From AI Routine Usage Experiment

AI has long been integrated into many companies. Often faster than the organisational prerequisites could be created for it.

Employees are testing AI tools, departments are trying out new applications, and software already in use is receiving new AI functions. Individual tests often become routine use before basic matters such as goals, data governance, risk management, and responsibilities have been clarified.

But that is precisely the real management task: it is about ensuring the controllable and responsible use of artificial intelligence. After all, the use of AI is associated with considerable control, liability, and reputational risks.

Who is the course suitable for?

The Quick-Dive is aimed at decision-makers, managers, and those in charge of specific areas within small and medium-sized businesses, who want to not only try out AI but also position and use it responsibly in a structured way.

The course is particularly suitable for:

• Company Management and Executive Board
• Commercial Management, CFOs and Finance Managers
• Subject matter experts from Controlling, Purchasing, Sales, HR, Operations or IT
• Heads of Digitisation, Automation and AI
• People who need to evaluate or prioritise initial AI use cases

The course is not technical AI training or legal advice. It is aimed at people who need to ask the right leadership questions. You do not need programming knowledge or in-depth data management expertise for this.

Achim Korten | Auditor
AI Officer (EU AI Act) · Lead Auditor ISO/IEC 27001:2022 · Lead Auditor ISO/IEC 42001:2023
Author of the „AI Governance Compass“ on LinkedIn

What you can better classify after the course

After the quick dive, you can better assess,

• why AI use cases should start with business processes,
• when digitisation or classic automation can be more sensible than AI,
• the role that data quality and context play in robust AI results,
• Management Commitment im Kontext von KI praktisch bedeutet,
• What expectations do key stakeholders have regarding the use of AI in the company?,
• how AI ideas can be assessed based on process relation, data basis, risk, and controllability,
• Here are the key questions that should be asked before any relevant AI project: * **What is the precise problem we are trying to solve?** (Be specific, avoid vague goals) * **What are the desired outcomes and how will we measure success?** (Define KPIs and metrics) * **Who are the intended users of this AI system and what are their needs?** (User-centric approach) * **What data do we have, what data do we need, and how will we acquire and prepare it?** (Data availability, quality, and ethical sourcing) * **What are the potential risks and ethical implications of this AI project?** (Bias, fairness, privacy, security, accountability) * **What are the regulatory and legal requirements that apply to this project?** (Compliance) * **What are the required technical capabilities and resources (people, technology, infrastructure)?** (Feasibility) * **What is the potential return on investment (ROI) or value proposition?** (Business justification) * **How will the AI system be deployed, maintained, and updated?** (Lifecycle management) * **Who will be responsible for the AI system's performance and any unintended consequences?** (Accountability and governance).

Why this course is relevant

Smaller and medium-sized businesses often start with AI pragmatically. This is not a mistake in itself. It only becomes problematic when pragmatic tests develop into a chaotic and uncontrolled usage that is difficult or impossible to manage.

Then significant control, liability and reputational risks can arise.

The Quick Dive helps you avoid typical false starts and approach the expansion of AI in your own company more systematically.

Ultimately, you will have compact management logic that allows you to better classify AI initiatives and prepare the next steps for viable, responsible, and controllable use of AI.