Beyond the Hype: Why Customer Trust is the True Frontier of the AI Revolution

In the fast-moving world of artificial intelligence, it is tempting for business leaders to view the technology as a silver bullet for efficiency. However, Tim Lawless, digital AI enablement lead at E.ON Next, warns that the industry is overlooking a critical variable: human psychology. As businesses race to integrate autonomous agents into their sales funnels, they risk alienating the very customers they aim to serve by assuming universal, rapid adoption.

Ahead of his appearance at the upcoming CustomerX conference, Lawless emphasizes that while AI is an unprecedented accelerator, it is not a monolithic solution. For the modern enterprise, AI is not merely a "tech project"—it is a complex challenge of customer experience, product strategy, and internal organizational evolution.

The Trust Gap: Why Human Behavior Doesn’t Follow Moore’s Law

The central tension in the current AI landscape is the mismatch between technological velocity and consumer trust. To illustrate this, Lawless points to his own mother, who required 15 years to trust the internet enough to input her credit card details.

"It took my mother 15 years to trust the internet," Lawless notes. "Yet, we are currently seeing predictions that consumers will hand over their purchasing decisions, payment credentials, and household account management to autonomous AI agents in a fraction of that time."

This rapid projection ignores the reality that customer confidence is a slow-burn process. Lawless argues that "trust will stop your customers from adopting AI solutions in the way that the frontier labs think is going to happen."

The Nuance of Uneven Adoption

The skepticism toward AI is rarely black and white. It is highly context-dependent. A customer might be perfectly comfortable using a generative AI tool to curate a travel itinerary or suggest a local restaurant, but draw a firm line when asked to let that same AI manage their energy supplier switch or access their bank account.

This creates a fragmented landscape where businesses must cater to three distinct groups:

The Early Adopters: Customers ready for "agentic commerce," where the AI makes decisions and executes transactions on their behalf.
The Research-Oriented: Users who treat AI as a "fancier Google," using it to synthesize data and reduce the effort of manual searching.
The Cautious Traditionalists: Consumers who prefer legacy digital interfaces or human interaction for high-stakes decisions.

AI as an Emerging Sales Channel

For many organizations, the knee-jerk reaction to AI has been to deploy chatbots—a move Lawless advises against. "Customers don’t wake up wishing a supplier would launch another chatbot," he observes. "They want something done more quickly, easily, or effectively."

Accelerating Existing Journeys

At E.ON Next, the focus is on "AI as an accelerator." The goal is to identify points of friction in the customer journey and use AI to eliminate them entirely. For example, instead of forcing a customer to manually input energy usage data—a process prone to abandonment—E.ON Next allows users to upload a bill from a competitor to receive an instant quote. Here, AI acts as a utility, not a gimmick.

The Rise of Agentic Commerce

The second, more disruptive role of AI is as an independent sales channel. Lawless suggests that platforms like OpenAI are becoming the new intermediaries between brands and consumers. As these tools evolve from search assistants to "agentic" negotiators, they will begin to act on behalf of the consumer.

"You’ve got to see OpenAI as an emerging sales channel," Lawless says. This requires a shift in how businesses market their products. If an AI is the one "shopping" for the customer, businesses must ensure their value propositions are optimized not just for human eyes, but for AI reasoning engines.

Dual Transformation: Customer-Facing vs. Internal

Lawless differentiates AI enablement into two distinct pillars. Organizations that conflate these two often face internal resistance and failed product launches.

1. The Product Transformation (Customer-Facing)

This pillar is about the external value proposition. It is easier to sell to the organization because the metrics for success are clear: higher conversion rates, reduced customer effort, and improved satisfaction scores.

2. The Organizational Transformation (Internal)

This is where the real cultural work happens. The introduction of AI often triggers anxiety regarding job displacement. To mitigate this, Lawless advocates for a "hands-on" approach. By framing AI as a "junior analyst"—a tool to offload repetitive, tedious tasks—management can empower employees to focus on high-value, creative, or strategic work.

"Until people have gone through that journey and actually played with the tech, there is a lot of anxiety," he says. "You need people to become hands-on, build things, and experiment."

Moving from "Use-Case-Led" to "Outcome-Led"

One of the biggest pitfalls in the current market is the tendency for companies to fund specific "AI projects" or "chatbot initiatives" without a broader strategic goal. This leads to isolated pilots that never achieve scale.

Lawless argues that companies must transition to outcome-led funding. Instead of allocating budget for a "chatbot," budget should be allocated to a team tasked with "reducing customer effort" or "improving retention."

"Really understand your end-to-end and where AI accelerates rather than just launching a chatbot," Lawless advises. "A chatbot isn’t going to move the dial for most companies." By focusing on the outcome, teams remain flexible, able to pivot their technical approach as the underlying AI capabilities evolve or as customer sentiment changes.

Implications for the Future of Business

As we look toward the next few years, the landscape remains uncertain. No single company has yet perfected the integration of AI across all facets of their business. However, the path forward is becoming clear.

The Value of Cross-Sector Collaboration

Lawless points to IKEA as a notable example of a company successfully linking customer-facing AI with internal operational shifts, specifically by redeploying employees to higher-value roles. Yet, he cautions against a "cookie-cutter" approach. What works for a global retailer may not work for an energy provider.

This is why events like CustomerX are vital. They allow leaders to step out of their industry "bubbles" and share learnings. The goal is not to replicate a competitor’s strategy, but to borrow the underlying principles of experimentation and agility.

Final Thoughts: The New Competitive Advantage

The winners in the AI era will not be the companies with the most expensive software stack or the grandest vision. The winners will be the organizations that:

Acknowledge the Trust Gap: Respect that customers will adopt AI at different speeds and in different contexts.
Solve Genuine Problems: Use AI to remove friction, not just to add "cool" features.
Foster a Culture of Participation: Reduce internal anxiety by turning employees into active experimenters.
Remain Flexible: View AI as a capability that requires an iterative, outcome-led approach rather than a one-off transformation program.

"It’s a new capability and an accelerator," Lawless concludes. "Those who learn faster and remain flexible enough to follow customers—wherever and however quickly they choose to go—will be the ones to define the future."

To hear more from Tim Lawless on the future of AI in commercial teams, register for CustomerX. This article was originally published on the CustomerX Substack. For further updates and industry insights, follow CustomerX on LinkedIn.