Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making

By Mohammad Hossein Jarrahi. Business Horizons, 2018

1,895 citations recorded by the source corpus.

Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans’ cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contributions.

Subfields: Safety Research, Sociology and Political Science, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/7ff3f12f27ef8e9e7d754c23b1eeb09b664223bd

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Citations in the corpus

Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale (2022, background): “At the same time, others (Jarrahi 2018; Stembert and Harbers 2019) have highlighted the positive e ﬀ ects of high AI competence on human – AI interactions (HAII).”
Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda (2019, background): “…(Miller, 2018a; Wilson & Daugherty, 2018) and “AI systems should be designed with the intention of augmenting, not replacing, human contributions” (Jarrahi, 2018 p. 584), but this assertion should be further supported with rigorous research and investigation with empirical evidence on how and why…”
Artificial Intelligence and Business Value: a Literature Review (2021, methodology): “In healthcare, computer vision vision can be used to process MRI images of the brain to mark tiny hemorrhages in the images for doctors (Jarrahi, 2018).”
RETRACTED ARTICLE: Impact of artificial intelligence on human loss in decision making, laziness and safety in education (2023, background): “But it is overcoming the human biological processors through lowing cognition capabilities (Jarrahi, 2018).”
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “Although these machines can surpass humans in terms of data processing [178], they lack accountability and interpretability and hence are referred to as “black boxes.””
Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors (2020, methodology): “Thus, organizations should foster DDDM as an organizational practice and prepare employees for a culture where AI provides complementary insights to support firm decisions (Jarrahi 2018).”
Artificial intelligence and big data in entrepreneurship: a new era has begun (2019, background): “This symbiotic, collaborative view seems to be evolving into the mainstream practice perspective in the contemporary business and management literature (Jarrahi 2018; Wilson and Daugherty 2018) and it should be a promising avenue for entrepreneurship as well.”
Artificial intelligence in supply chain management: A systematic literature review (2021, positive): “…(Huin et al., 2003); however, only recently have technological developments shown that AI has a vast set of applications (Min, 2010), making headlines by adapting processes in numerous diverse areas (Martínez-L ´ opez and Casillas, 2013; Jarrahi, 2018), including supply chain management (SCM).”
Artificial intelligence in operations management and supply chain management: an exploratory case study (2021, background): “Expert System (ES): ES is a computer program that solves problems or gives advice based on well-deliberated calculations and unmanageable amounts of data: these tools produce analyses and help to evaluate alternative decision options (Jarrahi 2018).”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “…(Miller, 2018; Wilson & Daugherty, 2018) and “ AI systems should be designed with the intention of augmenting, not replacing, human contributions ” (Jarrahi, 2018, p. 584), but this assertion should be further supported with rigorous research and investigation with empirical evidence on how…”
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies (2022, background): “By combining the predictive power of AI with the expertise and experience of human researchers [53], it is possible to optimize the drug discovery process and accelerate the development of new medications [54].”
Digital Sustainability and Entrepreneurship: How Digital Innovations Are Helping Tackle Climate Change and Sustainable Development (2020, background): “While PwC (2017) estimates AI will add some 14%—or USD 15.7 trillion—to the global economy by 2030, observers raise concerns ranging from adverse employment impacts to the ethical implications of AI-based decision mak - ing (Jarrahi, 2018 ).”