A new interview on CX Today with Dr. Ben Granger, Chief Workplace Psychologist at Qualtrics, puts into plain language what many operations leaders are quietly observing: AI is being pushed into customer-facing workflows faster than customer trust is adapting to it. The result is not neutral. When AI rollouts go badly, they do not just fail to help — they actively erode the relationship between a brand and the people it serves.

This is not a niche concern. It is quickly becoming one of the defining operational risks of 2026.

What the Research Is Actually Saying

Dr. Granger's core argument is that trust in AI-assisted service is not built through capability alone. Customers are not simply evaluating whether a bot resolved their issue. They are evaluating whether the interaction felt legitimate, fair, and respectful of their time and intelligence. When those conditions are not met — when an AI confidently gives wrong answers, deflects without resolution, or makes a customer feel like a ticket rather than a person — the damage extends beyond that single interaction. It shapes how that customer perceives the entire brand going forward.

The compounding problem is that many enterprises are deploying AI under pressure — competitive pressure, cost pressure, board-level pressure to show digital transformation progress. The urgency is real, but the shortcuts taken in the name of speed are now surfacing as measurable trust deficits in CSAT scores, churn data, and social sentiment.

The Operational Reality CX Leaders Are Facing

For CX managers and contact centre leads, this creates a genuine dilemma. You cannot ignore AI — the efficiency gains in deflection rates, handle times, and 24/7 availability are too significant to leave on the table. But you also cannot afford to deploy it carelessly across customer journeys that involve complexity, emotion, or high stakes.

The failure mode is predictable and repeatable: an organisation automates the wrong interactions, or automates the right ones without adequate fallback design, and the AI encounters an edge case it cannot handle. The customer gets stuck. Frustration builds. If there is no clear, fast path to a human agent, the interaction ends badly — and the brand takes the reputational hit.

What makes this particularly costly is that the customers most likely to hit AI limitations are often your highest-value ones. Complex queries, nuanced complaints, and high-emotion situations are not evenly distributed. They cluster around the customers who need the most careful handling.

Why Hybrid Is Not a Compromise — It Is the Architecture

The lesson here is not that AI should be slowed down. It is that AI should be deployed within a deliberate human-AI structure, not as a replacement for one. The organisations getting this right are not choosing between automation and human service. They are engineering the handoff between the two with the same rigour they would apply to any critical workflow.

This is precisely the model Conveneo is built on. Our approach treats AI automation and multilingual human expertise as complementary layers of the same operation — not competing budget lines. Automated systems handle high-volume, low-complexity interactions with speed and consistency. Skilled human agents take ownership the moment a conversation requires judgment, empathy, or contextual nuance that no model can reliably replicate yet.

The result is a CX operation that is both scalable and trustworthy. Customers who reach automation get fast, accurate resolution. Customers who need a human get one — without friction, without dead ends, and without the brand damage that comes from a poorly designed AI experience.

The Question Worth Asking This Quarter

If your team is planning an AI deployment or has already rolled one out, the most important diagnostic question is not 'what is our deflection rate?' It is 'what happens to the interactions the AI cannot handle?' If the answer to that second question is unclear, you have a trust problem waiting to surface. The good news is that it is entirely solvable — with the right operational architecture in place.