Are You Ready for an AI Workshop? A Checklist

We get enquiries from organisations at very different stages of AI readiness. Some teams are genuinely ready — curious, motivated, with clear problems they want to solve. Others are doing it because someone senior saw an article about AI and decided training was needed. A few are in active resistance mode.

All of these teams can benefit from AI training. But what good training looks like, and when it makes sense, varies significantly. This checklist helps you honestly assess where your team is so you can make better decisions about what to do next.

No score judgement here. Just clarity.

Signs Your Team Is Ready

These are the conditions that make AI training land well and stick.

People are already experimenting on their own

If some team members are already using ChatGPT, Claude, or other AI tools for work tasks — even informally — that's a strong readiness signal. It means there's curiosity and intrinsic motivation. Training can channel and formalise what's already happening rather than trying to create motivation from scratch.

There's genuine frustration with existing inefficiencies

Teams who are frustrated with specific bottlenecks — too many meetings, too much time on first drafts, too much manual research — are primed to adopt tools that fix real problems. The readiness here is emotional: people want solutions, not just knowledge.

Leadership is visibly involved

When senior leaders are curious about AI (not just mandating training), ask questions in sessions, and share what they're experimenting with themselves, adoption accelerates dramatically. Teams take cues from leadership about what's actually valued. If your manager attends the workshop and mentions it in your 1:1 the following week, you're going to try something.

There's clarity on what outcomes matter

You can name specific things you want to be different after training. Not "everyone understands AI" (vague and immeasurable) but "our team spends less time on meeting follow-up summaries" or "our client proposals are more polished with less late-night effort." Specific outcomes make it possible to design training that actually achieves them.

You have time to follow through

Training is a starting point, not a solution. If you're booking a workshop for a team that's currently working flat-out on a major deadline, the training will happen and nothing will change. Readiness includes having the capacity to practise new habits after the session.

Signs You're Not Quite Ready Yet

These aren't disqualifying — they're things to address before or alongside training so you get real value from it.

The purpose is unclear or performative

Training requested primarily because "everyone else is doing AI" or to appear progressive without clear application goals tends to produce low engagement and minimal change. If you can't answer the question "what specifically do you want people to do differently after this?", spend time on that first.

There's significant AI anxiety in the team

If team members are worried about AI replacing their jobs, or feel threatened by the training, they'll go through the motions without genuinely engaging. This isn't a reason to skip training — in fact, good training addresses these concerns directly — but it needs to be acknowledged and handled, not ignored. If there's real anxiety, the first session should address it head-on.

There's no permission to experiment

Some organisational cultures don't actually allow people to try new tools without going through a lengthy approval process. If every AI tool needs IT sign-off and legal review before anyone can touch it, you'll have a training that produces motivated people with no way to act. Sort out tool access before investing in skills training.

There's a leadership disconnect

When leadership mandates AI training but doesn't participate, or is openly sceptical about its value, the team picks up on that quickly. You don't need universal enthusiasm at the top — but you do need genuine support, or at minimum non-active resistance.

What to Do Before the Workshop

Preparation makes a significant difference to workshop outcomes. Here's a practical pre-workshop checklist:

• Survey the team on AI awareness: Ask a quick five-question survey: which AI tools have you used? For what? What frustrates you about your current workflows? What are you most curious about? What are you most concerned about? The responses shape the session content dramatically and make people feel heard before they arrive.
• Sort out tool access: Decide which tools you'll use in the session — ChatGPT, Claude, Gemini — and make sure everyone has accounts set up before the day. Don't waste 20 minutes in a workshop on account creation.
• Communicate the why: Send a clear message before the training explaining what it's for, why it matters, and what people can expect. Not a corporate announcement — a genuine explanation. If the training is a response to competitive pressure, say so. If it's because leadership wants to explore efficiency, say that. People engage more honestly when they understand the context.
• Identify one or two AI champions: These are people who are already curious and willing to be early adopters. Brief them before the session. Ask them to share their perspective during the workshop. Peer voices carry more weight than trainer voices for adoption.
• Prepare real examples: The single best thing you can do before a workshop is gather two or three examples of real work from your team — an actual email thread, a real project brief, a typical report — that can be used as practice material. Generic exercises produce generic learning. Real material produces real insight.

How to Set Goals for Your AI Workshop

Goal-setting for AI training follows the same principles as any good learning objective: specific, observable, and tied to real work outcomes.

Weak goals: "Understand AI better." "Feel more confident with AI tools." "See what's possible."

Strong goals: "By the end of this workshop, every participant will have a personal workflow for using AI to draft one specific type of communication they send regularly." "Three months after the workshop, participants will report spending less time on [specific task]." "The team will have a shared prompt library with at least ten entries within four weeks."

Share these goals with whoever is running the training before the session. Good trainers will design toward your specific outcomes. Generic trainers will deliver the same content regardless — which is a sign to look elsewhere.

What to Expect From a Cocoon Workshop

It's fair to be specific about what we do and don't offer, so you can decide if it's right for your team.

What we do

Cocoon workshops are hands-on and role-specific. We don't do generic AI overviews. Before the session, we talk to you, understand your team's actual work, and build content around your specific context. During the session, participants practice with real tasks from their own roles — not made-up examples. We're honest about what AI is bad at, not just what it's good at. Sessions include specific next steps and commitments, not just inspiration.

What we don't do

We don't do one-size-fits-all workshops that could apply to any industry. We don't oversell AI capability or promise transformational outcomes from a single session. We don't train a team and walk away — our programmes include follow-through support. And we won't take a booking from a team that isn't ready, because it wastes everyone's time and damages the case for AI training generally.

What to bring to the conversation

When you reach out to us, the most useful things to share are: what your team actually does day-to-day, what problems you're hoping AI might solve, what the current awareness and sentiment in the team is, and what success looks like to you. The more specific you are, the better we can serve you.

The Honest Bottom Line on Readiness

Most teams are "good enough" ready for an AI workshop — they don't need to be perfectly prepared, because part of the workshop's job is to surface and address concerns in real time. The conditions that genuinely prevent value are: no permission to use tools afterwards, leadership actively undermining the initiative, or a team crisis that means nobody has bandwidth to apply anything.

If none of those apply to you, you're probably ready enough. The question then shifts from "are we ready?" to "what do we want to get out of this?"

The teams who get the most from AI training aren't the most technically sophisticated ones. They're the ones who arrive with honest questions and genuine willingness to change how they work.

If you're asking whether you're ready, that's a good sign. Teams who genuinely don't care don't ask.

Use this checklist to prepare honestly, set clear goals, and arrive with real material to work on. The rest is what happens in the room — and that's our job to make worthwhile.