Generative AI in innovation and marketing processes: A roadmap of research opportunities

Paola Cillo () and Gaia Rubera ()
Additional contact information
Paola Cillo: Bocconi University
Gaia Rubera: SDA Bocconi School of Management

Journal of the Academy of Marketing Science, 2025, vol. 53, issue 3, No 3, 684-701

Abstract: Abstract Nowadays, we are witnessing the exponential growth of Generative AI (GenAI), a group of AI models designed to produce new content. This technology is poised to revolutionize marketing research and practice. Since the marketing literature about GenAI is still in its infancy, we offer a technical overview of how GenAI models are trained and how they produce content. Following this, we construct a roadmap for future research on GenAI in marketing, divided into two main domains. The first domain focuses on how firms can harness the potential of GenAI throughout the innovation process. We begin by discussing how GenAI changes consumer behavior and propose research questions at the consumer level. We then connect these emerging consumer insights with corresponding firm marketing strategies, presenting research questions at the firm level. The second set of research questions examines the likely consequences of using GenAI to analyze: (1) the relationship between market-based assets and firm value, and (2) consumer skills, preferences, and role in marketing processes.

Keywords: Generative artificial intelligence; Innovation; Marketing strategy; Marketing capabilities; Firm value (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11747-024-01044-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joamsc:v:53:y:2025:i:3:d:10.1007_s11747-024-01044-7

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/11747

DOI: 10.1007/s11747-024-01044-7

Access Statistics for this article

Journal of the Academy of Marketing Science is currently edited by John Hulland, Anne Hoekman and Mark Houston

More articles in Journal of the Academy of Marketing Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().