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.

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.

A função Operação vislumbra-se como um veículo positivo e eficaz para lidar com as imperativas questões da Sustentabilidade, devendo, então, se tornar a área central da organização e a força motriz da estratégia. Ressalta-se que as tecnologias digitais da Indústria 4.0 têm potencial para gerar capacidades para o cumprimento de tais objetivos sustentáveis. Assim, esta pesquisa temcomo objetivo principal propor um modelo processual de Operações Sustentáveis baseado em tecnologias digitais da I40. Para tanto, duas abordagens são integradas: de processo (Cambridge) e multicritério. Mais especificamente, foram desenvolvidas três principais fases. A primeira fase - denominada "Requerimentos do mercado" - tem como objetivo diagnosticar os objetivos de desempenho mais prioritários pela Matriz Importância & Desempenho. O segundo - denominado "Auditoria sobre a Operação" - visa classificar as tecnologias digitais mais críticas pelo modelo PROMETHEE-ROC. Na terceira fase tem-se o levantamento e priorização das capacitações consideradas mais críticas utilizando o modelo FI-Tradeoff.

Manufacturers are facing increasing pressures to adapt their operations to meet sustainability goals. Research and developments on industrial digitalization (aka Industry 4.0) present new opportunities to create and capture value in ever-more complex and connected industrial systems. However, digitalization does not always align with sustainability, and case studies combining these two topics are still scarce. To address these gaps, we adopted a bottom-up approach to (1) identify existing environmental solutions and their implemen\tation challenges by reviewing 208 empirical studies, and (2) formulate eight propositions to guide further work so that digitalization supports environmental improvements more systematically. Finally, a framework for Digitalized Sustainable Manufacturing consisting of four research themes is proposed, pointing to future research needed to align industrial development with sustainable development goals. The propositions and framework aim to structure and focus future research by targeting specifically the challenges encountered when implementing environmental solutions in manufacturing.