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.
Abstract
Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative research across industries.
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
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
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
Digital transformation in Knowledge-intensive Processes is shifting toward Agentic Business Process Management to overcome challenges posed by unstructured data complexity. This research-in-progress evaluates the structural tension between operational efficiency and compliance in Knowledge-intensive Processes automation by examining Generative AI extraction, database architectures, and performance trade-offs between Python and Low-Code/No-Code platforms under AI governance frameworks. Using Design Science Research, this study proposes a five-pillar conceptual framework and establishes a qualitative baseline through domain expert interviews. Initial findings reveal structural fragmentation as core barriers, driving a 20 to 40% waste premium. This paper provides the architectural foundation and experimental protocol for future quantitative benchmarking.
Ume Rubab, Bernhard Axmann· Engineering review· 0 citations
Aim/Purpose
This article reports an exploratory case study that examines workflow perceptions in two hybrid engineering teams comprising 26 professionals, focusing on temporal experience, meeting character, and engagement.
Background
Hybrid work configurations pose coordination challenges for distributed teams, yet the hybrid context remains understudied, particularly in terms of temporal coordination and synchronous communication.
Methodology
Following an exploratory case study design, we conducted semi-structured interviews with four team leaders and administered a six-item categorical questionnaire to 26 team members. A modified C4.5 decision tree algorithm was applied to organize response patterns for interpretive purposes, though severe limitations from the small sample size and unvalidated measures are acknowledged.
Contribution
Three response patterns and one outlier emerged (n= 1), with sense of time and meeting character appearing as the most differentiating variables. Extracted patterns from decision trees serve as a foundation for describing a provisional profile. Three provisional profiles were observed: a subgroup reporting temporal acceleration alongside task-focused meeting preferences (n=4); a subgroup reporting awaiting-completion states with task-focused preferences (n=17); and a subgroup reporting connection-oriented meeting preferences (n=4). These profiles are offered as tentative, sample-bound observations. We introduce preliminary terminology to describe workflow dynamics and apply a machine learning algorithm in a new context, as an exploratory pattern-organization tool. No claims of generalizable patterns are made due to the absence of performance measurement and comparison groups.
Findings
The observed patterns suggest possible directions for further studies on temporal experience, meeting character, and informing processes in hybrid work settings. These patterns require extensive validation before offering reliable guidance for practice.
Recommendations for Practitioners
The managers and team leaders may find it useful to reflect on temporal experience, meeting structure, and information flow in hybrid teams, but the present study does not provide validated intervention principles or managerial guidance.
Recommendations for Researchers
The interpretations were made on a small data set that depends heavily on the specificity of the team’s common language and may serve as a basis for researching new concepts of group engagement patterns in a business environment. Larger datasets, along with the application of contemporary machine learning methods, could be used to uncover hidden patterns in qualitative data.
Impact on Society
This study contributes to ongoing discussion about hybrid work by drawing attention to underexplored issues of temporal coordination and meeting function, but it should not imply direct societal impact or reliable practical guidance.
Future Research
Future research with larger datasets, validated instruments, performance measurement, and cross-context replication is essential, positioning this study as hypothesis-generating groundwork for understanding temporal dynamics in hybrid teams.
Vanja Bevanda, Zoltán Baracskai, Bernadett Domokos et al.· Informing Science· 0 citations
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