Strategic Integration of AI for Data ‑ Driven Decisions and Strategic Integration of AI for Data Driven Decisions and Automation in Operations Management Automation in Operations Management
This study develops an evidence-informed framework for the strategic integration of AI through a PRISMA-guided systematic literature review and design science artifact construction and offers a rigorous and practical blueprint for scalable and trustworthy AI-enabled operations.
This research demonstrates that when designed with ergonomics, human values, and socio‑technical principles at the center, agentic AI become powerful enablers of human‑centric, resilient, and adaptive enterprises.
Elizabeth Koumpan, Laurentiu Gabriel Ghergu, Łukasz Strack et al.· AHFE International· 0 citations
This study provides a systematic review of the academic sources to explore the role of the combination of AI, automation, and data-driven strategies in supporting the metamorphosis of conventional businesses and enhancing their operational efficiency, organizational agility, and long-term competitive advantage.
Gurpreet Kaur· The American Journal of Mana...· 0 citations
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
Choudhry Bilal Mazhar· International Journal for Re...· 0 citations
A Data-Driven Operations Synchronization Stack is proposed that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture in high-throughput manufacturing contexts.
A. Y. I. ElGabroni, Paulo Peças· Systems· 0 citations
Enterprise analytics is undergoing a fundamental transformation as organizations deploy AI-augmented systems that produce probabilistic outputs, generate plausible but incorrect recommendations, and require organizational change management at a scale that traditional data products never demanded. The data product managers who build and steward these systems face challenges for which established product management frameworks designed around deterministic systems with binary correctness criteria are structurally inadequate. This article argues that AI product management is a distinct professional discipline requiring its own framework, methods, and practices. Drawing on analysis of AI-augmented analytics deployments in enterprise environments, we propose a five-dimension framework: (1) uncertainty and confidence user experience design, (2) failure mode architecture, (3) stakeholder congruence and conflict management, (4) organizational change and capacity building, and (5) responsible decision making encompassing fairness, transparency, and accountability. Four best practices for mature AI product deployments accompany the framework, along with a profile of the emerging AI product manager skillset. Organizations that operationalize this framework gain systematic advantages in adoption, user trust, decision quality, and sustainable AI value realization. Those that continue treating AI product management as faster data engineering expose themselves to adoption failures, fairness and compliance risks, and the organizational resistance that undermines AI investment returns.
Akhil Kumar Kandakatla· International journal of com...· 0 citations
Autonomous AI agents operating in enterprise environments require both standardized connectivity to production tools and a governance architecture for doing so safely. The Model Context Protocol (MCP), introduced in November 2024 and transferred to the Agentic AI Foundation under the Linux Foundation in December 2025, has reached 97 million monthly SDK downloads and adoption across all major AI providers within sixteen months of launch. The specification addresses connectivity; it does not address governance. Enterprise architects deploying agents in regulated, mission-critical environments face a structural gap: no architectural guidance exists for permission enforcement, risk-tiered execution, or audit trail requirements at the MCP protocol layer. This article reports three contributions derived from an eighteen-month production deployment connecting autonomous agents to fourteen enterprise systems across 270 globally distributed data centers. First, a three-tier integration pattern taxonomy maps tool risk profiles to appropriate governance mechanisms. Second, a permission manifest architecture embeds role-based access control (RBAC), rate limiting, and scope constraints into MCP server registration — making safety a protocol-level property rather than an application-level afterthought. Third, empirical measurement confirms a 73 percent reduction in per-tool engineering effort and complete cross-platform portability across three AI providers. These patterns provide enterprise architects with a validated governance framework for production MCP deployment.
Satish Chandra, Guruvelli· International Journal of Com...· 0 citations
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