# Transformations in the Roles and Skills of Middle Managers in the Era of Generative AI: An Activity Theory Perspective

Philippe Jean-Baptiste

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# Transformations in the Roles and Skills of Middle Managers in the Era of Generative AI: An Activity Theory Perspective

Philippe JEAN-BAPTISTE

CNRS - Aix-Marseille University, philippejb@icloud.com

Follow this and additional works at: https://aisel.aisnet.org/amcis2025

## Recommended Citation

JEAN-BAPTISTE, Philippe, "Transformations in the Roles and Skills of Middle Managers in the Era of Generative AI: An Activity Theory Perspective" (2025). *AMCIS 2025 Proceedings*. 20. https://aisel.aisnet.org/amcis2025/sig_cnow/sig_cnow/20

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# Transformations in the Roles and Skills of Middle Managers in the Era of Generative AI: An Activity Theory Perspective

*Emergent Research Forum (ERF) Paper*

**Philippe JEAN-BAPTISTE**

Laboratory of Economics and Sociology of Work

LEST - Laboratoire d'Économie et de Sociologie du Travail

UMR 7317 - CNRS – Aix Marseille University

[philippejb@icloud.com](mailto:philippejb@icloud.com)

## Abstract

The adoption of Generative Artificial Intelligence (GAI), including tools like ChatGPT, is profoundly reshaping organizational landscapes. These technologies enhance creativity, decision-making, and automation, challenging traditional workflows and managerial practices. Middle managers, positioned between strategic decisions and daily operations, play a pivotal role in bridging these transformations. However, the micro-level dynamics of their evolving roles and competencies remain underexplored, particularly within specific industries like telecommunications or hybrid service firms.

This study uses activity theory to analyze the tensions and contradictions arising from GAI adoption, focusing on its impact on middle managers. It identifies key mechanisms such as Bottom-Up innovation dynamics, Shadow IT, and shifts in governance frameworks. Findings from diverse organizational contexts highlight the need for structured approaches to skill development, balancing technical and human-centric competencies. This research contributes to understanding managerial transformation and offers actionable strategies for organizations to navigate the complexities of GAI integration effectively

## Keywords

Generative Artificial Intelligence (AGI); middle managers; organizational transformation; Shadow IT; Managerial skills.

## Introduction

The rapid emergence of Generative Artificial Intelligence (GAI) technologies, such as ChatGPT, Perplexity, and GitHub Copilot, is profoundly reshaping organizational practices by redefining both operational and strategic processes (Brynjolfsson & McAfee, 2014; Baird & Maruping, 2021; Krakowski et al., 2023). These technologies, with their ability to automatically generate complex content, facilitate decision-making processes, and optimize workflows, are transforming traditional organizational structures, leading to significant disruptions and tensions within established governance frameworks (Silic & Back, 2014; Hillebrand et al., 2025).

At the center of these transformations are middle managers, who occupy strategic positions bridging executive governance and daily operational management (Mintzberg, 1973; Van Doorn et al., 2023). Historically perceived primarily as implementers of top-down decisions, these actors now emerge as critical facilitators of technological integration and drivers of organizational change. They foster bottom-up innovation dynamics and increasingly confront the phenomenon of Shadow IT, wherein employees spontaneously adopt technological tools without official authorization (Behrens & Sedera, 2004; Tarakci et al., 2023). These phenomena highlight structural tensions between spontaneous innovation and top-down
institutional governance, underscoring significant managerial and strategic challenges linked to GAI adoption in contemporary organizations.

Despite the growing recognition of the disruptive potential of GAI, few studies have specifically examined how these technologies influence the roles and competencies of middle managers at the micro-organizational level, particularly by adopting a systemic perspective capable of analyzing contradictions inherent in rapid technological evolution. Recent studies have emphasized the need to manage AI not merely as a technical asset but as an agentic element deeply embedded within organizational dynamics, requiring new theoretical and managerial approaches (Berente et al., 2021). Addressing this gap, this research employs Activity Theory (Engeström, 1987; Engeström & Sannino, 2021) as its central analytical framework, enabling an examination of complex interactions among middle managers, technological tools, professional communities, governance rules, and divisions of labor. Applying this theoretical framework is especially relevant for identifying and modeling organizational contradictions and tensions specific to GAI integration.

This study thus aims to address the following research question: How does the introduction of Generative Artificial Intelligence (GAI) technologies transform the roles and competencies of middle managers? To answer this question, the research adopts a qualitative methodology, utilizing semi-structured interviews conducted with middle managers across three distinct organizational contexts: a large enterprise in the energy sector, a medium-sized enterprise (MSE) specializing in digital services, and a small enterprise (SME) in the telecommunications sector. These contexts provide varied frameworks for analyzing different approaches to technological integration, associated tensions, and the specific GAI tools deployed (such as ChatGPT, Copilot, and Perplexity).

By combining a robust theoretical approach, an in-depth comparative analysis of organizational practices, and a clear mapping of emerging competencies, this research aims to make both theoretical and managerial contributions. Theoretically, it enriches our understanding of technological integration dynamics within organizational contexts. Practically, it offers concrete strategic pathways to effectively support middle managers in adopting and optimally utilizing GAI technologies, reducing risks associated with Shadow IT, and fostering enhanced organizational coherence.

## Motivation & Research Gap

While the disruptive potential of Generative Artificial Intelligence (GAI) technologies is increasingly acknowledged, existing literature predominantly emphasizes technical aspects or macro-organizational implications, neglecting detailed analyses at the micro-organizational level (Krakowski et al., 2023; Wilden et al., 2023). In particular, the concrete transformations of middle managers’ roles and competencies remain under-explored, despite their pivotal position in technological adoption processes and their central role in managing organizational tensions arising from these emerging technologies (Tarakci et al., 2023; Van Doorn et al., 2023).

While Artificial Intelligence (AI) has long supported organizations through supervised machine learning (ML) models focused on predictive tasks, Generative AI (GAI) represents a paradigmatic shift. Unlike traditional ML, which relies on training algorithms to classify, predict, or recommend based on structured data, GAI models such as ChatGPT, Copilot, or Perplexity autonomously generate new content (text, code, images) based on learned patterns from massive datasets (Krakowski et al., 2023; Hillebrand et al., 2025).

This capacity to create novel outputs rather than simply analyze existing data profoundly alters the nature of decision-making, creativity, and operational workflows within organizations. Therefore, GAI introduces new organizational dynamics distinct from earlier AI generations, particularly impacting middle managers’ competencies in interpretation, ethical evaluation, and hybrid orchestration.

This gap is compounded by the lack of sufficiently robust theoretical frameworks capable of explaining how these technologies concretely transform managerial activities and how these transformations generate organizational contradictions (Silic & Back, 2014). Activity Theory (Engeström, 1987, 2001; Engeström & Sannino, 2021), although highly relevant, remains underutilized in this context, especially regarding the
clear articulation of how concepts such as bottom-up innovation and Shadow IT fit within a systemic vision of organizational activity.

This research addresses these gaps explicitly by employing Activity Theory to precisely analyze these transformations, integrating recent references, and clearly articulating the interplay between key concepts (Baird & Maruping, 2021; Hillebrand et al., 2025).

## Research Question

This study is guided by the following central research question:

**How does the integration of Generative Artificial Intelligence (GAI) precisely redefine the roles and competencies of middle managers, and what organizational tensions arise from these transformations?**

This question seeks to uncover the mechanisms by which GAI adoption influences managerial practices and the interplay between technological innovation and organizational adaptation.

## Theoretical Framework

This research adopts Activity Theory as a systemic analytical framework to explore organizational transformations driven by GAI (Engeström, 1987, 2001; Engeström & Sannino, 2021). This framework allows for the analysis of dynamic interactions between subjects (middle managers), tools (GAI technologies such as ChatGPT, Copilot), rules (IT governance policies), communities (operational and executive teams), and the division of labor (redistribution of tasks). The study explicitly illustrates how bottom-up innovation dynamics and Shadow IT can be conceptualized as internal contradictions within these activity systems, representing both sources of tensions and opportunities for organizational development. The following conceptual model, adapted from Engeström's activity theory, provides a visual representation of these components and their interactions within the organizational system:

<figure><img src="image_2.png" /><figcaption>**Figure 1. Conceptual model based on activity theory (adapted from Engeström, 1987)**</figcaption></figure>

Figure 1 illustrates the organizational activity system adapted to the integration of Generative AI (GAI) technologies.

* Subjects (middle managers) interact with Tools (GAI technologies such as ChatGPT and Copilot) to achieve the Object (organizational innovation and role transformation).

* Rules (IT governance policies) and Community (executives, IT teams, operational employees) influence this activity, shaping opportunities and constraints.

* The Division of Labor component reflects the redistribution of tasks among human actors and AI systems.

* Arrows represent the dynamic interactions between components: for example, the introduction of GAI tools (Tools) influences the redefinition of roles (Division of Labor), while governance structures (Rules) can facilitate or constrain bottom-up innovation efforts.

Contradictions often arise between these components — for instance, tensions between operational needs for agile adoption and IT governance policies — and are key drivers of organizational change in the context
of GAI. Within Activity Theory, contradictions are not mere tensions or conflicts; they are deeply rooted structural inconsistencies that emerge within or between elements of an activity system (Engeström, 1987; Engeström & Sannino, 2021). These contradictions act as primary drivers for systemic transformation and expansive learning. In the context of GAI adoption, for instance, contradictions may arise between traditional governance frameworks (Rules) and new practices of tool usage (Tools), leading to potential redefinitions of organizational roles and competencies. Recognizing and analyzing these contradictions is essential for understanding how technological integration reshapes organizational structures over time.

By highlighting the tensions and contradictions within and between these components, activity theory provides a robust framework for understanding the complexities of organizational change. For instance, the introduction of GAI may challenge traditional governance models, requiring middle managers to navigate conflicts between operational needs and IT policies.

In addition to activity theory, this research incorporates complementary concepts such as Bottom-Up innovation dynamics and the Shadow IT phenomenon. Bottom-Up dynamics emphasize how middle managers often take initiative in experimenting with and adopting GAI, even outside formal organizational frameworks (Behrens & Sedera, 2004). This aligns with the Shadow IT concept, which refers to the unofficial use of technological tools by employees to meet immediate operational demands. While these practices can foster innovation, they also introduce risks related to data security and compliance.

Together, these theoretical perspectives offer a comprehensive view of how GAI reshapes managerial roles and competencies, bridging the gap between technological potential and practical organizational adaptation.

## Research context

This qualitative research draws upon three distinct organizational contexts, allowing for an in-depth exploration of how Generative Artificial Intelligence (GAI) is integrated into managerial practices:

* **Large enterprise in the energy sector (LE):** 179,560 employees, €139.7M in revenue, characterized by a rigid hierarchical structure posing specific challenges for GAI integration.

* **Medium-sized digital services enterprise (MSE):** 540 employees, €552M in revenue, displaying strong innovation dynamics but fragmented governance structures.

* **Small telecommunications enterprise (SME):** 175 employees, €75M in revenue, showing high agility but limited resources for formal GAI integration.

Data were collected through 60 planned semi-structured interviews (30 in the large enterprise, 15 in the MSE, and 15 in the SME) with middle managers directly involved in the adoption and experimentation of GAI tools such as ChatGPT, Perplexity, and Copilot.

<figure><img src="image_3.png" /><figcaption>Figure 2. Overview of the research contexts.</figcaption></figure>

The diversity of these settings ensures that the findings are both contextually rich and broadly applicable across different organizational scales and industries.
# Data Collection and Data Analysis Method

The data collection process was conducted between [period to specify if needed, e.g., October 2024 – March 2025] using virtual platforms such as Zoom and Microsoft Teams. All interviews were recorded with the prior informed consent of the participants, following ethical research standards.

The interviews followed a semi-structured interview guide organized around key themes:

* Perceptions of GAI technologies,

* Changes in managerial roles and competencies,

* Innovation dynamics (bottom-up adoption and Shadow IT practices),

* Emerging organizational tensions related to GAI integration.

Each recording was fully transcribed verbatim to ensure accuracy and depth of analysis. These transcripts form the basis for thematic coding and subsequent analysis aligned with the Activity Theory framework (Engeström, 1987; Engeström & Sannino, 2021).

This rigorous methodological approach ensures the richness, validity, and replicability of the data, enabling a systemic exploration of the contradictions and transformations observed.

At this stage of the project, all interviews have been fully transcribed and individually summarized to identify the main ideas expressed by the participants. For each interview, keywords were associated with the key themes mentioned, enabling a preliminary structuring of the emerging findings.

This inductive process follows a “Grounded Theory-informed approach,” as it aims to let concepts emerge directly from the data without relying on a predefined theoretical framework.

However, a more systematic analysis process — including open coding, axial coding, and detailed categorization — remains to be conducted. A more formalized analysis will be undertaken in the next phases of the research, using computer-assisted qualitative data analysis software (such as NVivo or Atlas.ti), to rigorously extract the tensions, contradictions, and evolving managerial role dynamics within the observed activity systems.

It is important to note that the analysis presented at this stage remains preliminary. The summarized keywords and emerging themes provide a first-level structuring of the interview material. The formal analytical phases — including open coding, category construction, and systematic contradiction analysis aligned with Activity Theory — are planned for the next stages of the research. This clarification ensures that the findings discussed herein should be interpreted as initial insights rather than definitive results.

# Preliminary Results

The preliminary analysis of the first 32 interviews reveals several emerging findings:

* **Bottom-Up Innovation and Shadow IT practices:** middle managers frequently engage in informal experimentation with GAI tools (especially ChatGPT and Copilot) outside of officially sanctioned IT frameworks. While fostering responsiveness and innovation, these practices create growing tensions with existing governance and compliance structures. For example, in the MSE context, the spontaneous adoption of Copilot by operational teams to automate coding tasks created immediate operational efficiencies. However, this bottom-up initiative conflicted with the IT department's formal governance policies, illustrating a primary contradiction between the operational need for agile technological adoption and the centralized requirement for security and compliance. This exemplifies how Shadow IT practices emerge and trigger systemic tensions within organizational activity systems.

* **Redefinition of Managerial Competencies:** the integration of GAI requires middle managers to develop a complex combination of technical (understanding AI tools), conceptual (strategic interpretation of AI outputs), and human (change management, communication, hybrid team coordination) skills. Early findings emphasize the increasing importance of human-centric and strategic skills.
* **Middle Managers as Hybrid Orchestrators:** positioned between strategic objectives and operational realities, middle managers are emerging as critical hybrid orchestrators, facilitating the integration of human and machine intelligence. This evolution resonates with the automation-augmentation paradox highlighted by Raisch and Krakowski (2021), where managers must simultaneously leverage AI capabilities and maintain critical human skills to create value. Their ability to manage organizational tensions around GAI adoption is becoming crucial for successful digital transformation.

These results highlight the necessity of a structured, supportive approach to foster the effective integration of GAI tools while mitigating associated risks and tensions.

## Contributions

This research offers several significant contributions at both theoretical and managerial levels:

* **Theoretical contributions:** by mobilizing Activity Theory (Engeström, 1987; Engeström & Sannino, 2021) to examine middle managers' transformations in the era of GAI, the study provides a systemic and dynamic understanding of the organizational contradictions and tensions generated by the integration of generative technologies. It bridges the gap between technological innovation, Shadow IT practices (Haag & Eckhardt, 2017), and micro-organizational dynamics. Furthermore, it extends the understanding of managerial work transformations initiated by early studies like Mintzberg (1973) and later refined by Tengblad (2006).

* **Managerial contributions:** The study identifies key strategies to support the adoption of GAI at the operational level:

* Fostering bottom-up innovation while aligning governance framework,

* Strengthening middle managers' hybrid competencies, combining technical, conceptual, and human skills (Katz, 1974; Peterson & Van Fleet, 2004),

* Managing risks associated with unauthorized innovation (Shadow IT) without stifling agility.

* **Practical implications for skills development:** the research highlights the need for training programs that balance technical upskilling (e.g., mastery of GAI tools like ChatGPT, Copilot) with the enhancement of human-centric competencies such as empathy, conflict management, and strategic communication (Katz, 1974; Peterson & Van Fleet, 2004). It emphasizes the critical role of developing hybrid orchestration capabilities among middle managers to sustain organizational competitiveness in increasingly AI-driven environments.

Overall, this research offers a structured roadmap for organizations aiming to integrate GAI technologies while ensuring sustainable, human-centric transformation.

## Conclusion

This study underscores the profound transformations driven by Generative Artificial Intelligence (GAI) in redefining the roles, competencies, and responsibilities of middle managers. Positioned as hybrid orchestrators, these managers play a pivotal role in mediating between strategic aspirations and operational realities, becoming key facilitators of technological transitions. In line with previous work on evolving managerial tasks (Tengblad, 2006), this study shows that middle managers are no longer merely operational executors but are becoming agents of digital and organizational transformation.

The findings advocate for a twofold investment by organization:

* Strengthening both technical and human skills among middle managers (Katz, 1974; Peterson & Van Fleet, 2004),

* Designing governance frameworks that are flexible enough to foster bottom-up innovation while maintaining organizational security and compliance (Haag & Eckhardt, 2017).

Building on these preliminary findings, several promising research avenues emerge.

Future research will expand on the current dataset by incorporating:

* Additional case studies in hybrid and digitally advanced organizations,
* Perspectives from IT specialists, executives, and frontline employees to enrich systemic understanding,

* A critical exploration of ethical, security, and governance issues linked to the increasing embeddedness of GAI technologies

By pursuing these avenues, this research aims to refine its theoretical contributions and provide actionable, sustainable strategies for managing the organizational impact of GAI

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