People and algorithms: Working with AI in marketing

Artificial Intelligence is no longer a distant technological trend in marketing. Used well, AI can boost marketing practice. Without an informed, critical approach to use, it can reproduce bias, generate misleading insights, weaken creativity, and create serious ethical, legal, and security risks.

This handbook has been prepared to support your work with Artificial Intelligence in marketing in a thoughtful, creative, and responsible way. Its goal is not only to introduce AI tools and concepts, but also to help you develop the judgment required to use them effectively.

The handbook is primarily addressed to marketing students at the master’s level. It has been devel­oped by marketing departments from European universities who are witnessing how AI the trans­formation of the profession and deciding how marketing education should respond to this change. The chapters are designed to connect conceptual understanding with practical application, helping students prepare for marketing roles in which AI will be part of everyday work.

This handbook is the result of collaboration between academics from Poznań University of Econom­ics and Business (Poland, project leader) and Corvinus University of Budapest (Hungary), University of Applied Sciences Upper Austria, Stockholm School of Economics (Sweden), Complutense Universi­ty of Madrid (Spain), Polytechnic University of Marche, Ancona (Italy), University of Zagreb, Faculty of Economicsand Business (Croatia), and University of Perpignan (France).

This handbook has been supported by the Polish National Agency for Academic Exchange under the NAWA Strategic Partnership program, project BNI/PST/2023/1/00082 DIGIT: People and algorithms in organisations

Introduction
1. Marketing 7.0 and beyond – Natalia Gluza & Jesús García de Madariaga
1.1 The evolution of marketing: from 1.0 to 7.0
1.2 What is marketing 6.0?
1.2.1 Task: Is this really marketing 6.0?
1.3 The changing role of the marketer
1.4 Key takeaways
1.5 Further reading

Case study: Harnessing generative AI for market segmentation and service
innovation: The case of MoovSmart (Belén Rodríguez Cánovas, Jesús García de Madariaga)

2. Machine learning and deep learning for marketing – Grzegorz Leszczyński & Amira Mouakher
2.1 How machine learning and deep learning work
2.1.1 Task: Self-assessment questionnaire on knowledge about Artificial Intelligence
2.2 ML or DL model development
2.3 Why machine learning and deep learning matter for marketing
2.4 Key takeaways
2.5 Further reading

Case study: Painting a clearer future: Novol’s path to smarter sales&operation planning with AI (Grzegorz Leszczyński, Amira Mouakher, Piotr Kwiatek)

3. Generative AI in marketing – Grzegorz Leszczyński & Amira Mouakher
3.1 What is generative AI & how does it work?
3.1.1 Task: self-assessment questionnaire on knowledge about generative Artificial Intelligence
3.2 How generative AI is used in marketing
3.2.1 Task: Learning from examples of generative AI application in marketing
3.3 Prompting
3.4 Key takeaways
3.5 Further reading

4. Data for AI in marketing – Grzegorz Leszczyński & Amira Mouakher
4.1 Data-driven marketing
4.2 What is data in AI context?
4.3 What makes a dataset suitable for AI?
4.4 Data preprocessing steps
4.5 Key takeaways
4.6 Further reading

Case study: From fuel to smart ecosystems: How AI redefines customer experience at MOL Group (Mirkó Gáti)

5. Collaborating with IT people – Maciej Ławrynowicz & Dubravka Sinčić
5.1 Understanding IT professionals — culture, mindset & first friction
5.2 Communication styles and common challenges
5.3 Practical collaboration techniques
5.3.1 Task: Case study analysis — what made IT work?
5.4 AI tools for enhanced IT – marketing collaboration
5.5 Key takeaways
5.6 Further reading

Case study: Fast, objective, and fully automated? Implementing AI-powered damage assessment in insurance market (Ružica Brečić, Andrea Lučić, Dubravka Sinčić)

6. Creative work with AI – Piotr Gaczek & Jesús García de Madariaga
6.1 What is creativity?
6.2 What drives creativity in the AI era?
6.2.1 Task: Idea generation
6.3 Human – AI collaboration
6.4 What research says about AI and creativity?
6.5 Replacement or augmentation?
6.6 Paradoxes of creativity and AI
6.7 Human roles in AI-supported creativity
6.7.1 Task: Exploring human roles in AI-supported creativity
6.8 Key takeaways
6.9 Further reading

Case study: Generative AI as enabler of value proposition innovation: The case of a digital marketing agency (Alessandra Cioli, Andrea Perna, Federica Pascucci)

7. Building critical skills for the AI age – Anna Rogala & Piotr Kwiatek
7.1 What critical thinking is
7.2 Cognitive skills for critical thinking
7.3 Tools for critical thinking
7.4 How digitalisation can weaken or strengthen critical thinking
7.5 How to question AI: prompts, data, and judgment
7.5.1 Task: Critical evaluation of AI-supported marketing data
7.6 Key takeaways
7.7 Further reading

8. Critical thinking when using AI in marketing – Anna Rogala & Piotr Kwiatek
8.1 Why do we need marketers to be critical thinkers?
8.2 Where AI can mislead marketers
8.3 Numbers in AI’s hands – analytical pitfalls
8.3.1 Task: Reasoning
8.4 Decision-making in marketing: What AI can support, what it should not replace, and why ethics still matter
8.5 Key takeaways
8.6 Further reading

Case study: Riding the AI wave: Building competencies for innovative marketing company (Hanna Nowak-Mizgalska, Anna Rogala)

9. Working with synthetic personas – Piotr Gaczek & Alessandra Cioli
9.1 What is a synthetic persona?
9.2 What are the advantages and limitations of synthetic personas?
9.2.1 Task: Creating, testing and evaluating synthetic personas
9.3 Three problems with AI-generated personas
9.3.1 Task: Testing data grounding
9.4 Key takeaways
9.5 Further reading

Case study: Is AI ready to decide? A learning case on organisational readiness for collaboration (Piotr Gaczek)

10. Competencies for working with AI in marketing – Maciej Ławrynowicz & Dubravka Sinčić
10.1 What is a competency in the AI era?
10.2 A map of AI marketing competencies
10.3 Developing AI competencies in practice
10.3.1 Task: The one-gap action plan
10.4 Key takeaways
10.5 Further reading

Case study: Parallel paths: Managing AI integration and neurodiversity in marketing workflows (Alicja Koperska, Maciej Ławrynowicz)

11. AI, cybersecurity, and digital hygiene – Krzysztof Durczak & Alicja Koperska
11.1.1 Task: The cost of convenience
11.1 Old threats, new scale
11.2 New problems in the AI era
11.3 Key takeaways
11.4 Further reading

Case study: Phantom in the outlook (Krzysztof Durczak, Allan Edh)

12. Sales and marketing synergy – Grzegorz Leszczyński & Piotr Kwiatek
12.1 How AI can help in marketing & sales management
12.2 AI tools for managing the sales funnel
12.2.1 Task: Analysing conversion in the sales funnel
12.3 Key takeaways
12.4 Further reading

Case study: From points to actions: Using generative AI to re-engineer a B2B loyalty program (Piotr Kwiatek, Grzegorz Leszczyński)

Authors bio