AI Foundations
Everything you need to understand Artificial Intelligence—explained clearly, without technical jargon, for business leaders and decision-makers.
The Business Case for Understanding AI
AI is reshaping industries at a pace unseen since the advent of the internet. For business leaders, understanding AI is no longer optional — it's a competitive necessity. You don't need to code; you need to know enough to lead.
This section gives you a solid conceptual foundation: what AI is, how it works at a high level, where it excels, and where it falls short — so you can make informed decisions.
AI Adoption in Business
Principles for Responsible AI Use
Transparency
Understanding what AI can and cannot do—and why—is the first step toward responsible adoption.
Ethics & Fairness
AI systems must be designed and deployed without perpetuating bias or causing harm to individuals or groups.
Data Privacy
Feeding sensitive data into public AI tools carries risks. Know what data governance rules apply to your organization.
Human Oversight
AI augments human decision-making—it does not replace it. Keeping humans in the loop is essential, especially for high-stakes decisions.
Featured Publications
The Non-Human Enterprise: How AI Agents Reshape Organizations
The rise of the agentic enterprise marks a profound transformation in the way organizations operate in an AI-driven world. Discover how AI agents are reshaping modern enterprises.
Innovation: Die sieben Dimensionen für einen nachhaltigen Unternehmenserfolg
Innovationen bescheren Unternehmen Aufstieg und sichern ihre Zukunft. Wie aber lässt sich die Entstehung des Neuen am besten fördern? Oliver Gassmann, Professor in St. Gallen, kennt die Antwort.
Key Concepts, Simply Explained
Artificial Intelligence (AI)
The study and design of intelligent computational agents capable of perceiving environments and performing tasks to maximize their chances of achieving defined goals (Source: Russell, S., & Norvig, P., 'Artificial Intelligence: A Modern Approach', 4th Ed., Pearson, 2020).
Machine Learning (ML)
A subfield of AI concerned with the design of algorithms that improve computationally through experience and generalizing from data architectures, rather than through explicit programming (Source: Mitchell, T., 'Machine Learning', McGraw-Hill, 1997 / Goodfellow, I., et al., 'Deep Learning', MIT Press, 2016).
Large Language Models (LLMs)
Extremely scaled neural networks trained via self-supervised learning on massive corpora of text. They predict sequences to generate and analyze complex natural language (Source: Zhao, W. X., et al., 'A Survey of Large Language Models', Computation and Language, arXiv, 2023).
Neural Network
Computational systems comprised of interconnected nodes (neurons) organized in layered topologies which calculate complex non-linear classification and generation problems (Source: Goodfellow, I., Bengio, Y., & Courville, A., 'Deep Learning', MIT Press, 2016).
Natural Language Processing (NLP)
The integration of computational linguistics and artificial intelligence enabling machines to decipher, understand, and generate human language in context (Source: Jurafsky, D., & Martin, J. H., 'Speech and Language Processing', 3rd Ed., Pearson, 2024).
Hallucination
An anomaly wherein AI generates a response conveying false or misleading information persistently presented as factual logic or truth, rather than generating a factual response grounded in context (Source: Ji, Z., et al., 'Survey of Hallucination in Natural Language Generation', ACM Computing Surveys, 2023).
Intelligent Agent / Agentic AI
Autonomous entities proactively perceiving environments and making iterative decisions securely over extended durations to achieve complex objectives by employing continuous learning (Source: Russell, S., & Norvig, P., 'Artificial Intelligence: A Modern Approach', 2020 / Wooldridge, M., 'An Introduction to MultiAgent Systems', 2009).