Manufacturing firms are increasingly pursuing AI-led transformations (e.g., predictive maintenance, quality inspection, and autonomous production), yet implementation often stalls after pilot projects. Prior literature links this “pilot-to-scale” gap to disorganised data and legacy IT (Clemens et al., 2023; Plathottam et al., 2023; Rauh et al., 2022), misaligned investment logics and budgeting routines (Hadhri et al., 2025), and capability deficits spanning technical skills and digital leadership (Mqoqi et al., 2026; Obi et al., 2025). At the same time, responsible AI introduces additional coordination challenges because accountability, transparency, and compliance remain difficult to operationalise across organisational layers and heterogeneous use cases (Besinger et al., 2025; Eng-ström et al., 2025). Based on previous studies (Engström et al., 2025), we argue that AI adoption is a socio-technical change where value arises from combining technology (models, data pipelines, and platforms) with decision-making rights, incentives, workflows, skills, and accountability. If this combination is weak, organisations might fall into “pilotism,” resulting in many projects but little learning or integration. This study asks: What organisational and governance mechanisms are required in manufacturing firms to convert AI aspirations into scalable and beneficial implementations? To answer our research question, a qualitative approach combining a focused literature review with co-creation workshops involving managers and specialists from six companies (A-F) was employed (Ahmed & Asraf, 2018) to identify AI goals, perceived blockers, and scaling mechanisms through discussions and group tasks (Ørngreen & Levinsen, 2017). The empirical material (field notes, audio recordings, and secondary data) was analysed using content analysis to find recurring patterns (Bengtsson, 2016). Across firms, evidence confirms literature: AI remains stuck in fragmented pilots because strategy, budgeting, data/legacy IT, skills, and governance are misaligned. More specifically, AI remains stuck because key organisational elements are misaligned. Firms often run many promising Proofs of Concept (PoCs), but the organisational system required for scaling (strategy → funding → data/IT → people → governance) does not “fit together”, preventing pilots from being industrialised into scalable, accountable deployments. There is a notable lack of shared direction, which results in isolated initiatives that fail to contribute to overall enterprise learning or effective scaling. The current funding model adheres to traditional ROI metrics, which create barriers to securing resources for scaling successful pilots, as they often fail to meet conventional thresholds or timelines. Many pilots are developed under conditions that do not generalise well, leading to challenges in scalability. Companies lack robust pipelines and deployable infrastructure necessary for broader implementation. There is an over-reliance on a limited number of individuals, as roles and learning routines are not well-defined. This situation hinders the establishment of standardised practices across teams. The absence of clear accountability and risk control mechanisms has blocked the operationalisation of successful pilot initiatives. On the other side, companies are working to tackle challenges by implementing a “governance spine” that merges centralised direction with decentralised execution. This approach features a dedicated AI strategy, clear decision rights, and local ownership through AI ambassadors and cross-functional teams. Key elements include a structured use-case pipeline (idea → business case → pilot → adoption), standardised tracking of ROI and KPIs, and role clarity with capability-building infrastructures for ongoing learning. Strong data governance ensures security and compliance. Successful scaling is more about organisational change than technology, requiring effective leadership and adaptive resourcing to align initiatives with operational needs.

This study examines how strategic sourcing contributes to supply chain resilience in CRM-dependent HVAC supply chains. Drawing on an explanatory single-case study of a Swedish HVAC manufacturer, the paper analyses four propositions concerning supplier diversification, formal risk criteria, cross-functional governance, and digital visibility. Using thematic analysis and proposition-based pattern matching, the findings show uneven support across the four mechanisms. Diversification is the most developed response, whereas formal planning, governance integration, and digital visibility remain less institutionalised. The study contributes by showing that resilience in CRM-dependent supply chains develops as a staged, differentiated process rather than as a single, coherent capability.

AI is viewed as the next major technological breakthrough for organizations. The range of areas, professions, and practices that can be improved with AI assistance or automation is overwhelming. However, this wide array of possibilities also brings a variety of expectations about how AI will change organizations and employees’ everyday work. Considering that voiced expectations influence adoption processes by both reflecting and shaping certain relational, belief-driven dynamics, we can learn a great deal about AI adoption by studying the organizational plurality of AI expectations. Therefore, this study examined AI expectations held by employees in three organizations currently adopting AI for use in the workplace. The study is based on a thematic analysis of empirical material form 15 focus groups in three Swedish AI-adopting organizations and shows how AI expectations shape the following: (1) a growing desire to move from exploring AI to establishing AI routines and regulations, (2) emerging dilemmas related to both the violation and fulfillment of AI promises, and (3) how dynamics and unpredictability in the AI field require organizations to adapt to shifting trends and innovations.

This study investigates how small and medium-sized enterprises (SMEs) in the metals sector adapt to sustainability regulations, particularly the European Union’s Corporate Sustainability Due Diligence Directive (CSDDD). Researchers combined interviews with SME managers, industry experts, and secondary data through an embedded case design to examine due diligence practices and barriers. Findings reveal that leadership commitment, collaboration, and robust performance management drive successful supply chain sustainability. Resource constraints and limited awareness hinder progress. The paper concludes that proactive strategies, strong governance, and multi-tier transparency enable SMEs to meet evolving regulatory demands and enhance competitive advantage.

Sustainable development has defined the competitive context in the last decade, forcing companies to review their business model to incorporate sustainability requirements. Stakeholders demand more transparency reflected in the present regulatory framework formed by standards, guidelines, and performance reports. These elements provide information for markets and society concerning sustainable behavior, which companies have certified or formally agreed upon. Organizations’ operations performance information could be related to evolutionary performance indicators set, representing sustainability present performance and orient companies to reach higher sustainability performance levels, following a maturity trajectory. This chapter aims to develop a process for designing an integrated set of performance indicators for managing sustainability at the operations system level. Applied research is conducted based on the ‘Cambridge Process Approach’ and worked in a research BPM cycle. Sustainability performance indicators are formally defined and detailed as metadata. Process application allows a better understanding of how sustainability indicators could be integrated and related to an ESG-based model and create an information system that could be used for auditing, assessment, and reporting sustainability performance.

Sustainable operations practices have been intensively assessed in the last few years and are opportunities to test the developed theory. This chapter aims to describe and analyze some ‘professional’ frameworks that professional associations and consulting firms are proposing. Fundamental content analysis is developed to identify evidence for sustainable concept adoption, CMMI adherence, and scope definition. Results show an incomplete implementation focusing primarily on environmental aspects; however, evidence for strategic sustainability management, governance structures based on multiple stakeholders, and models’ evolution based on continuous improvement and innovation management were found. Recently ESG frameworks are being revisited, circular economy models, and regenerative strategies.

For over 15 years, the concept of Industry 4.0, now transitioning into Industry 5.0, has been a focal point for the manufacturing sector. Yet, the success of companies in embracing digital transformation varies. There are numerous models and assessment tools for assessing digital readiness and maturity. Several models have been developed over the years, but firms also realize no “one-size-fits-all” exists when testing them. Previous studies show that firms must take charge of their own digital transformation (DT) journey to find a path that suits their specific needs.This qualitative paper is driven by a case study supported by a within-case analysis conducted with a heavy-machine industry with fourteen production plants worldwide – data collected from 2020 to 2023.Volvo Construction Equipment (Volvo CE), created Factory 4 Tomorrow (F4T) to address Industry 4.0. The central challenge for the F4T initiative was how to facilitate an inside-outside approach to identify an inclusive maturity model that emphasizes learning and collaboration. A diagnostic of opportunities model was created to aid the organisation’s transformation journey. It aimed to support all plants by evaluating their maturity in digital transformation, identifying gaps, and support in prioritising. Unlike traditional models that assess and compare plant levels, this model aimed to foster awareness and alignment, establishing a shared language. Thus, a unique model was explicitly crafted for the firm. The process of developing the model itself enhanced awareness and alignment. Therefore, this paper explores the development process - failures and successes - to compile a digital transformation maturity model tailor-made to a firm’s needs and goals. The objective is to offer comprehensive advice for firms to implement DT initiatives effectively in a way that suits them.