Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Originality/value

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.

中文翻译：

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人工智能在控制方面的局限性和前进的方向：呼吁未来的会计研究

目的

研究人员着眼于人工智能（AI）的局限性并基于复杂性和系统理论的研究进行控制，旨在得出对可能应用的未来展望，并对人机信息处理的未来补充提供见解。从这些例子中得出结论，作者提出了五个领域的研究议程，以推动这一领域的发展。

设计/方法/方法

本文本质上是概念性的，然而，来自各个学科的具有理论根据的半系统文献评论以及经过实验验证的未来研究问题，为整个叙述提供了背景。

发现

人们发现，人工智能在控制方面的应用受到严格限制，并从复杂性和控制论的角度进行了讨论。共有三个这样的限制，即布雷默曼限制，复杂系统的部分可检测性和可控制性问题以及人机信息处理的互补性中的固有偏差，作为突出的代表性示例。然后，作者继续并仔细说明了人机协作的外观，具体取决于任务和环境的具体情况。以此，作者对未来的研究提出了不同的角度，这些观点可能会革命性地改变人工智能在会计领导中的应用。

研究局限/意义

关于AI在控制中的价值前景的未来研究需要考虑到物理和计算效果，并且可能包含复杂性的观点。

实际影响

由于复杂系统中的海量信息，人工智能在会计和控制中的应用可能受到严格限制。

创意/价值

研究议程包括五个领域，这些领域是从先前的讨论中得出的。这些领域包括：组织变革，人机协作，法规，技术创新和道德考量。对于这些领域中的每个领域，都提供了研究问题，潜在的理论基础以及方法论上的考虑。