Benn R. Konsynski ([email protected]; corresponding author) is the George S. Craft Distinguished University Professor of Information Systems and Operations Management at the Goizueta Business School, Emory University. He holds a PhD in Computer Science from Purdue University, and held faculty positions at the University of Arizona and Harvard Business School. He was also named Baxter Research Fellow at Harvard and Hewlett Fellow at The Carter Center, and has served as adviser to and board member of public and private corporations. Dr. Konsynski specializes in digital commerce and information technology in relationships across organizations, and has published in such diverse journals as Journal of Management Information Systems, Communications of the ACM, Harvard Business Review, MIS Quarterly, Data Communications, Decision Sciences, Information Systems Research, IEEE Transactions on Software Engineering, and IEEE Transactions on Communications.
Abhishek Kathuria ([email protected]) is an Assistant Professor of Information Systems and ISB Alumni Endowment Research Fellow at the Indian School of Business. He received his PhD from the Goizueta Business School at Emory University. Dr. Kathuria’s research examines the antecedents and business value of firm-level IT, focusing on innovation, globalization, and emerging economies. His work has been accepted in such journals as Journal of Management Information Systems, MIS Quarterly, Information Systems Research, Journal of the Association for Information Systems, and Production and Operations Management, among others, and received multiple best paper nominations and awards at various academic conferences. Dr. Kathuria has co-founded & advised multiple startups, and consults on business and digital transformation and organizational turnarounds with public and private corporations in GREAT (growing, rural, eastern, aspirational, transitional) domains such as India, China, and the Middle East.
Prasanna P. Karhade ([email protected]) is an Associate Professor at the CUHK Business School of Chinese University of Hong Kong. He received his PhD from the University of Illinois Urbana-Champaign. Dr. Karhade is interested in digital innovation, design thinking, and entrepreneurship. His research has been published in top academic journals such as Information Systems Research (ISR), Journal of Management Information Systems (JMIS), and MIS Quarterly (MISQ). According to the rankings based on the Association for Information Systems’ List of High-Quality Journals (ISR, JMIS, and MISQ) from 2019-2021, he is placed in the top 1 percent of scholars worldwide. He has started a major research program on micro-finance for GREAT economies. Prasanna loves hiking and practices meditation under the guidance of Ajahn Brahm.
IntroductionAs we enter the age of generative artificial intelligence (AI) and its ever-more powerful successors, the allocation of decision rights will undergo a profound transformation. The past few years have witnessed rapid progress in AI and our ability to leverage its capabilities in diverse systems. As a result, AI used to aid in the automation of tasks and to assist in decision making, is becoming increasingly capable of making decisions independently, without human intervention [2, 3, 18]. The expectations from this technology are unprecedented, with prognosticators envisaging futures wherein AI will have human-like interactional competencies and take over most forms of repetitive, creative, and decision tasks from humans [4, 7]. For decades, we have built trust in leveraging these abilities in systems of transport and system control. However, this raises important questions about how decision rights should be allocated between humans and AI. To manifest the potential of AI, there is a need to consider the employment of these capabilities in the redesign of organizations and decision making within them.
In the near future, we are likely to see a number of different ways to allocate decision rights between humans and AI [17]. One possibility is that AI systems will be used to make decisions in collaboration with humans. Humans will provide the overall direction and guidance, while the AI will handle the details. Another possibility is that AI systems will be given more autonomy to make decisions independently, with humans providing oversight and review. Yet another possibility is that humans and AI systems will independently handle different portions of a decision, with the final solution being an ensemble of the two outputs. How decision rights are allocated will have a significant impact on organizations, commerce, and society. For example, if AI systems are given too much autonomy, they could make decisions that are harmful to humans. On the other hand, if humans are reluctant to cede decision rights to AI, we may miss out on the potential benefits of AI, such as increased efficiency, productivity, and safety effects. However, time is of the essence; it is essential to consider the allocation of decision-making authority between humans and AI before sophisticated AI systems begin to make decisions beyond our understanding or control.
One way to organize our thinking about the allocation of decision rights is to consider the different types of decisions that must be made. Some decisions are relatively routine and can be safely delegated to AI systems. For example, AI systems are already used to make decisions about pricing products, routing traffic, and managing inventory. Other decisions are more complex and require human judgment and discretion [22]. For example, decisions about hiring employees, launching new products, and setting corporate strategy are still typically made by humans. As AI systems become more sophisticated, it is likely that they will be able to make more and more complex decisions on their own [25]. This implies that the line between decisions that are made by humans and decisions that are made by AI is likely to become increasingly blurred.
The distinction between decision types, when viewed in conjunction with the historical endeavors on cognitive reapportionment [8–10, 13], can serve as a lodestar for theorizing a framework for the allocation of decision rights. Cognitive reapportionment is “the design of organizational decision-making processes to allocate decision rights and authorities across humans, systems, and combinations of humans and systems in a way that optimizes the use of cognitive resources” [12, p. 3]. This manuscript offers scholars a theoretical edifice to design future organizations that shall consist of cognition and intelligence spread across humans and technical systems. It serves as the introduction to the special section on Cognitive Reapportionment in the Era of Generative AI and provides a setting for the portfolio of the four papers constituting this special section and the framework that embraces the portfolio and raises challenges and opportunities for others going forward. As we stand at the cusp of a new era in AI-based managerial decision-making, it is pertinent for us to challenge our way of conceptualizing business practice and decision-making therein, while learning the art of letting go.
The rest of this paper is organized as follows. We first present a definitional background on intelligent systems, cognition, apportionment, and reapportionment. We then articulate a middle way for future business practice through the swollen middle consisting of the coexistence of human and machine actors intertwined via cognitive reapportionment. Next, we present our Helix Model of Decision Journeys and explicate how cognitive reapportionment depends on the type of scientific inquiry at each decision element. We propose two intertwined mechanisms: Technology Trust Thresholds and Redesign in the Remix Regime, for the art of letting go, that facilitate deployment of the Helix model. Finally, we introduce the four papers in this special section and draw their varied connections to the concept and context of cognitive reapportionment.
Acknowledgment
This special section consists of a selection of the best research that was initially presented at the Workshop on e-Business. The author teams of the nine best papers out of thirty-one submitted their revised work, which then underwent several rounds of revisions, with each paper refereed by three reviewers and the three editors of the special section. We express our gratitude to all the authors and reviewers for their contributions. The four papers that appear in this special section represent the very best of the research from this endeavor. We conclude by acknowledging and thanking Devina Chaturvedi for her assiduous and excellent editorial assistance and research support.
Disclosure statement
No potential conflict of interest was reported by the author(s).
References
1.Anandarajan, M. Profiling Web usage in the workplace: A behavior-based artificial intelligence approach. Journal of Management Information Systems, 19, 1 (2002), 243–266.
2.Baird, A., and Maruping, L.M. The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45, 1 (2021), 315–341.
3.Berente, N.; Gu, B.; Recker, J., and Santhanam, R. Managing artificial intelligence. MIS Quarterly, 45, 3 (2021), 1433–1450.
4.Chandra, S.; Shirish, A.; and Srivastava, S. C. To be or not to be … human? Theorizing the role of human-like competencies in conversational artificial intelligence agents. Journal of Management Information Systems, 39, 4 (2022), 969–1005.
5.Chomsky, N. Three models for the description of language. IRE Transactions on Information Theory, 2, 3 (1956), 113–124.
6.Clippinger, J.; and Konsynski, B.R. Information refineries. Computerworld, 28 (1989), 73.
7.Dennis, A.R.; Lakhiwal, A.; and Sachdeva, A. AI agents as team members: Effects on satisfaction, conflict, trustworthiness, and willingness to work with. Journal of Management Information Systems, 40, 2 (2023), 307–337.
8.Elofson, G.; and Konsynski, B. Delegation technologies: Environmental scanning with intelligent agents. Journal of Management Information Systems, 8, 1 (1991), 37–62.
9.Fedorowicz, J.; and Konsynski, B. Organization support systems: Bridging business and decision processes. Journal of Management Information Systems, 8, 4 (1992), 5–25.
10.Fjelstad, O.D., and Konsynski, B.R. The role of cognitive apportionment in information systems. In The Proceedings of the International Conference on Information Systems, 1986. San Diego, CA, USA.
11.Kathuria, A.; Karhade, P.P.; and Konsynski, B.R. In the realm of hungry ghosts: Multi-level theory for supplier participation on digital platforms. Journal of Management Information Systems, 37, 2 (2020), 396–430.
12.Konsynski, B.; and Sviokla, J. Cognitive reapportionment: Rethinking the location of judgement in managerial decision making. Harvard Business School Discussion Draft, Boston: Harvard University, 1993.
13.Konsynski, B.R., and Fjeldstad, O.D. Reapportionment of cognitive responsibilities in DSS dialogues. In H. Sol and E. McLean (eds.), Decision Support Systems: A Decade in Perspective. Amsterdam, Netherlands: Elsevier Publishers, 1986, pp. 183–203.
14.Mousavi, R.; Raghu, T.; and Frey, K. Harnessing artificial intelligence to improve the quality of answers in online question-answering health forums. Journal of Management Information Systems, 37, 4 (2020), 1073–1098.
15.Newell, A.; and Simon, H. The logic theory machine—A complex information processing system. IRE Transactions on Information Theory, 2, 3 (1956), 61–79.
16.Peirce, C.S. Collected Papers of Charles Sanders Peirce. Cambridge, MA: Harvard University Press, 1974.
17.Rai, A.; Constantinides, P.; and Sarker, S. Next generation digital platforms: Toward human-AI hybrids. MIS Quarterly, 43, 1 (2019), iii–ix.
18.Ransbotham, S.; Kiron, D.; Gerbert, P., and Reeves, M. Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59, 1 (2017). 1–15.
19.Reichertz, J. Abduction: The logic of discovery of grounded theory. In A. Bryant and K. Charmaz, (eds.), The SAGE Handbook of Grounded Theory. 2007, pp. 214–228. London: Sage.
20.Revilla, E.; Saenz, M.J.; Seifter, M.; and Ma, Y. Human–Artificial intelligence collaboration in prediction: A field experiment in the retail industry. Journal of Management Information Systems, 40, 4 (2023), 1071–1098.
21.Schuetzler, R.M.; Grimes, G.M.; and Scott Giboney, J. The impact of chatbot conversational skill on engagement and perceived humanness. Journal of Management Information Systems, 37, 3 (2020), 875–900.
22.Senoner, J.; Netland, T.; and Feuerriegel, S. Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Science, 68, 8 (2022), 5704–5723.
23.Simon, H.A. The Sciences of the Artificial. Cambridge, MA: MIT Press, 1996.
24.Stohr, E.A., and Konsynski, B.R. Information Systems and Decision Processes. IEEE Computer Society Press, 1992. Washington D.C.
25.Tofangchi, S.; Hanelt, A.; Marz, D.; and Kolbe, L.M. Handling the efficiency–personalization trade-off in service robotics: A machine-learning approach. Journal of Management Information Systems, 38, 1 (2021), 246–276.
Key words and phrases: Artificial intelligence, AI, organizational design, decision rights, cognition, intelligent systems, cognitive reapportionment, human-AI decisions