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# Redefining Middle Management: How Generative AI Reshapes Roles and Competencies

Philippe Jean-Baptiste

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Philippe Jean-Baptiste. Redefining Middle Management: How Generative AI Reshapes Roles and Competencies. Knowledge Futures: AI, Technology, and the New Business Paradigm, IFKAD (International Forum on Knowledge Asset Dynamics), Jul 2025, Naples, Italy. hal-05124707

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# Redefining Middle Management: How Generative AI Reshapes Roles and Competencies

Philippe JEAN-BAPTISTE

LEST - CNRS / Aix-Marseille University

Laboratory of Economics and Sociology - National Center of Scientific Research / Aix-Marseille University

35, Avenue Ferry - 13626 Aix-en-Provence - FRANCE

phillippejb@icloud.com

## Abstract

This ongoing research investigates how Generative Artificial Intelligence (GAI) technologies are reshaping the roles and competencies of middle managers. While much of the existing literature emphasizes macro-level impacts of AI—such as productivity gains and strategic transformation—this study focuses on micro-level changes, particularly managerial adaptation, decision-making, and skill reconfiguration.

Grounded in activity theory (Engeström, 1987, 2001) and informed by a critical realist epistemology, the study adopts a qualitative, inductive approach. Data is being collected through semi-structured interviews with middle managers from three contrasting organizational contexts: a large enterprise in the energy sector (30 interviews), a medium-sized digital services firm (15 interviews), and a small telecommunications company (15 interviews). This multi-site design enables the analysis of systemic tensions across different governance models and organizational cultures.

Preliminary findings reveal that middle managers are often at the forefront of GAI experimentation, initiating Bottom-Up innovation processes outside official IT channels. These informal practices—frequently associated with Shadow IT—allow for agile problem-solving but also pose significant risks in terms of data security and strategic misalignment. Managers report that GAI tools not only automate routine tasks but also support cognitive structuring, thereby transforming how they plan, communicate, and lead.

The integration of GAI calls for hybrid competencies that go beyond technical know-how. Conceptual skills (e.g., sense-making and system thinking), human skills (e.g., empathy, leadership, conflict mediation), and ethical awareness are increasingly vital. Middle managers thus emerge as key facilitators of digital transformation, mediating between frontline innovation and top-down strategic frameworks.

This paper contributes to the literature by offering a multi-dimensional reading of managerial transformation through the lens of activity theory. It proposes actionable insights for organizations aiming to responsibly integrate GAI, emphasizing the need for adaptive governance, targeted upskilling, and the creation of experimental safe zones. Future work will extend the dataset and explore longitudinal trajectories of GAI adoption and governance.

**Keywords:** Generative Artificial Intelligence (GAI); middle managers; organizational transformation; Shadow IT; managerial competencies.

**Paper type:** Academic Research Paper

## 1. Context and Research Problem

Recent advances in Generative Artificial Intelligence (GAI), such as ChatGPT and large language models, are profoundly transforming organizational environments. These technologies—capable of generating original content, producing ideas, automating complex cognitive tasks, and delivering strategic analyses (Jordan & Mitchell, 2015; Goodfellow et al., 2014; Vaswani et al., 2017)—are emerging as strategic intangible assets (Brynjolfsson & McAfee, 2014; Silic & Back, 2014). Their integration into companies is reshaping not only work practices but also the modalities of cooperation, decision-making, and control, in a context of continuous innovation (LeCun et al., 2015; Senge, 2006).

At the heart of this transformation, middle managers hold a pivotal position. Historically described as facilitators between strategy and operations (Mintzberg, 1973, 1989, 2006; Tengblad, 2006), they are now facing new expectations. On one hand, they are required to integrate GAI into their managerial practices and support their teams in adopting these technologies; on the other, they must navigate an environment where these tools
are reconfiguring value chains and redefining professional agency (Sannino & Engeström, 2021; Baird & Maruping, 2021). These “agentic artifacts” shift traditional roles by delegating certain decision-making functions to the machine, thereby challenging the legitimacy and relevance of specific human competencies (Van Doorn et al., 2023).

The emergence of Shadow IT—unauthorized use of technological solutions by employees—illustrates these tensions. The accessibility of GAI fosters bottom-up innovation dynamics, sometimes in conflict with top-down technological governance structures, leading to organizational friction (Silic & Back, 2014; Hillebrand et al., 2025). This phenomenon highlights the need to rethink power structures, risk management, and strategic IT governance (Behrens & Sedera, 2004).

As strategic actors, middle managers must contend with these multiple shifts: new forms of leadership, trade-offs between innovation and compliance, and skill management within unstable technological environments (Tarakci et al., 2023; Wilden et al., 2023). This transformation calls for both conceptual and operational agility, whereby the manager's role extends beyond strategy execution to include the co-construction of technological and cultural innovation (Engeström, 1987; Engeström & Sannino, 2021).

Yet, current research lacks a robust conceptual framework to thoroughly analyze these changes—both in terms of managerial roles (Mintzberg, 1973; Kraut et al., 1989) and competencies (Katz, 1974; Peterson & Van Fleet, 2004). This gap is precisely what the present research aims to address through the following research question: How does the introduction of Generative Artificial Intelligence transform the roles and competencies of middle managers?

## 2. Research objectives

This research aims to understand how the introduction of Generative Artificial Intelligence (GAI) is reconfiguring the roles and competencies of middle managers in a context of rapid technological transformation and increasing organizational tensions. Using a systemic approach grounded in activity theory (Engeström, 1987, 2001), the study seeks to:

* Identify the tensions and contradictions that emerge during GAI adoption;

* Develop a robust conceptual framework to analyze the transformation processes of managerial practices;

* Map the conceptual, human, and technical competencies required for the effective integration of these technologies (Katz, 1974; Peterson & Van Fleet, 2004);

* Propose organizational strategies to support managers during this transition, particularly with regard to Shadow IT and bottom-up innovation dynamics (Behrens & Sedera, 2004; Silic & Back, 2014; Tarakci et al., 2023).

This work thus addresses the need to reconcile technological mastery, ethical awareness, and strategic adaptability in the evolving role of middle managers (Van Doorn et al., 2023; Baird & Maruping, 2021).

## 3 Theoretical Framework

### 3.1 Activity Theory

Used as the primary analytical framework, activity theory (Engeström, 1987, 2001) provides a systemic model to understand the organizational transformations triggered by GAI. It consists of six interconnected components: the subject (middle manager), the object (transformation of roles and competencies), the tools (GAI technologies), the rules (IT norms and policies), the community (stakeholders involved), and the division of labor (redistribution of responsibilities). This framework reveals systemic contradictions that may become drivers of expansive learning and organizational change (Sannino & Engeström, 2021).

GAI technologies, as agentic artifacts (Baird & Maruping, 2021), deeply transform the mediation between actors and activities. They create tensions between innovation and compliance (Jordan & Mitchell, 2015; Hillebrand et al., 2025), formal and informal practices (e.g., Shadow IT), and strategic centralization and local agility (Engeström, 2001; Silic & Back, 2014).

The conceptual model used in this study, grounded in activity theory, is presented below. It illustrates the core components of the managerial activity system reconfigured by the introduction of GAI and highlights the systemic tensions likely to emerge.
<figure><img src="image_3.png" /><figcaption>Figure 1: Conceptual model of the activity system applied to middle managers and GAI integration
This model, inspired by Engeström's activity theory, shows the six core elements (Subject, Object, Tools, Rules, Community, Division of Labor) and helps identify contradictions between governance structures and emerging GAI uses.</figcaption></figure>

## 3.2 Bottom-Up Innovation Dynamics and Shadow IT

In addition, the study draws on bottom-up innovation models, which emphasize the proactive role of middle managers in technological adoption. These informal dynamics highlight both the agility of frontline actors and the risks of strategic and security misalignment (Behrens & Sedera, 2004; Haag & Eckhardt, 2017). Shadow IT—fueled by the accessibility of GAI tools—acts as both a signal of organizational creativity and a source of tension with top-down governance structures.

## 3.3 Synthesis of the Theoretical Framework

The articulation between activity theory and bottom-up innovation dynamics enables a multidimensional reading of the ongoing transformations. By combining:

* a systemic analysis of structures and tools (Engeström, 1987),

* a competency-based approach to managerial work in digital contexts (Katz, 1974; Wilden et al., 2023),

* and a socio-technical perspective on governance/innovation tensions (Silic & Back, 2014; Hillebrand et al., 2025).

This framework supports a better understanding of the conditions for a successful, ethical, and sustainable adoption of GAI. Positioned at the intersection of these tensions, middle managers emerge as key agents of this organizational transition.

# 4 Literature Review

## 4.1 Generative Artificial Intelligence as Strategic Resources

Generative Artificial Intelligence (GAI) technologies represent a major shift in organizational dynamics. Beyond their ability to generate content or automate complex cognitive tasks, they are becoming hybrid strategic resources, combining human and algorithmic intelligence (Brynjolfsson & McAfee, 2014; Jordan & Mitchell, 2015). These tools are actively reshaping decision-making processes, knowledge flows, and governance structures (Baird & Maruping, 2021; Van Doorn et al., 2023).

GAI contributes to the democratization of access to analytical and decision-making tools (Engeström, 2001), fostering greater autonomy among frontline actors. It also reconfigures value chains by enabling local innovations (bottom-up), often outside formal frameworks (Shadow IT)—a phenomenon that simultaneously enhances agility and creates friction with centralized structures (Silic & Back, 2014; Hillebrand et al., 2025).
## 4.2 Transformations in Managerial Roles and Competencies

The introduction of GAI disrupts the traditional roles of middle managers, who are increasingly expected to act as facilitators of technological transformation (Senge, 2006; Tarakci et al., 2023). Their intermediary position requires them to balance often conflicting demands: strategic vision, operational proximity, and resistance management.

The literature identifies three main categories of skills necessary for this transition (Katz, 1974; Peterson & Van Fleet, 2004):

* Conceptual competencies: systems thinking, the ability to structure and communicate the meaning of change (Engeström, 1987);

* Human competencies: leadership, relational intelligence, and group dynamics facilitation (Silic & Back, 2014);

* Technical competencies: understanding GAI technologies, data-related challenges, and ethical implications (Jordan & Mitchell, 2015; Wilden et al., 2023).

In light of growing automation, managers are also required to reprioritize their responsibilities—leading innovative projects, managing uncertainty, and balancing compliance with experimentation (Baird & Maruping, 2021; Van Doorn et al., 2023).

## 4.3. Conceptual Ambiguities Around Middle Managers

The literature highlights the lack of consensus on the definition of middle managers. This function is alternately described as frontline manager, operational supervisor, or cross-functional coordinator (Hales, 2005; Benedetto-Meyer et al., 2021), which often blurs the analysis of their strategic impact.

This terminological vagueness becomes particularly problematic in the context of digital transformation, where these actors perform complex and evolving roles that lie between execution and decision-making (Floyd & Wooldridge, 1994; Mahieu, 2007). The integration of GAI technologies further amplifies this complexity, placing middle managers at the center of tensions between technological innovation and organizational governance.

# 5. Methodology

This research adopts a qualitative approach rooted in a critical realist epistemology (Bhaskar, 1978; Archer et al., 1998; Meissonier, 2022), aimed at exploring the underlying mechanisms behind the transformation of middle managers’ roles and competencies in response to Generative Artificial Intelligence (GAI). By combining contextual analysis, situated understanding of practices, and the exploration of systemic tensions, the study seeks to go beyond descriptive approaches and propose a structural and transformational reading of the phenomenon.

## 5.1. Methodological Framework and Epistemological Stance

Activity theory (Engeström, 1987, 2001; Engeström & Sannino, 2021) serves as the central theoretical lens. It allows the analysis of role and competency reconfigurations within an activity system composed of: the subject (manager), the tool (GAI), the object (managerial transformation), the rules (IT governance), the community (executives, IT, teams), and the division of labor. This systemic view aligns with critical realism, which posits a stratified reality and a continuous tension between structures and agency.

By contrast, other frameworks such as TAM (Davis, 1989), UTAUT (Venkatesh et al., 2003), socio-technical systems (Trist, 1981), or structuration theories (Giddens, 1984; Orlikowski, 2000) were set aside as less suited for capturing the multi-level, informal, bottom-up and often contradictory dynamics observed in the field.

## 5.2. Data Collection Method

At this stage, data is being collected through semi-structured interviews, primarily with middle managers. Additional perspectives (executives, team members, IT staff) will be integrated in upcoming phases to enrich the analysis. This method allows for an in-depth exploration of the perceptions, experiences, and strategies adopted by actors facing GAI-induced transformations. All interviews are recorded, anonymized, and fully transcribed to ensure rigorous qualitative analysis.
## Diversity of Field Sites

Interview participants were selected for their key roles in technological change implementation and proximity to operational teams. Three organizational profiles were included:

* Large Enterprise: A mature organization with structured processes and centralized governance, providing a view of planned GAI adoption.

* Medium-Sized Enterprise (MSE): A growing company facing tensions between operational flexibility and strategic alignment.

* Small and Medium Enterprise (SME): A resource-constrained company often adopting technology opportunistically.

The following figure presents a typology of the organizational settings included in this study, detailing their sector, size, and structure to contextualize the diversity of GAI appropriation.

<figure><img src="image_4.png" /><figcaption>Figure 2. Typology of the three organizational contexts studied</figcaption></figure>

This table summarizes the characteristics of the three cases: a large enterprise (LE), a medium-sized enterprise (MSE), and a small and medium-sized enterprise (SME), providing a comparative perspective on GAI adoption environments.

## 5.3. Sampling Strategy

A purposive and theoretical sampling strategy was used, targeting managers positioned between strategy and execution with significant exposure to technological issues. Selection criteria include hierarchical position, involvement in AI-related projects, and sector diversity (telecom, energy, digital services).

Particular attention was given to cross-organizational cultural consistency. Despite varied settings, a shared engineering culture—emphasizing rationality, technical mastery, and problem-solving—shapes how AI is represented and used (Van Maanen & Barley, 1984; Kunda, 1995; Faulkner, 2015).

## 5.4. Data Analysis

Data is analyzed using an inductive thematic approach (Braun & Clarke, 2006), combined with the dimensions of activity theory. The goal is to identify organizational tensions (technological, normative, human) and understand the mechanisms of managerial adjustment and practice reconfiguration.

Key stages include:

* Initial inductive coding, structured by activity theory components;

* Identification of systemic tensions, such as conflicts between Shadow IT and formal governance policies;

* Analysis of collective learning trajectories, including secondary, tertiary, and expansive contradictions (Engeström);

* Data triangulation through cross-checking with documents and field feedback.

## 5.5. Validity and Reliability

Methodological rigor is ensured through:

* Source triangulation;

* Validation sessions with interviewees (member-checking);

* A reflexive journal documenting analytical decisions;

* Attention to organizational learning conditions, especially regarding bottom-up innovation and systemic resistance (Sannino & Engeström, 2021; Silic & Back, 2014).
This qualitative method thus enables the identification of internal contradictions within activity systems and the formulation of managerial transformation trajectories adapted to the challenges of GAI

# 6. Preliminary Findings

The initial semi-structured interviews (32 out of the 60 planned) provide significant, though non-generalizable, insights into the evolving roles and competencies of middle managers in the age of Generative Artificial Intelligence (GAI). These exploratory results help formulate hypotheses about emerging dynamics that will be further investigated in later phases.

## 6.1. New Roles and Bottom-Up Dynamics

Middle managers appear as key actors in organizational transformation. They translate strategic objectives into concrete actions and facilitate the adoption of GAI within teams (Engeström, 1987; Brynjolfsson & McAfee, 2014). Numerous accounts point to informal and localized use of GAI, sometimes beyond official IT guidelines, illustrating a Bottom-Up innovation dynamic (Silic & Back, 2014). This proactive appropriation is a pragmatic response to operational needs, but it also creates tension with existing IT governance frameworks (Behrens & Sedera, 2004; Senge, 2006).

These grassroots initiatives often arise as adaptive responses to bureaucratic complexity, particularly in large matrix organizations. As one technical manager explains: "To ask a question to a supplier, I have to send an email to Infrastructure, who contacts Intervention, who then reaches out to the manufacturer. [...] That's three emails just to get to the supplier. [...] It takes three weeks."

The reorganization of priorities in constrained environments leads some managers to seek automation tools to compensate for increasing workload: "We have meetings, but no time to write the minutes. Turning on the transcription tool is a productivity gain." (Senior manager, B2B distribution)

## 6.2. Evolving Managerial Competencies

The interviews point to a reconfiguration of required competencies:

* Conceptual: strategic vision, anticipation of organizational impacts, and managing contradictions (Engeström, 2001)

* Human: transformational leadership, strategic communication, change facilitation (Jordan & Mitchell, 2015)

* Technical: familiarity with GAI tools, understanding of ethical and security challenges (Behrens & Sedera, 2004)

Managers must integrate these skills to effectively use GAI while preserving team cohesion and performance (Katz, 1974; Peterson & Van Fleet, 2004). The following figure presents a synthesized framework of managerial competencies required in the GAI era, combining insights from Katz (1974) and Peterson & Van Fleet (2004).

<table><caption>Figure 3. Evolving managerial competencies in the context of GAI</caption><thead><tr><th>Katz (1974)<br>&<br>Peterson & Van Fleet (2004)</th><th>Middle<br>Manager<br>(before IAG)</th><th>Middle<br>Manager<br>(after AGI)</th><th>The skills of managers</th></tr></thead><tbody><tr><td><strong>Conceptual:</strong><br>See the company as a whole<br>Interdependence of functions</td><td>+</td><td>↑↑↑</td><td><strong>Conceptual:</strong> Expressing<br>meaning (Vision), continuous<br>learning</td></tr><tr><td><strong>Human being:</strong><br>Work effectively as a member of a group<br>Develop a cooperative effort within the team<br>Interpersonal Behavior</td><td>++</td><td>↑↑↑</td><td><strong>Human being:</strong> Expressing<br>meaning, continuous learning and<br>interpersonal behaviour</td></tr><tr><td><strong>Technique:</strong><br>Methods / Processes / Procedures / Techniques</td><td>+</td><td>↑↑</td><td><strong>Technique:</strong> Hard Skills,<br>continuous learning</td></tr></tbody></table>

This table illustrates how conceptual, human, and technical skills are redefined in light of GAI integration, based on a cross-reading of foundational models of managerial competencies.

The growing complexity of managerial tasks reinforces the human and organizational dimensions of their role. In critical environments, managers must ensure safety, coordination, and workflow regulation: "I have to ensure the safety of the site in all situations. [...] It's highly precise team management." (Manager, production site)
GAI is also changing how managers access knowledge, accelerating learning processes: "I used to waste a lot of time reading forums or PDFs. Now AI gives me context and direction." (Project manager, digital sector)

**6.3. Shadow IT: Innovation or Risk?**

Shadow IT is frequently cited as a pathway to GAI adoption. It allows operational agility but introduces risks related to compliance, data security, and strategic alignment (Györy et al., 2012; Zimmermann, 2014; Berente et al., 2021). Middle managers, acting as mediators, adopt awareness strategies, self-regulation, and even informal coordination with IT departments to mitigate these risks.

GAI usage by middle managers is not limited to technical or productivity-oriented applications—it also supports personal learning and relational development: "I use AI a bit to help myself—it helps me grow in how I behave and carry myself." (Technical lead, nuclear engineering)

Meanwhile, some internal training programs are beginning to formalize upskilling: "We have e-learning training that teaches you how to prompt. [...] It's totally autonomous." (Engineer, engineering department)

In parallel with proactive use, symbolic or emotional forms of resistance are emerging, particularly among experienced staff. These fears, often irrational or shaped by science fiction imaginaries, reflect outright rejection: "They don't want to use AI. I have a colleague who says it's the devil. [...] He's stuck on Terminator. To him, Skynet is going to take over." (Manager, large company, engineering).

In some cases, AI use is driven by a need for autonomy in the face of organizational inertia: "I didn't want to wait until next week, so I used ChatGPT. [...] I refined the prompt to get pictograms and flags for my slides." (Product lead, B2B distribution).

**6.4. Facilitators of Technological Change**

Middle managers are emerging as facilitators of change:

* Managing internal resistance

* Aligning strategic vision with operational execution

* Gradually integrating GAI into work practices

However, this position also exposes them to strong tensions between compliance requirements and innovation pressures.

The integration of AI tools challenges the added value of certain managerial tasks, particularly administrative and reporting functions. One manager puts it bluntly: "We need to become more efficient. [...] I think an AI could handle all time tracking. [...] Some managers will be left behind." (Technical manager, engineering)

Some see AI as a tool for cognitive structuring, helping them with project planning: "AI helps me structure my thinking—it reminds me of the steps I shouldn't skip when starting a project." (Technical project manager, tech company)

AI adoption can also create tensions between business units and support teams, especially IT: "I'm advocating for connecting Copilot Plus in Office 365. But IT says it won't work. [...] I'm talking from a business need." (Cross-functional manager, B2B distribution)

**6.5. Summary of Preliminary Findings**

The findings suggest that GAI is not merely a technological innovation but a structural and functional transformation of managerial work. The rise of hybrid skills, informal uses, and constant adjustment to internal tensions are key indicators of this ongoing shift.

Some respondents expressed clear awareness of the risk of skill obsolescence and are seeking to reposition themselves as informed technology partners: "I'd rather be the one telling AI what to do than the one saying 'go away.' [...] You need to identify your added value to stay relevant." (Manager, technical project environment).

Finally, the rapid evolution of tools is creating growing concern over potential internal divides: "There will be those who have AI—and those who don't." (Technical lead, tech company).

**7. Next Steps**

The next phase of this research will focus on completing the qualitative data collection by reaching the target of 60 semi-structured interviews across three contrasting organizational settings (large enterprise, medium-sized company, and SME). Special attention will be given to the diversity of respondent profiles, incorporating perspectives from senior management, IT teams, and HR departments to triangulate managerial perceptions.

The thematic analysis will be deepened using the Braun and Clarke method (2006), combined with activity theory, to highlight mechanisms of collective learning, sector-specific tensions, and GAI appropriation trajectories.
Furthermore, the conceptual articulation between bottom-up innovation, Shadow IT, and role transformation will be consolidated to develop a general interpretive model applicable to other organizational contexts. Lastly, a strong emphasis will be placed on the dissemination and valorization of the results across scientific and managerial communities (conferences, publications, professional reports).

**6. Ethics declaration**

This research did not require formal ethical clearance, in accordance with standard practices in the field of social sciences and humanities. All interviews were conducted following fundamental ethical principles: informed consent, systematic anonymization of data, and respect for participant confidentiality. Participants were informed of the study's objectives, their right to withdraw at any time, and the strictly academic use of the collected data.

**7. AI declaration**

Artificial Intelligence tools were used for language support, primarily to enhance the quality of the English translation of this paper, as the author is not a native English speaker. AI assistance was used to rephrase complex sections, harmonize academic style, and ensure terminological consistency across sections. No AI-generated content was included without human oversight, revision, or reformulation.

The author remains solely responsible for the argumentation, analysis, and all interpretations presented in the manuscript.

**8. Discussion and Conclusion**

The preliminary findings of this study suggest that the introduction of Generative Artificial Intelligence into organizations is not merely a technological development but signals a deeper transformation of the managerial role. Through the lens of activity theory, middle managers are no longer seen as mere executors of strategic directives, but rather as active mediators of the organizational tensions generated by GAI—between autonomy and control, innovation and compliance, individual appropriation and collective governance.

Three key contributions can already be identified:

* Conceptual Contribution: The systemic approach adopted here makes it possible to articulate bottom-up innovation logics and the Shadow IT phenomenon within a robust theoretical framework, positioning contradictions as drivers of transformation.

* Empirical Contribution: The initial interview data reveal emerging managerial practices, often informal, reflecting the development of hybrid skillsets combining relational leadership, technological reflexivity, and ethical sensitivity.

* Managerial Contribution: This research initiates a competency mapping for middle managers who support GAI-related practices—especially in informal or “grey” innovation zones outside formal governance structures.

Beyond its contributions, the study raises several critical questions:

* What is the sustainability of the adaptation burden placed on middle managers?

* How can we prevent the isolation of these facilitators within organizations under pressure?

* Most importantly, how can we design ethical governance models for GAI usage that reconcile local agility with strategic coherence?
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