• The AI Landscape: We survey the landscape of machine learning, deep learning and AI — clarifying key terms, how these fields relate, and why they are reshaping how organisations compete. Using the Horizon 1–3 framework, you learn to distinguish between optimising operations, extending capabilities, and exploring new business models. This session also introduces the capstone assignment that runs through the entire programme.
• Generative AI and Large Language Models: A hands-on session on the technology that has most visibly transformed AI. You gain a conceptual understanding of how LLMs work — training, transformers, fine-tuning, retrieval-augmented generation, cowork and skills — without requiring programming skills. In a substantial workshop, you work with ChatGPT, Claude, Copilot and other tools on realistic business tasks. We also cover limitations: hallucinations, reasoning failures, and data privacy risks.
• Machine Learning and Deep Learning: Going deeper into foundational techniques: supervised and unsupervised learning, model fitting, feature engineering, overfitting and generalisation. We discuss common model types at a conceptual level, focusing on when to use what. The goal: the vocabulary needed to evaluate model quality, ask the right questions of a data science team, and make informed deployment decisions.
• Determining the Right Business Question: The single most important determinant of AI project success: starting with the right question. You learn structured methods for identifying high-impact opportunities, assessing data readiness, and building roadmaps. The session introduces the Business Model Canvas, CRISP-DM framework and the DAPS diagram for translating business goals into data and AI questions.

• AI Strategy & Organisational Transformation: A structured approach to formulating and executing an AI strategy. You work through the AI strategy cycle: defining ambition, assessing maturity across data, technology, talent, processes and culture, building a capability roadmap, and translating strategy into a concrete project portfolio. We also discuss the role of an AI Centre of Excellence.
• Organising, Managing, and Implementing AI Projects: AI projects differ fundamentally from traditional IT: they are uncertain, data-dependent, and iterative. This session covers scoping and execution best practices, team composition, the build-vs-buy-vs-partner decision, and running effective proofs of concept. It includes a focused module on data visualisation and communicating results.
• Trustworthy AI: Governance, Risks, and Compliance: From ethical principles through responsible practices to trustworthy outcomes. We work through the Ethics-to-Trust pyramid and the Responsible AI Framework Landscape: the EU AI Act, NIST AI RMF, ISO/IEC 42001, and foundation controls including GDPR. Case studies illustrate what happens when governance fails.
• AI Agents and Agentic AI: The next frontier: autonomous agents that can plan, reason, use tools, and execute multi-step tasks. We cover agent architecture, current platforms, realistic business use cases, and the governance dimensions: human-in-the-loop design, scoping authority, and monitoring autonomous systems. Participants get hands-on experience with agentic tools.
• Data Governance and Data Management: High-quality data is the foundation of every successful AI initiative. We cover the distinction between data management and data governance, introduce a framework covering the 11 data management disciplines, and address emerging GenAI-era challenges: proprietary data exposure to LLMs, governing AI-generated content, and preventing data leakage.
• AI Economics & ROI / Leading AI-Augmented Teams: Two interconnected challenges: making the business case and leading the human side of AI adoption. You learn to estimate costs, quantify benefits, and build compelling cases for executive stakeholders. The second half focuses on leading AI-augmented teams: managing resistance, redesigning workflows, and communicating AI initiatives honestly.
• Capstone Assignment & Presentations: A practical assignment running through the entire programme. You identify a business opportunity in your own organisation where AI could create value, then translate it into a concrete AI project business case including data requirements, the appropriate AI approach (classical ML, generative AI, agentic AI, or a combination), governance considerations, and an implementation roadmap. Mid-programme coaching and peer feedback help you refine your proposal. On the final day, you present your capstone as an executive pitch. A positive assessment earns the Certified Analytics Translator.

Bart Lameijer is an Associate Professor of Business Analytics at the Amsterdam Business School of the University of Amsterdam. His expertise focuses on productivity technologies, including Artificial Intelligence and process automation. His research and teaching examine how organisations can implement and adopt these technologies effectively, both at project level and across the organisation. 

Bart is also Director of the Institute for Business and Industrial Statistics (IBIS). In his teaching he connects academic insight with practice, with a strong focus on helping participants structure improvement projects to deliver organisation-wide results, emphasising a strong focus on data and achieving stakeholder buy-in.

Reinier van den Biggelaar is managing partner at BastaGroup and Director Data Science at ORTEC. He brings together digital-transformation expertise and strong mathematical and data-science skills. Reinier holds an MBA from Erasmus University and has worked with large organisations in the Benelux, including AkzoNobel, Shell, Heineken and Philips. He has been involved in many Internet of Things, AI and data projects. Reinier often teaches management teams about digital transformation and is happy to explain how digital and data can strengthen an organisation’s strategic position.

David Stephenson is an external lecturer at the University of Amsterdam and managing director at DSI Analytics. He helps organisations set up and improve data-science programmes and has worked with companies such as eBay, adidas, Het Financieele Dagblad, IKEA, ABN Amro, Sky, Miro and several smaller firms. David also supports teams with talent sourcing and training. He is a regular speaker at international conferences and has been programme chair for machine-learning events in the UK and the USA. He is the author of the best-selling book Big Data Demystified, translated into seven languages, and Business Skills for Data Scientists.