Next steps to your hackathon
- Intro call. Clarify starting point, teams and goals.
- Tools and challenges call. Pick the real challenge, set the toolstack.
- Finalization and preparation. Set up access and data, GDPR compliant.
- Tool workshop and sprint. Hands-on on your own case with live coaching.
- Result pitches. Each team shows its prototype.
Challenges for scaleups
Typical challenges are a support ticket triage agent, internal tooling for recurring ops work, an email reply assistant, a reporting autopilot and an internal knowledge bot for fast-growing teams. Your challenge comes from your real work.
AI training vs. AI hackathon (short)
A classic training ends with a certificate and retention that is unclear without transfer measurement. The hackathon ends with a working prototype and measurable practical transfer. See the full comparison at /en/comparison/ai-training-vs-ai-hackathon.
Does the prototype really reach production? Often yes, because the people who built it keep maintaining it afterwards. Instead of a drawer it lands in your stack. The process is on /en/how-we-work, the comparison to a pure hackathon at /en/comparison/ai-training-vs-ai-hackathon.
Which format fits a scaleup? Spark runs 1 day for up to 10 people, Ignite 2 days for 10 to 15, Blaze 3 to 5 days for 15 plus. See the formats at /en/formats, and we explain the internal AI literacy measures record at /en/eu-ai-act.
Häufige Fragen
Wie bleibt KI-Schulung bei schnellem Wachstum konsistent?
Ein rollenbasiertes Kerncurriculum wird mit Toolregeln, Onboarding-Artefakten und regelmäßigen Praxisreviews verbunden. Neue Rollen erhalten nur die für ihren Kontext nötigen Module.
Was sollte ein Scaleup zuerst schulen?
Zuerst die häufig genutzten KI-Workflows mit relevantem Daten- oder Kundenrisiko. Priorität haben Rollen, die Entscheidungen oder externe Inhalte mit KI vorbereiten.
Wie wird aus der ersten Kohorte ein wiederholbarer Rollout?
Durch dokumentierte Aufgaben, Moderationshinweise, Bewertungsraster, Toolzugänge, Owner und einen festen Review-Zyklus für Änderungen am Stack.
Frequently asked questions
How can AI training stay consistent during rapid growth?
Connect a role-based core curriculum with tool rules, onboarding artefacts and regular practice reviews. New roles receive only the modules relevant to their context.
What should a scaleup train first?
Start with frequently used AI workflows that carry meaningful data or customer risk, especially roles preparing decisions or external content with AI.
How does the first cohort become a repeatable rollout?
Document tasks, facilitation notes, assessment rubrics, tool access, owners and a fixed review cycle for changes to the stack.
