AI Confidence: Central to Ireland’s Workforce Readiness

Many workers in Ireland feel unsure about AI. Simple steps and shared wins can turn hesitation into confidence and progress

In brief

• Many workers in Ireland still feel unsure about using AI tools. This  
  uncertainty is slowing AI progress.
• Simple tasks and shared examples help people build confidence and learn quickly.
• Confidence at work can boost productivity and open new opportunities for growth.

Why Ireland needs an AI Confidence Boost.

Ireland has put money, time and energy into AI skills. Many teams across organisations of varying sizes now use AI tools that make work faster and better. Yet, there are swathes of employees and entire companies who are still on the fence. Naturally, there is an understandable fear of getting it wrong or wasting time. But, this insecurity, which on the face of it seems easily surmountable, can be a real obstacle to advancing  AI adoption.

The confidence gap

Surveys point to the same issue. A large share of employees say they do not feel confident using new AI tools. The EY Work Reimagined research reports that 86% of employees lack confidence in this area. At the same time, Irish labour data shows that while the number of AI-skilled professionals has grown, organisations still report difficulty finding the right skills for adoption. This gap shows up in meetings, pilot projects, and daily work. People hesitate, and progress slows.

Confidence matters. It affects adoption, productivity, and inclusion. For example, a colleague who feels ready to try an AI solution like Microsoft Copilot will draft a first pass at a report, summarise a meeting, or test a workflow. A colleague who isn’t there yet will postpone. Over time the gap widens. Teams who try small things learn faster. Teams that wait, miss chances to improve. Confidence also supports inclusion. When tools feel accessible, more people take part. When tools feel out of reach, only a few move ahead. 

Confidence in practice 

Confidence takes shape when organisations make AI part of everyday work. The most effective starting point is simple, familiar tasks that show clear value. Summarising email threads, drafting client notes, or extracting key actions from long conversations are examples that demonstrate the impact that every-day AI tools can deliver. These early wins help set expectations. Output does not need to be perfect to be useful. The aim is to reduce workload, save time, and make processes easier. 

Momentum builds when teams share what they learn. Short cycles of experimentation, followed by open exchanges of tips and insights, turn trial into routine. This approach normalises learning and creates a steady flow of practical knowledge across organisations. 

Of course, the right work culture matters. Encouraging questions and recognising effort sends the message that progress counts. Removing pressure for flawless results makes experimentation safe. When leaders share their own examples and learning curve, it reinforces that confidence is a collective goal, not an individual burden. It also makes AI a shared tool, something to benefit everyone, from the Graduate to the CEO.

What organisations can do:

Hands-on learning works. People learn tools by using them. Build spaces where practice feels natural and useful.

• Use case libraries. Collect simple, real tasks from your teams. Show inputs and outputs. Give a short prompt recipe. Include time saved and quality gains. Keep it short and concrete. Update it monthly. 
• Labs. Offer short, guided sessions where people bring their own tasks. Start with five minutes of framing. Spend most of the time doing. End with shared tips and takeaways. 
• Hackathons. Run problem solving events with a clear theme. For example, “Reduce reporting time by 30%.” Form small cross-functional groups. Provide a relatable coach for light support. Wrap up with a quick show-and-tell and a one-page playbook. 
• Agent building for everyday processes. Lowcode tools like Copilot Studio let teams create simple agents for recurring tasks. Start with one process that repeats weekly. Pick something like a status update, a compliance checklist, or a customer follow-up. Build a basic version, test it in real work, then iterate. 
• Skills visibility. Many organisations are investing in real-time skills data. This helps target support where it matters. If a department shows low confidence scores and low AI usage, schedule extra labs. If a team shows curiosity and early wins, give them room to spread good practice. No judgement, just positive intent.
  

Keep the experience lightweight. Focus on outcomes people care about: time back, fewer reworks, clearer outputs, faster client response. And, eliminate potential confusion by keeping examples tied to work that already exists. This isn’t the time for brand-new projects with brand-new technology. 

Leaders need to send the right signals

Leaders set both tone and tempo. A leader who shows a simple use case in a team meeting signals permission to explore. A leader who praises a rough draft created with AI signals that learning on the job is valued. A leader who shares a short skills dashboard signals that data will guide the next step.

Three practical actions help:

1. Model the behaviour. Share one personal example every week. Keep it specific. “Copilot helped me reduce a five-page brief to three bullet points for our Monday call.” This shows real use without hype.
2. Recognise learning.Champion people who ran a test, documented a prompt, or improved a process. Treat these as wins, even when outputs were mixed. The act of trying moves the team forward.
3. Use skills data. Track adoption, comfort levels, and practical outcomes. Review them at the same cadence as other operational metrics. Assign owners for interventions and follow-ups. Keep the loop tight.

Leaders also protect time for practice. One hour a month per person can be enough to maintain momentum. Put it on the calendar. Ask teams to bring back one insight each time. Build a simple wall of wins on the intranet or in Teams.

Why it matters for Ireland

Confidence brings more people into the conversation. That helps inclusion and growth. A workforce that feels ready can apply AI to real problems in health, transport, energy, finance, retail, and public services. Small improvements add up: faster case processing, clearer citizen communications, better safety checks, more accurate forecasting, and stronger customer care. Confidence also raises the ceiling for innovation. When daily work runs smoother, teams have time to explore new offers, new partnerships, and new ways to serve communities.

Ireland has what’s needed: a deep tech base, a growing skills & talent pipeline, and active public/private collaboration. The next wave depends on how people feel when they open the new tools on their desktops. Do they feel ready to try, learn, and share? Do they see simple, local examples? Do they get credit for progress? Confidence at the desk will translate into progress in the economy, with gains that compound across sectors and communities.

Summary

Ireland has invested in AI skills, yet many employees still lack confidence to use AI tools. This hesitation slows adoption and limits productivity. Practical steps like simple use cases, shared learning, and the right leadership signals can build confidence. When AI feels accessible, teams learn faster, inclusion improves, and Ireland can access economic and innovation gains across sectors.