What businesses are getting wrong about consumer confidence
Artificial intelligence is now embedded in everyday business operations, from customer service and marketing to pricing, credit decisions, and internal workflows. For many organisations, AI is no longer experimental — it’s routine.
What’s drawing attention now is a widening gap between how confident business leaders feel about their AI practices and how consumers actually experience them. New survey data suggests many companies believe they’ve addressed the risks, while the public remains unconvinced.
Why this issue is surfacing now
AI adoption has accelerated faster than most governance and communication structures were designed to handle. Large language models and automated decision systems are being rolled out at scale, often as extensions of existing tools rather than clearly defined new systems.
At the same time, consumers are encountering AI more directly — through automated decisions, synthetic content, and opaque processes — making trust a practical issue rather than an abstract one.
What business leaders think is happening
According to a recent survey by Ernst & Young, many C-suite executives believe their organisations are already aligned with public expectations around responsible AI use. A majority say they feel confident in their controls, principles, and internal safeguards.
That confidence tends to increase in organisations that describe their AI systems as “fully integrated,” suggesting that maturity in deployment is often equated with maturity in oversight.
How consumers see it differently
Consumer sentiment data tells a different story. Across issues such as accuracy, privacy, transparency, explainability, and accountability, consumers consistently report higher levels of concern than business leaders expect.
The gap is especially pronounced around misinformation, manipulation, and the impact of AI on vulnerable groups. These concerns don’t necessarily reflect opposition to AI itself, but uncertainty about how decisions are made and who is responsible when systems fail.
Why AI maturity can increase overconfidence
One counterintuitive finding is that organisations still integrating AI tend to express more caution than those that say they have already scaled it. Earlier-stage adopters often report greater awareness of unresolved risks because governance structures are still being actively built.
By contrast, leaders in more advanced deployments may assume existing controls are sufficient, even as newer AI capabilities introduce different kinds of exposure that older frameworks were never designed to address.
What this affects in practice
When confidence gaps persist, adoption slows — not because systems don’t work, but because users hesitate to rely on them. In sectors like finance, healthcare, and public services, even small trust deficits can reduce engagement or trigger backlash.
The issue is less about whether responsible AI principles exist, and more about whether stakeholders understand how they’re applied in real decisions that affect them.
How responsibility is typically assessed
Responsibility for AI outcomes is rarely automatic or singular. Oversight usually depends on how systems are classified, how decisions are delegated between humans and machines, and how risks are documented and reviewed over time.
Accountability can sit across leadership, technology teams, governance committees, and operational managers — with discretion playing a significant role. This means confidence alone does not determine exposure; context, design choices, and communication matter just as much.
What remains unresolved
As AI systems become more autonomous and harder to explain, the challenge of maintaining public confidence grows. Many organisations have principles in place, but fewer can clearly demonstrate how those principles operate in day-to-day use.
Whether trust can keep pace with deployment remains an open question — and one that will likely shape how willingly people engage with AI-driven services in the years ahead.
