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

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 adopti...

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Bibliographic Details
Published in:Business horizons Vol. 63; no. 2; pp. 183 - 193
Main Authors: Canhoto, Ana Isabel, Clear, Fintan
Format: Journal Article
Language:English
Published: Elsevier Inc 01.03.2020
Subjects:
Artificial intelligence
Chatbot
Decision making
Enterprise value
Machine learning
Technology adoption
Value creation
Value destruction
Technology adoption
Value destruction
Decision making
Machine learning
Value creation
Chatbot
Artificial intelligence
Enterprise value
ISSN:0007-6813, 1873-6068
Online Access:Get full text
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Summary: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.
ISSN:0007-6813
1873-6068
DOI:10.1016/j.bushor.2019.11.003