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Looking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.

This article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.

AI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.

Future research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.

AI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.

The research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

The purpose of this paper is to reflect on how to apply the abductive research process for developing a theoretical explanation in studies on performance management and management control systems. This is important because theoretically ambitious research tends to require explanatory study outcomes, but prior research frameworks provide little guidance in this regard, potentially facilitating ill-defined research designs and a lack of common vocabulary and criteria for evaluating studies.

The authors introduce a methodological framework that distinguishes three interwoven theoretical abstraction levels: descriptive, analytical and explanatory. They use a recently published qualitative field study to illustrate an application of the framework.

The framework and its illustrated application make the systematic logic of the abductive research process visible and accessible to researchers. The authors explain how the framework supports moving from empirical description to theoretical explanation during the research process and where the three levels might open spaces for the positioning of novel practices and conceptual and theoretical innovations.

The framework provides guidance for an explanatory research design and theory-building purpose and has been developed in response to recent criticism in the field that highlights the wide gap between leading-edge practice and the lagging state of theory. It offers interdisciplinary vocabulary and evaluation criteria that can be applied by any accounting and management researcher regardless of whether they pursue critical, interpretive or positivist research and whether they primarily use qualitative or quantitative research methods.