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Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROI

Lee Ackerman

TL;DR

The paper investigates how organizations perceive and implement responsible AI amid the rise of agentic AI, focusing on ROI considerations. It adopts an interpretive qualitative approach, surveying 44 North American AI professionals and synthesizing findings with a heatmap framework that maps responsible AI dimensions to emergent themes. Key results show that agentic AI's novelty and complexity generate knowledge gaps, control-focused attitudes, and inadequate stakeholder engagement, all of which threaten effective adoption and ROI realization. The work offers practical implications for leadership education, governance enhancements, and participatory design approaches to close theory-practice gaps and foster responsible ROI in agentic AI deployments.

Abstract

As artificial intelligence (AI) systems rapidly gain autonomy, the need for robust responsible AI frameworks becomes paramount. This paper investigates how organizations perceive and adapt such frameworks amidst the emerging landscape of increasingly sophisticated agentic AI. Employing an interpretive qualitative approach, the study explores the lived experiences of AI professionals. Findings highlight that the inherent complexity of agentic AI systems and their responsible implementation, rooted in the intricate interconnectedness of responsible AI dimensions and the thematic framework (an analytical structure developed from the data), combined with the novelty of agentic AI, contribute to significant challenges in organizational adaptation, characterized by knowledge gaps, a limited emphasis on stakeholder engagement, and a strong focus on control. These factors, by hindering effective adaptation and implementation, ultimately compromise the potential for responsible AI and the realization of ROI.

Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROI

TL;DR

The paper investigates how organizations perceive and implement responsible AI amid the rise of agentic AI, focusing on ROI considerations. It adopts an interpretive qualitative approach, surveying 44 North American AI professionals and synthesizing findings with a heatmap framework that maps responsible AI dimensions to emergent themes. Key results show that agentic AI's novelty and complexity generate knowledge gaps, control-focused attitudes, and inadequate stakeholder engagement, all of which threaten effective adoption and ROI realization. The work offers practical implications for leadership education, governance enhancements, and participatory design approaches to close theory-practice gaps and foster responsible ROI in agentic AI deployments.

Abstract

As artificial intelligence (AI) systems rapidly gain autonomy, the need for robust responsible AI frameworks becomes paramount. This paper investigates how organizations perceive and adapt such frameworks amidst the emerging landscape of increasingly sophisticated agentic AI. Employing an interpretive qualitative approach, the study explores the lived experiences of AI professionals. Findings highlight that the inherent complexity of agentic AI systems and their responsible implementation, rooted in the intricate interconnectedness of responsible AI dimensions and the thematic framework (an analytical structure developed from the data), combined with the novelty of agentic AI, contribute to significant challenges in organizational adaptation, characterized by knowledge gaps, a limited emphasis on stakeholder engagement, and a strong focus on control. These factors, by hindering effective adaptation and implementation, ultimately compromise the potential for responsible AI and the realization of ROI.

Paper Structure

This paper contains 22 sections, 15 figures.

Table of Contents

  1. Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROI
  2. Contextualizing the Narrative: Literature Review
  3. The Storytelling Process: Methodology
  4. Research Approach and Rationale
  5. Ethical Considerations
  6. Study Limitations: Strengths and Weaknesses of the Chosen Method
  7. Research Tools and AI Collaboration
  8. Researcher Positionality and Bias Mitigation
  9. Ethical Research Commitment
  10. The Respondents' Stories: Findings
  11. The Respondents
  12. Foundations for Interpretation
  13. Interpretive Synthesis
  14. Autonomy, Control, and Ethical Alignment
  15. Organizational Culture, Practices, and Societal Impacts
  16. ...and 7 more sections

Figures (15)

  • Figure 1: Heat map showing how the dimensions for Responsible AI intersect with the key themes.
  • Figure 2: Perceptions of Agentic AI in Organizations - Distribution of Responses to Likert Questions
  • Figure 3: This pie chart represents the results of an interpretive analysis of AI-related language found in the open-ended responses from the survey.
  • Figure 4: Perceptions of Agentic AI's impact on financial performance and ROI.
  • Figure 5: Respondents reported years of experience working with AI technologies
  • ...and 10 more figures