Blog
From the Agentic University blog
What CTOs, COOs and MDs in DACH really need to know about AI adoption.
AI Act Training: proving AI literacy under Art. 4 (funding and deadline 2026)
AI literacy under AI Act Art. 4: in force since 02.02.2025, enforcement from 02.08.2026. What Art. 4 requires, what the proof of competence looks like, who is liable, and how QCG funding works.
AI Coding Training: Copilot vs. Cursor vs. Claude Code training compared
Which coding tool should you train your team on? Copilot, Cursor and Claude Code training compared: focus areas, licence tier, data governance, the 3-phase approach and the 4 KPIs.
In-house training vs. open AI courses: what works for the Mittelstand
In-house training or open AI course? The decision from a procurement and L&D angle: the five real criteria, when each format wins, the procurement vocabulary, QCG funding and a clear decision matrix.
Rolling out Claude Code in the team: training, configuration, governance
A Claude Code training that actually makes a team productive. Why most rollouts stall, the 3-phase approach, CLAUDE.md, Skills, Permissions and Multi-Agent, in-house vs. open, funding, and the 4 KPIs.
Cursor Training: from tool access to a productive agentic workflow
A Cursor training that goes beyond activating the licence. Why rollouts stay shallow, the 3-phase approach, using Rules, Agents and MCP properly, in-house vs. open, and the 4 KPIs.
GitHub Copilot Training for Engineering Teams: what actually drives productivity
A GitHub Copilot training that is more than a feature tour. Why rollouts fail on adoption, the 3-phase approach that fixes it, in-house vs. open, funding, and the 4 KPIs that prove value.
AI Board Agenda 2026: 8 Topics That Belong in Every Board Meeting
AI is a board responsibility in 2026, not an IT agenda footnote. 8 board agenda topics for every meeting in the DACH Mittelstand, 5 to 10 minutes each.
From AI Pilot to AI Program: the Scaling Leap for the Mittelstand 2026
The first AI pilot is the easy exercise. The leap to the second, third, tenth use case is where most Mittelstand companies stall in 2026. Six building blocks for the jump to a real AI program.
Human-in-the-Loop 2026: How Much Autonomy Should an AI Agent Have (Mittelstand Guide)
Fully autonomous AI agents are mostly a marketing claim in 2026. Here is the 4-stage autonomy spectrum, the 6 axes that drive the stage decision, and 4 anti-patterns that destroy trust.
AI in Controlling and Finance 2026: what mid-market CFOs can actually automate
Controlling teams still spend 60 to 70 percent of their time on manual aggregation in 2026. 7 use-cases, 3 anti-patterns, and a clear line between autonomous and human-approved.
AI in Marketing 2026: from Content Hype to Real Pipeline in the Mittelstand
AI in marketing 2026: highest hype ratio, thinnest ROI story. 6 use cases along the pipeline that move the needle in the DACH Mittelstand, plus 3 anti-patterns that damage brands.
How to Introduce Agentic AI in Your Company: the SME Guide
How to introduce agentic AI in your company: the practical guide for the Mittelstand, from use-case selection through cost and governance to the AI Act competence duty.
AI in Customer Service: What Actually Works in 2026 and What Drives Customers Away
No area forgives bad AI as little as customer service. What actually takes load off in 2026, what drives customers away, and the 4 rules in between.
AI in Sales: the 8 Use-Cases That Actually Work in the DACH Mittelstand in 2026
AI in sales in 2026 is not the robot that sells. It is 8 concrete use-cases that give time back and improve the pipeline, plus the 3 that do not work yet.
Agentic AI 2026: 6 Developments That Actually Affect the DACH Mittelstand
Most AI-trends-2026 lists are hype bingo. This post counts only 6 developments that are ALREADY measurable in 2026, each with a concrete consequence for your Mittelstand.
AI Agent vs RPA vs Classic Automation: the Difference in 2026 (and When You Need Which)
Automation, RPA and AI agents get confused constantly in 2026. They solve different problems. Here is the difference in plain language, with a rule of thumb.
Your First AI Agent: The Realistic Path From Use-Case to Production
The use-case is chosen, the budget is approved, and three months later the agent is in the pilot graveyard. Not the tech is missing, but 6 steps. Here is the realistic path.
How an AI Agent Really Works: Loop, Tools, Memory and Planning Explained
Most people think an AI agent is a smarter chatbot. Wrong. The 4 building blocks that make an agent an agent: loop, tools, memory, planning.
Self-Hosting an LLM or Buying Managed: The CIO Decision 2026
Self-hosting sounds like data sovereignty and cost control, but for most Mittelstand companies in 2026 it is the more expensive and slower choice. The 4-axis framework for CIOs.
RAG vs Fine-Tuning vs Prompting: The CTO Decision Framework 2026
Most Mittelstand CTOs overestimate fine-tuning and underestimate RAG plus good prompting. The 2026 decision framework, with a decision tree and cost reality.