AI-Supported Organizational Decision-Making

AI-Supported Organizational Decision-Making

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Decision-making is a crucial skill for managers and organizations, but the following issues also make it challenging: bounded rationality, cognitive biases, information overload, and various others [1–3]. A large body of research has therefore investigated how managers and organizations can be supported when making decisions. One approach is the use of information systems, commonly referred to as decision support systems (DSS), which can be defined as “any computerized system that supports decision-making in an organization” [4].

Artificial Intelligence (AI) is currently an intensely discussed topic in both research and practice due to recent breakthroughs in machine learning (ML) and the increased availability of large amounts of data and processing power [5]. Various authors have posited that AI will transform the way that organizational decisions are made and will redefine the role of managers [6] [7, 8]. Specifically, over the past decade, researchers have begun exploring DSS that incorporate AI techniques, known as Intelligent Decision Support Systems (IDSS) [9].

While there is a significant amount of research on decision-making, DSS, AI applications, and even some on applying AI to support decision-making, there is, nonetheless, a noteworthy research gap. The existing publications focus on a technical perspective and/or present single applications of AI to support decision-making in specific contexts and do not offer theories that could explain or predict AI techniques’ effect on organizational decision-making. Interestingly, it appears that many of the research gaps in the field identified almost 25 years ago are still relevant [10]. Certain scholars argue that while companies increasingly adopt AI solutions, there is still a lack of understanding of the “practical issues associated with the interaction of AI, management, and organizations”[10].

Multiple commercial AI-based systems, such as IBM Watson and Salesforce Einstein, can support decision-making [11–13]. Their usage underscores this topic’s practical relevance and the need for researchers to study how organizations use these systems and the effects they have on decisions and decision-making processes. Investigating how and the degree to which AI can support decision-making is therefore a promising research topic.

Furthermore, practitioner-oriented outlets frequently maintain that the use of AI changes decision-making processes. These claims are not scientific hypotheses and, at this point, mere speculations that should be critically considered. Practitioners usually distinguish between AI’s ability to automate or augment decision-making, with automation meaning that AI systems can make decisions independently, without the need for human input, whereas augmentation requires collaboration between AI-based systems and human decision-makers [14, 15]. A common claim from practice is that while some routine and operational decisions can be automated, more challenging and less structured decisions require augmentation [14]. In our research, we focus on augmented decision-making involving, which require both humans and AI-based DSS.

Other researchers [16] argue that IDSS can have a significant effect on the outcomes, as well as on the process of decision-making. We agree that it is prudent to investigate both aspects. However, other researchers [17] argue that organizations can only generate value from business analytics if the organizations at the same time transform their business processes accordingly. Thus, these researchers call on scholars to investigate how the related processes, organizational structures, and routines could influence organizations’ abilities to deliver insights and value. We therefore aim to analyze the use of AI for organizational decision support with a comprehensive look at the related processes, structures, and routines.

Duration

2017 – ongoing

Publications

-

Student contributions

Jonas Kuhlmann, M. Sc.

References

  1. Anderson, D.R. ed: Quantitative methods for business. South-Western Cengage Learning, Mason, OH (2009).
  2. Cooke, S., Slack, N.: Making management decisions. (1991).
  3. Stanovich, K.E.: Decision making and rationality in the modern world. Oxford University Press, New York (2010).
  4. Turban, E., Aronson, J.E., Liang, T.-P.: Decision support systems and intelligent systems. Prentice-Hall : Pearson, Upper Saddle River (2005).
  5. Gurkaynak, G., Yilmaz, I., Haksever, G.: Stifling artificial intelligence: Human perils. Comput. Law Secur. Rev. 32, 749–758 (2016).
  6. Ryan Shanks, Sunit Sinha, Robert J. Thomas: Judgment Calls: Preparing Leaders to Thrive in the Age of Intelligent Machines. Accenture Institute for High Performance (2016).
  7. Dewhurst, M., Willmott, P.: Manager and machine: The new leadership equation | McKinsey & Company, www.mckinsey.com/global-themes/leadership/manager-and-machine.
  8. Kolbjørnsrud, V., Amico, R., Thomas, R.J.: The promise of artificial intelligence. (2016).
  9. Phillips-Wren, G., Jain, L.: Knowledge-based intelligent information and engineering systems: 10th international conference, KES 2006, Bournemouth, UK, October 9-11, 2006: proceedings. Springer, Berlin ; New York (2006).
  10. Duchessi, P., Keefe, R.O.’, Leary, D.O.’: A Research Perspective: Artificial Intelligence, Management and Organizations. ResearchGate. 2, (1993).
  11. Benaich, N.: Investing In Artificial Intelligence, social.techcrunch.com/2015/12/25/investing-in-artificial-intelligence/, (2015).
  12. Carey, S.: Inside Google DeepMind’s Latest Attempts to Achieve a General Artificial Intelligence, www.techworld.com/big-data/inside-deepminds-latest-attempts-achieve-general-artificial-intelligence-3646730/.
  13. Sinai, J.: Introducing Salesforce Einstein–AI for Everyone, www.salesforce.com/blog/2016/09/introducing-salesforce-einstein.html, (2016).
  14. Bataller, C., Harris: Turning Artificial  Intelligence into  Business Value.  Today. (2016).
  15. Kolbjørnsrud, V., Amico, R., Robert, T.: How Artificial Intelligence Will Redefine Management.
  16. Phillips-Wren, G., Mora, M., Forgionne, G.A., Garrido, L., Gupta, J.N.D.: A Multicriteria Model for the Evaluation of Intelligent Decision-making Support Systems (i-DMSS). In: Intelligent Decision-making Support Systems. pp. 3–24. Springer London, London (2006).
  17. Sharma, R., Mithas, S., Kankanhalli, A.: Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. Eur. J. Inf. Syst. 23, 433–441 (2014).