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From analytics to artificial intelligence

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  • Thomas H. Davenport

Abstract

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.

Suggested Citation

  • Thomas H. Davenport, 2018. "From analytics to artificial intelligence," Journal of Business Analytics, Taylor & Francis Journals, vol. 1(2), pages 73-80, July.
  • Handle: RePEc:taf:tjbaxx:v:1:y:2018:i:2:p:73-80
    DOI: 10.1080/2573234X.2018.1543535
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    Cited by:

    1. Alessandra Colombelli & Elettra D’Amico & Emilio Paolucci, 2023. "When computer science is not enough: universities knowledge specializations behind artificial intelligence startups in Italy," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1599-1627, October.
    2. Sullivan, Yulia & Fosso Wamba, Samuel, 2024. "Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation," Journal of Business Research, Elsevier, vol. 174(C).
    3. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    4. Kanungo, Rama Prasad & Liu, Rui & Gupta, Suraksha, 2024. "Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation," Journal of Business Research, Elsevier, vol. 182(C).
    5. Cumali Kılıç & Gaye Atilla, 2024. "Industry 4.0 and sustainable business models: An intercontinental sample," Business Strategy and the Environment, Wiley Blackwell, vol. 33(4), pages 3142-3166, May.
    6. Luca Dal Bene & Paolo Franchi & Antonella Garna & Giacomo Pieraccioli & Monica Piovi & Paolo Torrico, 2022. "La digitalizzazione dei servizi di supporto in sanità. L?esperienza di ESTAR," MECOSAN, FrancoAngeli Editore, vol. 2022(123), pages 195-209.
    7. Pramanik, Paritosh & Jana, Rabin K. & Ghosh, Indranil, 2024. "AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    8. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    9. Ilaria Tutore & Adele Parmentola & Michele Costagliola di Fiore & Francesco Calza, 2024. "A conceptual model of artificial intelligence effects on circular economy actions," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4772-4782, September.

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