India’s AI Future Hinges on Data Quality, Not Models: EY Partner

Thomas, a partner at Ernst & Young, made the remarks during a panel discussion on India's technology roadmap, where he emphasized that the country's vast data generation potential remains underutilized due to fragmentation and quality issues.

He argued that while India has made strides in developing AI models, the real bottleneck is the lack of integrated data ecosystems across government and private sectors. 'The model is only as good as the data it consumes,' Thomas said, pointing to inconsistent data standards and siloed repositories as key hurdles.

To address this, he called for a national framework to standardize data collection, sharing, and governance, which would enable cross-sectoral AI applications in healthcare, agriculture, and urban planning. He also stressed the importance of building trust through transparent data practices to encourage wider participation from citizens and businesses.

Thomas's comments come amid India's push to position itself as a global AI hub, with recent government initiatives to boost computing infrastructure and research. However, industry experts have repeatedly flagged data readiness as a critical gap that could slow progress if left unaddressed.

Looking ahead, Thomas predicted that investments in data quality and connectivity would yield higher returns than model development alone, as they would unlock practical AI solutions for India's unique challenges, from rural healthcare delivery to climate-resilient farming.