Authors : Aditya Kashyap

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/43vy2bem

Scribd : https://tinyurl.com/xw6ymah

DOI : https://doi.org/10.38124/ijisrt/26apr503

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : AI governance frameworks such as the EU AI Act, the NIST AI Risk Management Framework, and ISO 42001 were built for a world where AI systems were visible, discrete, and clearly owned. That is not the world enterprises operate in today.AI now exists inside enterprise software. It influences decisions across ERP, CRM, contract management, and supply chain platforms. Organizations are not deploying AI systems. They are inheriting AI capabilities. This shift creates a structural accountability problem. Enterprises do not train these models and often cannot inspect them. Yet they remain fully accountable for the outcomes those systems produce. Vendors control the technology, but do not share operational responsibility. The result is a governance gap that current frameworks such as the EU AI Act, NIST AI RMF, and ISO 42001 are not designed to address. This paper explores how that gap appears in real enterprise environments and proposes the Enterprise AI Integration Governance model, a practical four-layer approach to reestablishing accountability in embedded AI ecosystems. With regulatory enforcement approaching, this is no longer a future concern.

Keywords : AI Governance, Enterprise AI, EU AI Act, NIST AI RMF, ISO 42001, ERP, Embedded AI, Agentic AI, Accountability, Digital Transformation, AI Risk Management, CLM, SAP, Oracle.

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