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Paper Type
Complete
Abstract
This article addresses the growing integration of artificial intelligence (AI) and blockchain in enterprise incentive systems, highlighting gaps in the Information Systems literature regarding integrated governance frameworks. By developing a comprehensive AI–Blockchain governance framework, we leverage IT governance theory and sociotechnical systems perspectives, employing a conceptual design methodology alongside scenario-based simulations to assess architectural feasibility and governance efficacy. The proposed framework unifies AI lifecycle governance, programmable smart contract execution, and a metricized oversight model that translates key performance indicators such as security and fairness into measurable metrics. This integrated approach facilitates evaluation, execution, and continuous monitoring, offering managers a structured roadmap for the scalable deployment of tokenized AI incentive systems while advancing IT governance research through operationalized decision-right redistribution via programmable infrastructures.
Paper Number
1802
Recommended Citation
Gadiraju, Divija and Khazanchi, Deepak, "Integrating AI Governance and Blockchain Execution: A Secure Framework for Enterprise Incentive Systems" (2026). AMCIS 2026 Proceedings. 36.
https://aisel.aisnet.org/amcis2026/sig_sec/sig_sec/36
Integrating AI Governance and Blockchain Execution: A Secure Framework for Enterprise Incentive Systems
This article addresses the growing integration of artificial intelligence (AI) and blockchain in enterprise incentive systems, highlighting gaps in the Information Systems literature regarding integrated governance frameworks. By developing a comprehensive AI–Blockchain governance framework, we leverage IT governance theory and sociotechnical systems perspectives, employing a conceptual design methodology alongside scenario-based simulations to assess architectural feasibility and governance efficacy. The proposed framework unifies AI lifecycle governance, programmable smart contract execution, and a metricized oversight model that translates key performance indicators such as security and fairness into measurable metrics. This integrated approach facilitates evaluation, execution, and continuous monitoring, offering managers a structured roadmap for the scalable deployment of tokenized AI incentive systems while advancing IT governance research through operationalized decision-right redistribution via programmable infrastructures.
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