Conference item : Abstract
Becoming an agentic enterprise: a practitioner methodology and frameworks for human-AI governance
- Abstract:
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The transition from pilot projects to enterprise-scale autonomous AI systems represents one of the most significant governance challenges facing organizations today. While much attention has focused on the technical capabilities of large language models and agentic systems, comparatively little research has addressed the practical governance frameworks and methodology required to manage portfolios of agents at scale within an Enterprise. This creates a governance gap that keeps enterprises in long pilot cycles. For enterprises to confidently deploy, they need governing frameworks: Boards lack comprehensive frameworks to assess, classify, and oversee these increasingly sophisticated human-algorithm systems. Transformation leaders need a way to assess the human oversight required as agent complexity evolves.
This paper introduces the “Becoming an Agentic Enterprise” methodology developed by the author (s) as a comprehensive practitioner framework, grounded in direct engagement with enterprise transformation initiatives. The methodology comprises four interlocking components: SCORE-AI for board-level governance assessment, the L1-L6 Agent Maturity Model for capability classification, the AURA framework for human oversight calibration & the extent of integration required with existing systems and processes, and the RRR transformation sequence for phased implementation. We illustrate the application of this integrated methodology through a detailed case study of an enterprise software company that uses this framework, resulting in a blueprint for ~100 agents across eight business domains spanning the spectrum of agent maturity. The paper contributes to the human-AI interaction literature by demonstrating how layered governance structures can maintain meaningful human oversight while enabling operational autonomy, and by providing a replicable blueprint for enterprise-scale agentic transformation.
- Publication status:
- Published
- Peer review status:
- Reviewed (other)
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- Files:
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(Preview, Version of record, pdf, 360.5KB, Terms of use)
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- Publisher copy:
- 10.2139/ssrn.7123579
Authors
- Publisher:
- Elsevier
- Journal:
- SSRN More from this journal
- Publication date:
- 2026-07-16
- Acceptance date:
- 2026-07-06
- Event title:
- Human-algorithm interaction workshop 2026
- Event location:
- Oxford, UK
- Event website:
- https://www.sbs.ox.ac.uk/events/human-algorithm-interaction-workshop-2026
- Event start date:
- 2026-07-06
- Event end date:
- 2026-07-08
- DOI:
- EISSN:
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1556-5068
- Language:
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English
- Keywords:
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- Subtype:
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Abstract
- Pubs id:
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2446815
- Local pid:
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pubs:2446815
- Source identifiers:
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W7168814097
- Deposit date:
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2026-07-27
- ARK identifier:
Terms of use
- Copyright holder:
- Bharath Yadla
- Copyright date:
- 2026
- Rights statement:
- ©2026 Bharath Yadla. This work is available under a Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0). You are free to share, copy, and redistribute the material in any medium or format, provided you give appropriate credit, do not distribute modified or derivative versions, and do not use it for commercial purposes without explicit permission. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/
- Notes:
- This paper was presented at Human-algorithm interaction workshop 2026, 6-8/07/2026, Oxford, UK
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