Artificial intelligence (AI) in strategic marketing decision-making: a research agenda
Research output: Contribution to journal › Article › peer-review
Design/methodology/approach: Review of literature and consultation with marketing experts who were invited to contribute to the article.
Findings: There is little research into applying AI to strategic marketing decision-making. This is needed as the frontier of AI application to decision-making is moving in many management areas from operational to strategic. Given the competitive nature of such decisions and the insights from applying AI to defence and similar areas, it is time to focus on applying AI to strategic marketing decisions.
Research limitations/implications: Applying AI to strategic marketing decision-making is known to be taking place, but the it is commercially sensitive, so data is not available to the authors.
Practical implications: There are strong implications for all businesses, particularly large businesses in competitive industries, where failure to deploy AI in the face of competition from firms who have deployed AI to improve their decision-making could be dangerous.
Social implications: The public sector is a very important marketing decision-maker. Although in most cases it does not operate competitively, it must still make decisions about making different services available to different citizens and identify the risks of not providing services to certain citizens, so this article is relevant to the public sector.
Originality/value: This is one of the first articles to probe deployment of AI in strategic marketing decision-making.
|Number of pages||18|
|Journal||The Bottom Line|
|Early online date||13 Apr 2020|
|Publication status||Early online - 13 Apr 2020|
- LABIB_2020_cright_Artificial intelligence (AI) in strategic marketing decisionmaking
Rights statement: Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., Laughlin, P., Machtynger, J. and Machtynger, L. (2020), "Artificial intelligence (AI) in strategic marketing decision-making: a research agenda", The Bottom Line, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BL-03-2020-0022. Copyright © 2020, Emerald Publishing Limited. All rights reserved.
Accepted author manuscript (Post-print), 814 KB, PDF document