What participants post publicly
No polished testimonial deck, just real posts published right after the hackathons.
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Why standard employee training does not land
A busy employee sits through a lecture, gets a slide deck, and returns to an inbox that has not changed. That is where most AI training for employees quietly dies. The problem is not the content, it is the shape. Four things go wrong when training is delivered as watching rather than doing.
The demo is not their work. A polished example on the trainer's screen does not touch the messy report, the awkward customer email or the spreadsheet the employee actually owns.
There is no first win. Without shipping one real thing, an employee never crosses from curious to confident, and confidence is what makes them open the tool again next week.
The context is missing. Generic prompts break the moment they meet a real document, a real deadline and a real constraint, so people conclude the tool "does not work for us".
Nothing is left running. After the session there is no assistant, no automation, no draft-bot on the desktop to keep the habit alive.
Employee training that lands flips the shape. From the first hour people build on their own work, with a coach beside them, so the skill has somewhere real to attach.
Goals of the employee training
The aim is that your employees can get real work done with AI by the end of the week and keep doing it, not that they can recite terminology.
Prompts that hold up against real documents, not clean demo text.
A small, working tool each person understands because they built it.
The confidence to open the right AI tool without waiting on IT or a central expert.
A documented AI literacy measure, useful as a building block toward EU AI Act Article 4.
What the training entails
This is a guided work sprint for employees who cannot and do not need to code. People work with ChatGPT and Custom GPTs for assistants and drafting, Cursor and Lovable for building tools and apps without prior coding, n8n for workflow automation, Claude Code for deeper builds, Gamma for decks and NotebookLM for research. We match the tools to the real problems each employee faces, so people practise what they will actually use. ChatGPT training that goes nowhere is a waste. ChatGPT training tied to a shipped tool sticks.
A typical sprint has four interlocking parts:
- Challenge scoping. Each employee or small group takes a real, narrow task with a named user and a desired output, for example a reply assistant that drafts in the company's tone, rather than a vague brief to "learn AI".
- Tool workshop. Before building, each group learns exactly the tools its task needs, a fast hands-on intro rather than a tour of everything.
- Build phase. People build with a coach beside them, unblocking and showing the next step, keeping the tool inside a boundary they can operate afterwards.
- Pitch and handoff. Each person or group shows what they built, names open risks, and keeps the artifact with a next step, so Monday looks different from last Monday.
Deliverables
Pre-scoping with a per-person or per-team task design.
A curated tool stack per task, including access.
A facilitated build sprint with a coach present throughout.
At least one working prototype per team (reply assistant, automation, internal bot, small app or reporting pipeline).
A handoff document per prototype with owner, access, open risks, acceptance criterion and next step.
A short reference sheet of the prompts and workflows each person built, so the skill survives the week.
Documentation of the AI literacy measure for participants.
Who it fits, and who it does not
Honest fit saves both sides time. Hands-on employee training is the stronger lever when these points hold. When they do not, we say so.
| Good fit when | Not a fit when |
|---|---|
| employees have real, recurring tasks they would love to shorten | you want a broad awareness talk for a large audience, nothing more |
| managers back people using the new tools the following week | the new tools will be blocked or discouraged after the session |
| data and tool access can be approved in principle | real data cannot be touched for legal reasons under any circumstances |
| you want employees who can build, not just describe | a completion certificate on file is genuinely enough |
Watch-and-forget training vs build-and-keep training
| Criterion | Watch-and-forget training | AI Hackathons build-and-keep training |
|---|---|---|
| Output | notes and a certificate | a working tool the employee keeps using |
| What people practise on | the trainer's demo | their own real task |
| Skill on Monday | mostly gone | attached to something they built |
| Confidence with tools | still theoretical | tested on a real deadline |
| Proof of value | attendance record | a tangible artifact plus a documented literacy measure |
| 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 employees who can build with AI accomplish more than a thousand who only sat in a training room, which is why we train employees to build, not just to attend.
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 an employee spends on one recurring manual chore, say re-keying data between two systems.
The share a small tool realistically removes.
Internal hourly rate times the number of employees doing that chore.
Formula: hours saved per week × hourly rate × 45 working weeks × employees − the one-off cost of the training and any hardening afterwards. Put your own numbers in. As an illustrative model only: if re-keying data costs each employee ninety minutes a week and a simple automation removes an hour, that is one hour across, say, fifteen employees 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-enablement-readiness/.
Does this match your team? 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 | employee tasks scoped, tool stack per task |
| 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 | every employee can operate their stack |
| 6 | Hackathon sprint | Employees (coached) | working prototypes on real tasks |
| 7 | Pitches and handoff | Employees + owner | shown results, handoff documents, a reference sheet |
Social proof
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us.
See the case studies
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