What happened, in plain language
TELUS Digital reported that roughly two thirds of enterprises are deploying AI into production without what they call safety nets. In other words, many organizations are moving quickly to put AI into customer-facing systems and contact center workflows without formal guardrails, monitoring, or rollback plans.
That is not surprising. AI tools promise fast gains in automation and efficiency, and teams often prioritize customer outcomes and speed of delivery. The gap is that production AI can behave unpredictably, and when it does, the consequences land squarely on customer experience and operations.
Why this matters for CX and contact center teams
When AI touches conversations, decisions, or agent workflows, failures are not just technical. They become customer trust and compliance problems. Consider these concrete risks:
- Incorrect or hallucinated responses from an AI voice or chat assistant can confuse customers and create compliance breaches. You cannot treat those the same way you treat a backend service failure.
- Automated quality assurance or coach suggestions that surface wrong insights can misdirect training and performance management. That affects agent morale and metrics you trust.
- Real-time agent assist that provides inaccurate prompts increases handle time and can escalate calls rather than resolve them.
- Autonomous agents or agentic AI workers acting on customer instructions can make irreversible actions, like changing account settings or initiating payments, if not tightly constrained.
These problems translate to real costs: rework, regulatory penalties, compliance investigations, calls to recover trust, and higher churn. Those costs usually exceed the cost of adding basic safety measures up front.
Practical safety controls to put in place now
You do not need to stop using AI. You need a plan that treats AI as software that interacts with people and regulated systems. Start with these actions:
- Map impact by use case and identify what is reversible, what requires human signoff, and what requires audit logging.
- Apply human-in-the-loop controls for high-risk decisions, including approvals for payments, refunds, or account changes.
- Build monitoring and alerting for performance drift, user complaints, anomalous outputs, and legal triggers.
- Create rollback and canary deployment paths so models and prompts can be reverted quickly.
- Maintain detailed audit trails for model inputs, outputs, and system actions, to support investigations and compliance requests.
A short checklist to operationalize safety
- Classify use cases by risk level. Prioritize safety for high-impact scenarios.
- Require human approval for irreversible actions.
- Log interactions, model versions, and prompts centrally.
- Monitor production outputs with synthetic and live tests.
- Establish escalation paths and rollback procedures.
Monitoring and feedback, not just one-time validation
Traditional QA approaches are not enough for AI. You will need continuous validation. Set up simple, practical telemetry that tells you when things change:
- Track core CX metrics alongside AI performance metrics. A spike in transfers, repeat contacts, or agent overrides can be an early sign of an AI issue.
- Use sampling and human review to audit AI decisions, not just outcomes. Random samples will surface edge cases you did not anticipate.
- Capture feedback from agents. They are the first line of defense. Make it effortless for agents to flag bad AI suggestions and for you to feed that data back into model improvements.
Governance and accountability
Make roles and responsibilities explicit. Someone needs to own the safety posture for each AI use case, and that ownership should cover testing, deployment, monitoring, and incident handling. This is about operations, not just the model team.
Include legal, compliance, and security early when you design guardrails. They help you define acceptable failure modes and data handling rules. Also involve business owners so safety measures align with customer experience priorities.
When to slow down, and when to iterate
If a use case touches payments, personal data, or legally sensitive decisions, slow down until you have strong safety controls. For low-risk use cases you can move faster, but still deploy monitoring, sampling, and an easy rollback path.
Iterate in short cycles. Validate assumptions with small pilot groups, observe real behavior, and expand only when metrics and audits look solid.
What this means for your CX team
This TELUS Digital finding is a warning and an opportunity. If two thirds of enterprises are skipping safety nets, that means many of your peers will face avoidable incidents. You can use safety as a competitive advantage. Put pragmatic controls in place now, lean on agents for feedback, and make monitoring part of your standard operating rhythm. Doing so will protect customers and preserve the benefits AI can deliver to your contact center.