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.

The concept of business networking is present in the entrepreneurial environment in Romania for at least 15 years, but in the last years it becomes very popular. The main goal of business networking is to help company representatives obtain new strategic clients through referrals. This paper presents key aspects collected from SME representatives that use business networking techniques to promote their businesses. We consider that structured networking can bring more value to the scope than just occasional networking. We decided to work with representatives that understand the difference between structured, professional networking and occasional networking. This paper presents the collected perspective on the benefits of business networking in Romanian entrepreneurial environment.

Organizations face rising market volatility, while legacy, plan-driven structures struggle to translate strategy into adaptive execution. Prior studies discuss product-centric operating models, yet typically treat decision rights, product definition, and technology-enabled execution separately. This paper introduces the WHO–WHAT–HOW framework, an authorial synthesis that links decision boundaries (WHO), product scope and value hypotheses (WHAT), and workflow and technology routines (HOW) into a single, operational model. A triangulated design is employed, comprising a systematic document analysis of 62 sources published between 2018 and 2024, illustrative case studies of Amazon and Spotify, and a scenario-based organizational illustration that contrasts a baseline hierarchy with a WHO–WHAT–HOW configuration. Rather than constituting empirical validation, these elements serve as illustrative demonstrations of conceptual plausibility. Indicative composite indices, synthetically constructed from document-coded constructs and simulated rules, suggest improvements in decision speed, cycle time, and coordination; these indices are heuristic and non-inferential. The contribution is threefold: First, it provides a pragmatic five-step implementation roadmap. Then, we make the mechanisms concrete via a construct-to-rule mapping and three rule-based vignettes (incident pathway, value-hypothesis experiment, cross-team dependency), showing how WHO–WHAT–HOW compresses decision time, cycle time, and coordination without introducing new measurement programs. Finally, the composite indices remain heuristic and non-inferential. Limitations include reliance on secondary evidence and a scenario-based, non-empirical illustration; robust validation requires longitudinal, multi-sector primary data and testing in regulated or low-automation settings.

Artificial Intelligence is expected to influence the biggest change in the history. Adapting to the new socio-economical behavior requires resources, energy, and focus. This paper examines essential insights gathered from decision-makers in Romanian SMEs regarding the role of Artificial Intelligence in business development. While it is still hard to estimate the whole impact of AI in the economic environment, some aspects are already concerning decision makers in companies. We argue that, while managers increasingly recognize the strategic potential of AI, its implementation remains constrained by organizational barriers and resistance to change. For this reason, our research focuses on respondents who are familiar with both the opportunities offered by AI and the internal challenges that hinder its adoption, aiming to highlight the gap between awareness and effective integration.

New decision approaches and automated workflow systems are acquired by organizations due to AI development. The Digital-Augmented Agility system is integrated into an information processing organization, where artificial intelligence-managed decision methods are used to improve operational resilience and adaptable workforce system implementation. The ability of AI automation to refine processes in real-time and execute prediction-based decisions cannot be adequately determined through analytical methods from either Dynamic Capabilities Theory or the Theory of Organizational Routines. Researchers assessed financial sector agility using mixed research methods, including quantitative and regression models. Operational efficiency, shorter time-to-market, and superior work-team performance that integrates human resources with AI assets were gained by organizations utilizing AI systems. Employee interviews validated that positive operational effects were brought by AI decision automation to the workforce, while challenges in adapting to this transition were found by employees. Practical insights about AI-agile integration were built from research and direct industry interviews, contributing to engineering practice knowledge. This academic approach does not remain under the reflection of financial uniqueness but sees it as a challenge; its extension may reach into healthcare or direct operational functionality for supply chain management or other industrial engineering systems.