IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v197y2023ics0040162523005632.html
   My bibliography  Save this article

Implementing and scaling artificial intelligence: A review, framework, and research agenda

Author

Listed:
  • Haefner, Naomi
  • Parida, Vinit
  • Gassmann, Oliver
  • Wincent, Joakim

Abstract

Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.

Suggested Citation

  • Haefner, Naomi & Parida, Vinit & Gassmann, Oliver & Wincent, Joakim, 2023. "Implementing and scaling artificial intelligence: A review, framework, and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005632
    DOI: 10.1016/j.techfore.2023.122878
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523005632
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122878?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    2. Boothby, Daniel & Dufour, Anik & Tang, Jianmin, 2010. "Technology adoption, training and productivity performance," Research Policy, Elsevier, vol. 39(5), pages 650-661, June.
    3. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    4. Tatiana Beliaeva & Galina Shirokova & William Wales & Elena Gafforova, 2020. "Benefiting from economic crisis? Strategic orientation effects, trade-offs, and configurations with resource availability on SME performance," International Entrepreneurship and Management Journal, Springer, vol. 16(1), pages 165-194, March.
    5. Berg, Andrew & Buffie, Edward F. & Zanna, Luis-Felipe, 2018. "Should we fear the robot revolution? (The correct answer is yes)," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 117-148.
    6. Ansari, Shahzad & Garud, Raghu, 2009. "Inter-generational transitions in socio-technical systems: The case of mobile communications," Research Policy, Elsevier, vol. 38(2), pages 382-392, March.
    7. Nikolas Zolas & Zachary Kroff & Erik Brynjolfsson & Kristina McElheran & David Beede & Catherine Buffington & Nathan Goldschlag & Lucia Foster & Emin Dinlersoz, 2020. "Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey," Working Papers 20-40, Center for Economic Studies, U.S. Census Bureau.
    8. Greg Fisher & Regan Stevenson & Emily Neubert & Devin Burnell & Donald F. Kuratko, 2020. "Entrepreneurial Hustle: Navigating Uncertainty and Enrolling Venture Stakeholders through Urgent and Unorthodox Action," Journal of Management Studies, Wiley Blackwell, vol. 57(5), pages 1002-1036, July.
    9. Geels, Frank W., 2002. "Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study," Research Policy, Elsevier, vol. 31(8-9), pages 1257-1274, December.
    10. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    11. Jay Dixon & Bryan Hong & Lynn Wu, 2021. "The Robot Revolution: Managerial and Employment Consequences for Firms," Management Science, INFORMS, vol. 67(9), pages 5586-5605, September.
    12. Sony, Michael & Naik, Subhash, 2020. "Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model," Technology in Society, Elsevier, vol. 61(C).
    13. Xing, Fei & Peng, Guochao & Zhang, Bingqian & Li, Shuyang & Liang, Xinting, 2021. "Socio-technical barriers affecting large-scale deployment of AI-enabled wearable medical devices among the ageing population in China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    14. Münch, Christopher & Marx, Emanuel & Benz, Lukas & Hartmann, Evi & Matzner, Martin, 2022. "Capabilities of digital servitization: Evidence from the socio-technical systems theory," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    15. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    16. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    17. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    18. André Hanelt & René Bohnsack & David Marz & Cláudia Antunes Marante, 2021. "A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change," Journal of Management Studies, Wiley Blackwell, vol. 58(5), pages 1159-1197, July.
    19. Stella Pachidi & Hans Berends & Samer Faraj & Marleen Huysman, 2021. "Make Way for the Algorithms: Symbolic Actions and Change in a Regime of Knowing," Organization Science, INFORMS, vol. 32(1), pages 18-41, January.
    20. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
    21. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nazareno, Luísa & Schiff, Daniel S., 2021. "The impact of automation and artificial intelligence on worker well-being," Technology in Society, Elsevier, vol. 67(C).
    2. Shepherd, Dean A. & Majchrzak, Ann, 2022. "Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship," Journal of Business Venturing, Elsevier, vol. 37(4).
    3. Nils Grashof & Alexander Kopka, 2023. "Artificial intelligence and radical innovation: an opportunity for all companies?," Small Business Economics, Springer, vol. 61(2), pages 771-797, August.
    4. Lupp, Daniel, 2023. "Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    5. Belloc, Filippo & Burdin, Gabriel & Landini, Fabio, 2022. "Robots, Digitalization, and Worker Voice," GLO Discussion Paper Series 1038, Global Labor Organization (GLO).
    6. Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    7. Florian Knobloch & Hector Pollitt & Unnada Chewpreecha & Vassilis Daioglou & Jean-Francois Mercure, 2017. "Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C," Papers 1710.11019, arXiv.org, revised May 2018.
    8. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    9. Jain, Sanjay, 2020. "Fumbling to the future? Socio-technical regime change in the recorded music industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    10. Sergi, Bruno S. & Ključnikov, Aleksandr & Popkova, Elena G. & Bogoviz, Aleksei V. & Lobova, Svetlana V., 2022. "Creative abilities and digital competencies to transitioning to Business 4.0," Journal of Business Research, Elsevier, vol. 153(C), pages 401-411.
    11. Chenhui Ding & Xiaoming Song & Yingchun Xing & Yuxuan Wang, 2023. "Bilateral Effects of the Digital Economy on Manufacturing Employment: Substitution Effect or Creation Effect?," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
    12. Usabiaga, Carlos & Núñez, Fernando & Arendt, Lukasz & Gałecka-Burdziak, Ewa & Pater, Robert, 2022. "Skill requirements and labour polarisation: An association analysis based on Polish online job offers," Economic Modelling, Elsevier, vol. 115(C).
    13. Cebreros Alfonso & Heffner-Rodríguez Aldo & Livas René & Puggioni Daniela, 2020. "Automation Technologies and Employment at Risk: The Case of Mexico," Working Papers 2020-04, Banco de México.
    14. Marcel Steffen Eckardt, 2022. "Minimum wages in an automating economy," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(1), pages 58-91, February.
    15. Vögele, Stefan & Poganietz, Witold-Roger & Kleinebrahm, Max & Weimer-Jehle, Wolfgang & Bernhard, Jesse & Kuckshinrichs, Wilhelm & Weiss, Annika, 2022. "Dissemination of PV-Battery systems in the German residential sector up to 2050: Technological diffusion from multidisciplinary perspectives," Energy, Elsevier, vol. 248(C).
    16. Lihua Zhang & Tian Gan & Jiachen Fan, 2023. "Do industrial robots affect the labour market? Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(3), pages 787-817, July.
    17. Santos, Susana C. & Liguori, Eric W. & Garvey, Erin, 2023. "How digitalization reinvented entrepreneurial resilience during COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    18. Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    19. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    20. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005632. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.