TELUS Digital has outlined three operational requirements contact center leaders should evaluate when selecting an AI agent assist partner, as the technology moves from pilot projects to production requirements in 2026. The company, which provides AI-powered customer experience services, says the shift means buying decisions now hinge on how the tool performs under real working conditions, not on feature lists or demo performance.
Erin Walker, Global VP of CX AI, Business & Delivery at TELUS Digital, said the technology works in production when three things are in place: the platform integrates natively with the CRM and knowledge base, latency is low enough for real-time recommendations, and the system captures which recommendations agents accept, ignore, or modify.
"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," Walker said.
What separates enterprise-grade agent assist from consumer copilots
AI agent assist technology listens to or reads live conversations across voice, chat, and email, then surfaces relevant information directly in the agent's workspace without requiring manual searches. The first production requirement is context plus a recommended action: the agent needs CRM data, order history, knowledge base content, and entitlements in one view, paired with a specific next step. Context without guidance leaves the decision burden on the agent.
The second requirement is real-time latency. Recommendations must land before the moment passes, benchmarked under load and across different languages and integrations. If guidance arrives late, agents stop trusting the tool regardless of recommendation quality. The third is a continuous improvement loop, where the system learns from what agents accept, ignore, and modify across every interaction. Without that loop, quality flatlines after launch as business context shifts.
For support teams evaluating these systems, the practical takeaway is that integration depth and latency testing matter more than demo features. A tool that requires screen switching during a call reintroduces the friction it was meant to remove. For call center supervisors implementing these tools, the AI Learning Path for Call Center Supervisors covers how to manage adoption and measure results.
Why deployments stall after launch
Without a mechanism to capture which recommendations agents accept or modify, a deployment cannot learn from real usage. A 2025 study tracking an enterprise AI assistant serving more than 30,000 employees found that performance degraded after deployment as business context shifted. A structured feedback loop reversed the decline, improving 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. Without that, the deployment hits a ceiling it cannot raise on its own.
For professionals expanding their skills in this space, AI for Customer Support resources cover the practical side of working alongside agent assist systems.
How agent assist improves productivity
TELUS Digital approaches agent assist technology as an implementation and orchestration partner, working with enterprises to evaluate whether to build on its Fuel iX platform directly or integrate with the technology they already run. Fuel iX provides the foundational infrastructure, spanning real-time agent assist technology, model orchestration, and enterprise-grade moderation and observability.
The company says the measurable impact includes reduced After-Call Work, lower average handle time, higher CSAT and NPS scores, and improved agent retention without increases in headcount or operational complexity.
Why this matters for customer support professionals
The decision about which agent assist tool your organization buys will determine how your daily work changes. A system that integrates deeply with your existing CRM reduces screen-switching during calls, while a poorly integrated one adds friction. The feedback loop also affects you directly: your acceptance and modification of recommendations trains the system, so adoption is not optional for the deployment to work. If you are evaluating these tools, ask vendors for latency benchmarks under load and proof that their systems capture and act on agent feedback after go-live, not just pilot performance.
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