IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v63y2020i2p183-193.html

Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential

Author

Listed:
  • Canhoto, Ana Isabel
  • Clear, Fintan

Abstract

Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system’s inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AI-powered chatbot in customer service, and we discuss how to remedy the problems that arise.

Suggested Citation

  • Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:183-193
    DOI: 10.1016/j.bushor.2019.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681319301570
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2019.11.003?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    2. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    3. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    4. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    5. Arun Rai & Xinlin Tang, 2014. "Research Commentary ---Information Technology-Enabled Business Models: A Conceptual Framework and a Coevolution Perspective for Future Research," Information Systems Research, INFORMS, vol. 25(1), pages 1-14, March.
    6. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    7. Merendino, Alessandro & Dibb, Sally & Meadows, Maureen & Quinn, Lee & Wilson, David & Simkin, Lyndon & Canhoto, Ana, 2018. "Big data, big decisions: The impact of big data on board level decision-making," Journal of Business Research, Elsevier, vol. 93(C), pages 67-78.
    8. Monica Johar & Vijay Mookerjee & Sumit Sarkar, 2014. "Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization," Information Systems Research, INFORMS, vol. 25(2), pages 285-306, June.
    9. Christian Homburg & Danijel Jozić & Christina Kuehnl, 2017. "Customer experience management: toward implementing an evolving marketing concept," Journal of the Academy of Marketing Science, Springer, vol. 45(3), pages 377-401, May.
    10. Cristina Alaimo & Jannis Kallinikos, 2017. "Computing the everyday: social media as data platforms," LSE Research Online Documents on Economics 81432, London School of Economics and Political Science, LSE Library.
    11. Raphael Amit & Christoph Zott, 2001. "Value creation in E‐business," Strategic Management Journal, Wiley Blackwell, vol. 22(6‐7), pages 493-520, June.
    12. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    13. Christoph Zott & Raphael Amit, 2007. "Business Model Design and the Performance of Entrepreneurial Firms," Organization Science, INFORMS, vol. 18(2), pages 181-199, April.
    14. Richard J. Arend, 2003. "Revisiting the logical and research considerations of competitive advantage," Strategic Management Journal, Wiley Blackwell, vol. 24(3), pages 279-284, March.
    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. Canhoto, Ana Isabel, 2021. "Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective," Journal of Business Research, Elsevier, vol. 131(C), pages 441-452.
    2. Costa Climent, Ricardo & Haftor, Darek M., 2021. "Value creation through the evolution of business model themes," Journal of Business Research, Elsevier, vol. 122(C), pages 353-361.
    3. Landoni, Paolo & Dell’era, Claudio & Frattini, Federico & Messeni Petruzzelli, Antonio & Verganti, Roberto & Manelli, Luca, 2020. "Business model innovation in cultural and creative industries: Insights from three leading mobile gaming firms," Technovation, Elsevier, vol. 92.
    4. Snihur, Yuliya & Lamine, Wadid & Wright, Mike, 2021. "Educating engineers to develop new business models: Exploiting entrepreneurial opportunities in technology-based firms," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    5. Sarel Gronum & John Steen & Martie-Louise Verreynne, 2016. "Business model design and innovation: Unlocking the performance benefits of innovation," Australian Journal of Management, Australian School of Business, vol. 41(3), pages 585-605, August.
    6. Mark Loon & Xiaohong Iris Quan, 2021. "Theorising business model innovation: An integrated literature review," Australian Journal of Management, Australian School of Business, vol. 46(3), pages 548-577, August.
    7. Baoliang Hu & Wei Huang & Shuai Yan & Guang Liu & Tao Zhang, 2020. "Business Model Design and Customer Loyalty: The Mediating Role of Customer Citizenship Behavior," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    8. Cinzia Battistella & Gianluca Murgia & Fabio Nonino, 2021. "Free-driven web-based business models," Electronic Commerce Research, Springer, vol. 21(2), pages 445-486, June.
    9. Meena Madhavan & Mohammed Ali Sharafuddin & Thanapong Chaichana, 2022. "Impact of Business Model Innovation on Sustainable Performance of Processed Marine Food Product SMEs in Thailand—A PLS-SEM Approach," Sustainability, MDPI, vol. 14(15), pages 1-33, August.
    10. Jianyu Zhao & Yining Huang & Xi Xi & Shanshan Wang, 2021. "How knowledge heterogeneity influences business model design: mediating effects of strategic learning and bricolage," International Entrepreneurship and Management Journal, Springer, vol. 17(2), pages 889-919, June.
    11. Marko Peric & Jelena Durkin & Vanja Vitezic, 2017. "The Constructs of a Business Model Redefined: A Half-Century Journey," SAGE Open, , vol. 7(3), pages 21582440177, September.
    12. Bingqin Dai & Wenquan Liang, 2022. "The Impact of Big Data Technical Skills on Novel Business Model Innovation Based on the Role of Resource Integration and Environmental Uncertainty," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    13. Zhang, Yucheng & Zhang, Meng & Li, Jing & Liu, Guangjian & Yang, Miles M. & Liu, Siqi, 2021. "A bibliometric review of a decade of research: Big data in business research – Setting a research agenda," Journal of Business Research, Elsevier, vol. 131(C), pages 374-390.
    14. Jianmin Song & Senmao Xia & Demetris Vrontis & Arun Sukumar & Bing Liao & Qi Li & Kun Tian & Nengzhi Yao, 2022. "The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models," Information Systems Frontiers, Springer, vol. 24(4), pages 1167-1187, August.
    15. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    16. Costa Climent, Ricardo & Haftor, Darek M., 2021. "Business model theory-based prediction of digital technology use: An empirical assessment," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    17. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    18. Mika Gabrielsson & Saara Julkunen & Svante Andersson, 2025. "The Influence of Entrepreneurial Mindsets and Business Model Practices on Firm Internationalization," Management International Review, Springer, vol. 65(2), pages 239-274, April.
    19. Candi, Marina & Kitagawa, Fumi, 2022. "Performance implications of business model centrality over technology-based firms’ life courses," Technovation, Elsevier, vol. 118(C).
    20. Belitski, Maksmi & Mariani, Marcello, 2023. "The effect of knowledge collaboration on business model reconfiguration," European Management Journal, Elsevier, vol. 41(2), pages 223-235.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:bushor:v:63:y:2020:i:2:p:183-193. 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.elsevier.com/locate/bushor .

    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.