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Artificial Intelligence in Strategic Marketing Decision-Making (NIM Research Spotlight)


The Role of AI in Strategic Marketing Decision-Making

How do managers work together with intelligent machines – today and in the future?

The power of AI in marketing: From the operational to strategic decision-making level

With increasing data availability and computing capacity, companies are intensifying their efforts to use artificial intelligence (AI) to generate business value. As capabilities of smart machines grow through advances in machine learning and natural language processing, the boundaries of AI in decision-making is shifting from the operational to the strategic level. 

Particularly in marketing, there is a great potential for the use of AI. To date, AI applications in marketing practice have focused primarily on automating processes at the operational level, such as customer support chatbots or product recommendation systems, and on gaining insights from big data, e.g., to analyze customer sentiment or build advanced segmentation models. However, there is a growing expectation in academia that AI has the potential to support, augment, or even replace marketing managers in strategic-level decision-making. So, in the future, will AI decide which markets companies tackle, which products they launch, or which communication and pricing strategy they pursue?

Digging deeper: How will humans and smart machines collaborate in strategic decision-making?

Previous studies indicate that managers accept giving algorithms some weight in managerial decisions, but they still want to keep a certain level of control. However, the question of how such a human-machine collaboration (HMC) on a strategic level might look like on concrete terms remains largely unanswered.

In this study, we provide a comprehensive overview of the status quo and venture a look into the future by examining managers' outlook of the future and their preferences for interacting with intelligent machines when making strategic marketing decisions.

Method: Combining qualitative expert insights with an international C-level survey

In the first phase of the project, we interview experts, including marketing managers, marketing consultants, data science experts, and marketing academics, to get an initial overview of the research field and gain deep qualitative insights.

In the second project phase, we use a standardized survey (CATI) to gain empirical evidence from high-level executives (C-suite or direct reports) responsible for marketing or corporate strategy. We focus on managers from B2C-companies on the Global Forbes 2000 list. This high-quality sample allows us to draw conclusions about state-of-the-art implementation on a global level.

In this study we...

  • present and validate a five-category model to evaluate the role of AI in collaboration with human managers, ranging from "no AI involved" to "assistant", "collaborator", "project manager" and "manager".
  • examine differences between concrete types of marketing decisions and between specific tasks within decision-making processes.
  • explore obstacles that hinder the implementation of AI in strategic level decision-making and gain insights on how pioneers overcame them.
  • examine implications for managers and marketing staff regarding future tasks and expertise.

Selected conference contributions

  • “The Role of AI in Strategic Marketing Decision-Making: How Will Managers Collaborate with Smart Machines - Today and Tomorrow?”, ISMS Marketing Science Conference, 16.–18.06.2022 (virtual) at the University of Chicago Booth School of Business
  • “The Future of Strategic Decision-Making: What Will Human-Machine Collaboration Look Like on a Strategic Level?”, Advances in Decision Analysis Conference, 22. – 24.06.2022 (hybrid) at the Darden Business School, Washington DC Campus
  • “Human-Machine-Collaboration in Strategic Business Decisions”, Exploring Next | APF Global Conference, 13. - 23.06.2022 (virtual)