AI skills training that turns into capability, because it is anchored in your own work.
What participants post publicly
No polished testimonial deck, just real posts published right after the hackathons.
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The skills gap this closes
The real question behind "ai skills training" is usually practical. Buyers want to know whether the result is credible, whether employees need to code, whether the format leaves proof, and whether it is safe for company data. Those doubts point at four gaps a generic curriculum does not close.
Skills stay abstract. A syllabus covering prompting, agents and automation reads well, but a skill you have never applied to your own work is a fact, not a capability.
Levels are mixed. An engineer and a marketer sit in the same room learning the same generic exercise, and neither leaves with the skill that matters for their role.
There is no proof. A completion badge does not show a manager that anyone can now do something new.
The tools do not match the stack. Training on a tool the company will not adopt teaches a skill that has nowhere to land.
Skills training that closes the gap ties each skill to a challenge in the team's own workflow, and matches the tools and level to the people in the room.
Goals of the skills training
The aim is capability you can point to, not a certificate on file.
Each participant leaves able to do one concrete thing they could not do before, on their own workflow.
Skills matched to role, so engineers go deeper with Cursor and Claude Code while non-technical teams build with Lovable, n8n, Gamma and Custom GPTs.
A prototype the team understands because they built it, which makes the skill defensible to HR, compliance and the business owner who funds the work.
A documented AI literacy measure, useful as a building block toward EU AI Act Article 4.
What the programme entails
This is a guided, one-week path built around skill-building. Discovery, tool setup, challenge design, facilitation, a prototype sprint and a handoff. Participants build skills by producing something usable, and the level of each challenge is matched to the level of the team, so nobody is bored and nobody is lost.
A typical sprint has four interlocking parts:
- Challenge scoping by role. Each group takes a real workflow it owns, with a named user and a desired output, so the skill that forms is the one that role actually needs.
- Tool workshop matched to level. Non-technical teams get Lovable, n8n, Gamma and Custom GPTs; technical teams can go deeper with Cursor and Claude Code. Each group learns exactly the stack its challenge needs.
- Build phase. Teams build with a coach beside them, unblocking and showing the next step, so the skill forms under real conditions.
- Pitch and handoff. Each team demonstrates its result, names open risks, and hands the artifact to an owner with a next step, so the skill has a place to keep growing.
Deliverables
Pre-scoping with a per-role challenge design.
A curated tool stack per challenge matched to team level, including access.
A facilitated build sprint with a coach present throughout.
Three to five usable prototypes across the cohort (reporting workflow, knowledge bot, support triage flow, sales briefing agent, code assistant).
A skills library recording the prompts, patterns and workflows built, so the capability spreads.
An impact report and an IT handoff package.
Documentation of the AI literacy measure for participants.
Who it fits, and who it does not
Honest fit saves both sides time. Workflow-anchored skills training is the stronger lever when these points hold. When they do not, we say so.
| Good fit when | Not a fit when |
|---|---|
| you have mixed-level teams who each own a real workflow | you want one uniform lecture for everyone regardless of role |
| you need proof of capability, not just a completion record | a badge on file already satisfies the requirement |
| data and tool access can be approved in principle | real data cannot be touched for legal reasons under any circumstances |
| the tools we use match, or could match, your stack | the mandated stack rules out every tool a team would build with |
Generic curriculum vs workflow-anchored skills training
| Criterion | Generic AI curriculum | AI Hackathons workflow-anchored training |
|---|---|---|
| What people leave with | notes on concepts | a capability tied to their own workflow |
| Level fit | one exercise for a mixed room | challenges matched to each role and level |
| Proof | completion badge | a working prototype plus a documented literacy measure |
| Tool relevance | often off-stack | matched to the team's real stack |
| Skill durability | fades without application | attached to something built and kept |
| Time to first result | weeks to months | ready to start in one week, outcome depends on scope |
The table compares ways of working, not vendors, and deliberately carries no invented percentages. Real figures come from your own baseline. A hundred people who can build with the tools ship more than a thousand who merely completed a course, which is why we measure skills training by what people can now build.
How the value can add up (a model, not a client number)
An honest view of the payback uses your own figures, not a borrowed case number. The model has three inputs and one formula.
Weekly time a role spends on one workflow the new skill touches, say triaging inbound support tickets.
The share a prototype realistically removes once the skill is applied.
Internal hourly rate times the number of people in that role.
Is workflow-anchored skills training right for us? Book a call
Formula: hours saved per week × hourly rate × 45 working weeks × people − the one-off cost of the training and any hardening afterwards. Put your own numbers in. As an illustrative model only: if ticket triage costs each agent five hours a week and a triage assistant removes two, that is two hours across, say, six agents over a year, an order of magnitude you can weigh the training against honestly. We run this in the discovery call with your real numbers, not ours.
Interactive calculator: /tools/ai-skills-gap-matrix/.
Does this match your teams? Decide with a person, or read first.
The one-week path, as an animated timeline
| # | Step | Owner | Output |
|---|---|---|---|
| 1 | Intro call | AI Hackathons + sponsor | agreed goal, rough scope, date |
| 2 | Tools and challenges call | AI Hackathons + team leads | per-role challenges, tool stack matched to level |
| 3 | Finalise info and data | Client (IT, privacy) | approved data access, documented boundaries |
| 4 | Hackathon prep | AI Hackathons | prepared environments, access, run of show |
| 5 | Tool workshop | AI Hackathons + teams | each level can operate its stack |
| 6 | Hackathon sprint | Teams (coached) | usable prototypes, skills formed by role |
| 7 | Pitches and handoff | Teams + owner | skills library, impact report, handoff documents |
Social proof
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us.
See the case studies
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