The growing complexity of data management in the Information and Communication Technology (ICT) industry presents significant challenges in achieving real-time insights, scalability, and operational efficiency. To address these challenges, this study proposes an AI-augmented data science pipeline framework tailored for ICT-driven industries. The framework incorporates advanced techniques, such as automated feature engineering, adaptive machine learning models, and real-time analytics, to enhance data quality, optimize predictive performance, and support critical applications, such as predictive maintenance and cybersecurity. By integrating AI methodologies with traditional data processing workflows, the framework addresses key pain points, including handling large-scale datasets, improving decision accuracy, and enabling proactive risk management. Case studies across domains, such as finance, retail, and supply chain management, demonstrate measurable outcomes, including enhanced agility, reduced downtime, and improved operational resilience. In addition, this study explores ethical considerations, scalability constraints, and deployment strategies, offering actionable insights for ICT professionals and industry stakeholders. By bridging research and practice, this study positions AI-augmented pipelines as a transformative tool to leverage data as a strategic asset to drive innovation and competitiveness in dynamic global markets.