We are at a moment where the decisions being made about how to design, deploy, and govern AI will shape systems that society will depend on for decades. Those decisions are not primarily technical. They are decisions about how responsibility is distributed, how human judgment interacts with automated systems, how trust is built and lost in high-stakes environments, and what it means to design for resilience rather than just efficiency. They are decisions that require people who can think across disciplines, hold complexity without collapsing it prematurely, and ask hard questions even when the pressure is to move fast and deliver.

This challenge, developed in the context of ECIU University at Linköping University in Sweden, was built on the belief that engineers, business students, and social scientists need to learn to work on problems together, not alongside each other, but genuinely together,bringing different kinds of knowledge to bear on questions that none of them can answer alone. We found search and rescue to be a particularly good problem for that. It involves autonomous systems and sensor networks, but also organisational culture and volunteer motivation. It involves data infrastructure, but also legal accountability and public trust. It is technically complex and humanly complex in equal measure, and neither complexity can be understood without the other.

The students who took on this challenge did so with seriousness and curiosity. They were asked to engage with real difficulty, to resist the pull toward easy solutions, to question their own assumptions, and to think carefully about what it actually takes for AI to create value in systems that involve many actors, imperfect information, and consequences that matter. The work in this report is the result of that effort.

We are grateful to Jesper Tordenlid and WARA-PS for bringing a real problem to the course,an active research agenda with genuine open questions. That generosity made a difference to what the students were able to produce. We are grateful to Katarina Iversen for the scenario game that opened the course, which placed students inside the operational complexity of a rescue mission before they had the language to describe what they were experiencing. That ordering, feeling the problem before analysing it, shaped much of what came after. And we are grateful to Pascal Le Masson and Antoine Bordas from Mines Paris PSL for introducing students to design theory and the ideas around managing the unknown. Their contribution gave students not just a framework but a different way of thinking about what innovation in uncertain conditions actually demands.

The problems this challenge addresses are becoming more urgent as AI moves to autonomous agents. The students who worked through this challenge are among those who will have to navigate that transition, not just as users of AI, but as the people designing the systems, the organisations, and the governance structures that determine whether AI integration goes well or badly. That is a serious responsibility, and it requires exactly the kind of cross-disciplinary,critically grounded thinking this challenge was designed to develop.

Rapid developments in artificial intelligence (AI) are driving the evolution of complex products and systems (CoPS) into complex intelligent systems (CoIS). The introduction of AI implies generativity and increasingly fluid boundaries in such systems and presents challenges for organizations to control and manage systems that are safety critical. Building on a case study representing future CoIS, this paper explores fluid boundaries in CoIS, including approaches for navigating system criticality and generativity. The findings point to the relationship between fluid boundaries and a stable organizational and system core, along with a shared core mission. Together, they serve as a platform that enables both contributions from various constituent systems and dynamic reconfigurations of the overall system-of-systems (SoS). System criticality and generativity are navigated through setting bounds to generativity by checks and balances involving both human and AI, including safety requirements for constituent systems and overall human oversight. Such an approach extends beyond traditional system integration activities and alters the role of CoIS integrators.

As artificial intelligence (AI) is increasingly integrated into the context of complex products and systems (CoPS), making complex systems more intelligent, this article explores the consequences and implications for engineering management in emerging complex intelligent systems (CoIS). Based on five engineering management aspects, including design objectives, system boundaries, architecting and modeling, predictability and emergence, and learning and adaptation, a case study representing future CoIS illustrates how these five aspects, as well as their relationship to criticality and generativity, emerge as AI becomes an integrated part of the system. The findings imply that a future combined perspective on allowing generativity and maintaining or enhancing criticality is necessary, and notably, the results suggest that the understanding of system integrators and CoPS management partly fundamentally alters and partly is complemented with the emergence of CoIS. CoIS puts learning and adaptation characteristics in the foreground, i.e., CoIS are associated with increasingly generative design objectives, fluid system boundaries, new architecting and modeling approaches, and challenges predictability. The notion of bounded generativity is suggested to emphasize the combination of generativity and criticality as a direction for transforming engineering management in CoPS contexts and demands new approaches for designing future CoIS and safeguard its important societal functions.

Many of the benefits of artificial intelligence (AI) are expected to emerge in the context of complex systems that become increasingly intelligent. The transformation of complex systems into complex intelligent systems (CoIS) create a new landscape, not only related to technology development but also to the management and decision-making processes connected to these systems. This chapter seeks to create a new understanding of decision-making with AI in the context of CoIS and outlines 3 central views of decisions making, including (1) the decision-maker, (2) the decision-making process and (3) the decision space. To illustrate several of the new and emerging prerequisites for CoIS, the emerging field of personalized medicine is used as an example disclosing several of the implication of AI integration in decision-making. By outlining the implications of these findings, the chapter contributes with a new understanding of dynamic of human- AI decision-making in the context of CoIS.

Research Summary This article explores EMNEs innovation capability building in emerging markets. The paper provides a longitudinal account of how the Brazilian cosmetics firm Natura transitioned from scant to ample innovation resources and processes. Building on the institution-based view and the resource-based view, we explain how EMNEs innovation capability building is anchored in open innovation and collaborative nonmarket strategies. The paper reveals a unique pattern of innovation capability building based on a combination of local and global open innovation processes and harnessing the country characteristics over time. It is shown how combining open innovation and collaborative nonmarket strategies can help mitigate weak formal and informal institutions in emerging markets. The study offers an integrated framework explaining innovation capability building and the effects on the institutional setting. Managerial Summary The increase of well-known EMNEs has raised interest in understanding how these firms build sustainable innovation capabilities. Based on a longitudinal study of the Brazilian-based cosmetics firm Natura, this paper shows how an open innovation strategy can be used to tap into home-market natural resources and connect to the global setting. This innovation capability process involves traditional market-based strategies like inter-organizational collaborations but also nonmarket strategies, such as developing local relationships, supporting socio-biodiversity, and contributing to local society. The findings point at the importance of developing an overall innovation strategy, directing attention to innovation processes, engaging in recursive practice in innovation projects, responding to the market and nonmarket environments, and linking the emerging market institutional setting and the global market context.

Currently, new business models can be observed in content creator-based e-commerce. The research on e-commerce has grown rapidly and new concepts have emerged such as social commerce, platforms, and user-generated content. However, no overarching perspective has yet been formulated for distinguishing new content creator-based business models within e-commerce. The aim of this paper is therefore to characterize content creator-based business models by formulating a taxonomy of e-commerce based on a structured literature review of the concepts mentioned above. The results of our study point toward eight types of content creator-based business models. Our paper outlines theoretical and practical implications for the emerging phenomenon of content creator-based business, which we refer to as intellectual commerce. In addition, we describe 19 concepts related to Web 1.0, Web 2.0, and e-commerce.