Artificial intelligence (AI) is changing how work is organized and carried out, particularly in contexts with uneven access to digital infrastructure, training, and organizational support. This exploratory study examines how AI-exposed professionals in Mexico perceive organizational readiness, work transformation, AI perception, and AI adoption. Using survey data from 122 respondents and partial least squares structural equation modelling (PLS-SEM), the study finds that readiness for change is strongly associated with perceived work transformation, which in turn is associated with higher AI adoption. In contrast, favorable perceptions of AI alone are not significantly associated with adoption in the proposed model. Complementary regression results and a common method bias assessment provide additional checks while reinforcing the need for cautious, non-causal interpretation. The findings suggest that managers should move beyond awareness campaigns and focus on readiness-building, skill development, transparent communication, and participatory work redesign. For engineering managers in large companies, small and medium-sized enterprises, and startups, the study highlights practical ways to align AI implementation with workforce development, role redesign, and sustainable labor transitions in AI-enabled workplaces. The article contributes practice oriented evidence on the organizational conditions that may help employees engage with AI responsibly and productively in an emerging economy context. These insights are relevant for organizations seeking to implement AI while preserving employee agency and long-term employability.
In today's competitive landscape, organizations are maneuvering through the complex challenge of balancing the high demand for skilled professionals with their scarce availability. This challenge is magnified by a growing global talent pool, emphasizing the necessity for cutting-edge technology in recruitment. A suite of technologies, including resume screening, chatbots, video interviews, and big data analytics, is transforming the way organizations find and assess potential hires. These tools not only streamline recruitment processes but also support diversity and improve candidate–organization matching, primarily through AI-driven analytics, while emerging approaches such as blockchain-based credential verification remain at an early stage of adoption. They facilitate a range of recruitment tasks, from initial contact to skill evaluation and interview scheduling, allowing organizations to efficiently and effectively connect with the right candidates. Despite these advancements, there is a noticeable gap in understanding how an organization's technological readiness impacts its adoption of these innovations. Most studies have explored these technologies in isolation, neglecting their collective effect on the recruitment ecosystem. This study seeks to bridge this gap, exploring how organizational technological maturity correlates with the strategic implementation of recruitment technologies, offering insights into a more integrated and efficient future for hiring. For practitioners, the findings translate into a maturity-based adoption roadmap that sequences technology investment to an organization's technological readiness—prioritizing foundational, widely adopted tools before emerging technologies such as predictive and blockchain-based solutions.
Bringing additive manufacturing (AM) into hard-metal production is often hindered by multiple, sequential “Valleys of Death” (VODs) that stall the transition from proof of concept to commercial scale. This study examines how firms can systematically navigate these interdependent challenges when commercialising AM technologies in a mature, subtractive manufacturing environment. Drawing on three longitudinal cases at differing stages of commercialisation, the study traces innovation activities over time to identify recurring patterns in how firms overcome successive VODs. The findings reveal three interrelated causation cycles, R&D, enabling, and entrepreneurial loops, through which innovation activities accumulate and interact. Actions that bridge early VODs reshape system conditions, either easing or amplifying downstream challenges. In particular, early knowledge development and cross-actor collaboration build legitimacy, while coordinated resource mobilisation and policy alignment accelerate later-stage scaling. The study contributes by demonstrating that VODs are not isolated barriers but interdependent phases across the product lifecycle, and by showing how process-based approaches can explain the evolution of innovation activities over time. For managers, the findings provide a structured way to diagnose risks, align resources, and sequence interventions to improve the likelihood of successful commercialisation.
The integration of Industry 4.0 technologies with Green Supply Chain Management (GSCM) represents a transformative strategy for achieving sustainability, efficiency, and resilience in supply chain operations. This paper explores the integration of Industry 4.0 technologies-such as Artificial Intelligence (AI), Blockchain, Big Data Analytics, Internet of Things (IoT), and Cloud Computing-with GSCM strategies, emphasizing their contribution to enhancing green supply chains (GSC). The paper delves into how these technologies enhance key GSCM practices, including green manufacturing, green procurement, reverse logistics (RL), and closed-loop supply chains, while addressing challenges such as traceability, environmental governance, and low-carbon economy goals. By analyzing existing academic and research works, this paper identifies critical enablers for the adoption of Industry 4.0 technologies in GSCM, highlighting their impact on supply chain resilience, cross-chain cooperation, and sustainable operations. Furthermore, the paper identifies research gaps in the reviewed technologies adoption in GSC. The findings reveal that the convergence of Industry 4.0 and GSCM not only improves operational efficiency but also fosters environmental sustainability by enabling real-time data fusion, predictive analytics, and transparent, decentralized systems. This exploration contributes to the growing body of knowledge by providing a comprehensive understanding of how advanced technologies can drive green supply chain innovation, offering valuable insights for researchers, practitioners, and policymakers aiming to achieve sustainable supply chain ecosystems in the era of Industry 4.0.
Program plans can appear complete because each workstream has an owner and backlog yet still omit the dependency structure required for integrated delivery. This article describes that failure mode as dependency blindness and presents end-to-end slice planning as a practical response. Building on established vertical-slicing literature, the article reframes slices as planning probes that expose interface assumptions, data contracts and sequencing constraints before implementation commitments harden. A practitioner scenario from a regulated software-intensive program illustrates the approach, followed by implementation guidance and measures managers can use to evaluate impact.
