This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.
The study demonstrates that the successful deployment of AI Builder is contingent more on organizational readiness and accountability than technological maturity, and offers an informed blueprint to organizations adopting low-code AI platforms.
A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.
Gerald Schneikart, Walter Mayrhofer· Engineering review· 0 citations
The findings indicate that GenAI use is widespread in the sample, but integration into business processes is incremental and localized rather than deeply embedded and governance practices are evolving alongside its use.
Shirin Hasavari, J. Zaveri· Knowledge and Process Manage...· 0 citations
The study demonstrates that the successful deployment of AI Builder is contingent more on organizational readiness and accountability than technological maturity, and offers an informed blueprint to organizations adopting low-code AI platforms.
P. Vutla, Triveni Yenugu· Journal of Information Techn...· 0 citations
Generative artificial intelligence (GenAI) has moved from experimental novelty to a central input in organizational decision-making, with McKinsey's Q1 2026 Global AI Survey finding that 65 percent of organizations now use generative AI in at least one business function, roughly double the adoption rate reported ten months earlier, and 72 percent report at least one AI workload in production. Despite this scale of adoption, the empirical record on business value remains sharply divided. This paper synthesizes recent academic and industry evidence, drawing on 24 sources published primarily between 2023 and 2026, to examine three interlocking questions: what theoretical frameworks currently explain GenAI's role in managerial and strategic decision-making, how enterprises are applying GenAI in practice across functional areas, and what structural challenges limit the translation of GenAI adoption into measurable business value. The paper synthesizes evidence from strategic management research on AI-assisted evaluation of business alternatives, organizational theory on GenAI's emerging roles in decision processes, and empirical field studies on ambiguity handling and sycophantic behavior in AI-generated business advice, alongside a widely cited 2025 MIT study finding that 95 percent of enterprise generative AI pilots fail to deliver measurable profit-and-loss impact. Findings indicate that GenAI functions most reliably as an augmentation tool that aggregates and structures diverse inputs for human judgment, rather than as an autonomous decision-maker, that single-model evaluations of strategic alternatives are frequently inconsistent and biased while aggregated multi-model evaluations approximate expert human judgment, and that the primary barrier to enterprise value is organizational and workflow integration rather than model capability. The paper concludes with a proposed decision-integration framework and implications for executives, AI governance functions, and researchers.
M.SANGEETHA, Una Suman Kumar Patro, K.ARPITHA et al.· International journal of com...· 0 citations
Software delivery in large organisations is shaped by two different needs. Management must maintain control over scope, cost, commitments, risk and compliance, while delivery teams must respond when technical findings, user feedback or external dependencies change the working assumptions. This paper develops a conceptual framework that connects traditional project governance, agile execution and artificial intelligence (AI)-supported decision-making within one operating model. Five applications are considered: effort and capacity estimation, backlog prioritisation, delivery-risk sensing, project knowledge capture and software quality analytics. The framework also treats bias in historical records, data quality, explainability and accountability as project-management concerns. Four propositions link the framework with schedule adherence, scope control, rework, throughput, release quality and stakeholder satisfaction. A staged route for empirical validation and a practical readiness assessment are proposed. The study is conceptual and draws on practitioner-informed observations. It argues that AI creates value only when its outputs are connected to an existing decision process at the point where action is still possible; responsibility for interpreting, accepting or rejecting the recommendation remains with the accountable project participants.
Unknown authors· SHS Web of Conferences· 0 citations
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