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The age of artificial intelligence (AI) is already upon us. The rapid development of AI tools is facilitating sustainable development and its corollary social good. For AI dedicated to social good to be impactful, it has to be human-centred, striving to achieve inclusiveness, sustainable livelihoods and community well-being. In short, it offers major opportunities to holistically enhance peoples' lives in diverse areas: education, health care, food security, disaster reduction, smart cities, etc. However, ethical, unbiased and ‘secure-by-design’ algorithms that power AI are crucial to building trust in this technology. Civil society's engagement can hopefully drive the features and values that should be embedded in AI.

This chapter focuses on the societal benefits that AI can deliver. Our initiatives and decisions of today will fashion the ‘Social Good’ AI applications of tomorrow. Sustainable Development Goals (SDGs) being addressed are 2–4 and 10–11.

Smart industry initiatives focus on intelligent and interconnected cyber-physical systems. These initiatives develop complex technical architectures that integrate heterogenous technologies, causing significant organizational complexity. Tapping into the digital capabilities of distant partners while capturing profit from such innovation is demanding. Furthermore, firms often need to establish and orchestrate inter-organizational collaborations without prior relations or established trust. As a result, smart industry initiatives bring together disparate organizational forms and institutional environments, distinctive knowledge bases, and geographically dispersed organizations. We conceptualize this organizational capability as ‘distant capabilities integration’. This research explores the governance mechanisms that support such integration and their relation to value capture. We analyse 11 IoT case studies organized in three categories (process, product and technologies) of smart industry initiatives. Building on existing literature, we consider different ways to describe distance, including knowledge heterogeneity and organizational, geographical, institutional, cultural and cognitive distance. Finally, we describe the governance mode appropriate for upstream (developing foundational technologies) and downstream (leveraging existing distant technologies) smart industry initiatives.