# Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption

arXiv: https://arxiv.org/abs/2502.15870 · HTML: https://mlsanity.com/paper/2502.15870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 77 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Multimodal Foundation Models: From Specialists to General-Purpose Assistants](https://mlsanity.com/paper/2309.10020/) (2023)
- [Selective Explanations: Leveraging Human Input to Align Explainable AI](https://mlsanity.com/paper/2301.09656/) (2023)
- [Decision-Oriented Dialogue for Human-AI Collaboration](https://mlsanity.com/paper/2305.20076/) (2023)
- [Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration](https://mlsanity.com/paper/2402.18498/) (2024)
- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes](https://mlsanity.com/paper/2311.18452/) (2023)
- [Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions](https://mlsanity.com/paper/2012.02592/) (2020)
- [Concerns on Bias in Large Language Models when Creating Synthetic Personae](https://mlsanity.com/paper/2405.05080/) (2024)
- ["I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials](https://mlsanity.com/paper/2309.08121/) (2023)
- [Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals](https://mlsanity.com/paper/2311.14720/) (2023)

## Built on by (most cited first, 2 of 2)

- [Cisco Integrated AI Security and Safety Framework Report](https://mlsanity.com/paper/2512.12921/) (2025)
- [ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language](https://mlsanity.com/paper/2607.05123/) (2026)

## Abstract (arXiv)

This study investigates how individuals' perceptions of artificial intelligence (AI) limitations influence organizational readiness for AI adoption. Through semi-structured interviews with seven AI implementation experts, analyzed using the Gioia methodology, the research reveals that organizational readiness emerges through dynamic interactions between individual sensemaking, social learning, and formal integration processes. The findings demonstrate that hands-on experience with AI limitations leads to more realistic expectations and increased trust, mainly when supported by peer networks and champion systems. Organizations that successfully translate these individual and collective insights into formal governance structures achieve more sustainable AI adoption. The study advances theory by showing how organizational readiness for AI adoption evolves through continuous cycles of individual understanding, social learning, and organizational adaptation. These insights suggest that organizations should approach AI adoption not as a one-time implementation but as an ongoing strategic learning process that balances innovation with practical constraints. The research contributes to organizational readiness theory and practice by illuminating how micro-level perceptions and experiences shape macro-level adoption outcomes.