From analytics to artificial intelligence
Open accessAbstract
Analytics have been employed by companies for several decades, but now many firms are interested in building their capabilities for artificial intelligence (AI). Many AI systems, however, are based on statistics and other forms of analytics. Companies can get a “running start” on AI by building upon their analytical competencies. The focus of this article is how to transition from analytics to AI. Three eras of analytical focus are detailed, with AI portrayed as a fourth era. The types of AI methods that are and are not based on analytics are described. AI applications that build on analytical strengths are discussed. Approaches to assessing analytical capabilities that relate to AI, and the development of an organizational plan and strategy for AI, are also described in brief.
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Upskilling develops interdisciplinary skillsets that are necessary for the successful implementation of AI projects.
“Upskilling develops interdisciplinary skillsets that are necessary for the successful implementation of AI projects (Davenport 2018).”
From Ready or not, AI comes—an interview study of organizational AI readiness factors (2021) · cites this paper as Davenport 2018
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- Davenport, T. H., & Harris, J. G. (2017). Competing on analytics. Boston: Harvard Business Review Press (revised and updated).
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- Authors
- Davenport, T. H.
- Affiliations
- Babson College (US)
- Corresponding author
- Davenport, Thomas H.
- ORCID
- Davenport, Thomas H.
- Venue
- Journal of Business Analytics
- Publisher
- Informa UK Limited
- Published
- 2018-07-03
- Volume
- 1
- Issue
- 2
- Pages
- 73-80
- ISSN
- 2573-234X (ISSN-L)
- 2573-2358
- OpenAlex
- W2900699623
- Cited by
- 341
- References
- 5
- Citations by year
- 2026: 31 · 2025: 58 · 2024: 75 · 2023: 74 · 2022: 40 · 2021: 41 · 2020: 14 · 2019: 8
- FWCI
- 23.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Business Analytics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- 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
- Keywords
- artificial intelligence, business analytics, analytical competencies
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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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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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