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7 - Organizing Intelligent Digital Actors

Published online by Cambridge University Press:  09 November 2023

Charles C. Snow
Affiliation:
Pennsylvania State University
Øystein D. Fjeldstad
Affiliation:
BI Norwegian Business School
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Summary

The ability to organize is our most valuable social technology. Organizing affects an enterprise’s efficiency, effectiveness, and ability to adapt. Modern organizations operate in increasingly complex, dynamic environments, which puts a premium on adaptation. Compared to traditional organizations, modern organizations are flatter and more open to their environment. Their processes are more generative and interactive – actors themselves generate and coordinate solutions rather than follow hierarchically devised plans and directives. Modern organizations search outside their boundaries for resources wherever they may exist. They coproduce products and services with suppliers, customers, and partners. They collaborate, both internally and externally, to learn and become more capable. In this book, leading voices in the field of organization design articulate and exemplify how a combination of agile processes, artificial intelligence, and digital platforms powers adaptive, sustainable, and healthy organizations.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Abbass, H. A. 2019. Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust. Cognitive Computation 11(2): 159171.Google Scholar
Acemoglu, D. & Restrepo, P. 2019. Automation and new tasks: how technology displaces and reinstates labor. Journal of Economic Perspectives 33(2): 330.CrossRefGoogle Scholar
Agrawal, A., Gans, J., & Goldfarb, A. 2019. Economic policy for artificial intelligence. Innovation Policy and the Economy 19(1): 139159.Google Scholar
Amabile, T. 2020. Guidepost: creativity, artificial intelligence, and a world of surprises. Academy of Management Discoveries 6(3): 351354.Google Scholar
Andersen, E., Johnson, J. C., Kolbjørnsrud, V., & Sannes, R. 2018. The data-driven organization: intelligence at SCALE. In Sasson, A. (ed.), At the Forefront, Looking Ahead: 2342. Universitetsforlaget, Oslo, Norway.CrossRefGoogle Scholar
Bast, H., Delling, D., Goldberg, A. et al. 2016. Route planning in transportation networks. In Kliemann, L. and Sanders, P. (eds.), Algorithm Engineering: 19–80. Springer, Berlin, Germany.Google Scholar
Blomqvist, K. & Levy, J. 2006. Collaboration capability – a focal concept in knowledge creation and collaborative innovation in networks. International Journal of Management Concepts and Philosophy 2(1): 3148.CrossRefGoogle Scholar
Bonabeau, E. & Théraulaz, G. 2000. Swarm smarts. Scientific American 282(3): 7279.Google Scholar
Brynjolfsson, E. & McAfee, A. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, New York, NY.Google Scholar
Buolamwini, J. & Gebru, T. 2018. Gender shades: intersectional accuracy disparities in commercial gender classification. ACM Conference on Fairness, Accountability and Transparency, New York, NY, 77–91.Google Scholar
Byrne, C. 2018. This AI helps find great startups before the world discovers them [Online]. Fast Company. www.fastcompany.com/40588028/this-ai-helps-find-great-startups-before-the-world-discovers-them.Google Scholar
Colford, P. 2014. A leap forward in quarterly earnings stories [Online]. Associated Press. https://blog.ap.org/announcements/a-leap-forward-in-quarterly-earnings-stories.Google Scholar
Cowgill, B. 2019. Bias and productivity in humans and machines. Columbia Business School, Research Paper. https://dx.doi.org/10.2139/ssrn.3584916CrossRefGoogle Scholar
Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. 2020. Working for an algorithm: power asymmetries and agency in online work settings. Administrative Science Quarterly 65(3): 644676.CrossRefGoogle Scholar
Daugherty, P. R. & Wilson, H. J. 2018. Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press, Boston, MA.Google Scholar
Davenport, T. 2020. The future of work now: Morgan Stanley’s financial advisors and the next best action system [Online]. Forbes. www.forbes.com/sites/tomdavenport/2020/05/16/the-future-of-work-now-morgan-stanleys-financial-advisors-and-the-next-best-offer-system/?sh=32bcba837027.Google Scholar
Delta. 2019. Delta TechOps expanding predictive maintenance capabilities with new Airbus partnership [Online]. Delta Air Lines. https://news.delta.com/delta-techops-expanding-predictive-maintenance-capabilities-new-airbus-partnership.Google Scholar
Endsley, M. R. 2017. From here to autonomy: lessons learned from human–automation research. Human Factors 59(1): 527.CrossRefGoogle Scholar
Engelbart, D. C. 1962. Augmenting Human Intellect: A Conceptual Framework. Stanford Research Institute, Menlo Park, CA.CrossRefGoogle Scholar
Esteva, A., Kuprel, B., Novoa, R. A. et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639): 115118.CrossRefGoogle ScholarPubMed
European Commission. 2021. Proposal for a regulation of the European Parliament and of the Council: laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. Brussels, Belgium. https://eur-lex.europa.eu/resource.html?uri=cellar:e0649735-a372-11eb-9585-01aa75ed71a1.0001.02/DOC_1&format=PDFGoogle Scholar
Evans, R. & Gao, J. 2016. DeepMind AI reduces Google data centre cooling bill by 40% [Online]. Deep Mind. https://deepmind.com/blog/deepmind-ai-reduces-google-datacentre-cooling-bill-40/.Google Scholar
Executive Office of the President. 2016. Artificial Intelligence, Automation, and the Economy. Executive Office of the President, United States of America, Washington, DC.Google Scholar
Fjeldstad, Ø. D., Snow, C. C., Miles, R. E., & Lettl, C. 2012. The architecture of collaboration. Strategic Management Journal 33(6): 734750.Google Scholar
Glikson, E. & Woolley, A. W. 2020. Human trust in artificial intelligence: review of empirical research. Academy of Management Annals 14(2): 627660.Google Scholar
Goldberg, K. 2012. What is automation? IEEE Transactions on Automation Science and Engineering 9(1): 12.CrossRefGoogle Scholar
Goldberg, K. 2019. Robots and the return to collaborative intelligence. Nature Machine Intelligence 1(1): 2.CrossRefGoogle Scholar
Goodman, B. & Flaxman, S. 2017. European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine 38(3): 5057.Google Scholar
Grønsund, T. & Aanestad, M. 2020. Augmenting the algorithm: emerging human-in-the-loop work configurations. The Journal of Strategic Information Systems 29(2): 116.CrossRefGoogle Scholar
Gunning, D. & Aha, D. 2019. DARPA’s explainable artificial intelligence (XAI) program. AI Magazine 40(2): 4458.CrossRefGoogle Scholar
Guo, W. & Caliskan, A. 2021. Detecting emergent intersectional biases: contextualized word embeddings contain a distribution of human-like biases. The 2021 AAAI/ACM Conference on AI, Ethics, and Society: 122–133.Google Scholar
Hodson, H. 2014. The AI boss that deploys Hong Kong’s subway engineers [Online]. New Scientist. www.newscientist.com/article/mg22329764-000-the-ai-boss-that-deploys-hong-kongs-subway-engineers/.Google Scholar
Hu, Y., Da, Q., Zeng, A., Yu, Y., & Xu, Y. 2018. Reinforcement learning to rank in e-commerce search engine: formalization, analysis, and application. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018: 368–377.CrossRefGoogle Scholar
Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. 2017. Imitation learning: a survey of learning methods. ACM Computing Surveys 50(2): 135.CrossRefGoogle Scholar
Kellogg, K., Valentine, M., & Christin, A. 2019. Algorithms at work: the new contested terrain of control. Academy of Management Annals 14(1): 366410.Google Scholar
Khalil, A., Ahmed, S. G., Khattak, A. M., & Al-Qirim, N. 2020. Investigating bias in facial analysis systems: a systematic review. IEEE Access 8130751-130761.CrossRefGoogle Scholar
Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. 2020. Algorithms as discrimination detectors. Proceedings of the National Academy of Sciences 117(48): 30096–30100.Google Scholar
Kolbjørnsrud, V. & Sannes, R. 2021. Augmented intelligence: the case of AI in early-stage property development. Strategic Management Society Annual Meeting, Toronto, Canada.Google Scholar
Kolbjørnsrud, V., Amico, R., & Thomas, R. J. 2017. Partnering with AI: how organizations can win over skeptical managers. Strategy & Leadership 45(1): 3743.Google Scholar
König, P. D. & Wenzelburger, G. 2021. The legitimacy gap of algorithmic decision-making in the public sector: why it arises and how to address it. Technology in Society 67(101688): 110.Google Scholar
Kwun, A. 2018. These chairs were designed by an AI bot, and they’re surprisingly good [Online]. Fast Company. www.fastcompany.com/90228357/these-chairs-were-designed-by-an-ai-bot-and-theyre-surprisingly-good.Google Scholar
Lee, J. D. & See, K. A. 2004. Trust in automation: designing for appropriate reliance. Human Factors 46(1): 5080.Google Scholar
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. 2018. Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology 31(4): 611627.CrossRefGoogle Scholar
Lin, T., Maire, M., Belongie, S. et al. 2014. Microsoft COCO: common objects in context. In Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (eds.), Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693: 740755. Springer, Cham, Switzerland.Google Scholar
Lindebaum, D., Vesa, M., & Den Hond, F. 2020. Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review 45(1): 247263.Google Scholar
Linsell, K. 2018. Meet the robot who knows how to trade bonds better than you do [Online]. Bloomberg. www.bloomberg.com/news/articles/2018-11-12/meet-the-robot-who-knows-how-to-trade-bonds-better-than-you-do.Google Scholar
Liu, M., Brynjolfsson, E., & Dowlatabadi, J. 2021. Do digital platforms reduce moral hazard? The case of Uber and taxis. Management Science 67(8): 46654685.Google Scholar
Lohia, P. K., Ramamurthy, K. N., Bhide, M., Saha, D., Varshney, K. R., & Puri, R. 2019. Bias mitigation post-processing for individual and group fairness. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 2847–2851.Google Scholar
Major, L. & Shah, J. 2020. What to Expect When You’re Expecting Robots: The Future of Human-Robot Collaboration. Basic Books, New York, NY.Google Scholar
Mason, R. O. 1969. A dialectical approach to strategic planning. Management Science 15(8): B-403–B-414.CrossRefGoogle Scholar
Metcalf, L., Askay, D. A., & Rosenberg, L. B. 2019. Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review 61(4): 84109.Google Scholar
Microsoft. 2020. Trigger hand-off to a live agent [Online]. Microsoft, Seattle, WA. https://docs.microsoft.com/en-us/power-virtual-agents/advanced-hand-off.Google Scholar
MiR. 2021. MiR launches two powerful autonomous mobile robots to optimize all logistics [Online]. Mobile Industrial Robots, Odense, Denmark. www.mobile-industrial-robots.com/en/about-mir/news/mir-launches-two-powerful-autonomous-mobile-robots-to-optimize-all-logistics/.Google Scholar
Moyer, C. 2016. How Google’s AlphaGo beat a Go world champion [Online]. The Atlantic. www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611.Google Scholar
Muro, M., Maxim, R., & Whiton, J. 2019. Automation and artificial intelligence: how machines are affecting people and places. Brookings Institution, Metropolitan Policy Program, Washington, DC.Google Scholar
Najibi, A. 2020. Racial discrimination in face recognition technology [Online]. Harvard University, The Graduate School of Arts and Sciences. https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/.Google Scholar
Newlands, G. 2021. Algorithmic surveillance in the gig economy: the organization of work through Lefebvrian conceived space. Organization Studies 42(5): 719737.CrossRefGoogle Scholar
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. 2011. The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decision Support Systems 50(3): 559569.Google Scholar
Nicolas-Alonso, L. F. & Gomez-Gil, J. 2012. Brain computer interfaces, a review. Sensors 12(2): 12111279.Google Scholar
Nilsson, N. J. 1998. Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers, San Francisco, CA.Google Scholar
O’Hear, S. 2019. Spacemaker scores $25M Series A to let property developers use AI [Online]. TechCrunch. https://techcrunch.com/2019/06/09/spacemaker/.Google Scholar
Overgoor, G., Chica, M., Rand, W., & Weishampel, A. 2019. Letting the computers take over: using AI to solve marketing problems. California Management Review 61(4): 156185.Google Scholar
Parasuraman, R. & Riley, V. 1997. Humans and automation: use, misuse, disuse, abuse. Human Factors 39(2): 230253.Google Scholar
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. 2000. A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 30(3): 286297.Google Scholar
Parker, S. K. & Grote, G. 2020. Automation, algorithms, and beyond: why work design matters more than ever in a digital world. Applied Psychology 71(4): 145.Google Scholar
Pasquale, F. 2015. The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge, MA.Google Scholar
Patel, B. N., Rosenberg, L., Willcox, G. et al. 2019. Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digital Medicine 2(111): 110.Google Scholar
Pawlowicz, L. M. & Downum, C. E. 2021. Applications of deep learning to decorated ceramic typology and classification: a case study using Tusayan White Ware from Northeast Arizona. Journal of Archaeological Science 130(105375): 114.CrossRefGoogle Scholar
Raj, M. & Seamans, R. 2019. Primer on artificial intelligence and robotics. Journal of Organization Design 8(1): 114.Google Scholar
Samek, W. & Müller, K.-R. 2019. Towards explainable artificial intelligence. In Samek, W., Montavon, G., Vedaldi, A. et al., Explainable AI: Interpreting, Explaining and Visualizing Deep Learning: 522. Springer, Cham, Switzerland.Google Scholar
Schmidt, A. 2017. Augmenting human intellect and amplifying perception and cognition. IEEE Pervasive Computing 16(1): 610.Google Scholar
Selkowitz, A. R., Lakhmani, S. G., & Chen, J. Y. 2017. Using agent transparency to support situation awareness of the Autonomous Squad Member. Cognitive Systems Research 46: 46134625.Google Scholar
Sharma, A., Zanotti, P., & Musunur, L. P. 2019. Enabling the electric future of mobility: robotic automation for electric vehicle battery assembly. IEEE Access 7: 7170961–170991.CrossRefGoogle Scholar
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. 2019. Organizational decision-making structures in the age of artificial intelligence. California Management Review 61(4): 6683.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K. et al. 2017. Mastering the game of Go without human knowledge. Nature 550(7676): 354359.Google Scholar
Simon, H. A. 1947. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization. Macmillan, New York, NY.Google Scholar
Simon, H. A. 1957. Models of Man: Social and Rational; Mathematical Essays on Rational Human Behavior in a Social Setting. Wiley, New York, NY.Google Scholar
Simon, H. A. 1993. Strategy and organizational evolution. Strategic Management Journal 14(S2): 131142.Google Scholar
Simon, H. A. 2002. Organizing and coordinating talk and silence in organizations. Industrial and Corporate Change 11(3): 611618.Google Scholar
Simon, H. A. & Newell, A. 1958. Heuristic problem solving: the next advance in operations research. Operations Research 6(1): 110.Google Scholar
Tambe, P., Cappelli, P., & Yakubovich, V. 2019. Artificial intelligence in human resources management: challenges and a path forward. California Management Review 61(4): 1542.Google Scholar
Tichy, N. M. & Bennis, W. G. 2007. Judgment: How Winning Leaders Make Great Calls. Penguin, New York, NY.Google Scholar
Tversky, A. & Kahneman, D. 1974. Judgment under uncertainty: heuristics and biases. Science 185(4157): 11241131.Google Scholar
Uliyar, S. 2017. A primer: Oracle intelligent bots – powered by artificial intelligence, white paper. Oracle, Redwood Shores, CA.Google Scholar
von Krogh, G. 2018. Artificial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries 4(4): 404409.Google Scholar
Wilson, H. J. & Bataller, C. 2015. How people will use AI to do their jobs better. Harvard Business Review: 16. https://hbr.org/2015/05/how-people-will-use-ai-to-do-their-jobs-betterGoogle Scholar
Winston, P. H. 1992. Artificial Intelligence, 3rd ed. Addison-Wesley Longman, Reading, MA.Google Scholar
Woschank, M., Rauch, E., & Zsifkovits, H. 2020. A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability 12(3760): 123.Google Scholar
Zhang, L., Tan, J., Han, D., & Zhu, H. 2017. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discovery Today 22(11): 16801685.Google Scholar
Ziemke, T., Schaefer, K. E., & Endsley, M. 2017. Situation awareness in human-machine interactive systems. Cognitive Systems Research 46(1): 12.CrossRefGoogle Scholar
Zuboff, S. 1988. In the Age of the Smart Machine: The Future of Work and Power. Basic Books, New York, NY.Google Scholar

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