The Three Stages of Trusting AI

Much of the conversation around AI focuses on capability. We talk about what the technology can do, what jobs it might affect, and how quickly it is advancing. Yet one of the most important questions is rarely discussed.

How do people learn to trust it?

After reading a recent Decoded Thinking article, consultant Paul Weald suggested an interesting framework for understanding AI adoption. Drawing on the concept of competence development, he proposed that individuals and organisations may move through three distinct stages as they learn to work with AI.

What struck me was how well this framework explains many of the conversations currently taking place across organisations. It also connects with themes raised in recent discussions around culture, adoption and cognitive offloading.

Stage 1: Consciously Incompetent

The first stage is often where organisations begin their AI journey.

At this point, people know AI can make mistakes. They have seen inaccurate responses, hallucinations and examples of the technology getting things wrong. As a result, they approach it with caution. Outputs are checked carefully, recommendations are questioned and trust remains limited.

While this stage can sometimes feel frustrating, it serves an important purpose. The awareness of limitations creates healthy scepticism. Organisations are learning where AI works well, where it struggles and where human judgement remains essential.

Many businesses are still operating in this phase today. Teams are experimenting with tools, testing use cases and exploring possibilities, but they have not yet built enough experience to trust the technology consistently.

Stage 2: Consciously Competent

Over time, confidence begins to grow. Organisations develop governance processes, establish workflows and gain a clearer understanding of where AI creates value. Trust becomes based on evidence rather than enthusiasm. Teams learn which tasks can be automated, which require oversight and how to manage risk effectively.

This is often the most important stage in the entire journey because it is where AI moves from being an interesting technology to becoming a practical business tool.

During a recent conversation, Commercial AI Consultant Jessica Thomas described a common challenge facing organisations today. Many are eager to implement AI but have not fully defined the problem they are trying to solve. In her experience, AI does not fix broken processes. More often, it simply scales them.

That distinction matters.

The organisations that succeed with AI are rarely the ones deploying it everywhere as quickly as possible. Instead, they are the organisations that understand their objectives, identify genuine opportunities for improvement and introduce AI in ways that support those goals.

At this stage, trust is earned through understanding. People know enough about the technology to recognise both its strengths and its weaknesses.

Stage 3: Unconsciously Competent

Eventually, something interesting happens. The technology begins to disappear. People stop talking about AI and start focusing on outcomes. The conversation shifts away from prompts, models and automation and towards productivity, customer experience, decision-making and results.

This idea echoes a point raised by Dr Nicola Millard that the most successful technologies are often the ones we stop noticing. We do not spend our days talking about electricity, cloud computing or Satnav. These technologies have become part of the infrastructure that supports modern life.

The same may ultimately be true of AI.

When organisations reach this stage, trust is no longer an active decision being made every day. It has been built gradually through experience, evidence and successful implementation. AI simply becomes another tool that helps people do their jobs.

Ironically, this may be the point at which AI begins to have the greatest impact. Not when it dominates every conversation, but when it quietly fades into the background.

The Risk of Skipping the Middle

Of course, not every organisation follows this journey successfully.

One of the concerns Nicola raised during our conversation was the growing issue of cognitive offloading. As AI becomes more capable, there is a temptation to hand over increasing amounts of thinking, analysis and decision-making to technology.

Used appropriately, this can be enormously valuable. It can reduce repetitive work, surface insights more quickly and free people to focus on higher-value activities.

The danger arises when trust develops faster than understanding.

Some organisations attempt to move directly from experimentation to reliance without building the knowledge and governance that sit in between. They begin depending on AI before they fully understand where it performs well, where it introduces risk and when human judgement should remain involved.

In effect, they skip the consciously competent stage altogether.

Rather than becoming unconsciously competent, they become unconsciously incompetent. The technology is trusted, but the foundations required to support that trust have never been built.

Trust Is Earned, Not Assumed

Perhaps the future of AI depends less on whether people trust it and more on how they learn to trust it.

The organisations seeing the greatest success are not necessarily those with the most advanced technology. More often, they are the organisations that have taken the time to understand where AI creates value, where it requires oversight and how it fits alongside human expertise.

Trust is not a switch that can simply be turned on. It develops through experience, evidence and understanding.

If Paul’s framework is correct, the ultimate goal of AI adoption is not for people to talk about AI more. It is for them to talk about it less. The most successful technologies eventually become part of the background, quietly enabling better outcomes without demanding constant attention. AI may follow the same path.

The question is whether organisations are willing to do the work required to get there.