What Good AI Training Looks Like (And How to Spot the Bad)

The AI training market is booming and, honestly, a lot of what's being sold isn't worth the time it takes to sit through it. Generic overview sessions that give you a tour of AI tools without changing how you work. One-size-fits-all workshops delivered to accountants, marketers, and HR managers at the same time. Training that focuses on features rather than workflows.

This isn't just wasted money — it's wasted credibility. When employees sit through bad AI training and leave unchanged, it reinforces their scepticism that AI isn't relevant to their work. That's worse than no training at all.

Here's what genuinely effective AI training looks like — and the red flags to watch for when you're evaluating providers.

The Core Criteria for Effective AI Training

1. Industry-specific, not generic

The most consistent difference between training that changes behaviour and training that doesn't is specificity. When a finance professional hears "you can use AI to analyse data," they think: "what data, which tool, in what context?" When they see AI being used to build a three-statement financial model or write a variance analysis memo, they understand immediately. The relevance is visceral.

Good AI training uses examples from the industry the participants actually work in. The prompts they practise are prompts they'll actually use. The tools demonstrated are tools relevant to their workflow. Generic examples — "here's how to use AI to write an email" with no context about who the sender is, who they're writing to, or what it's about — produce zero insight.

2. Hands-on, not passive

Research on professional learning consistently shows that passive instruction — watching a presentation, listening to a lecture — produces very low retention and behaviour change. The National Training Laboratory estimates retention of about 5% from lectures versus 75% from "practice by doing."

Good AI training is active. Participants try things. They use the actual tools during the session. They write their own prompts, see the outputs, discuss what worked and what didn't, and iterate. The trainer demonstrates — then participants do. The uncomfortable feeling of not being sure you're doing it right is essential to the learning process.

A workshop where the trainer does all the prompting and participants watch is not a training — it's a demo. Demos don't change habits.

3. Outcome-focused, not tool-focused

The question good training starts from: "What do you want participants to be able to do differently after this session?" Not: "How many tools will we cover?"

Tool-focused training produces tool literacy — people who know what features exist. Outcome-focused training produces capability — people who can do specific things they couldn't do before. "After this session, you'll be able to draft a client proposal in half the time using AI" is a different promise from "we'll cover ChatGPT, Claude, Perplexity, and Midjourney today."

Effective training typically covers fewer tools and fewer features, but covers them deeply enough that participants leave genuinely capable of using them.

4. Appropriate to the audience's starting point

AI training fails when it assumes either too much or too little. Advanced users sitting through basic definitions of what a language model is will disengage. Beginners being shown complex automation workflows will feel overwhelmed and shut down.

Good providers assess the actual starting point of participants before designing the training — not just the "average" but the range. They differentiate the learning experience so advanced users are extended while beginners get the scaffolding they need.

Red Flags to Watch For

The session is the same regardless of your industry

If a provider can't tell you specifically how they'll adapt the content for your team's context — your industry, your tools, your specific workflows — treat that as a serious warning sign. Generic content is easier to produce and sell. Specific content requires more from the provider and delivers far more for participants.

The agenda lists more than 5-6 tools

A half-day training that covers 10 AI tools is not a training — it's a product tour. You'll leave knowing that these tools exist and having a vague sense of what they do. You won't be able to use any of them fluently. Depth beats breadth. Always.

There's no practice component

If the proposal or agenda has no time allocated for participants to actively use tools themselves, the session will not change behaviour. Observation without practice doesn't stick.

The trainer can't demonstrate using the tools in real time

Some AI trainers deliver slide-based overviews without ever opening an actual AI tool. This is a fundamental problem. Trainers should be comfortable using AI tools live, handling unexpected outputs, and showing participants how to iterate when the first result isn't right. That's the skill participants need to see modelled.

Success is measured by attendance, not behaviour change

If a provider can only tell you how many people attended their sessions — not whether those people actually changed how they work — they're measuring the wrong thing. Good providers care about downstream behaviour: Are participants using AI in their workflows? Are they using it better? Are time savings measurable?

What to Measure After Training

If you're responsible for AI training in your organisation, measurement matters. Not to justify the budget (though that helps) but because measurement tells you whether the training is working and what needs to follow up.

Measure at three levels:

• Immediate reaction: Did participants find it useful and relevant? (Satisfaction survey — necessary but insufficient)
• Behaviour change at 30 days: Are participants using AI tools in their actual work? Which tasks? How often? (Manager observation, self-report)
• Impact at 90 days: Is there measurable improvement in the outcomes that matter? Time saved on specific tasks? Quality improvements? (Work quality assessment, time tracking)

The gap between what people say in immediate post-training surveys and what they actually do 30 days later is substantial. Training that produces high satisfaction scores but no behaviour change isn't effective training — it's enjoyable entertainment.

The Follow-Through Problem

Even genuinely good training fades without reinforcement. The learning science is unambiguous: skills that aren't practised within the first week after training are largely forgotten. Skills that are practised immediately, and then regularly, become habits.

The organisations seeing the strongest AI training ROI are pairing good initial training with structured follow-through: weekly practice challenges, peer learning groups, manager check-ins on AI use, shared prompt libraries that teams build together. The training session is the beginning, not the end.