Track 09: Leadership and Organizational Development with the advent of AI

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Call for Papers

Track 9 of IMRC 2026, hosted by Ashank Desai Centre for Leadership and Organizational Development, invites papers that study, evaluate, and contribute to the impact and adoption of artificial intelligence in multiple facets of organizations including operations, management, HR, IT and others.

The integration of Artificial Intelligence has moved beyond experimental pilot programs into the very fabric of organizational existence. We find ourselves at a critical juncture where the “technical” implementation of AI has outpaced our “human” understanding of its consequences (Davenport & Ronanki, 2018). AI-driven transformation is fundamentally reshaping how decisions are made, how work is structured, and how people relate to their organizations (Brynjolfsson & McAfee, 2014).

AI presents a profound paradox for modern organizations. On one hand, it offers unprecedented positive impact: the elimination of drudgery, hyper-efficient operations, and the democratization of data-driven insights (Chui et al., 2016). On the other hand, we are witnessing significant negative pressures: rising employee anxiety over job displacement (Frey & Osborne, 2017), the erosion of traditional workplace social capital (Newlands, 2021), and the ethical “black box” of algorithmic bias (Raghavan et al., 2020).

The question is not about the technology itself, but about the human experience of it. How do we ensure that “transformation” leads to actual “value-creation” rather than just a race for speed at the cost of culture (Westerman et al., 2014)? Do we understand how the experience of work itself is changing because of AI?

This track provides an opportunity for both scholars and practitioners to dissect these tensions. This track invites papers that look at both the organizational and human impact of AI, and the ways in which organizations, leadership, and employees can navigate this period of change and emerge victorious at the other end. Leadership plays a particularly consequential role in this transition — research has shown that effective organizational change depends on leaders who can manage workforce anxieties and build cultures of psychological safety during disruption, while also stewarding the ethical and human dimensions of AI deployment (Tschang & Almirall, 2021).

The track invites empirical and theoretical papers that address topics such as (this list is not inclusive of all possible topics).

Given that organizations are at the edge of adoption or have adopted AI deeply, it is necessary that scholars in the field start collecting data, understanding the process and the phenomenon, and creating insights that can then be used to design further studies and impact practice (Shrestha et al., 2019). The HR function is simultaneously being called upon to bridge workforce transitions, design adaptive structures, and embed ethical frameworks into AI deployment decisions (Tambe et al., 2019). Despite significant theoretical speculation on these consequences (Santana & Cobo, 2020; Schlogl et al., 2021), a significant empirical gap remains.

Thus, we are not calling for generic papers on leadership and organizational development this time. We are asking you to collect data from organizations (of all kinds in terms of size, age, ownership, industry, mission etc.) in the form of case studies, ethnographic studies, interviews, or surveys in organizations. You can look at the phenomenon of interest from the perspective of industry bodies, decision makers, senior leadership, teams, and individuals.

Systematic literature reviews on themes presented above will also be welcomed.

The attempt is to create a body of knowledge and a community of scholars who would be interested in studying the phenomenon as it is emerging. Even if your paper is not publication ready there is a chance it will get accepted as long as it is focused on an aspect of the adoption of AI within organizations.

References

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company. 

Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans — and where they can’t (yet). McKinsey Quarterly, 30(2), 1–9.  https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019

Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through Lefebvrian conceived space. Organization Studies, 42(5), 719–737. https://doi.org/10.1177/0170840620937900

Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 469–481). ACM. https://doi.org/10.1145/3351095.3372828

Santana, M., & Cobo, M. J. (2020). What is the future of work? A science mapping analysis. European Management Journal, 38(6), 846–862. https://doi.org/10.1016/j.emj.2020.04.010

Schlogl, L., Weiss, E., & Prainsack, B. (2021). Constructing the ‘Future of Work’: An analysis of the policy discourse. New Technology, Work and Employment, 36(3), 307–326. https://doi.org/10.1111/ntwe.12202Digital Object Identifier (DOI) 

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), 66–83. https://doi.org/10.1177/0008125619862257

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910

Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642–659. https://doi.org/10.5465/amp.2019.0062

List of themes (suggested but not inclusive)

• Insights into the role of leadership in the successful adoption of AI (case studies, narratives based on adopting organizations)
• Impact of adoption of AI on job satisfaction, commitment, engagement, morale, performance etc. within organizations (empirical studies, cases, systematic literature reviews).
• The impact of AI adoption on team dynamics (real studies and narratives)
• Exploring the process of decision making on adoption of AI (what are the forces shaping the adoption-imitation, in-depth studies, use cases, leadership exposure etc.)
• The impact of digitalization and AI on selection decisions and processes
• Exploring change processes that facilitate adoption of AI in organizations. Are the processes the same or different for AI adoption as compared to other changes
• The skills that are required for meeting the demands of digitalization and AI among a variety of job roles and functions.
• Reimagining learning and development for the emerging realities at work.
• Implications of AI adoption for unionized voice and regulation.
• Emerging job roles, job polarization, job redundancies etc among organizations.
• The role of HR professionals in aiding transition to hybrid organization models.
• Designing of reward systems for AI heavy departments.
• Impact on wellbeing of employees in the context of AI adoption.
• Fostering and measuring job satisfaction, commitment, engagement in the current context.
• Exploring issues of emerging ethics in the current context and the role of the leader in handling them.

Papers accepted in this track are expected to contribute significantly to scholarly discourse by offering novel insights, advancing theoretical frameworks, and providing evidence-based recommendations for leadership practice in the context of AI adoption.

This track will serve as a vital platform for scholars, practitioners, and thought leaders to engage in rigorous discussions, share research findings, and exchange innovative ideas at the intersection of theory and practice in leadership.

We invite-

Extended abstracts (250 words)

The abstract may describe the question, the method you would use and the progress you have made in the work that you wish to present. The abstract will be evaluated on the choice of topic and demonstration of progress of work and expected contribution to the track theme.

Proposals for symposiums.

A symposium will have 3-4 papers on a theme or topic. The symposium must include a 250-word description of the theme and its significance and the extended abstracts of all the papers to be presented in the symposium. If the proposal of the symposium is accepted then the proposers of the symposium will be responsible for ensuring that all papers included in the proposal are presented. 

The symposiums are meant to help the development of programmatic research on a topic.

All submissions will be blind peer reviewed. Thus, do ensure that you follow the guidelines for submission.