Francesca Roche sits down with Priscilla Lee, Senior Director of Product Marketing at Quiq, and John Anderson, AI Architect at Quiq.
AI pilots often look polished because they happen in controlled conditions, with limited scenarios, close oversight, and a narrower view of what customers actually do when a service issue becomes urgent, messy, or emotionally charged.
Once AI moves into production, the real test begins: customers switch channels, repeat old problems, ask follow-up questions, expect context to carry over, and judge the brand when the experience feels disconnected.
Anderson points to a common enterprise mistake, explaining that:
“Most companies tend to deploy AI by channel, not necessarily by customer journey.”
That channel-by-channel approach can create a customer experience where voice, chat, SMS, and human agents all operate from different fragments of the same story.
For customers, that means repeating the same issue when they move from an AI agent to a human agent, or restarting from scratch when they return a day later to continue an unresolved case.
Anderson says one of the most common failure points is the handoff, where a human agent receives “a cold transfer with no context,” forcing the customer to explain everything again.
That moment matters because AI does not have to fail dramatically to damage trust, it only has to make the customer feel unseen, slowed down, or bounced between systems.
Lee says expectations have changed quickly, noting that “a year ago, if AI could answer a simple question or just get you to the right human agent without too much hassle, customers were pretty happy about that.”
Now, customers increasingly expect AI to resolve issues from start to finish, which demands stronger architecture, better access to business data, and a clearer understanding of when to escalate.
The production trust problem also reaches beyond the customer journey and into the leadership team, where visibility becomes essential for governance, compliance, and brand protection.
“AI almost never stalls because AI isn’t handling the task,” Lee acknowledges.
“It’s stalling because someone on the leadership side isn’t comfortable with AI that they can’t see inside of.”
That is why simulations, guardrails, and observability become central once AI leaves the pilot stage and starts shaping live customer experiences.
Anderson adds that teams should avoid optimizing for the demo and instead focus on what happens when “a real customer has a complex problem” and the AI is the only available support.
For CX leaders, the takeaway is clear: production-ready AI needs continuity, control, and enough visibility for the business to trust what customers experience before they experience it.
