AI will move at the speed of customer trust, not technology, says E.ON Next

TEXT START: AI may be creating new sales channels and removing friction from customer journeys, but businesses cannot assume that every customer will adopt it in the same way or at the same speed, says Tim Lawless, digital AI enablement lead at E.ON Next ahead of CustomerX

The Dissection

The article converts AI from a labor-displacement event into a manageable customer-experience and channel-strategy problem. Its practical advice is competent: improve existing journeys, prepare for agentic channels, preserve multiple interfaces, fund measurable outcomes, and let employees experiment.

But its deeper function is transition management. It gives incumbents a vocabulary for introducing automation without confronting the terminal implication: “removing frustrating tasks” is often the first visible stage of removing the people whose work consisted of those tasks. The article acknowledges agentic commerce and workforce anxiety, then contains both inside a reassuring narrative of gradual adoption, experimentation, and flexibility.

The Core Fallacy

The central error is treating customer trust as a structural brake on AI’s economic consequences. Trust can slow adoption, segment users, and delay specific transactions. It cannot permanently override the competitive pressure to deploy systems that are cheaper, faster, and more capable.

The article confuses the speed of customer acceptance with the speed of productive displacement. A customer may resist letting an agent change an energy supplier today. That does not preserve the human labor involved in quoting, servicing, analyzing, routing, selling, or managing the transaction. Firms can maintain legacy channels during the lag, but those channels become expensive accommodation rather than protected economic territory.

“Outcome-led funding” does not alter this mechanism. Funding sales, retention, or reduced customer effort instead of a chatbot may improve execution, but the winning team will still be pressured to produce those outcomes with fewer humans. The chatbot is not the threat. The capital allocation logic behind the chatbot is.

Hidden Assumptions

• Customers retain genuine freedom to reject AI even when price, speed, convenience, or market access favor automated channels.
• Businesses can support traditional, AI-assisted, and agentic channels indefinitely without human-service costs becoming untenable.
• AI that functions as a “junior analyst” remains an assistant rather than becoming the reason fewer analysts are required.
• Employee participation and experimentation can convert displacement into durable augmentation.
• Redeploying workers creates lasting productive roles rather than postponing redundancy.
• Trust barriers remain stable instead of eroding under repeated exposure, default settings, competitive pressure, and institutional normalization.
• Firms will control the customer relationship rather than losing it to AI platforms that mediate discovery, recommendation, and purchase.
• Cross-sector collaboration can solve an essentially structural ownership and productivity problem.
• Customer adoption is the decisive variable, while ownership of AI capital and access to the resulting gains are treated as background conditions.

Social Function

This is primarily transition management and ideological anesthetic, with a substantial partial-truth component.

The partial truth is real: adoption will be uneven, trust will vary by task, and legacy channels will persist during the transition. The anesthetic is the implication that uneven adoption gives workers or incumbent institutions a durable refuge. It does not. It gives management a softer rollout strategy and gives employees a less threatening description of the same automation trajectory.

The article also performs elite self-exoneration. It frames the problem as learning how to introduce AI responsibly and flexibly, rather than asking who owns the systems, who captures the productivity surplus, and what happens when human participation is no longer economically necessary.

The Verdict

Customer trust controls the pace of AI adoption, not its direction. The article correctly identifies a lag defense and mistakes it for a counterforce. Under the Discontinuity Thesis, trust is hospice care for the old customer journey: it can prolong human-facing channels, create temporary niches, and slow visible disruption, but it cannot preserve the mass employment–wage–consumption circuit once AI becomes competitively superior.