The Tribune · 15 Sep, 11:32 · Business briefing

As AI adoption scales in India, data quality and governance emerge as key factors influencing AI reliability, per global study findings

A recent global study indicates that as artificial intelligence adoption expands across India, the reliability of these systems increasingly depends on robust data quality and governance frameworks.

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The briefing

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A recent global study indicates that as artificial intelligence adoption expands across India, the reliability of these systems increasingly depends on robust data quality and governance frameworks. The findings suggest that technical implementation alone is insufficient without stringent oversight mechanisms. For organisations scaling AI initiatives, establishing clear protocols for data management is now critical to ensuring consistent and trustworthy outcomes. This shift highlights a growing disconnect between rapid technological deployment and the foundational data infrastructure required to support it effectively.

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You must prioritise data governance skills immediately, as AI reliability now hinges on clean, well-managed information. Professionals in data science, compliance, and IT leadership are seeing increased demand, but every role interacting with AI needs a stronger grasp of data ethics and quality control. Do not just learn to build models; learn to audit them. Focus on understanding regulatory frameworks and data lifecycle management. Upskill in compliance and data stewardship to become indispensable. Review your current projects for data integrity gaps. If you ignore this, your AI outputs may become liabilities. Smart professionals are already integrating governance into their daily workflows, positioning themselves as the bridge between technical potential and operational trustworthiness.

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