Leveraging AI to Scale Product Development: Technical Approaches and Implementation Strategies

Rajeshkumar Rajubhai Golani

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 2, 2853-2866

Abstract: This article examines how artificial intelligence technologies are transformatively scaling product development across multiple dimensions. As organizations seek to enhance both quality and efficiency in their product offerings, AI-powered solutions are providing competitive advantages through automation, personalization, and data-driven decision frameworks. The article explores the technical architectures supporting effective AI integration, including event-streaming systems, feature stories, and machine-learning infrastructures that form the foundation for advanced analytics capabilities. The article investigates how generative AI accelerates development workflows through code generation, UI design, and automated testing while examining sophisticated personalization technologies, including vector-based recommendation engines and dynamic interface adaptation. Additionally, it covers predictive analytics applications in market intelligence, exploring how ensemble forecasting methods and competitive analysis tools provide strategic insights. Technical challenges related to data privacy and algorithmic fairness are addressed alongside implementation strategies, offering organizations a comprehensive roadmap for incremental AI adoption across development lifecycles. By examining both architectural considerations and practical applications, this article provides a technical framework for leveraging AI to optimize product development at scale.

Keywords: Product Development; Artificial Intelligence; Personalization Systems; Predictive Analytics; Implementation Strategies (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112742
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i2:id:1328

DOI: 10.32628/CSEIT25112742

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