When a Bot Evaluates Work: Five Moves for Reclaiming Authority from AI
14 Apr, 2026
Enterprise AI systems are increasingly being given authority over human work, including evaluating performance, moderating content, flagging noncompliance, and even reversing human-made decisions. Unlike consumer AI tools that people choose to use, these systems are embedded in organisational infrastructure, and the people subject to their evaluations often have no say in whether or how the AI was deployed: a captive relationship that makes questions of trust and legitimacy unavoidable.
In a paper co-authored by Nadine Ostern, Likoebe Maruping, Enterprise AI Alliance member Marek Kowalkiewicz, Jörg Weking, and Jason Thatcher, published in MIS Quarterly (the top-ranked journal in information systems), the authors examined what happens when an organisation delegates evaluative authority to an AI. The single most important finding: the people subject to AI authority will reshape it, whether the organisation plans for that or not. Using Wikipedia’s antivandalism bot as a case, the authors traced how those subject to the bot’s evaluations progressively contested and reclaimed authority through five mechanisms. The authors term these mechanisms SAFER: Scoping, Adapting, Flagging, Escalating, and Requesting. What began as ad hoc pushback evolved into institutionalised governance structures that gave people durable influence over AI decisions.
The paper develops a grounded theory of how authority delegated to AI-based bots is dynamically redistributed through recursive negotiation between developers, the humans subject to the bot’s evaluations, and the bot itself. It identifies three phases of authority configuration: malleable adaptation, on-request intermediation, and continuous influence, showing how human agency over AI governance can be progressively institutionalised rather than eroded.
“For enterprise AI leaders, the practical lesson is direct. Delegating evaluative authority to AI is not a one-time design decision. It initiates an ongoing negotiation. Organisations that build mechanisms for contestation and adjustment into their AI systems from the start, rather than discovering the need reactively, will achieve more reliable, trusted, and legitimate AI deployments.” – Professor Marek Kowalkiewicz
Marek Kowalkiewicz is Professor and Chair in Digital Economy at QUT Business School, and a member of the Australian Research Alliance for Enterprise AI. His research focuses on algorithms as economic actors: how they acquire authority, reshape work, and create new governance challenges for organisations. His current work at QUT’s Centre for Future Enterprise investigates how enterprises transition from automating tasks to autonomising decisions, and the organisational implications of that shift.