying decisions in this category can stall on feature comparisons when the harder variable is implementation. TELUS Digital approaches agent assist as an implementation and orchestration partner, working with enterprises to evaluate whether to build on the Fuel iX platform directly or integrate with existing technology. Fuel iX provides the foundational infrastructure, spanning real-time agent assist, model orchestration, and enterprise-grade moderation and observability.
For customer support leaders evaluating AI agent assist, the practical takeaway is to ask three questions before committing: Does the platform integrate natively with the CRM and knowledge base? Is latency low enough for real-time recommendations? And does the system capture agent accept, ignore, and modify signals at scale? TELUS Digital received the first Privacy by Design certification (ISO 31700-1) for its GenAI-powered customer support chatbot built on Fuel iX, which may matter for teams in regulated industries.
Why this matters for customer support professionals
The shift from pilots to production requirements means customer support teams will increasingly be measured on how well they adopt AI agent assist, not whether they trial it. The operational requirements TELUS Digital outlined - native integration, low latency, and feedback loops - give agents and supervisors a concrete checklist for evaluating tools. For those building skills in this area, an AI Learning Path for Call Center Supervisors covers implementation and optimization approaches relevant to this transition. The broader category of AI for Customer Support continues to expand as production deployments replace pilot programs across enterprise contact centers.
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