IW103BKK
AI Native: Competing in the Age of Intelligent Machines
Faculty
Tristan Post
CEO & Founder at AI Strategy Institute, Munich
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This course provides a comprehensive introduction to Artificial Intelligence (AI) for business and technology students. Students will explore the foundations of AI, from its historical development and core technical principles to modern generative AI, large language models, and agentic AI systems.
The course covers the full AI value chain: understanding how AI works; developing AI fluency and effective human–AI collaboration; building and understanding AI agents; identifying and evaluating AI use cases; managing AI products; developing AI strategy; and navigating responsible AI, compliance, and regulation.
Through lectures, hands-on workshops, applied exercises, and project work, students will gain both theoretical knowledge and practical skills to strategically deploy AI in organisational contexts.
Learning highlights
- Understand the fundamentals of AI and machine learning, and how AI differs from traditional programming
- Understand the ‘magic triangle’ of generative AI (model, input/prompting, and data/context) and how large language models work
- Develop AI fluency: collaborate effectively, efficiently, ethically, and safely with AI systems using hands-on prompting techniques
- Understand AI agents and agentic AI, including autonomous systems, tool use, and multi-step reasoning
- Identify, evaluate, and prioritise AI use cases across the value chain
- Understand the AI product development lifecycle and manage AI products from concept to deployment
- Develop AI strategy, including enabling factors, infrastructure, and organisational readiness
- Apply principles of responsible AI, ethics, risk management, and bias mitigation
- Navigate AI regulation, including the EU AI Act, data protection, and compliance frameworks
- Assess the societal impact of AI on work, security, and the future of humanity
Course outline
15 classes
Introduction to Artificial Intelligence
A comprehensive introduction to AI covering its history, core definitions, how AI differs from traditional programming, and why defining AI is challenging. Exploration of the key prerequisites for AI: computing power, big data, algorithmic advances, and human talent.
From Data to Prediction
An understanding of the processes behind AI: how computers learn to interpret the world through data. The journey from raw data to trained models, including data collection, feature engineering, model training, and validation.
Applied AI: Under the Hood
A hands-on applied session in which students look under the hood of machine learning, exploring traditional data collection, building a simple model, and gaining practical experience of the end-to-end AI process.
Introduction to Generative AI
How generative AI works: the foundations of large language models, probability-based text generation, and the ‘magic triangle’ of generative AI (model, input/prompting, and data/context). Initial insights into how generative AI can be used strategically within organisations.
AI Fluency Training
A hands-on AI fluency session focused on collaborating with AI effectively, efficiently, ethically, and safely. Mastery of the ‘four Ds’ framework for productive human–AI collaboration. Practical prompting exercises, task delegation, and critical evaluation of AI outputs.
Agents and Agentic AI
An introduction to AI agents and agentic AI systems, focusing on autonomous systems that can reason, plan, use tools, and execute multi-step tasks. The session explores agent architectures, tool use, reasoning loops, and real-world applications of agentic workflows in business contexts.
AI Strategy Part 1: AI Use Cases
An introduction to AI strategy through a focus on use cases: identifying promising AI applications along the value chain, evaluating them in terms of impact, feasibility, and cost, and progressing from ideation to implementation. The session also covers requirements engineering and make-or-buy decisions.
AI Product Management
Building an AI product from concept to deployment. The AI product development lifecycle includes needs analysis, the data phase, model development, integration, operationalisation, and management. A hands-on exercise focuses on designing an AI product.
AI Strategy Part 2: Enabling Factors
Deepening AI strategy by exploring enabling factors for successful AI adoption: organisational readiness, data infrastructure, skills development, processes, and the AI operating system concept. Developing an AI roadmap.
Responsible AI & Risk Management
Responsible AI: transparency, fairness, accountability, privacy, and human oversight. AI ethics through real-world case studies. Bias in AI systems and fundamental approaches to risk identification and management.
AI Regulation & the EU AI Act
The regulatory landscape for AI with focus on the European AI Act as a product safety regulation. Risk-based classification of AI systems, GDPR in the AI context, copyright and IP considerations, and compliance obligations.
AI Compliance in Practice
Practical AI compliance: preparing for the AI Act. Standardisation frameworks (ISO 42001, NIST AI Framework), building an AI Registry for organisational governance, risk assessment and classification, cybersecurity for AI.
AI and Society: Future of Work
Societal impact of AI: AI-related risks and misuse (deepfakes, fraud), future of work and how AI transforms jobs, political and societal perspectives on AI governance, and a forward look at AGI, superintelligence, and long-term AI trajectories.
Project Work (Supervised)
Supervised project work session. Students finalise their AI projects and prepare presentations, integrating all course themes: technical foundations, agentic AI, strategy, responsible AI, and societal impact.
Final Exam / Presentation
Final Exam / Presentation
Course materials
Books
Media
Prerequisites
No specific technical prerequisites are required. Students should have basic computer literacy and an interest in technology and business. No prior programming experience is necessary. An openness to hands-on experimentation with AI tools is expected.
Methodology
The course combines multiple teaching formats to ensure deep understanding and practical application:
* Lectures: Interactive sessions introducing core concepts, frameworks, and real-world case studies. Students are encouraged to ask questions and engage in discussion throughout.
* Hands-on workshops: Applied sessions in which students work directly with AI tools, practise prompting techniques, build simple models, explore AI agents, and use AI platforms.
* Group exercises: Collaborative activities including AI use case ideation, strategy development, and risk assessment.
* Project work: Students undertake an individual or group AI project throughout the course, applying concepts learned in class. A dedicated project session provides supervised working time with instructor guidance.
* Final presentation: The course culminates in a final presentation or examination in which students demonstrate their project outcomes and key learnings.
* Progression: The course follows a structured arc from foundational AI knowledge (Sessions 1–3), through generative AI, fluency, and agentic AI (Sessions 4–6), to strategic application (Sessions 7–9), governance, compliance, and societal impact (Sessions 10–13), and concludes with project work and a final presentation.
Grading
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AI Native: Competing in the Age of Intelligent Machines
by Tristan Post
Total hours
45 Hours
Dates
Sep 28 - Oct 16, 2026
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
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