Open Access

A Data-Centric Approach to Transforming Digital Retail Through Artificial Intelligence-Based Shopping Systems

https://doi.org/10.55640/ijaair-v03i09-08

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Priya Sharma ¹ ,

⁴ School of Computer Science and Engineering, National Center for Intelligent Systems, Bengaluru, India

Abstract

: The rapid evolution of artificial intelligence (AI) has transformed digital retail by enabling personalized recommendations, intelligent search, predictive analytics, and automated decision-making. Modern shopping systems increasingly rely on data-centric architectures that prioritize data quality, feature engineering, and continuous model optimization rather than solely focusing on algorithmic sophistication. This research-review paper examines the role of data-centric AI in digital retail transformation by synthesizing machine learning, computer vision, pattern recognition, and classification methodologies reported in the provided literature. The study develops a conceptual framework that integrates data acquisition, preprocessing, feature extraction, predictive modeling, and customer interaction optimization. Findings indicate that data quality, scalable learning architectures, and adaptive analytics significantly improve customer engagement, operational efficiency, and retail decision-making. The paper further discusses implementation challenges, ethical considerations, and future opportunities associated with AI-driven shopping ecosystems. The proposed analysis contributes to the understanding of how data-centric AI architectures can support intelligent retail platforms capable of delivering personalized, efficient, and scalable shopping experiences.

Keywords

Artificial Intelligence, Digital Retail, Data-Centric AI, Machine Learning

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A Data-Centric Approach to Transforming Digital Retail Through Artificial Intelligence-Based Shopping Systems

Abstract

Keywords

References

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