AI workshops
Workshops after which your team can actually build with AI.
I help leaders, product teams, and software teams put vibe coding and AI-assisted development to work: from the first prototype to safe workflows with clear quality rules.
Who it serves
The right workshop depends on the job, not the tool.
Some teams first need to understand what AI makes possible. Others want to build a prototype in a day. Software teams need standards for specs, reviews, tests, and privacy.
Leadership
For CEOs, founders, and executives who want to experience AI building and make better decisions about opportunities, risks, and first pilots.
- What is substance and what is hype?
- Which use cases are worth testing?
- Where do data, quality, and expectation risks appear?
Product & domain teams
For product, operations, marketing, sales, or finance teams that want to turn ideas into prototypes, internal tools, and better requirements.
- How does an idea become a useful prototype?
- How do we describe workflows so AI can work with them?
- When should engineering take over?
Engineering
For CTOs, engineering leads, and software teams that want productive AI coding without weakening architecture, tests, and review quality.
- Which tools fit our codebase and security posture?
- What do good specs, prompts, and review routines look like?
- How do we avoid fast code with slow downstream costs?
Workshop formats
From aha moment to reliable working practice.
The workshops are designed to go beyond tool demos. Your team understands the possibilities, builds hands-on, and leaves with routines for day-to-day work.
Vibe Coding for Executives
A compact executive workshop with live demo, tool landscape, opportunity assessment, and risk check.
AI Building Workshop
Your team turns its own ideas into first prototypes, internal-tool concepts, or automation workflows while learning specification work.
AI Coding for Software Teams
Hands-on professional development format: ticket to spec, spec to implementation, AI-supported tests, code review, privacy, and team guidelines.
Prototyping Sprint as Follow-up
When a use case is ready, the sprint creates a testable prototype with technical assessment, risk notes, and a roadmap.
Why different
Not a prompt course. Not a motivational talk. A building workshop.
Many AI workshops show tools. The team leaves inspired but alone in daily work. These workshops combine the fast experience of vibe coding with 15+ years of software engineering.
Typical AI workshop
Einfach AI workshop
Outcome
Many examples, little transfer into the team’s own work.
Your team works on real ideas, workflows, or code scenarios from its own context.
Depth
Tool demo, prompt tips, and general productivity promises.
Spec writing, review, tests, privacy, engineering handoff, and realistic limits.
Afterward
After the workshop, there is often no shared standard.
You leave with workshop results, decision logic, and concrete team routines.
Possible outcomes
Your team does not leave with theory.
The goal is visible progress: better decisions, a first prototype, clearer requirements, or a repeatable AI coding workflow.
Prioritize AI opportunities realistically
Leaders identify which use cases are worth testing and which rules are needed before the first pilot.
- Opportunity map for first AI-building pilots
- Tool and risk assessment for decision makers
- Clear go, no-go, or sprint criteria
Turn processes into prototypes
Teams translate recurring work into clear workflows, build first internal tools, and learn what they can own.
- Briefing, analysis, or reporting workflows
- Internal tool prototypes for real tasks
- Better requirements for later implementation
Make AI coding work for the team
Engineering teams create shared standards for AI-assisted development instead of isolated experiments.
- Workflow from ticket to spec to pull request
- Review checklists for AI-generated code
- Guidelines for tools, tests, and privacy
What we cover
Content that fits real company questions.
Each workshop is adapted to audience, maturity, and data constraints. The modules stay deliberately practical.
From idea to prototype
How teams describe a problem, cut scope, iterate with AI, and see early whether an idea has substance.
- Use-case sharpening and scope
- Prompting vs. spec writing
- Prototyping without false production promises
Professional development workflows
How AI coding fits existing engineering processes with architecture guardrails, tests, reviews, and clear ownership.
- Position Cursor, Claude Code, Copilot, and ChatGPT
- Ticket, spec, implementation, review
- Quality control for AI-generated code
Safety, data, and team rules
Which data may go into which tools, how to avoid shadow IT, and which rules teams actually use.
- Make privacy and IP risks understandable
- Tool policy for daily situations
- First guidelines instead of long rulebooks
FAQ
What companies want to know before an AI workshop.
The common objections are valid. That is why the workshop connects speed with clear judgment.
Next step
Let us find the right workshop format.
In a short call, we clarify audience, maturity, possible use cases, and whether an executive workshop, AI building workshop, software team workshop, or prototyping sprint is the best next step.
- Recommendation for the right workshop format
- Assessment of suitable use cases
- Clarification of tool, data, and safety questions
- Concrete proposal for agenda and next steps