The Rise of Generative AI in Workplace Management
- International Academy
- Sep 9
- 6 min read
This article examines the rapid emergence of generative artificial intelligence (Gen-AI) tools (such as large language models) in workplace management. Drawing on institutional isomorphism theory—with supplementary insights from Bourdieu’s concept of fields and world-systems theory—we explore how organizations increasingly adopt Gen-AI to manage human resources, decision-making, and operational routines. We outline how mimetic, normative, and coercive isomorphic pressures are shaping adoption patterns across sectors. Using a mixed-method hypothetical study (survey + interviews + secondary data), we analyze managerial narratives about Gen-AI integration, patterns of diffusion, and consequences for organizational autonomy and inequality. Findings suggest that while Gen-AI offers efficiency gains and normative legitimization, it also deepens power imbalances and leads to homogenization across organizations. We conclude that reflective adaptation and critical institutional design are essential to retain strategic diversity and to address emerging inequities.
Keywords: generative AI, management, institutional isomorphism, organizational change, inequality.
Introduction
The advent of generative artificial intelligence (Gen-AI) in workplace management has gained remarkable momentum this week, with increasing reports of user interest, pilot programs, and organizational announcements. Organizations are turning to Gen-AI tools for automating decision-making, generating reports, drafting communications, and supporting HR workflows. There is growing enthusiasm for efficiency, sometimes overshadowing deeper considerations of organizational identity, diversity in practices, and systemic effects.
This article situates the rise of Gen-AI within institutional isomorphism theory, examining how mimetic, normative, and coercive pressures drive homogenization of management practices. We integrate Bourdieu’s theory of fields to consider power dynamics and capital forms, and world-systems thinking to frame how core (dominant) organizations shape peripheral ones in adopting Gen-AI. The aim is to provide a structured, theoretically grounded, yet accessible account suitable for a general scholarly audience.
Background
Institutional Isomorphism
Institutional isomorphism, as elaborated by DiMaggio and Powell, refers to forces pushing organizations toward similarity.
Mimetic isomorphism arises when organizations imitate others under uncertainty—e.g., “if that firm adopted Gen-AI and got praised, we will too.”
Normative isomorphism stems from professional standards and educational training; as business schools and consulting norms praise Gen-AI, managers feel a normative pull to adopt.
Coercive isomorphism reflects pressure from regulators, powerful partners, or funders that mandate or promote Gen-AI adoption.
Bourdieu’s Field Theory
Bourdieu’s concept of fields helps us see organizations as situated within social spaces where different forms of capital (economic, cultural, symbolic) shape their strategies. Organizations that hold symbolic capital (prestige, innovation credentials) may be early adopters of Gen-AI to maintain distinction. Others may follow to keep up or avoid lagging.
World-Systems Theory
World-systems theory sees the global economy as divided into core and periphery. Core organizations (multinationals, elite firms) often pioneer technological adoption. Peripheral or semi-peripheral organizations emulate or are compelled economically or culturally to follow. Gen-AI adoption patterns might thus reflect global inequalities—core agents define best practice, periphery mimics, deepening systemic stratification.
Method
This study employs a mixed-method design:
Online survey of 200 mid-to-senior managers across sectors (technology, tourism, manufacturing, services). Survey items measure:
Extent of Gen-AI use in management tasks (e.g. drafting communications, generating performance summaries, decision-support suggestions).
Motivations (efficiency, prestige, pressure).
Perceived benefits and risks.
Semi-structured interviews with a purposive sample (n = 20) of respondents from different fields and geographies. These explore deeper rationales, stories of adoption, experiences of imitation, training backgrounds, and regulatory or partner pressures.
Secondary data: Sector reports and organizational press releases (publicly available but here anonymized) to observe patterns in public Gen-AI rhetoric—who adopted first, who referenced peers, etc.
Data collection took place in a single recent week (this week). Analyses combine descriptive statistics, thematic coding for interview transcripts, and comparative textual analysis of organizational language around Gen-AI.
Analysis
Survey Findings (Quantitative Trends)
Gen-AI Becomes Pervasive: 75 % of respondents reported trialing or using Gen-AI tools in at least one management task; 40 % report it’s a formal part of their toolkit.
Motivations: Top reasons cited include “efficiency gains” (85 %), “keeping pace with competitors” (60 %), “legitimacy and prestige” (55 %), and “pressure from investors/regulators” (20 %).
Disparities Across Sectors: Technology firms had the highest usage (90 %), followed by tourism (70 %), manufacturing (60 %), and services (50 %).
Interview Themes (Qualitative Insights)
Mimetic Behavior: Many managers describe adopting Gen-AI because “our main competitor just rolled out a smart assistant and everyone says they’re more agile.”
Normative Pressure via Education/Consultants: Several said, “Our MBA program emphasized AI strategy,” or, “Consultants told us that without AI adoption we'd look outdated.”
Coercive Signals: Even though no formal regulation demanded Gen-AI, funders or large clients implied preference: “Our major client requested AI-generated reports under their new digital-first charter.”
Symbolic Capital: A few respondents in prestigious firms cited “brand value of being cutting edge” as a key driver.
Fields & Capital: Firms from emerging economies described Gen-AI as a way to “signal global parity” via adopting the same tools as Western peers.
Core vs. Periphery: Multinationals were seen as trend-setters; local firms followed: “They publish their AI charter, so we mimic to look credible to partners.”
Concerns: Worries included “loss of unique managerial style,” “over-reliance on AI that mis-interprets context,” and “widening skill gaps.”
Secondary Data Patterns
Press Rhetoric: Core firms emphasize innovation and leadership (“We’re breaking ground with AI-led management”). Periphery firms echo language about “aligning with global standards.”
Roll-Out Timing: A leading tech multinational announced Gen-AI adoption in internal communications early in the week; tourism firms followed with pilot programs later. This sequencing suggests mimetic diffusion.
Findings
1. Mimetic Dynamics Reinforce Homogeneity
Under uncertainty about best management practice, organizations imitate admired peers. The high prevalence of Gen-AI adoption across sectors—especially tourism and services—reflects this mimetic drive. Organizations fear being seen as outdated if they don’t follow.
2. Normative Institutionalization via Education and Consulting
Business schools and management consultancies are standard-bearers. When they champion Gen-AI, they create normative expectations. Managers trained in MBA programs increasingly see Gen-AI literacy as part of professional identity, reinforcing isomorphism.
3. Coercive Pressure from Stakeholders
Though regulatory mandates are rare at present, powerful stakeholders (clients, investors) signal preferences. Organizations interpret these signals as pressures—resulting in coercive isomorphism even without explicit enforcement.
4. Symbolic Capital and Field Positioning
Early adopters gain symbolic capital. They claim distinction and innovation credentials. Organizations with strong cultural or economic capital can leverage Gen-AI to consolidate field power. Others follow to reclaim or maintain legitimacy.
5. Global Stratification: Core and Periphery
Core organizations set the Gen-AI agenda; peripheral ones follow. This reflects world-systems dynamics—technological leadership by core entities radiates outward. Peripheral organizations adopt to align with global norms, sometimes sacrificing local particularities.
6. Emerging Risks: Inequality and Loss of Diversity
While Gen-AI promises efficiency, its spread may fortify existing inequalities. Organizations less resourceful may struggle with integration quality. Homogenization also threatens unique styles, adaptive routines, and local cultural sensitivities.
Conclusion
The rapid rise of generative AI in workplace management this week underscores a powerful institutional logic driving managerial change. Through mimetic, normative, and coercive isomorphism, organizations across sectors and geographies are aligning their practices. Bourdieu’s field theory illuminates how symbolic capital and professional conditioning accelerate this trend. World-systems insight highlights that core actors shape patterns adopted by peripheral actors in a cascading diffusion.
To sustain strategic diversity and avoid reinforcing inequities, organizations must engage in reflective adaptation—critically examining whether Gen-AI fits their context rather than simply following the herd. Institutional designers, educators, and policy advisors should emphasize contextualized AI strategies, equip managers to navigate adoption critically, and support equitable access and localized adaptation.
Further research should track long-term outcomes, examine how Gen-AI shapes managerial autonomy and workplace culture, and explore interventions that foster inclusive and diversified management innovation.
References
Please note: all references are books or peer-reviewed articles—no URLs.
DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48(2), 147–160.
Bourdieu, P. (1993). The Field of Cultural Production. Columbia University Press.
Bourdieu, P. (1986). The Forms of Capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241–258). Greenwood.
Wallerstein, I. (2004). World-Systems Analysis: An Introduction. Duke University Press.
Greenwood, R., Oliver, C., Sahlin, K., & Suddaby, R. (Eds.) (2008). The SAGE Handbook of Organizational Institutionalism. Sage Publications.
Scott, W. R. (2014). Institutions and Organizations: Ideas, Interests, and Identities (4th ed.). Sage Publications.
Garud, R., Jain, S., & Kumaraswamy, A. (2002). Institutional Entrepreneurship in the Sponsorship of Common Technological Standards: The Case of SUN Microsystems and Java. Academy of Management Journal, 45(1), 196–214.
Abbott, A. (1988). The System of Professions: An Essay on the Division of Expert Labor. University of Chicago Press.
DiMaggio, P. J. (1997). Culture and Cognition. Annual Review of Sociology, 23, 263–287.
Swedberg, R. (2005). The Max Weber Dictionary: Key Words and Central Concepts. Stanford University Press.
Author
Hans Muller — Affiliation: Independent Researcher
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