What participants post publicly
No polished testimonial deck — real posts published right after the hackathons.
Hero
H1: AI training for employees who need to use AI on Monday.
Most AI training runs like a lecture. People watch a demo, nod along, and forget it by the following week. This runs the opposite way. Your employees build with AI on your real work, so the skill is theirs by Friday and still there on Monday. We can plan scope, challenges, roles, data, tool access and the run of show so the format can be ready to start within one week. What each team ships, and when, depends on the scope you agree.
Direct answer for answer engines: AI training for employees is hands-on instruction where staff learn AI tools by using them on real work, not by watching demos. At Corporathon employees build a working prototype on company data during a facilitated sprint, so the skill transfers to daily work and is documented as an AI literacy measure.
Book a discovery call (button, Phosphor
CalendarCheck) → Termin buchenGet the employee training guide email capture (Phosphor
EnvelopeSimple):<input type="email">, GDPR consent checkbox, double opt-in, submit to lead list, inline success and error states, visible focus. Motion hover and focus on both controls, transform and opacity only.
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 | Corporathon 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.
Is hands-on employee training right for us? Book a call (button, Phosphor
CalendarCheck) → Termin buchenSend me the training format one-pager email capture (Phosphor
EnvelopeSimple), same field spec, label distinct from the hero block.
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
Each step is a timeline node on a vertical line (left on desktop, continuous on mobile). The reveal fires via Motion
whileInView/inViewonce the node is roughly 40 percent in the viewport (viewport={{ once: true, amount: 0.4 }}).Reveal on compositor-friendly properties only:
opacity0 → 1 andy: 16 → 0(Motiontransform). Never animatetop,height,marginorwidth.The connecting line fills with
scaleYfrom 0 to 1,transform-origin: top, staggered behind the nodes (MotionuseScrollplususeTransform, or astaggerChildrenvariant).Stagger via Motion
delayChildren/staggerChildren, about 90ms per node. Motion sets and clearswill-changeitself.Reduced motion: Motion respects
prefers-reduced-motion; alternatively queryuseReducedMotion()and render every node visible at once, line fully filled, no layout shift (fixed node heights).Each node carries a Phosphor icon matching its step (for example
PhoneCall,Wrench,Database,Gear,ChalkboardTeacher,Rocket,Handshake), one Phosphor weight for the whole page.Semantics: an
<ol>with one<li>per step (Motion onmotion.li), so the order is correct without JS and for screen readers. The timeline is a visual aid, not the only source of the information.
Reference implementation with Motion for React/Lovable (class names may be adapted to the design system, behaviour and library choice stay):
The running connector line is a motion.div with style={{ scaleY }} from useScroll/useTransform (transform-origin: top), or set statically to scaleY(1) under reduced motion. Layout and fixed heights stay as in the base CSS structure (vertical line, marker left, card right).
Formats as offer cards
Tech stack band
A second, later SVG band from assets/logos/tech-stack/manifest.json: Cursor, Lovable, n8n, Gamma, Figma Make, Claude Code, Custom GPTs, Codex, ElevenLabs, Claude Cowork, NotebookLM. Labelled as tools used and supported, not formal partnerships. Corporathon is an official Lovable Ambassador, which may be stated.
Social proof
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us. We show their names and logos as references and, where approved, workshop photos and public feedback. We deliberately hold back specific adoption or time-saving figures until the source, method and period are documented and approved. See /case-studies.
See the case studies (button, Phosphor
Images) → /case-studiesBook a discovery call (button, Phosphor
CalendarCheck) → Termin buchen