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

arXiv: https://arxiv.org/abs/2504.11564 · HTML: https://mlsanity.com/paper/2504.11564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [International AI Safety Report](https://mlsanity.com/paper/2501.17805/) (2025)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents](https://mlsanity.com/paper/2501.00881/) (2025)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [The Return on Investment in AI Ethics: A Holistic Framework](https://mlsanity.com/paper/2309.13057/) (2023)
- [An ethical study of generative AI from the Actor-Network Theory perspective](https://mlsanity.com/paper/2406.11860/) (2024)
- [Criticizing Ethics According to Artificial Intelligence](https://mlsanity.com/paper/2408.04609/) (2024)

## Abstract (arXiv)

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.