Behavioral Operations Strategies for Managing Worker Effort with AI-Based Goal Contracts
Keywords:
behavioral operations, AI-based contracts, goal setting, algorithmic management, gig economy, worker effort, platform governance, fairnessAbstract
The integration of artificial intelligence into the design of performance targets is reshaping how organizations manage worker effort across rapidly scaling digital platforms. This paper examines the system-level dynamics of AI-based goal contracts, a class of behavioral operations instruments in which granular performance targets are generated, adapted, and enforced algorithmically. Moving beyond classical principal-agent models, the analysis addresses structural trade-offs between adaptive precision and motivational crowding, between individualized goal calibration and collective fairness, and between real-time optimization and long-term sustainability of worker engagement. Drawing on established research in behavioral operations, algorithmic management, and platform governance, the paper develops a multi-layered architecture of AI-driven goal-setting systems, discussing the underlying infrastructure, model governance, and feedback mechanisms that stabilize effort over time. Particular attention is paid to the manner in which algorithmic transparency, worker autonomy, and fairness perceptions interact to shape the viability of such contracts in practice. The discussion extends to deployment challenges in large-scale gig and remote work settings where geographically dispersed labor pools and asymmetric information complicate traditional incentive design. The paper further explores governance frameworks that can reconcile the efficiency gains of automated goal adaptation with the ethical imperatives of procedural justice and worker well-being. System robustness, adaptability to heterogeneous worker populations, and the institutional conditions required for responsible scaling are analyzed as integral components of a sustainable AI-based behavioral operations strategy. By synthesizing insights across operations management, information systems, and organizational behavior, the paper provides a comprehensive conceptual foundation for designing, deploying, and governing AI-based goal contracts that preserve worker motivation and platform integrity at scale.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
