Purpose

The purpose of this study is to explore the process of initial sensemaking that organizational members activate when they reflect on AI adoption in their work settings, and how the perceived features of AI technologies trigger sensemaking processes which in turn have the potential to influence workplace learning modes and trajectories.

Design/methodology/approach

We adopted an explorative qualitative and interactive approach to capture free fantasies and imaginative ideas of AI among people within the industry. We adopt a conceptual perspective that combines theories on initial sensemaking and workplace learning as a theoretical lens to analyze data collected during 23 focus groups held at four large Swedish manufacturing companies. The data were analyzed using the Gioia method.

Findings

Two aggregated dimensions were defined and led to the development of an integrated conceptualization of the initial sensemaking of AI technology adoption. Specifically, sensemaking triggered by abstract features of AI technology mainly pointed to an exploitative learning path. Sensemaking triggered by concrete features of the technology mainly pointed to explorative paths, where socio-technical processes appear to be crucial in the process of AI adoption.

Originality/value

This is one of the first studies that attempts to explore and conceptualize how organizations make sense of prospective workplace learning in the context of AI adoption.

Artificial intelligence (AI) has for the last decade been expected to revolutionize the way we work. However, as many industries are facing more complex problems as an effect of technological development and globalizations, companies are experiencing challenges in finalizing AI-projects and fully integrating AI into their operations, not least in the manufacturing industry. To face these challenges, it is suggested that manufacturing industry could benefit from enhancing their work-integrated learning related to AI and AI transformation. In addition, one of the considered success factors in technological development and organizational change is the way that it is reflected on by the organization. This paper employs an action-based perspective, in which learning is generated by and in action, that is, in the reflection on experiences. And so, with a qualitative focus group study, we sought to explore how manufacturing industry organizations reflect on action in a potential AI-transformation. However, the results showed that the participating organizations instead displayed an inaction stance when discussing AI-transformation, demonstrating a general passivity towards the transformation as a concrete change process. We employ theories of psychological safety, maneuver space and sense of coherence to analyze our empirical results. Moreover, we discuss our findings based on the idea of action and inaction as contradictory forces that adverse each other in terms of learning as well as the theoretical and practical implications of our study.

When implementing a technology e.g., machine learning (ML) in a business process, a return of investment is desired if not required. This exploratory study, in a single business process case where ML will be implemented, showed the complexity of trust, where many aspects of trust were addressed by different themes at different times. It also showed that process trust, technology trust and the interaction, integrated trust, are important to consider when applying a new technology in a business process. These critical aspects have been gathered in a model that can be useful for both scholars and practitioners.

Purpose The purpose of this paper is twofold: to identify and map contemporary research on advanced technology implementations for problem-solving purposes in the manufacturing industry, and to further understand the organizational learning possibilities of advanced technology problem-solving in the manufacturing industry.

Design/methodology/approach This paper outlines a scoping review of contemporary research on the subject. The findings of the review are discussed in the light of theories of contradicting learning logics.

Findings This paper shows that contemporary research on the subject is characterized by technological determinism and strong solution-focus. A discussion on the manufacturing industries’ contextual reasons for this in relation to contradicting learning logics shows that a Mode-2 problem-solving approach could facilitate further learning and expand knowledge on advanced technology problem-solving in the manufacturing industry. A research agenda with six propositions is provided.

Originality/value The introduction of advanced technology implies complex effects on the manufacturing industry in general, while previous research shows a clear focus on technological aspects of this transformation. This paper provides value by providing novel knowledge on the relationship between advanced technology, problem-solving and organizational learning in the manufacturing industry.

AI has the potential to be a disruptive technology causing paradigm shifts in industries and greatly impacting both operational and strategic decision making. Adopting AI technologies requires proactively engaging both the technical and social system of the organization as processes, workflows, as well as individual employees, are influenced. This paper explores potential tensions between the social and technical systems in the early change process of AI transformation to understand the nature and degree of AI transformation. We do this by analyzing in-depth inquiry from 23 focus groups involving 112 white-collar industrial employees in large multinational industrial firms using a change management perspective. Our study revealed nine categories of tensions divided into tensions between the current and future state and tensions between humans and machines. This stresses the need to adopt a socio-technical perspective in the attempt to understand organizations approaching AI and provides practical implications for organizations considering adopting AI technologies.