How to Choose an AI Workshop for Your Team
The AI training market is a mess. You have enterprise consultants charging six figures for slide decks. You have influencers selling courses that are mostly hype. And somewhere in between, there are people doing useful work.
Here’s how to tell the difference.
Start with What You Actually Need
Before evaluating options, get clear on what you’re trying to achieve:
Who needs training?
- Engineers who build software?
- Business teams who use software?
- Executives who need to understand AI strategy?
Different audiences need different workshops. A session that works for developers will bore marketers. A session designed for non-technical teams will frustrate engineers.
What should change after training?
- Specific tasks done faster?
- New capabilities unlocked?
- Better decision-making about AI?
Vague goals lead to vague outcomes. “We want to be better at AI” isn’t a goal—it’s a wish.
What’s your timeline?
- Quick win in the next month?
- Building capability over a quarter?
- Long-term transformation?
One workshop won’t transform your organization. Know what you’re buying and what comes after.
What Good Workshops Include
Hands-On Building
If attendees aren’t creating something during the workshop, it’s a lecture with a fancy price tag.
Good workshops have participants:
- Build tools they’ll actually use
- Work on problems from their real work
- Leave with deliverables, not just notes
Ask: “What will my team have built by the end?”
Customization to Your Context
Generic AI tips apply to everyone and help no one. Useful training connects to your specific:
- Industry and use cases
- Tools and workflows
- Constraints and requirements
Ask: “How will this be tailored to our situation?”
Practical, Not Theoretical
AI changes fast. Theory from six months ago might already be outdated. What matters is:
- What works today
- How to adapt as tools evolve
- Principles that transfer across tools
Ask: “When did you last use these techniques in real work?”
Follow-Through Support
Training creates excitement. Follow-through creates change. Look for:
- Resources to reference later
- Support channel for questions
- Check-ins to reinforce learning
Ask: “What support exists after the workshop ends?”
Red Flags to Watch For
Promising Transformation in a Day
Anyone claiming a single workshop will “transform” your team is overselling. Real capability takes practice. A workshop is a starting point.
What they say: “This will revolutionize how your team works!” What it means: They’re focused on selling, not on what actually happens.
No Practitioner Experience
Some trainers teach AI without using it for real work. They’ve read about it, maybe built demos, but haven’t shipped production code or run business processes with it.
What to ask: “Can you show me something you built with these tools recently?” Red flag answer: Pivoting to case studies from other companies instead of their own work.
All Theory, No Application
Workshops heavy on “the future of AI” and light on “here’s how to do it” waste everyone’s time. Your team needs skills, not philosophy.
What to ask: “What percentage of the workshop is hands-on?” Good answer: 50% or more is practical exercises.
No Customization
If the same workshop is delivered to every client without modification, you’re getting a product, not a service. Your team’s challenges are specific.
What to ask: “What do you need from us to prepare?” Red flag answer: “Nothing, we have everything we need.”
Only Tool Training
Tools change constantly. A workshop focused entirely on which buttons to click will be outdated in months. Principles and thinking patterns last longer.
What to ask: “How much of this will still be relevant in a year?” Good answer: “The specific tools might change, but here’s what transfers…”
Questions to Ask Any Provider
About Their Experience
- What AI tools do you use in your own work?
- Can you show examples of what you’ve built?
- How long have you been doing this kind of training?
- What industries have you worked with?
About the Workshop
- What will participants be able to do after that they can’t do now?
- How is the content customized for our situation?
- What’s the ratio of lecture to hands-on work?
- How many people can effectively participate?
About Outcomes
- What do successful teams look like 3 months after training?
- What are common reasons training doesn’t stick?
- How do you measure whether it worked?
- What follow-up support is included?
About Fit
- When might this NOT be the right workshop for us?
- What should we have in place before training?
- Who should attend, and who shouldn’t?
The last question is particularly revealing. Anyone who says their workshop is right for everyone isn’t being honest.
Comparing Options
If you’re evaluating multiple providers, here’s a framework:
Enterprise Consulting Firms
What they offer: Comprehensive AI strategy, large-scale transformation programs, executive-level credibility.
Good for: Large organizations needing board-level buy-in, complex multi-department rollouts.
Watch out for: High costs ($50K-500K+), strategy-heavy but light on practical skills, junior consultants doing the actual work.
Online Courses and Platforms
What they offer: Self-paced learning, low cost, broad coverage of topics.
Good for: Individual skill development, foundational knowledge, teams that can self-direct learning.
Watch out for: No customization, no accountability, often outdated by the time you take them.
Independent Trainers/Consultants
What they offer: Hands-on expertise, customization, direct access to the practitioner.
Good for: Teams wanting practical skills, mid-size organizations, specific capability building.
Watch out for: Variable quality, limited scale, need to verify their actual experience.
Internal Training
What they offer: Deep context on your systems, free (sort of), ongoing availability.
Good for: Teams with existing AI expertise, reinforcing external training, ongoing skill development.
Watch out for: Limited by your team’s current knowledge, “expertise” might be one person’s experiments.
The Decision Framework
Use this to evaluate any AI training option:
1. Do they understand your actual situation?
- Have they asked good questions about your work?
- Do they know your industry’s challenges?
- Can they speak to your specific use cases?
2. Is the content practical and current?
- Are they teaching from real experience?
- Is the focus on doing, not just knowing?
- Will participants build something useful?
3. Is there a path to real results?
- What happens after the workshop?
- How will learning be reinforced?
- Who’s accountable for outcomes?
4. Is the investment proportional to the value?
- What’s the cost per person?
- How does this compare to the problem it solves?
- What’s the opportunity cost of not doing it?
Making It Stick
Even the best workshop fails if nothing changes after. Before committing, plan for:
Before Training
- Identify specific problems you want to solve
- Choose participants who will actually implement
- Clear calendars so people can focus
- Set expectations about what changes after
During Training
- Active participation, not passive observation
- Real work examples, not hypotheticals
- Documentation of what’s learned
After Training
- Time to practice new skills
- Support for questions and problems
- Accountability for implementation
- Follow-up to measure progress
The workshop is maybe 10% of getting value from AI training. The other 90% is what happens before and after.
One More Thing
Be skeptical of anyone—including me—who makes it sound too easy.
AI tools are genuinely useful. Training can genuinely help. But:
- Not every team will succeed
- Not every use case is a fit
- Not every investment pays off
The best providers will tell you when their offering isn’t right for you. The worst ones will sell you something regardless.
Ask hard questions. Expect honest answers. And remember: the goal isn’t to attend training. The goal is to build capability that creates value.
If you’re evaluating options and want an honest conversation about whether training makes sense for your team, that’s what the discovery call is for. Sometimes the best advice is “not yet” or “not this way.”