Trust in AI hinges on a trusted context built across four interrelated layers: data context, business context, user context, and governance context, all working together.

India’s aggressive AI push, showcased by the IndiaAI Mission with an outlay exceeding 10,300 crore rupees and expansion to more than 38,000 GPUs, aims to make AI a national infrastructure with potential to add up to $1.7 trillion to the economy by 2035.

Major scaling hurdles in India hinge on governance and data security, with the vast majority of business leaders identifying governance-related data issues as a key obstacle to AI scale.

Regulatory developments reinforce governance: DPDP Rules 2025 will impose substantive data fiduciary obligations by 2027, and RBI’s FREE-AI framework requires board-approved AI policies, model audits, explainability, and human oversight.

The central challenge is trust—India’s AI pilots fail at a higher rate (about 38%) due to missing business context, not due to cost, talent, or model quality, according to the Salesforce study.

Enterprises that win with AI will be those building the contextual trust layer now, enabling scalable, reliable agentic AI rather than retrofitting governance later.

India is rapidly embracing AI, with roughly 40% of Indian business and tech leaders utilizing AI significantly or fully, well above the global average of 28%.

Five essential capabilities to operationalize trusted AI: metadata catalog, real-time data integration, continuous data quality monitoring, master data management for single golden records, and governance embedded within data workflows.

Without all four layers, AI can produce correct data but still draw unreliable conclusions, as shown by a procurement example where valid pricing masked non-compliant supplier history.