Augment, Don’t Replace: Human-AI Collaboration as a Success Factor in Modern Marketing

Carolin Wölfle & Emanuele Natale

Artificial intelligence (AI) is no longer a futuristic concept – it is a present-day reality, increasingly embedded in daily life and gaining traction across industries. This trend is especially evident in marketing, where data-driven strategies, automation, and intelligent systems are reshaping how brands engage with consumers. While AI has already transformed operational tasks – such as personalized newsletters and automated customer interactions – AI is now venturing into strategic domains. The emergence of GenAI (Generative AI) tools such as ChatGPT or DALL·E is accelerating this shift, raising a critical question: is AI ready to fully replace humans in the marketing domain, or should it instead be employed to enhance human capabilities?

How GenAI is Reshaping the Industry

AI can broadly be defined as encompassing all technologies that can mimic human traits and abilities. Within that context, GenAI represents a distinct subcategory of AI, characterized by its ability to create entirely new content. While traditional AI primarily focuses on analyzing and interpreting existing data, GenAI goes a step further: it independently generates texts, images, videos, and other media based on user prompts. These applications thus differ from traditional AI in that they specifically imitate human creative abilities from the full spectrum of human skills. A practical example: whereas conventional AI predicts which type of advertisement works best for a target audience, GenAI tools independently create advertisements by designing and implementing the desired content, visualizations, and messages.

Hybrid Intelligence: The Key to Collaborative Success

AI and humans possess distinct yet complementary strengths, which is why emerging research highlights the value of human-AI collaboration. AI, for instance, excels at processing vast amounts of data, recognizing complex patterns, and delivering consistent and efficient performance. Humans, in contrast, benefit from empathy, intuition, adaptability, and analogical thinking – the ability to draw insights from one domain and apply them to another. Combining human strengths with AI’s analytical capabilities leads to more effective collaboration, particularly in tasks that require both creativity and precision. For instance, recent studies have shown that marketing emails created and personalized by GenAI were opened more frequently and resulted in higher engagement compared to emails drafted solely by trained marketing professionals. However, it is important to note that GenAI-generated mailings still required human review to correct minor errors or unnatural phrasing. In this case, both AI and humans contribute their respective strengths to achieve optimal results. This complementary has given rise to the concept of Hybrid Intelligence – a model in which humans and AI collaborate by strategically distributing tasks to solve complex problems. In this dynamic partnership, both entities continuously learn from one another, enabling a more adaptive, efficient, and innovative approach to decision-making.

Hybrid Intelligence in Marketing

The following use cases illustrate how humans and AI can collaborate most effectively by applying the principles of Hybrid Intelligence:

Marketing Analysis and Strategy: AI automates data collection, identifies competitors and trends, and builds detailed customer profiles through behavioral analysis. It can segment customers and support positioning strategies using sentiment analysis. Humans, in contrast, define market strategies, interpret AI-generated insights and refine them based on business goals. Empathy and creative thinking are essential for evaluating target segments and developing meaningful brand narratives.

New Product Development: AI predicts trends, identifies market gaps, and extracts consumer insights. GenAI accelerates ideation by producing product concepts and designs. Humans guide product vision and ensure alignment with customer expectations. They refine AI-generated concepts through strategic insights and ensure authenticity, usability, and emotional resonance.

Campaign management: AI streamlines campaign execution, handles media planning and ad targeting, and generates personalized content. It also enhances ad performance through automated A/B testing and optimization. Humans define campaign goals and select appropriate advertising channels. They also need to enter prompts to initiate content creation with a generative model and evaluate its output. Human oversight ensures emotional impact, brand alignment, and cultural relevance – preventing campaign from feeling robotic.

Success Stories: Human-AI Synergy in Action

Spotify uses AI to personalize playlists based on users’ listening habits. However, AI can only personalize playlists when data already exists. Human editors therefore curate content for new artists and releases – blending algorithmic recommendations with human judgement to maintain the platforms musical diversity and promote emerging artists.

Unilever leverages AI to create superior products by analyzing vast amounts of biological data, allowing scientistic and marketers to focus on creativity and product innovation. AI speeds up experimentation, but human insight ensures scientific breakthroughs are both relevant and valuable.

Coca-Cola’s Y3000 limited-edition flavor was co-created by AI and humans. Coca-Cola invited fans around the world to share their visions of the future – expressed through emotions, aspirations, colors, and flavors – and then used AI to synthesize these insights into a unique flavor profile and futuristic packaging design.

Strategic Takeaways for Marketers

To fully unlock the potential of Hybrid Intelligence, companies need to consider the following:

Refocus human effort on strategic tasks. Let AI manage mechanical and analytical duties.

Ensure human oversight. Maintain brand consistency and address internal resistance to AI adoption.

Invest in technological infrastructure and AI training. Enable seamless collaboration through technical capabilities and AI literacy.

Establish data governance. Protect consumer trust by using consumer data ethically and transparently.

As AI reshapes the marketing landscape, professional must evolve alongside it. In-demand skills will include data literacy, critical thinking, emotional intelligence, empathy, creativity, cross-functional collaboration, and the ability to effectively work alongside AI. New roles are also likely to emerge – such as AI marketing strategist, prompt engineer, or marketing data ethicist – reflecting the growing need for expertise at the intersection of technology and human insight.

Augmentation Over Replacement

AI is not able to completely replace human marketers anytime soon. While it performs exceptionally well in structured, data-driven environments, it still lacks the nuance, judgement, and traits such as empathy that humans uniquely provide. Marketing processionals, therefore, do not need to fear immediate job loss. However, routine entry-level tasks, such as content creation or keyword selection, will be increasingly subject to automation. The true value of human marketers will continue to lie in strategic thinking, emotional intelligence, and interpretive insight – areas were AI still falls short.

The future of marketing lies in Hybrid Intelligence, where AI augments rather than replaces human potential. By embracing this synergy, marketers can enhance both efficiency and creativity, driving deeper customer engagement and sustained business success.

References
  • Campbell, Colin, Sean Sands, Carla Ferraro, Hsiu-Yuan (Jody) Tsao, and Alexis Mavrommatis (2020), “From data to action: How marketers can leverage AI,” Business Horizons, 63 (2), 227–43.
  • Chan, Terri H. (2023), “How brands can succeed communicating social purpose: engaging consumers through empathy and self-involving gamification,” International Journal of Advertising, 42 (5), 801–34.
  • Chen, Jing, Jose Humberto Ablanedo-Rosas, Gary Frankwick, and Fernando Arévalo (2021), “The State of Artificial Intelligence in Marketing With Directions for Future Research,” International Journal of Business Intelligence Research, 12 (2), 1–26.
  • Chintalapati, Srikrishna and Shivendra Kumar Pandey (2022), “Artificial intelligence in marketing: A systematic literature review,” International Journal of Market Research, 64 (1), 38–68.
  • Chui, Michael, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel (2023), “The economic potential of generative AI: The next productivity frontier,” McKinsey & Company.
  • Coca-Cola (n.d.), “Coca-Cola® Creations Imagines Year 3000 With New Futuristic Flavor and AI-Powered Experience,” (accessed June 11, 2025), [available at https://www.coca-colacompany.com/media-center/coca-cola-creations-imagines-year-3000-futuristic-flavor-ai-powered-experience].
  • Davenport, T. H., & Mittal, N. (2022), “How Generative AI Is Changing Creative Work,” Harvard Business Review, (accessed March 4, 2025), [available at https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work].
  • Dellermann, Dominik, Adrian Calma, Nikolaus Lipusch, Thorsten Weber, Sascha Weigel, and Philipp Ebel (2019), “The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems,” in Proceedings of the 52nd Hawaii International Conference on System Sciences, Hawaii, USA.
  • Dellermann, Dominik, Philipp Ebel, Matthias Söllner, and Jan Marco Leimeister (2019), “Hybrid Intelligence,” Business & Information Systems Engineering, 61 (5), 637–43.
  • Eriksson, Theresa, Alessandro Bigi, and Michelle Bonera (2020), “Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation,” The TQM Journal.
  • Gao, Biao, Yiming Wang, Huiqin Xie, Yi Hu, and Yi Hu (2023), “Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization,” Sage Open, 13 (4), 1–20.
  • Haleem, Abid, Mohd Javaid, Mohd Asim Qadri, Ravi Pratap Singh, and Rajiv Suman (2022), “Artificial intelligence (AI) applications for marketing: A literature-based study,” International Journal of Intelligent Networks, 3 (1), 119–32.
  • Hesel, Nina, Fabian Buder, and Matthias Unfried (2022), “The Next Frontier in Intelligent Augmentation: Human-Machine Collaboration in Strategic Marketing Decision-Making.,” NIM Marketing Intelligence Review, 14 (2), 49–53.
  • Huang, Ming-Hui and Roland T. Rust (2021), “A strategic framework for artificial intelligence in marketing,” Journal of the Academy of Marketing Science, 49 (1), 30–50.
  • Huang, Ming-Hui and Roland T. Rust (2022), “A Framework for Collaborative Artificial Intelligence in Marketing,” Journal of Retailing, 98 (2), 209–23.
  • Jarrahi, Mohammad Hossein, Christoph Lutz, and Gemma Newlands (2022), “Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation,” Big Data & Society, 9 (2), 1–6.
  • Paschen, Jeannette, Jan Kietzmann, and Tim Christian Kietzmann (2019), “Artificial intelligence (AI) and its implications for market knowledge in B2B marketing,” Journal of Business & Industrial Marketing, 34 (7), 1410–19.
  • Petrescu, Maria and Anjala S. Krishen (2023), “Hybrid intelligence: human–AI collaboration in marketing analytics,” Journal of Marketing Analytics, 11 (3), 263–74.
  • Sheikh, Haroon, Corien Prins, and Erik Schrijvers (2023), “Artificial Intelligence: Definition and Background,” in Mission AI: The New System Technology, H. Sheikh, C. Prins, and E. Schrijvers, eds., Cham: Springer International Publishing, 15–41.
  • Shrestha, Yash Raj, Shiko M. Ben-Menahem, and Georg von Krogh (2019), “Organizational Decision-Making Structures in the Age of Artificial Intelligence,” California Management Review, 61 (4), 66–83.
  • Steel, Anne (2025), “The Playlist Power Broker Who Makes or Breaks New Artists,” The Wall Street Journal, (accessed June 11, 2025), [available at https://www.wsj.com/arts-culture/music/spotify-playlist-sulinna-ong-algorithm-7835e0ac].
  • Unilever (2025), “AI, machine learning and data behind Unilever’s new launches,” (accessed June 11, 2025), [available at https://www.unilever.com/news/news-search/2025/sxsw-ai-machine-learning-and-data-behind-unilevers-latest-launches/].

Leave a Reply

Your email address will not be published. Required fields are marked *