AI Training for Teams: What Works and What Doesn't
AI training works when it’s built around your team’s actual tasks, tools, and workflows, not slide decks about the history of AI. We’ve run sessions for teams across Ireland and the pattern is consistent: role-specific, hands-on training gets people productive within days, while generic workshops are forgotten within a week.
What doesn’t work
Slide decks about the history of AI. Abstract demos that have nothing to do with your business. Sessions that end with “now go explore on your own.” These waste time and leave people more confused than before.
A 2024 McKinsey survey found that 72% of organisations have adopted AI in at least one function, yet only 26% report meaningful productivity gains. The gap is almost always a training and integration problem, not a technology one.
What does work
Sessions built around real tasks from the team’s actual workflow. Show a marketing person how to use AI for their specific content process. Show an operations lead how to automate their specific reporting. Make it immediate and relevant.
The difference between training that sticks and training that doesn’t comes down to specificity:
| Approach | Retention after 30 days | Productivity impact |
|---|---|---|
| Generic AI overview | ~15% | Minimal, no behaviour change |
| Tool-specific demo | ~40% | Moderate, some adoption |
| Role-specific, hands-on | ~75% | High, measurable within 2 weeks |
Signs your team needs better AI training
- People use AI for the wrong tasks. They’re generating content nobody needs instead of automating the reporting that eats 5 hours a week.
- Adoption is uneven. One enthusiast on the team does everything with AI while everyone else avoids it.
- No measurable results. You can’t point to specific time savings, error reduction, or output improvements from AI tool usage.
Our approach
We spend time understanding your team’s roles and daily work before the training. Then we build sessions around their real tasks, with real tools, producing real output they can use the next day.
Before the session: We audit current tool usage and identify 3-5 high-value workflows per role where AI can make an immediate difference.
During the session: Every participant works on their own tasks. No hypotheticals. By the end, they’ve built prompts, automations, or workflows they’ll use tomorrow.
After the session: We follow up at 2 and 4 weeks to check adoption, answer questions, and refine the workflows based on what’s actually working.
If your team is experimenting with AI but not seeing consistent results, get in touch. We’ll start with your workflows, not a slide deck.