Confidence is low, even among users
Only a small share feel sure they get the most out of AI. Many use it without knowing how to use it well.
AI research · Berghs · 2026
What does AI confidence actually look and feel like?
And what would someone do or say differently once they have it?
A research project at Berghs, run together with four other UX designers. Access to AI tools was never the problem. What people struggled with was knowing when to trust AI, when to question it, and when not to use it at all. We set out to understand what AI confidence really is, and why it matters right now.
Context
Team research project, Berghs School of Communication, 2026
Team
5 UX designers
My role
Desk research, interviews, synthesis and insight gathering
Methods
Desk research, interviews, affinity mapping, workflow mapping
01
The brief
Not about AI skills, but AI judgment: when to trust, question, or reject AI at work.
The gap is emotional and cognitive, not technical.
What we asked in the interviews
Definition
What is AI confidence, and what behaviours signal it in real work?
Barriers
At what moments, and in which tasks, does uncertainty arise?
Trust & judgment
How do people decide whether an AI output is good, correct or usable?
The gap
What separates knowing how to prompt from knowing when to trust the result?
Learning
How do people learn AI, and how do peers and culture shape their confidence?
Context
What support helps, and when do people feel safe to experiment?
Outcomes
What changes in someone's work once they become more confident with AI?
02
The landscape · Desk research
40%
of employees use AI at work
5%
are actually AI fluent
AI is everywhere, but real fluency is rare. The gap isn't interest. It's support, guidance, and room to practice.
Source: Google × Ipsos, “The Path to AI Fluency”, 2026
Guidance is missing
Most people get no formal training or clear guidance. The problem isn't motivation, it's missing support structures.
Learning is social
People turn to colleagues rather than formal systems. Fluency grows where experimentation is supported.
03
Two gaps · From our desk research
What really stands in the way.
01
Emotional gap
Fear, shame, identity threat
- People find AI useful, but worry about how their data is used.
- Many fear that AI will take their jobs.
- Some feel AI works against careful decision-making, and are disappointed by it.
- AI can make people less willing to speak up or admit mistakes.
02
Cognitive gap
Skills ≠ judgment
- Using AI well takes new skills: writing good prompts and judging the results.
- Organizations haven't yet changed how they think to work well with AI.
- The biggest risk is believing you understand AI before you really do.
How the two gaps feed each other
Fear blocks practice → no practice → no judgment → more fear.
04
Where judgment matters
AI can help at every step. Judgment is still needed at every step.
We mapped a typical UX workflow and marked, phase by phase, what AI can take on and where a human has to decide.
Our workflow map, straight from the Figma board
Scroll →
05
The answer: AI confidence in practice
Confident people don't know more tools. They have four habits.
01
Intentional use
Regular, deliberate AI use in real tasks, not occasional experiments.
02
Knowing the limits
A clear sense of when AI is appropriate, and when it is off-limits.
03
Double-checking
A habit of verifying outputs before acting on them.
04
Owning the decision
It feels like augmentation, not replacement, and you can explain every AI-assisted decision.
06
Why practice is the way out
AI fluency isn't learned in a course. It's built by doing.
Research backs up what we saw: fluent people don't know more, they use AI more, testing it in real work. The biggest barrier isn't motivation, but missing time, guidance and a safe place to experiment. Teams that learn together get there faster.
Source: Harvard Business Publishing, “Learning Through Experimentation”
That's what I take into my own work: judgment comes from practice, not from more tools.
Main sources
Our own user interviews with UX designers · Google × Ipsos, The Path to AI Fluency (2026) · Harvard Business Publishing, Learning Through Experimentation · Worklearning.ai, AI Fluency in Learning and Development · NN/g on evaluating AI-generated designs · Master's thesis on how UX designers evaluate and override AI-generated designs · Articles and community discussion on human-in-the-loop AI workflows