• understand the role of AI in a marketing context
• design the structure for an effective implementation of AI in marketing
• understand the value of AI in personalizing the consumer experience and journey
• effectively incorporate AI into marketing strategy (including in product, price, communication, and sales marketing) 
• critically evaluate the benefits and risks of AI for companies and for consumers 

What is the main purpose of this course?

This course is designed to provide undergraduate students with basic understanding how artificial intelligence (AI) is changing the marketing environment and how it can be applied in various marketing contexts. The main purpose of the course is to learn from best-practice use cases (e.g., Amazon, Netflix, Starbucks) in order to develop and implement an AI-based strategy in a firm. This course is not about programming AI tools or statistical issues behind AI; the course takes a more practical approach and looks at how and where AI can be applied in today's marketing.

Why is this course relevant?

Artificial intelligence (AI) is changing the way how businesses interact with consumers and vice versa. According to an Accenture global survey among 1500 C-suite executives from companies across several industries, 84% of executives believe they will not achieve their growth objectives without scaling AI in their organization. Importantly, a large majority of the surveyed executives report that they struggle with how to scale it. Moreover, a McKinsey analysis of more than 400 actual use cases shows that marketing is the functional area in an organization where AI contributes the greatest value. This is in line with the CMO (Chief Marketing Officer) Barometer 2025 showing that AI is the key topic in today's marketing.

Given the growing importance of AI in business and especially for marketing activities, it seems fundamental to understand how and where AI can be applied in the field of marketing to provide substantial benefits for companies (and consumers) and to stay competitive in the market. This course is designed to provide such understanding and help students to get an overview on how AI can be effectively used and applied in today's marketing contexts.

What topics will be discussed in this course?

The course introduces the students to the history and meaning of AI, how it evolved in marketing, what impact it has on today's marketing strategy to create value for both organizations and consumers, and what role data play for an effective AI implementation. The course discusses both strategic and operational elements of AI in marketing. For example, the followiong topics will be covered:

• Strategic elements of AI
  • AI and marketing research
  • AI and market segmentation
  • AI and marketing automation (standardization, personalization)
• Operational elements of AI
  • AI in product management (e.g., improving fit of product offering and consumer preferences)
  • AI in price management (e.g., automated pricing decisions)
  • AI in communication management (e.g., automated social media campaigns)
  • AI in sales management (e.g., sales forecasts)
  • AI in customer relationships (e.g., chatbots for customer support)
• Psychological elements of AI (e.g., benefits and costs for consumers)
• Ethical, environmental, and privacy issues of AI

• Classic and basic theory
• New theory
• Teacher’s own research
• Methodology

• Development of research questions
• Data collection
• Analysis
• Discussion, critical reflection, modelling
• Activities that contribute to new or existing research projects

Selected readings:

• Bertini and Koenigsberg (2024): Dynamic Pricing Doesn’t Have to Alienate Your Customers, Harvard Business Review, Digital: https:/​/​hbr.org/​2024/​05/​dynamic-pricing-doesnt-have-to-alienate-your-customers?ab=HP-hero-for-you-text-1
• Dawar and Bendle (2018), Marketing in the Age of Alexa, Harvard Business Review, 96, 3, 80-86.
• De Freitas, Agarwal, Schmitt, and Haslam (2023): Psychological Factors Underlying Attitudes towards AI Tools, Nature Human Behaviour, 7, 1845-1854.
• Hagiu and Wright (2020): When Data Creates Competitive Advantage, Harvard Business Review, 98, 1, 94-101.
• Hermann and Puntoni (in press): Artificial Intelligence and Consumer Behavior: From Predictive to Generative AI, Journal of Business Research.
• Huang and Rust (2021): A Strategic Framework for Artificial Intelligence in Marketing, Journal of the Academy of Marketing Science, 49, 30-50.
• Iansiti and Lakhani (2020): Competing in the Age of AI, Harvard Business Review, 98, 1, 60-67.
• Castelo, Boegershausen, Hildebrand, and Henkel (2023): Understanding and Improving Consumer Reactions to Service Bots, Journal of Consumer Research, 50 (4), 848-863.
• Kozinets and Gretzel (2021): Commentary: Artificial Intelligence: The Marketer’s Dilemma, Journal of Marketing, 85 (1), 156-159.
• Palumbo and Edelman (2023): What Smart Companies Know About Integrating AI, Harvard Business Review, 101 (4), 116-125.
• Peres, Schreier, Schweidel, and Sorescu (2023): On ChatGPT and Beyond: How Generative Artificial Intelligence may Affect Research, Teaching, and Practice, International Journal of Research in Marketing, 40, 269-275.
• Puntoni, Walter Reczek, Giesler, and Botti (2021): Consumers and Artificial Intelligence: An Experiential Perspective, Journal of Marketing, 85, 1, 131-151.
• Sinha, Shastri, and Lorimer (2024): Can AI Assistants Add Value to Your Sales Team?, Harvard Business Review: https:/​/​hbr.org/​2024/​09/​can-ai-assistants-add-value-to-your-sales-team