Hybrid Accountability in AI-Augmented Organizations: Rethinking Performance Measurement and Organizational Governance

Abstract Book of the 19th International Conference on Modern Research in Management, Economics and Accounting

Year: 2026

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Hybrid Accountability in AI-Augmented Organizations: Rethinking Performance Measurement and Organizational Governance

Prof. Gianmario Strappati

ABSTRACT:

The growing adoption of Artificial Intelligence (AI) is transforming organizational decision-making and challenging traditional approaches to performance measurement and governance. As AI increasingly supports forecasting, performance evaluation, and strategic decisions, accountability for outcomes remains primarily assigned to human actors. This creates an “accountability gap,” whereby algorithmic recommendations significantly influence decisions while responsibility is ambiguously distributed between managers and intelligent systems. Although existing research on AI accountability has mainly focused on ethical and regulatory concerns, limited attention has been devoted to understanding how AI reshapes performance measurement systems and organizational governance. This study develops a conceptual framework to explain the emergence of the accountability gap and introduces the concept of hybrid accountability, whereby organizational outcomes are co-produced through the interaction of human judgment and algorithmic intelligence. Methodologically, the study adopts a conceptual research design based on an integrative literature review of performance measurement, organizational governance, accountability theory, and AI-enabled decision-making. The findings identify four dimensions of hybrid accountability—algorithmic transparency, human oversight, decision authority, and responsibility allocation—and suggest that traditional performance measurement systems become inadequate when decision authority is shared with algorithmic actors. In response, the framework proposes governance mechanisms that integrate human oversight, algorithmic transparency, and shared responsibility structures. By bridging performance measurement and AI governance research, the study advances a theoretical foundation for redesigning accountability systems in AI-enabled organizations and provides practical guidance for managers seeking to ensure responsible, transparent, and effective governance in increasingly complex decision environments.

Keywords: Artificial Intelligence; Hybrid Accountability; Performance Measurement; Corporate Governance; AI Governance