Thoughts on AI & Processes
Analyses, case studies and practical insights from AI consulting.
Which questions show that important know-how lives in one person’s head?
Practical questions for teams that want to make practical know-how visible before a handover.
The first mission: understanding one business task in a few steps
How a real question from daily work becomes a manageable assignment with a usable outcome.
When process analysis should lead to a digital prototype
A prototype is useful when the task, goal and test case are clear enough.
Why workflows work differently in practice than on paper
How teams examine a real workflow without claiming causes or solutions too early.
AI and Knowledge Loss Through Staff Turnover: Securing Experience, Building the Future
One of the greatest risks for SMEs, associations and social organizations is knowledge loss through turnover or retirement. When experienced employees leave, decades of experiential knowledge, informal networks and process know-how often disappear. AI offers new ways to preserve this knowledge.
How to Review AI Results in Teams: Quality Control as a New Core Skill
AI delivers drafts in seconds – but responsibility for quality stays with the human. Anyone accepting AI results without review risks embarrassment, errors and legal consequences. Quality control becomes a new core skill in modern organizations.
Why AI Projects Should Start Small: The Law of the Controlled Pilot
The temptation is great: anyone seeing the potential of AI wants to transform the whole organization at once. But "big bang" projects often fail with AI on complexity, data protection or team resistance. The safest path goes through the law of the small start.
AI for Research and Decision Documents: Precision at the Push of a Button
Decision-makers in SMEs and nonprofits often suffer not from lack of information but from its excess. The challenge: pulling out from data piles, long reports and market analyses what enables a sound decision. AI is the ideal tool for this distillation – when used carefully.
AI in Internal Communication: Impact Without Losing the Personal Touch
Internal communication is the connective tissue of every organization. In daily business, however, time is often missing for well-written updates, understandable instructions or engaging newsletters. AI can relieve noticeably here – when used right.
Building a Team Prompt Library: Share Knowledge, Secure Quality
In many organizations, AI knowledge is still island knowledge: individual employees write excellent prompts while the rest of the team is still experimenting. Anyone wanting to harness AI's full potential must liberate this knowledge from individual heads and structure it. An internal prompt library is the backbone for consistent quality.
When Automation Becomes Dangerous: The Limits of AI Sovereignty
AI promises to free us from tedious routine tasks. But anyone surrendering control too early or too completely to algorithms risks not just professional errors but trust, liability and human judgment. Where automation becomes dangerous – and why the human-in-the-loop is the most important safety concept of our time.
The Difference Between Chatbot, Copilot and Agent: A Guide for Decision-Makers
Anyone talking about AI today encounters terms like chatbot, copilot or agent. For decision-makers, it's hard to keep an overview. Are these all synonyms? Where do they differ in benefit, risk and cost? This guide sorts the terms out – and helps choose the right technology for your goals.
AI in Social Organizations: Between Efficiency and Empathy
Social work is relationship work. But staff shortages and growing documentation loads leave less and less time for the person. AI can build a bridge: it takes over administrative routines and creates space for the core task.
AI in Associations and Volunteer Work: Relief for the Heart of Society
In volunteer work and associations, time is the scarcest resource. Administrative tasks like minutes, grant applications or member communication often eat up exactly the hours that should go to substantive work. AI offers a real chance for relief here – even without an IT department and on a small budget.
Why Good AI Results Need Source Work: Protection Against Hallucinations
Language models aren't search engines. They are masters of probability, not of truth. Anyone using AI professionally must understand the phenomenon of hallucinations and actively counter it – through clean source work and techniques like RAG (Retrieval Augmented Generation).
AI Literacy Under the EU AI Act: From Law to Team Capability
The EU AI Act contains a far-reaching provision in Article 4: organizations must ensure their staff has sufficient AI literacy. What sounds at first like administrative burden is actually the most important prerequisite for safe and productive AI use.
Local AI Models and Isolated Setups: Maximum Security for Sensitive Data
When patient data, business secrets or highly sensitive strategy documents are involved, cloud-based AI solutions hit their limits – not technically, but regulatorily and ethically. The solution exists: local language models today offer powerful AI without a single bit leaving your own network.
Microsoft Copilot in Daily Work: Realistic Assessment Instead of Blind Trust
Microsoft Copilot is often marketed as the ultimate tool for the modern workplace. Anyone using it in practice quickly notices: it isn't an autopilot, it's an assistant that needs guidance. When Copilot shines, why your data structure decides success – and where the system's limits lie.
AI Policy for Small Organizations: Simple Rules Beat Thick Manuals
Many organizations hesitate to use AI because they think they need an extensive legal framework first. Especially for small businesses, associations and nonprofits, a pragmatic, concise policy is often safer than no policy at all. It gives the team something to lean on – and creates clarity without paralyzing.
What Leaders Really Need to Know About AI
Leaders don't need to become prompt experts. But they must be able to assess benefits, risks, limits and responsibilities. A compact orientation framework for decision-makers who want to anchor AI responsibly in their organization.