Blog
All Corporathon articles and playbooks.
Comparisons, frameworks, EU AI Act context and practical AI adoption from the full content package.
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01
AI Hackathon vs. AI Training: the honest comparison
AI hackathon or AI training? A decision framework, honest cost logic, a worked ROI example, EU AI Act context and a clear final recommendation.
02
AI Hackathon vs. Development: Proof of Participation or Prototype
AI hackathon or professional development? A decision framework, honest cost logic, a worked ROI model, EU AI Act context and a clear recommendation.
03
AI hackathon vs. AI workshop: input or shipped output?
AI hackathon or AI workshop? A decision framework, honest cost logic, a worked ROI model, EU AI Act context and a clear final recommendation.
04
AI hackathon vs. consulting: slides or prototype?
AI hackathon or AI consulting? A decision framework, honest cost logic, a worked ROI model, EU AI Act context and a clear final recommendation.
05
AI adoption playbook: how teams start building in one week
A playbook for real AI adoption in one week: a readiness framework, honest cost logic, a worked ROI model, EU AI Act context and next steps to take.
06
EU AI Act competence records through AI hackathons
How an AI hackathon can document AI literacy measures under EU AI Act Article 4. A qualified framework, decision logic, cost logic and an ROI model.
07
Spark, Ignite, Blaze: which AI hackathon format fits?
Spark, Ignite or Blaze? A decision framework for the right AI hackathon format, with honest cost logic, a worked ROI model and EU AI Act context.
08
Prototype ROI: how an AI hackathon makes impact measurable
How an AI hackathon makes prototype ROI measurable: a measurement framework, honest cost logic, a worked ROI model and EU AI Act context to consider.
09
Build AI champions: from tool test to internal movement
How an AI hackathon turns into internal AI champions: an enablement framework, honest cost logic, a worked ROI model and EU AI Act context to consider.
10
AI hackathon provider selection: buyer checklist
How to select an AI hackathon provider: an evaluation framework with signals and red flags, honest cost logic, an ROI model and EU AI Act context.
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FAQ
Frequently asked questions
For teams in marketing, sales, operations, product and IT. Coding skills are not required, the facilitation is built so every team ships a working prototype.
Usually one week: scoping call, use-case selection, access and tool setup. After that the sprint is ready to run.
You decide which data enters the sprint. Access, privacy and boundaries are agreed upfront and we only work with approved material.
You do. Prototypes, automations, workflows, prompts and generated code belong to your company after the sprint.
We work with the stack that fits you: LLM APIs, automation and data platforms, and your existing systems.
Teams of four to six work best. Overall we run everything from a single team up to several dozen participants.
All three work. On site creates the strongest momentum, remote is leaner logistically, hybrid is the usual compromise for distributed sites.
You get a documented handoff: prototypes, setup, open items and a recommendation on which results deserve to go to production.
We define target metrics before the sprint, hours saved, cycle time, quality or revenue contribution, and review them in the closing session.
Slots are usually available within two to four weeks. With a clearly scoped use case it can be sooner.