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AI-Driven Leadership
Abstract
A clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart.
Descriptions of AI's possible effects on businesses and their employees cycle between utopian hype and alarmist doomsaying. This book from MIT Sloan Management Review avoids both these extremes, providing instead a clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. The contributors show that organizations can create business value with AI by cooperating with it rather than relinquishing control to it. The smartest companies know that they don't need AI that mimics humans because they already have access to resources with human capability—actual humans.
The book acknowledges the prominent role of such leading technology companies as Facebook, Apple, Amazon, Netflix, and Google in applying AI to their businesses, but it goes beyond the FAANG cohort to look at AI applications in many nontechnology companies, including DHL and Fidelity. The chapters address such topics as retraining workers (who may be more ready for change than their companies are); the importance of motivated and knowledgeable leaders; the danger that AI will entrench less-than-ideal legacy processes; ways that AI could promote gender equality and diversity; AI and the global loneliness epidemic; and the benefits of robot–human collaboration.
Contributors
Cynthia M. Beath, Megan Beck, Joe Biron, Erik Brynjolfsson, Jacques Bughin, Rumman Chowdhury, Paul R. Daugherty, Thomas H. Davenport, Chris DeBrusk, Berkeley J. Dietvorst, Janet Foutty, James R. Freeland, R. Edward Freeman, Julian Friedland, Lynda Gratton, Francis Hintermann, Vivek Katyal, David Kiron, Frieda Klotz, Jonathan Lang, Barry Libert, Paul Michelman, Daniel Rock, Sam Ransbotham, Jeanne W. Ross, Eva Sage-Gavin, Chad Syverson, Monideepa Tarafdar, Gregory Unruh, Madhu Vazirani, H. James Wilson
... • Data literacy means being able to understand and use data in different forms and situations. 69 Being able to understand and work with data is really important for educational leaders. They need this skill to use AI technology and data in education, and to make smart choices based on evidence. ...
... • Data literacy means being able to understand and use data in different forms and situations. 69 Being able to understand and work with data is really important for educational leaders. They need this skill to use AI technology and data in education, and to make smart choices based on evidence. ...
Artificial Intelligence (AI) is changing the way students are educated in many ways, like improving teaching and learning, personalizing learning experiences, making things more efficient and effective, and promoting creativity and inclusivity. AI can cause problems for educational leaders. It can bring up ethical, legal, social, and cultural issues. It also requires leaders to learn new skills and abilities for the AI age. There is a need for Educational leaders to be proactive and strategic in order to make the most of the advantages and find solutions for the issues related to AI in education. This article aimed to examine how artificial intelligence is used in leadership in education. It also proposed a framework to understand and develop AI-based leadership in education. The basic idea of the conceptual framework relies on the theories of AI, leadership, and decision-making. It has four parts: inputs, processes, outputs, and feedback. The impact of the conceptual framework on education practices and policies was also examined. The article also discussed how education practices and policies are influenced by the conceptual framework. Additionally, the article explored the relationship between the conceptual framework and its effects on education practices and policies. The article highlighted the framework’s limitations and suggests potential areas for future research. This study contributes to the existing literature on enhancing learning through technology. Keywords: Artificial Intelligence, Educational Leadership, Decision-Making, Conceptual Framework
Background
The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.
Objective
In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.
Methods
We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines.
Results
A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.
Conclusions
In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.
This conceptual, interdisciplinary paper will start by introducing the commencement of a new era in which human society faces continuously accelerating technological revolutions, named the Post Accelerating Data and Knowledge Online Society, or ‘Padkos’ (“food for the journey; prog; provisions for journey”—in Afrikaans) for short. In this context, a conceptual model of sustainable development with a focus on knowledge management and sharing will be proposed. The construct of knowledge management will be unpacked into a new three-layer model with a focus on the knowledge-human and data-machine spheres. Then, each sphere will be discussed with concentration on the learning and decision- making processes, the digital supporting systems and the human actors’ aspects. Moreover, the recombination of new knowledge development and contemporary knowledge management into one amalgamated construct will be proposed. The holistic conceptual model of knowledge management for sustainable development is comprised by time, cybersecurity and two alternative humanistic paradigms (Homo Technologicus and Homo Sustainabiliticus). Two additional particular models are discussed in depth. First, a recently proposed model of quantum organizational decision-making is elaborated. Next, a boundary management and learning process is deliberated. The paper ends with a number of propositions and several implications for the future based on the deliberations in the paper and the models discussed and with conclusions.
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