kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-2-440-454kaz29-2030Research ArticleЭКОНОМИКА И БИЗНЕСECONOMY AND BUSINESSАНАЛИЗ МОДЕЛИРОВАНИЯ БИЗНЕС-ПРОЦЕССОВ: НА ПРИМЕРЕ ЧАСТНОГО АГЕНТСТВА ЦЕЛЕВОЙ РЕКЛАМЫANALYSIS OF BUSINESS PROCESS MODELING: A CASE OF PRIVATE TARGETED ADVERTISING AGENCYhttps://orcid.org/0009-0006-4348-959XОспановИ. М.OspanovI. M.

 магистр управления ИТ 

 г. Алматы 

 Master’s student in IT management 

 Almaty 

is_ospanov@kbtu.kzhttps://orcid.org/0000-0002-6791-673XАвинашБ. М.AvinashB. M.

 доцент 

 г. Алматы 

 Associate Professor 

 Almaty 

a.bigadevanahalli@kbtu.kzhttps://orcid.org/0009-0005-5446-2141ИльхамжановР. К.IlhamzhanovR. K.

 руководитель рекламного агентства 

 г. Алматы 

 Head of advertising agency 

 Almaty 

rufat.kudratovich@mail.ruКазахско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan202506072025222440454Copyright © Оспанов И.М., Авинаш Б.М., Ильхамжанов Р.К., 20252025Оспанов И.М., Авинаш Б.М., Ильхамжанов Р.К.Ospanov I.M., Avinash B.M., Ilhamzhanov R.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2030

Моделирование бизнес-процессов (BPM) играет ключевую роль в оптимизации принятия решений для целевой рекламы в цифровом маркетинге. Традиционные ручные модели BPM, основанные на рабочих процессах, управляемых человеком, сталкиваются с ограничениями в масштабируемости, эффективности и адаптивности к динамическим требованиям рынка. Целью данного исследования является разработка и оценка гибридной структуры BPMN Human-AI, которая объединяет автоматизацию, управляемую ИИ, с человеческим опытом для повышения адаптивности процессов, точности таргетинга и соответствия нормативным требованиям (например, GDPR, CCPA) для частных рекламных агентств в Казахстане. Методология использует сравнительный подход смешанных методов с использованием инструментов построения диаграмм BPMN (Bizagi), инструментов разработчика Meta и Google Analytics для оценки трех моделей BPM (ручной, управляемой ИИ и гибридной) по ключевым показателям эффективности, включая эффективность процесса, сокращение ошибок и масштабируемость автоматизации. Эмпирические результаты реальных исследований и моделирования показывают, что интеграция ИИ снижает ручную нагрузку на 30–50%, повышает точность таргетинга на 25–40% и сводит к минимуму ошибки принятия решений. Однако ранние вмешательства ИИ требуют обратной связи от человека для смягчения предвзятости и обеспечения этического соответствия. Исследование также затрагивает пробелы в существующей литературе, такие как отсутствие практических рамок для BPM на основе ИИ в рекламе и необходимость гибридных моделей, уравновешивающих автоматизацию с человеческим надзором. Будущие направления исследований включают использование обучения с подкреплением для адаптивности ИИ и отраслевой настройки. Эта работа вносит вклад в масштабируемую, соответствующую требованиям и эффективную структуру BPM для целевой рекламы, устраняя теоретические и практические пробелы в оптимизации процессов на основе ИИ.

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.

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The authors declare that there are no conflicts of interest present.