Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance.

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced.

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack.

This scientific investigation presents a groundbreaking exploration of integrating the Scaled Agile Framework® (SAFe®) with Azure DevOps, setting a new benchmark for innovation and strategic agility in IT project management. The study meticulously examines how this fusion optimizes operational efficiency and fosters a culture of continuous improvement and collaboration across diverse project teams. Adopting a multi-metric research approach, it assesses the transformative potential of combining the Agile principles of SAFe® with the robust tools and automation capabilities of Azure DevOps. This synthesis is proposed as a dynamic model for managing complex IT projects where traditional methodologies fail to address the rapid pace of technological advances and market requirements. Key findings highlight the improved flexibility, visibility, and productivity achieved through this integration, highlighting its importance in achieving successful project outcomes. It adds the area of strategic recommendations for IT leaders trying to navigate the challenges of digital transformation, advocating adopting these innovative practices as a cornerstone for future excellence in project management. This work not only contributes to the academic discourse on IT project management but also provides practical insights for practitioners seeking to leverage the latest advances in Agile Methodologies and Azure DevOps practices.

Circular economy-driven business model innovation is an emerging field of study that emphasizes sustainable practices across industries. This study explores how circular economy strategies can act as guiding tools for business model innovation and investigates the main challenges faced by companies adopting circular economy-driven innovations. Building on existing literature, and proposing a 14R circular strategies framework, the research employed a qualitative methodology based on three case studies using semi-structured interviews to collect data, followed by individual case analysis and cross-case comparisons. Findings highlight how circular economy strategies, such as redesign, reuse, and repurpose, can enhance business model elements. The research also emphasizes the critical role of stakeholder relationships in advancing circular economy practices and reveals key challenges, like logistical barriers, limited infrastructure, and shifting market dynamics. Contributions to the field include validating the flexibility of the proposed 14R framework and demonstrating its applicability across diverse industries. The research offers actionable insights for practitioners and policymakers aiming to align the circular economy principles with business model innovation while addressing systemic barriers and fostering sustainable growth.