# Generative AI and Middle Managers: Exploring Bottom-Up Innovation and Organizational Tensions Through Activity Theory

Philippe Jean-Baptiste

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## To cite this version:

Philippe Jean-Baptiste. Generative AI and Middle Managers: Exploring Bottom-Up Innovation and Organizational Tensions Through Activity Theory. Innovation Powered by Nature, ISPM (The International Society for Professional Innovation Management), Jun 2025, Bergen, Norway. [DOI: 10.5113172v1](https://doi.org/10.5113172v1)

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Distributed under a Creative Commons Attribution 4.0 International License
Generative AI and Middle Managers: Exploring
Bottom-Up Innovation and Organizational Tensions
Through Activity Theory

Philippe JEAN-BAPTISTE

LEST, CNRS, InCIAM / Aix-Marseille University, 35 Avenue Jules
Ferry, 13626 Aix-en-Provence, France
E-mail: philippejb@icloud.com

**Abstract:** This research explores how Generative Artificial Intelligence (GAI) transforms the roles and competencies of middle managers. Grounded in activity theory, it examines organizational tensions, particularly between Bottom-Up innovation and centralized governance. A qualitative methodology, based on 60 semi-structured interviews conducted across large enterprises, medium-sized enterprises, and small businesses, investigates these dynamics in diverse contexts.

Preliminary findings reveal that middle managers play a pivotal role in adopting GAI, often bypassing formal frameworks through Shadow IT. They are emerging as facilitators of change, requiring enhanced human and conceptual skills to interpret AI tools and manage organizational tensions effectively.

This research proposes practical recommendations to balance innovation with
compliance while strengthening the role of middle managers in technological
transitions. Feedback is sought on analysing tensions, identifying managerial
competencies, and ensuring the transferability of results.

**Keywords:** Generative Artificial Intelligence (GAI), Middle managers, Organizational transformation, activity theory, bottom-up innovation, Shadow IT, managerial competencies, skilling, technological adoption.

PROBLEM

The emergence of Generative Artificial Intelligence (GAI) technologies—particularly large language models such as ChatGPT, Copilot, or Perplexity—represents a profound disruption of the technological and organizational landscape. Unlike traditional supervised machine learning models focused on predictive tasks based on structured data, GAI autonomously generates new content (text, code, images) based on large-scale learning patterns (Krakowski et al., 2023; Hillebrand et al., 2025). This paradigmatic shift is reshaping decision-making, creativity, workflows, and organizational structures (Brynjolfsson & McAfee, 2014; Wilson & Daugherty, 2018).

At the heart of these transformations, middle managers—positioned between strategic leadership and operational teams—are experiencing a profound redefinition of their roles. Historically responsible for coordination and execution (Floyd & Wooldridge, 1997;
Tengblad, 2006), they are now expected to facilitate GAI integration, arbitrate between bottom-up innovation and centralized governance, and maintain strategic alignment in increasingly volatile environments (Berente et al., 2021; Wilden et al., 2023).

The bottom-up adoption of GAI fosters the emergence of Shadow IT practices (Behrens & Sedera, 2004), enhancing agility but exposing organizations to risks of fragmentation and non-compliance. At the same time, new forms of distributed and invisible control (Leclercq-Vandelannoitte, 2017; Leclercq-Vandelannoitte & Isaac, 2013) increase pressure on middle managers, who must supervise digital initiatives they did not initiate and do not fully control.

This reconfiguration demands an evolution in managerial competencies: beyond technical capabilities, human skills (e.g., empathy, change management) and conceptual abilities (e.g., interpreting AI-generated insights, understanding algorithmic logic) become increasingly critical (Mintzberg, 1989; Katz, 1974; Peterson & Van Fleet, 2004).

Yet despite the magnitude of these changes, academic research remains fragmented. Few studies systematically address how GAI adoption specifically reshapes the roles and competencies of middle managers. In this regard, Activity Theory (Engeström, 1987; Blackler et al., 2003) offers a relevant socio-technical framework to map interactions between actors, tools, rules, division of labor, and communities, while exposing emerging contradictions and enabling organizational learning.

Developing a contextualized and systemic understanding of the challenges posed by GAI for middle managers is thus crucial to support responsible technology adoption, anticipate resistance, and design support structures that reinforce the pivotal role of middle managers in organizational adaptation.

## CURRENT UNDERSTANDING

Generative Artificial Intelligence (GAI) is attracting growing interest in both academic and professional communities. However, most existing research remains focused on the technical capabilities of these tools and their macroeconomic impacts: productivity gains, task automation, value chain reconfigurations, and sectoral transformations (Brynjolfsson & McAfee, 2014; Wilson & Daugherty, 2018). While valuable, these approaches often overlook the micro-organizational dynamics, particularly the adjustments experienced by middle managers. In particular, GAI tools can be viewed as agentic artifacts requiring new models of delegation and interaction between human actors and intelligent systems (Baird & Maruping, 2021).

Middle managers occupy a strategic pivotal position. They perform critical functions of coordination, mediation, change facilitation, and strategic translation (Floyd & Wooldridge, 1997; Tengblad, 2006). In the context of rapid GAI adoption, their role becomes increasingly complex and ambivalent, balancing bottom-up innovation with the maintenance of organizational coherence (Wilden et al., 2023).

Classic models of managerial skills (Katz, 1974; Mintzberg, 1973, 1989) did not foresee the specific tensions generated by autonomous generative technologies. Earlier research emphasized how managerial roles varied significantly depending on organizational needs
and contexts (Kraut et al., 1989), a perspective that requires reexamination in light of
GAI disruptions. Today, new contradictions emerge:

* Between operational agility and centralized IT governance standards;

* Between the expansion of Shadow IT, initiated by teams, and formal compliance obligations (Behrens & Sedera, 2004);

* Between the expectation to support emerging digital practices and the lack of adapted technical competencies (Leclercq-Vandelannoitte & Isaac, 2013; Leclercq-Vandelannoitte, 2017).

These tensions reveal the partial obsolescence of traditional theoretical frameworks and call for more systemic approaches. Activity Theory (Engeström, 1987; Blackler et al., 2003) offers a relevant analytical lens, capable of modeling interactions among subjects, tools, rules, communities, and division of labor, while surfacing organizational contradictions.

Despite its relevance, this framework remains underutilized for understanding how GAI concretely reshapes middle managers’ roles and competencies (Tarakci et al., 2023; Van Doorn et al., 2023). Leveraging Activity Theory more fully would enable a better analysis of the negotiation, learning, and recomposition dynamics that characterize everyday adaptation to GAI.

In summary, while macro-level studies on AI provide valuable insights, there remains a lack of fine-grained, contextualized understanding of the daily challenges faced by middle managers—situated between hopes for innovation and risks of organizational misalignment.

## RESEARCH QUESTION

The rapid diffusion of Generative Artificial Intelligence (GAI) is profoundly transforming work environments, decision-making processes, and collaboration modes. In this dynamic context, middle managers play a crucial role: they must integrate new technologies within operational teams while maintaining strategic alignment with governance structures.

Empirically, they stand at the crossroads of bottom-up innovation initiatives, top-down compliance constraints, and increasing tensions between agility, security, and individual accountability. Yet, the mechanisms through which GAI redefines their roles, responsibilities, and competencies remain largely underexplored, both empirically and theoretically.

Building on a systemic approach grounded in Activity Theory, this research aims to analyze how GAI integration reshapes managerial practices through the interaction of technological tools, organizational rules, and stakeholder communities.

The central research question is formulated as follows:

**How does the integration of Generative Artificial Intelligence transform the roles and competencies of middle managers within digitally transforming organizations?**

This question specifically seeks to explore:

* the new technical, human, and conceptual skills required by GAI integration;
* the reconfiguration of managerial roles in response to bottom-up innovation dynamics;

* the emerging tensions around autonomy, control, and managerial legitimacy in increasingly hybrid human-machine environments

## RESEARCH DESIGN

This study adopts a qualitative and exploratory approach, particularly suited to understanding a complex, emerging, and underexplored phenomenon: the transformation of middle managers' roles and competencies in the era of Generative Artificial Intelligence (GAI). This methodological positioning is justified by the dynamic nature of GAI adoption, its strong contextual dependency, and the importance of informal practices in shaping usage patterns.

### Research Setting and Data Collection Strategy

Data were collected through 60 semi-structured interviews with middle managers across three types of organizations, selected based on their digital maturity, exposure to GAI, and diversity in governance structures:

* **Large Enterprise (LE):** 30 interviews conducted within a highly centralized environment, providing insights into tensions between bottom-up innovation and rigid governance.

* **Medium-Sized Enterprise (MSE):** 15 interviews conducted within an agile organization encouraging local experimentation with GAI.

* **Small Enterprise (SE):** 15 interviews conducted in a startup-like context characterized by rapid technology adoption and high managerial flexibility.

These contrasting organizational settings allow comparisons of how tensions related to innovation, control, and competency transformation are managed across different governance logics.

<table><thead><tr><th></th><th>Typology</th><th>Sector of Activity</th><th>Revenue</th><th>Nb of Employees</th></tr></thead><tbody><tr><td>30</td><td>Large French enterprise (LE)</td><td>Production and distribution of electricity</td><td>139,7 Md€</td><td>179 550</td></tr><tr><td>15</td><td>Medium-sized enterprise (MSE)</td><td>IT services and consulting</td><td>552 M€</td><td>540</td></tr><tr><td>15</td><td>Small and medium-sized enterprise (SME)</td><td>Telecommunications</td><td>75 M€</td><td>175</td></tr></tbody></table>

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<figure><img src="image_5.png" /><figcaption>**Figure 1** Overview of the organizational contexts included in the study.</figcaption></figure>

### Analytical Framework and Theoretical Lens

Interviews were fully transcribed, anonymized, and subjected to thematic analysis (Braun & Clarke, 2006), with a particular focus on identifying systemic contradictions in managerial practices. Activity Theory (Engeström, 1987) constitutes the central conceptual framework, guiding both the data collection and the structuring of the analysis.

Each component of the activity system (subject, tools, rules, object, community, division of labor) is used to detect reconfigurations and tensions triggered by the introduction of GAI.
<figure><img src="image_6.png" /><figcaption>**Figure 2** Conceptual model of GAI transformation dynamics based on Activity Theory (adapted from Engeström, 1987).</figcaption></figure>

## Methodological Rigor and Validity

To ensure analytical rigor:

* A systematic triangulation of data across the three organizational types is conducted.

* Participant validation processes are implemented to strengthen interpretive credibility.

* Reflective analytical memos are produced throughout the research to document theoretical evolution and minimize interpretive bias.

The aim is to produce a systemic, nuanced, and context-sensitive analysis that not only describes the observed transformations but also supports theoretical and practical recommendations for assisting middle managers in navigating GAI adoption. At the time of writing, all interviews have been transcribed and initially coded using thematic analysis. Deeper axial coding and cross-context comparisons are currently ongoing.

# PRELIMINARY RESULTS

The preliminary analysis of interviews across diverse organizational contexts highlights four major dynamics reshaping managerial roles in the era of Generative Artificial Intelligence (GAI):

## 1. Bottom-Up Innovation and Governance Tensions

Middle managers frequently initiate local experimentation with GAI without always seeking formal validation. While these initiatives foster innovation and responsiveness, they create tensions with centralized control standards, exposing organizations to risks associated with Shadow IT. Shadow IT, as characterized by Haag and Eckhardt (2017), involves the deployment of technological tools outside the formal oversight of IT departments, intensifying the risk of security breaches and governance inconsistencies. This dynamic illustrates a core contradiction between innovative tool adoption and formal governance rules.

## 2. Emergence of Hybrid Competencies
GAI requires middle managers to develop a combination of technical skills (understanding AI tools), conceptual skills (critical interpretation of AI-generated outputs), and human skills (communication, adaptive leadership). However, these emerging competencies are rarely formalized in current HR frameworks, creating a gap between evolving expectations and available support structures. This evolution illustrates the automation-augmentation paradox, where managers must simultaneously automate routine tasks while reinforcing uniquely human competencies such as critical judgment and ethical discernment (Raisch & Krakowski, 2021).

### 3. Change Facilitation and Continuous Arbitration

Situated between strategic leadership and operational teams, middle managers increasingly act as facilitators, translating uncertain or evolving directives into pragmatic actions. Their mediation role becomes critical to balance bottom-up innovation with strategic alignment, within a context marked by rapid learning and technological uncertainty.

### 4. Pragmatic Ambivalence Toward Shadow IT

While aware of the risks posed by unofficial technological initiatives, many managers pragmatically tolerate—or even encourage—local experimentation to avoid stifling innovation. This ambivalence highlights the difficulty of balancing innovation freedom with the need for organizational security and compliance. Previous studies have shown that informal IT practices often arise as responses to rigid governance frameworks, fostering both innovation and significant risk exposure (Silic & Back, 2014).

These preliminary findings demonstrate that GAI acts as a powerful driver of managerial role reconfiguration while amplifying classical tensions between autonomy, control, and strategic alignment. Further analysis will aim to model these dynamics more precisely within the framework of Activity Theory and to develop actionable recommendations for supporting the responsible integration of GAI into organizational practices.

## CONTRIBUTION

This research offers a multifaceted contribution to the understanding of the impact of Generative Artificial Intelligence (GAI) on organizational dynamics, with a particular focus on the transformation of middle managers’ roles and competencies.

### 1. Theoretical Contribution

By applying Activity Theory (Engeström, 1987), this study proposes a systemic framework to analyze the organizational tensions generated by GAI adoption. Recent developments further explore how heterogeneous coalitions and distributed agency reshape work practices, offering an essential lens to understand GAI integration (Engeström & Sannino, 2021). This perspective enables:

* the identification of contradictions between tools, rules, objectives, and communities of actors

* the interpretation of managerial transformations not merely as technical adaptations, but as processes of negotiation, learning, and organizational recomposition
Thus, the study enriches the understanding of managerial evolution dynamics in the digital era, extending prior work by Blackler et al. (2003) and Engeström & Sannino (2010).

**2. Empirical Contribution**

Based on 60 semi-structured interviews conducted across diverse organizational settings, the study provides:

* a comparative analysis of GAI appropriation practices;

* the identification of both common dynamics (such as the rising demand for hybrid competencies and the enhanced mediation role) and context-specific variations linked to governance structures and digital culture.

The findings, while rich, are limited to specific organizational contexts and call for complementary studies to validate their transferability to other sectors

**3. Practical Contribution**

The research outlines concrete strategies to support responsible GAI integration:

* the recognition of new hybrid managerial competencies (technical, human, and conceptual);

* the creation of controlled innovation frameworks that support agility without compromising governance;

* the repositioning of middle managers as strategic actors in digital transformation.

**PRACTICAL IMPLICATIONS**

Building on these findings, this research provides concrete recommendations to support the responsible adoption of GAI technologies. Four priority areas emerge:

**1. Developing Hybrid Competencies Adapted to GAI**

In increasingly complex digital environments, middle managers must cultivate both conceptual skills (e.g., critical analysis of AI outputs) and human skills (e.g., communication, change management, leadership in hybrid environments). Training programs must evolve to incorporate these dimensions, which are essential for successful digital transformation.

**2. Structuring Bottom-Up Innovation Without Hindering It**

Recognizing local innovation as a strategic asset requires structuring experimentation through controlled environments (“innovation sandboxes”). This approach enables organizations to balance operational creativity with regulatory compliance by providing a secure framework for testing new practices.

**3. Integrating Shadow IT Into a Regulated Innovation Strategy**

Rather than suppressing informal initiatives, organizations should channel Shadow IT practices by gradually integrating them into formal systems. This approach ensures data security while preserving the agility and entrepreneurial spirit that fuel bottom-up innovation.
## 4. Equipping Middle Managers to Become True Change Facilitators

Strengthening the role of middle managers as facilitators requires providing them with effective collaborative tools, ensuring clear and consistent top-down communication, and creating peer-sharing spaces to accelerate the dissemination of best practices across the organization.

These levers aim to orchestrate a sustainable balance between agility, security, and human support — a critical condition for the effective and strategic integration of GAI into organizational environments.

## FEEDBACK

As this research project progresses, feedback is actively sought to enrich theoretical contributions, deepen empirical analysis, and enhance the operational relevance of the findings. Four key areas of reflection have emerged from the preliminary results and would greatly benefit from exchanges with both the academic community and organizational practitioners.

### 1. Organizational Tensions: better understanding the innovation-governance interplay

Tensions between bottom-up initiatives and centralized control requirements emerge as both drivers of transformation and sources of organizational vulnerability.

* Which complementary theoretical frameworks (e.g., organizational ambidexterity, structuration theory, institutional work) or methodological tools could help to model these tensions more precisely?

* In addition, what methodological approaches could best capture the evolution of these tensions longitudinally?

### 2. Managerial Competencies: redefining evaluation frameworks

The rapid evolution of expectations toward middle managers calls for an update of competency models.

* Which frameworks, tools, or field experiences could better formalize the emerging hybrid competencies (technical, interpretive, human) required in GAI-driven environments?

* Are there validated competency frameworks that could be adapted specifically for AI-augmented managerial roles?

### 3. Transferability of Findings: broadening the applicability

The diversity of organizational contexts raises the question of the generalizability of observed dynamics.

* What sectoral, structural, or cultural factors could shape the transferability of these findings to domains such as healthcare, education, or the public sector?

* What sectoral characteristics (e.g., regulatory intensity, digital maturity) would most influence the transferability of findings?

### 4. Integrating Shadow IT: balancing experimentation and compliance

Shadow IT dynamics foster innovation but pose governance challenges.
* What examples or organizational strategies have successfully channeled informal practices into secure, formal systems?

* What organizational policies have proven successful in channeling informal IT practices without stifling innovation?

## Final Aim

The feedback collected will help refine the analysis, strengthen the conclusions, and adjust the recommendations to meet the expectations of:

* researchers interested in how GAI reshapes managerial practices,

* and practitioners facing the challenges of technological integration in complex organizational settings.

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