Dr.Walter KurzMBA, M.Sc.

Verstehen Sie, was KI, maschinelles Lernen und generative KI wirklich sind, in klarer Sprache und ohne technische Vorkenntnisse.

• Künstliche Intelligenz definieren
• Was generative KI ist
• Verbreitete Mythen über KI
• + 3 weitere

Setzen Sie KI für Ihre Korrespondenz, wiederkehrende Aufgaben und die alltäglichen Werkzeuge ein, die Sie bereits nutzen, um schneller echte Ergebnisse zu erzielen.

• Geschäftskorrespondenz entwerfen
• Eine Aufgabe in modellgerechte Schritte zerlegen
• KI mit alltäglichen Werkzeugen verbinden

Entwerfen Sie Texte, erzeugen Sie Ihr erstes Bild und verfeinern Sie beides auf professionelles Niveau, mit KI-Werkzeugen, die Sie noch heute nutzen können.

• Für Klarheit und Prägnanz überarbeiten
• Ihr erstes erzeugtes Bild
• Einen Bild-Prompt schreiben

Sehen Sie, was im Inneren eines Chat-Assistenten geschieht: wie Token, Vorhersage und Training zusammenwirken, damit er so flüssig klingt.

• Wie Modelle Text als Token lesen
• Wie die Vorhersage des nächsten Tokens Sätze bildet
• Das Kontextfenster

Erkennen Sie erfundene Antworten und erfundene Quellen und entwickeln Sie die alltägliche Gewohnheit, zu prüfen, bevor Sie einer Antwort vertrauen.

• Was eine Halluzination ist
• Erfundene Quellen und Zitate
• Die Haltung des Überprüfens

Verstehen Sie die ethischen und datenschutzrechtlichen Grenzen von KI und lernen Sie, synthetische Medien zu erkennen, um KI verantwortungsvoll einzusetzen.

• Was KI-Ethik umfasst
• Datenschutz durch Technikgestaltung bei der KI-Nutzung
• Synthetische Medien erkennen

Führen Sie eine produktive erste Sitzung mit einem Assistenten und lernen Sie, klare Prompts zu schreiben, die Ihnen das gewünschte Ergebnis liefern.

• Eine Assistenten-Sitzung führen
• Die vier Bestandteile eines Prompts
• Eine Antwort kritisch lesen

How AI creates and destroys economic value in an organisation that competes, and how to read a proposal in those terms: where value lands, what makes an advantage hold, what it costs to keep rather than to build, and what a mistake costs.

• Name the business problem first
• AI changes tasks before it changes jobs
• Cost, revenue, risk: where AI value lands
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Building the apparatus that decides and evidences in the officer's absence: the mandate and its limits, the inventory, decision rights and evidence standards, the incident and harm path, refusal, and the management system as a whole.

• What a real mandate consists of
• What stays with other people
• Where you sit and who you can reach
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A defined AI implementation carried from concept to something that runs, with practitioners alongside, together with the governance, evaluation and legal artefacts the earlier parts taught. The integration point of the programme.

The English the role actually has to survive: a supplier negotiation, an audit conversation, a regulator's question, a board's scepticism.

Presenting AI work to decision-makers and expert panels, negotiating, handling hostile questions, and holding a refusal under pressure.

Choosing where the organisation competes with AI and what it declines, how AI changes what it sells and on what economics, and how to commit to a direction before the capability is proven.

• A strategy is what you decline
• From ambition to an AI thesis
• What this does to the industry, not only to you
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Running AI as a standing function rather than a series of projects: portfolio and funding cadence, benefit realisation, supplier control, adoption, and knowing when operating evidence has overturned the strategy.

• Set the operating calendar
• Intake and triage
• Run the portfolio review
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How these systems work at the level where a non-engineer can reason about them, where their error comes from, and why they are attackable at all. Produces somebody who can ask the question that exposes a weak answer.

• The words people use in the room
• What learning from data actually means
• Where the data came from, and what it leaves out
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What an AI system consists of end to end, what working means for a given use and who sets that floor, how evaluation and human oversight are designed, and how to accept or refuse what a supplier delivers.

• From a use case to a system boundary
• The parts of an AI system, end to end
• What "good enough" means for this use
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What a sourcing choice commits an organisation to: where the system runs, where models come from, how cost behaves as use grows, what lock-in actually consists of, and what a real exit requires.

• The five things that actually differ between offers
• Where the system runs, and what that decides
• Whose ground it sits on
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Placing a system inside the regulation that governs it: classification, the role the organisation occupies, the obligations that follow, and the point where it stops being the officer's own call. Taught as a method, since the applicable framework set varies by jurisdiction and sector.

• The fact pattern you will keep reusing
• What AI regulation is trying to do
• Is this even an AI system in the legal sense
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Lawful use of data in AI systems, the rights of the people in it, what transparency is owed, and the judgements that remain once the law is satisfied.

• Map the data in the system
• Is there personal data here at all
• Lawful basis for using data this way
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Where liability lands, what a supplier contract does and does not give you, intellectual property in both directions, employment consequences, and the documentation that answers a question two years later.

• Where harm becomes liability
• Your own exposure as the officer who signed
• What a standard supplier contract does not give you
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