Leveraging AI to Scale Product Development: Technical Approaches and Implementation Strategies
DOI:
https://doi.org/10.32628/CSEIT25112742Keywords:
Product Development, Artificial Intelligence, Personalization Systems, Predictive Analytics, Implementation StrategiesAbstract
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.
Downloads
References
Luis Mizutani, "AI for software development: can we actually measure its impact?," Medium, 2024. [Online]. Available: https://medium.com/@LuisMizutani/ai-for-software-development-can-we-actually-measure-its-impact-31af45dccdab
Athanasios Polyportis, "A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education," Frontiers in Artificial Intelligence, 2024. [Online]. Available: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1324398/full
Actian Corporation, "How To Build Scalable Data Architectures," Actian Technical White Paper. [Online]. Available: https://www.actian.com/building-scalable-data-platform-architectures/
Grig Duta, "What is a Feature Store in ML, and Do I Need One?," Qwak Blog, 2024. [Online]. Available: https://www.qwak.com/post/what-is-a-feature-store-in-ml
Mariana Coutinho et al., "The Role of Generative AI in Software Development Productivity: A Pilot Case Study," arXiv:2406.00560v1, 2024. [Online]. Available: https://arxiv.org/html/2406.00560v1
Santhosh Kumar Shankarappa Gotur et al., "A Framework for AI-Powered Performance Test Results Analysis," ResearchGate, 2024. [Online]. Available: https://www.researchgate.net/publication/389696329_A_Framework_for_AI-Powered_Performance_Test_Results_Analysis
Hady Khamis Khan, "How to Create a Vector-Based Recommendation System," E2E Cloud, 2023. [Online]. Available: https://www.e2enetworks.com/blog/how-to-create-a-vector-based-recommendation-system
Abdulrahman Khamaj and Abdulelah M. Ali, "Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time," Alexandria Engineering Journal, Volume 95, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1110016824002874
Chen-Fu Chien et al., "Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study," Computers & Industrial Engineering, Volume 185, November 2023. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0360835223006940
Andrejs Cekuls, "AI-Driven Competitive Intelligence: Enhancing Business Strategy and Decision Making," Journal of Intelligence Studies in Business 12(3):4-5, 2023. [Online]. Available: https://www.researchgate.net/publication/369166724_AI-Driven_Competitive_Intelligence_Enhancing_Business_Strategy_and_Decision_Making
DialZara, "Privacy-Preserving AI: Techniques & Frameworks," 2024. [Online]. Available: https://dialzara.com/blog/privacy-preserving-ai-techniques-and-frameworks/
Xiaomeng Wang et al., "A brief review on algorithmic fairness," Springer, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s44176-022-00006-z
Landing AI, "AI Transformation Playbook: How to lead your company into the AI era," Landing AI. [Online]. Available: https://landing.ai/case-studies/ai-transformation-playbook
Michael Chui et al., "The State of AI in 2023: Generative AI's Breakout Year," McKinsey & Company, 2023. [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.