AI skills training checklist: what buyers should require

A team reviewing a real work product during a facilitated AI skills session

Author and editorial responsibility

Tim Jamboula, Founder of Corporathon. Last reviewed 24 August 2026. Client-specific claims stay behind the proof gate before publication.

AI summary (citable)

A useful AI skills training checklist scores the things that predict adoption, an outcome you can point to, work on your own approved data, a named owner for afterwards and evidence you can keep for compliance. Topic lists and slide counts predict almost nothing. Score each provider on these dimensions, then choose the format that fits the outcome you actually need, a short training for awareness or a build sprint when a real artifact is required.

Contents

  1. The real question a checklist should answer
  2. The ten checklist items that matter
  3. How to score a provider
  4. The decision framework in four questions
  5. Honest cost logic without invented prices
  6. A worked ROI model with math
  7. Checklist coverage, visualised
  8. Why "topics covered" is a weak signal
  9. EU AI Act: what Article 4 asks for
  10. What you should do next
  11. FAQ

1. The real question a checklist should answer

Most training checklists ask "does it cover prompting, does it cover our tools, how many modules". Those are inputs, not outcomes. The question a buyer should really answer is "how will I know, four weeks later, that this changed anything". A checklist built around that question scores outcome, data relevance, ownership and evidence, because those are what separate a training that lands from one that fades. Everything else is negotiable.

Buyers ask what is in the course. The better question is what will be different in the work after it, and who owns making that true. – Tim Jamboula, Founder of Corporathon

Team during the AI hackathon

2. The ten checklist items that matter

#Checklist itemWhy it mattersStrong answer looks like
1Named outcomeanchors the whole engagement"an analyst ships a reusable report assistant"
2Work on real datarelevance and transferapproved company data, not demo sets
3Reviewable artifactproof the skill was applieda working prototype or template you keep
4Named owner for afterprevents the stalla person accountable in production
5Role-appropriate contentfits actual jobstracks by function, not one-size-fits-all
6Facilitation qualitydrives momentumexperienced facilitators, clear time boxes
7Data and access planavoids day-one blockersscope, permissions and tools agreed upfront
8Handoff definitioncontinuity after the eventartifact, owner, risks, acceptance criterion
9Evidence for compliancesupports Article 4documented application by role and context
10Honest pricing logiccredibilityvariables named, no invented flat price

If a provider scores well on items one to four and eight to nine, the rest usually follows. If they only talk about modules and topics, treat that as a warning.

3. How to score a provider

Score each item 0, 1 or 2. Zero means absent, one means vague, two means concrete and evidenced. A provider that scores two on outcome, real data, artifact and owner is worth a serious conversation even if the topic list looks thin. A provider that scores two on topics but zero on artifact and owner is selling awareness, which is fine only if awareness is all you need.

4. The decision framework in four questions

  1. What outcome must exist afterwards? Awareness, or a working artifact in the process. That single answer sorts most providers.
  2. Can you approve real data? If yes, favour a build format. If not yet, a training on demo data is the honest choice for now.
  3. Is there an owner for after? Without one, even a great sprint stalls, so fix ownership before format.
  4. What evidence do you need to keep? If compliance matters, require documented application, not just an attendance list.

Three or four "build and evidence" answers point to a hackathon-style sprint. Mostly "awareness" answers point to a training first.

5. Honest cost logic without invented prices

Credible pricing depends on variables, not a flat rate.

  • Depth of outcome. An awareness session costs less than a sprint that ships an artifact with a handoff.

  • Number of roles and cohorts. More tracks means more design and facilitation.

  • Preparation. A build format needs scope, data approval and access, which is real work and real value.

  • Evidence requirements. Documented, compliance-ready evidence adds effort you should see priced honestly.

Corporathon deliberately shows no fixed prices yet. The right shape comes out of these variables in a short call.

Team during the AI hackathon

6. A worked ROI model with math

A purely illustrative model you can replace with your own figures.

A marketing team of six spends four hours per person per week on repetitive first-draft content. A build format ships a reviewed drafting assistant that removes about half.

  • Time saved: 2 hours × 6 people = 12 hours per week.

  • Across 44 working weeks: 528 hours per year.

  • At an internal rate of 65 EUR per hour: about 34,320 EUR of modelled annual value in this one workflow.

This is a model, not a guarantee and not a client figure. A checklist that scores only topics cannot predict this value, because value comes from the artifact and its use, which is exactly what items three and four measure.

7. Checklist coverage, visualised

Visualization: two providers scored on the checklist (schematic).

score (0-2 per item)
 2 | A  A  A  .  A  A  .  A  A  A     Provider A (outcome-led)
 1 | .  .  .  B  .  .  B  .  .  .
 0 | B  B  B  .  B  B  .  B  B  B     Provider B (topic-led)
   +--------------------------------------
     1  2  3  4  5  6  7  8  9  10   checklist item

8. Why "topics covered" is a weak signal

Without repetition and application, retained knowledge drops fast, which is what the forgetting curve has described since the 19th century. A course can cover every topic perfectly and still leave nothing behind if nobody applies it to a real task within days. That is why the checklist weights artifact, owner and evidence over topic coverage, those items force application to be scheduled, not hoped for. A topic-rich, application-poor programme is the classic way to spend a budget and see no change.

9. EU AI Act: what Article 4 asks for

Since 2 February 2025, Article 4 of the AI Regulation asks for a sufficient level of AI literacy among staff, appropriate to role and context. Checklist item nine exists for this reason, require documented application by role, not just attendance. Any format can be a building block, none is an official certificate, and none guarantees automatic compliance. The company assesses the adequacy of its overall programme itself.

Team during the AI hackathon

Score us against your own checklist

Two ways in, depending on how far along you are.

  • Book directly: Book a strategy call. Thirty minutes, bring your checklist and we will answer it item by item on a real use case.

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10. What you should do next

Score two or three shortlisted providers on the ten items, weighting outcome, real data, artifact and owner most heavily. If your top provider scores well there and you can approve a real task, run the build format and keep the artifact as both value and evidence. If you cannot approve data yet, start with an awareness training and revisit. In just one week you can go from first call to a working prototype that answers most of the checklist by itself.

Ready when you are

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Team during the AI hackathon

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Client voices

What teams say after the week

Short feedback from hackathons with engineering, marketing, operations and leadership teams.

Our engineers built a code-review assistant in two days that the whole team still uses. No training ever did that.
Engineering leadNavVis
The whole agency was building. Marketers with zero coding background shipped content pipelines that saved real hours.
Managing directorYOYABA
Procurement workflows that used to sit on a roadmap were prototyped and demoed inside the same week.
Head of operationsOnventis
Our leadership cohort left with five working agents and a completely different sense of what AI can do for us.
Programme ownerAdobe cohort

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