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The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions
Abstract
Artificial Intelligence tools have attracted attention from the literature and business organizations in the last decade, especially by the advances in machine learning techniques. However, despite the great potential of AI technologies for solving problems, there are still issues involved in practical use and lack of knowledge as regards using AI in a strategic way, in order to create business value. In this context, the present study aims to fill this gap by: providing a critical literature review related to the integration of AI to organizational strategy; synthetizing the existing approaches and frameworks, highlighting the potential benefits, challenges and opportunities; presenting a discussion about future research directions. Through a systematic literature review, research articles were analyzed. Besides gaps for future studies, a conceptual framework is presented, discussed according to four sources of value creation: (i) decision support; (ii) customer and employee engagement; (iii) automation; and (iv) new products and services. These findings contribute to both theoretical and managerial perspectives, with extensive opportunities for generating novel theory and new forms of management practices.
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“some studies specifically showing that the strategic use of AI technologies for customer and employee engagement has not been well exploited yet (Borges et al., 2021;Gursoy, Chi, Lu, & Nunkoo, 2019).”
From Artificial intelligence in information systems research: A systematic literature review and research agenda (2021) · cites this paper as Borges et al., 2021
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“However, it is widely believed that these well-known technologies came to the fore because of advances in computing power, development of algorithms and the availability of large datasets [21,65].”
From From computer ethics and the ethics of AI towards an ethics of digital ecosystems (2022) · cites this paper as [21]
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Metadata
- Authors
- Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A.
- Affiliations
- Universidade de São Paulo (BR)
- Universidade Paulista (BR)
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 57
- Pages
- 102225
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W3085480867
- Cited by
- 760
- References
- 169
- Citations by year
- 2026: 112 · 2025: 163 · 2024: 193 · 2023: 128 · 2022: 101 · 2021: 59
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Information Management (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- artificial intelligence, organizational strategy, corporate value creation, artificial intelligence adoption, decision support, customer engagement, employee engagement, automation, new product and service development, AI implementation challenges, conceptual framework
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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