Jun 2026· Journal of Environmental Management· Vol 413, pp.
130343
· 0 citations· 55 references
Medicine
TL;DR
The core findings show that both augmentation- and automation-based AI strategies contribute to product performance, though through different mechanisms, and underscore the importance of evaluating the role of AI technologies in product processes, depending on whether the strategic objective of such technologies emphasizes adaptability and responsiveness to customer demands or environmental outcomes.
Abstract
The study evaluates how the implementation of AI-enabled strategies (i.e., augmentation and automation) influences product environmental and operational performance. We also explore if the positive impact of such AI-based strategies amplifies in products whose processes are characterized by high levels of operational flexibility and adaptiveness. To test the proposed hypotheses, we apply multilevel regression models to a unique dataset of 138 product lines from Costa Rican manufacturing and professional service firms in 2024. The core findings show that both augmentation- and automation-based AI strategies contribute to product performance, though through different mechanisms. 'AI-augmented process co-adaptation' improves the connection between process adaptiveness and operational performance by leveraging human-AI collaboration to support rapid and effective product reconfigurations, whereas 'AI-automated process co-adaptation' amplifies the positive effect of process adaptiveness and environmental performance, thus ensuring that eco-efficient routines are executed consistently across product processes. These findings underscore the importance of evaluating the role of AI technologies in product processes, depending on whether the strategic objective of such technologies emphasizes adaptability and responsiveness to customer demands or environmental outcomes. By focusing on the product line as the unit of analysis, this study contributes to both the AI strategy and environmental management literatures by showing how distinct AI-based strategic logics interact with products' adaptive capabilities to generate superior environmental and operational performance. From a practical perspective, the study offers guidance for managers on configuring AI-enabled strategies in ways that align operational flexibility, sustainability objectives, and product-level value creation.
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
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
This study aims to examine the role of artificial intelligence (AI)-enabled integrating capabilities (AIEIC) and environmental dynamism (ED) in enhancing supply chain agility (SCa) and customer integration (CI) for superior supply chain performance (SCp). The study further addresses the mediating roles of SCa and CI, adopting a human-centric perspective in which these mechanisms represent adaptive and relational responsiveness to improve SCp.
The primary data were collected from 373 valid respondents and analysed the linear relationship primarily through “structural equation modelling (SEM)”; and “fuzzy set qualitative comparative analysis (fsQCA)” was used for non-linear configurational insights into high performance outcomes. This study draws on the dynamic capability view and contingency theory to provide theoretical support for the analyses and evidence from India.
The study reveals that AIEIC and ED have a positive impact on SCp. SCa and CI were treated as human-centric responses, where SCa captures human-centric capabilities, and CI captures human-centric coordination to improve SCp. Additionally, the necessary conditions for fsQCA and the combined effect of all variables on solution consistency were examined, indicating that a high-performance outcome might be achieved in a dynamic environment even in the absence of AIEIC, highlighting the strong influence of human-centric responses.
Given the current results, managers should prioritise human-centric mechanisms, such as job security for employees and customer responsiveness. Note that, in resource-constrained organisations where managers cannot afford AI investments, they should focus on human-centric solutions.
The contribution of this study shifted from a technology-oriented solution to a human-centric approach, indicating that AIEIC performs better when integrated with SCa and CI in dynamic environments to improve SCp. By using both SEM and fsQCA, this study assesses the robustness of the interconnections among the study variables through multiple pathways.
Sananda Das, Rauf Iqbal, Vivek Khanzode et al.· Journal of Enterprise Inform...· 0 citations
The rapid developments in artificial intelligence (AI), intelligent automation, and data-driven decision-making have changed the way that competitive dynamics play out across industries and businesses, forcing them to rethink and reimagine their traditional working methods and key strategies. While there's an increase in investments in digital technologies, many organizations are still experiencing disparate implementations, legacy systems, organizational barriers, and inadequate data capabilities that lead to inconsistent transformation results. 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. A methodical literature review approach was used to present and synthesize peer-reviewed studies from the main academic databases according to specific inclusion and exclusion criteria to guarantee methodological rigor and transparency. The review brings together insights from various industries, including manufacturing, retail, healthcare, finance, logistics, and small and medium-sized businesses, to find out what technological capabilities all have in common in these industries, what challenges they encounter when implementing them, what enablers they require at the organizational level and what measurable business outcomes they achieve. Evidence synthesized suggests that digital transformation is not just about technology, but also about investing in complementary aspects such as organizational capabilities, leadership commitment, workforce reskilling, process redesign, and strong data governance. The adoption and integration of AI into intelligent automation and evidence-based decision-making consistently leads to increased productivity, cost savings, customer satisfaction, operational resilience, and innovation capabilities - which, however, is heavily dependent on the ability of the organization to implement AI and become digitally mature. From these insights, this review suggests an integrated conceptual model linking technological, organizational and data capabilities and business transformation outcomes. The study advances the digital transformation literature by offering a comprehensive, evidence-based synthesis that is able to bridge between the fragmented research streams and provide recommendations for managers, policy makers, and researchers aiming at accelerating sustainable transformation using AI in traditional business settings.
Gurpreet Kaur· The American Journal of Mana...· 0 citations
From descriptive analytics to generative systems and, most recently, autonomous agentic architectures, artificial intelligence (AI) is transforming how enterprises achieve digital transformation and develop innovation capabilities and how they shape strategic decision-making. This study highlights an AI adoption-value paradox; despite significant investments, many organizations report a lack of business value from AI. The authors present a conceptual framework for the relationship between AI capability inputs (generative, predictive, and agentic) and organizational absorption processes (culture, skills, and governance) and downstream effects (digital transformation maturity, innovation performance, and decision quality), which are moderated by the regulatory environment and innovation industry context and mediated by innovation capability and employee AI literacy. Based on decision intelligence theory, the technology-organization-environment (TOE) framework, and dynamic capabilities theory, this study follows a mixed-methods approach with a structured survey of mid-to large-sized enterprises, followed by semi-structured interviews with C-suite and senior IT executives that will be analyzed through partial least squares structural equation modelling (PLSEM) and thematic analysis. This study extends the dynamic capabilities theory to the domain of agentic AI, provides managers with practical guidelines for governing AI decisions, and provides regulators with insights into responsible AI adoption. The implications, limitations, and directions for empirical validation are discussed.
Zarin Subha Progga, Mohammad Ali· Journal of business and mana...· 0 citations
Artificial intelligence (AI) adoption is accelerating, yet enterprise value remains uneven because technical capability often outpaces organizational redesign, workforce adaptation, and governance maturity. This paper develops an original theoretical model, the AI productivity-governance frontier (PGF), to explain why the same agentic AI capability can generate measurable value in one organization but produce negligible or negative returns in another. Using integrative theoretical modelling, the study synthesizes recent empirical evidence on generative AI productivity, enterprise adoption, AI risk management, labor-market exposure, and prior conceptual work by Kwan Hong TAN on AI-form organizations, AI stakeholder recognition, and temporal displacement-adaptation equilibrium. The resulting PGF model formalizes AI value as the interaction between automation-augmentation gains, learning spillovers, decision velocity, scalability, and institutional absorptive capacity, offset by governance drag, risk externalities, and identity-coordination costs. The paper proposes six testable propositions and a practical maturity pathway moving from experimental AI use to validated autonomy. The central argument is that sustainable AI value does not increase monotonically with either automation intensity or governance intensity. Instead, organizations approach maximum value when they design human-AI work systems that combine use-case fit, accountable autonomy, adaptive reskilling, and proportionate assurance. The contribution is threefold: a formal value equation for enterprise AI, a governance-sensitive interpretation of AI productivity heterogeneity, and an implementation framework for managers, policymakers, and researchers studying AI-enabled business transformation.
K. Tan· International Journal of Sci...· 0 citations
