Improving Data to Boost AI Results in India

Indian enterprises face a stark AI paradox: surging investments yet stalled outcomes due to inadequate data foundations. IDC-Qlik research reveals 54% struggle with poor data quality—highest in APAC—while 62% flag governance/privacy shortcomings. Salesforce notes 94% of production AI yields inaccurate outputs from untrustworthy data (25% deemed unreliable).​

Critical Readiness Gaps:

Fragmented/unstructured data (70% volume) undermines model accuracy; 39% doubt AI output reliability.​

Bias, legacy integration, and compliance (GDPR/DPDP) risks amplify, with 28% facing data bias vs. APAC averages.​

Hitachi survey: 58% tie AI success to quality data; 43% fear insufficient training datasets amid skills shortages.​

Despite 47% running multiple GenAI cases (EY-CII), leaders prioritize modernization—89% per Salesforce—for ROI via integration tools and governance.​

Sources: IDC-Qlik, Salesforce, Hitachi Vantara, EY-CII.