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The AI Advantage: How to Put the Artificial Intelligence Revolution to Work

Author

Listed:
  • Davenport, Thomas H.

    (Babson College)

Abstract

In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM’s Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don’t go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won’t replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.

Suggested Citation

  • Davenport, Thomas H., 2018. "The AI Advantage: How to Put the Artificial Intelligence Revolution to Work," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262039176, April.
    • Handle: RePEc:mtp:titles:0262039176
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    Cited by:

    1. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    2. Ulrich Lichtenthaler, 2020. "Building Blocks of Successful Digital Transformation: Complementing Technology and Market Issues," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-14, February.
    3. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    4. Karim, Mohammed Shamsul & Nahar, Sharmin & Demirbag, Mehmet, 2022. "Resource-Based Perspective on ICT Use and Firm Performance: A Meta-analysis Investigating the Moderating Role of Cross-Country ICT Development Status," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    5. Milad Mirbabaie & Stefan Stieglitz & Felix Brünker & Lennart Hofeditz & Björn Ross & Nicholas R. J. Frick, 2021. "Understanding Collaboration with Virtual Assistants – The Role of Social Identity and the Extended Self," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 21-37, February.
    6. Peter Buxmann & Thomas Hess & Jason Thatcher, 2019. "Call for Papers, Issue 1/2021," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 545-547, August.
    7. Katharina Blöcher & Rainer Alt, 2021. "AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 529-551, September.
    8. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    9. Gilbert Giacomoni, 2022. "Towards a general framework for innovation shaped with AI to create and transform market offerings [Vers un cadre général d’innovation façonné par l’IA pour créer et transformer les offres du march," Post-Print hal-04083376, HAL.
    10. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    11. Omid Omidvar & Mehdi Safavi & Vern L. Glaser, 2023. "Algorithmic Routines and Dynamic Inertia: How Organizations Avoid Adapting to Changes in the Environment," Journal of Management Studies, Wiley Blackwell, vol. 60(2), pages 313-345, March.
    12. Dhruv Grewal & John Hulland & Praveen K. Kopalle & Elena Karahanna, 2020. "The future of technology and marketing: a multidisciplinary perspective," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 1-8, January.
    13. Achrol, Ravi S. & Kotler, Philip, 2022. "Distributed marketing networks: The fourth industrial revolution," Journal of Business Research, Elsevier, vol. 150(C), pages 515-527.
    14. Liwei Chen & J. J. Po-An Hsieh & Arun Rai, 2022. "How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience," Information Systems Research, INFORMS, vol. 33(3), pages 1042-1071, September.
    15. Riccardo Camilli & Hira Salah ud din Khan, 2023. "Book Review," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2023(2), pages 137-144.
    16. Kimberly Alba Mc Cord, 2019. "The personalized medicine challenge: shifting to population health through real-world data," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(9), pages 1255-1256, December.
    17. Ming-Ching Hsu, 2023. "The Construction of Critical Factors for Successfully Introducing Chatbots into Mental Health Services in the Army: Using a Hybrid MCDM Approach," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    18. Holmström, Jonny, 2022. "From AI to digital transformation: The AI readiness framework," Business Horizons, Elsevier, vol. 65(3), pages 329-339.
    19. Gokhan Ozkaya & Ayse Demirhan, 2023. "Analysis of Countries in Terms of Artificial Intelligence Technologies: PROMETHEE and GAIA Method Approach," Sustainability, MDPI, vol. 15(5), pages 1-27, March.
    20. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.
    21. Mehran Farzadmehr & Valentin Carlan & Thierry Vanelslander, 2023. "Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-44, December.
    22. Zeyneb GUELLIL & Sarah BOURI, 2024. "The role of artificial intelligence in shaping Islamic finance services," Management Intercultural, Romanian Foundation for Business Intelligence, Editorial Department, issue 53, pages 53-61, December.

    More about this item

    Keywords

    artificial intelligence; AI; cognitive technology; cognitive technologies; future of work; business strategy; business process reengineering; worker automation; deep learning; natural language processing; expert systems; robots; robotic process automation; statistical machine learning; neural networks; worker augmentation; enterprise AI;
    All these keywords.

    JEL classification:

    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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