Аннотация
Business Process Modeling (BPM) plays a pivotal role in optimizing decision-making for targeted advertising in digital marketing. Traditional manual BPM models, reliant on human-driven workflows, face limitations in scalability, efficiency, and adaptability to dynamic market demands. This study aims to develop and evaluate a Human-AI Hybrid BPMN framework that integrates AI-driven automation with human expertise to enhance process adaptability, targeting precision, and regulatory compliance (e.g., GDPR, CCPA) for private advertising agencies in Kazakhstan [3, 9]. The methodology employs a comparative mixed-methods approach using BPMN diagramming tools (Bizagi), Meta developer tools, and Google Analytics to evaluate three BPM models (manual, AI-driven, and hybrid) against key performance indicators including process efficiency, error reduction, and automation scalability. Empirical findings from real-world case studies and simulations demonstrate that AI integration reduces manual workload by 30–50%, improves targeting accuracy by 25–40%, and minimizes decision-making errors. However, early-stage AI interventions require human feedback to mitigate biases and ensure ethical compliance [10]. The study also addresses gaps in existing literature, such as the lack of practical frameworks for AI-driven BPM in advertising and the need for hybrid models balancing automation with human oversight. Future research directions include leveraging reinforcement learning for AI adaptability and industry-specific tuning. This work contributes a scalable, compliant, and efficient BPM framework for targeted advertising, bridging theoretical and practical gaps in AI-driven process optimization.