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AI can act reliably only when it has more than accurate data: it needs to know where that data came from, which business rules apply, who is asking, and what the system is permitted to do. Sumeet Agrawal, vice president of product management at Informatica from Salesforce, frames that combination as trusted context—the layer that can help enterprises move AI beyond pilots toward dependable use.
What trusted context means
In an article published by ETCIO on September 23, 2026, Agrawal describes trust as a relationship between information and the rules governing its use. His formulation: “Data becomes trusted once that information has been verified, is reliable, and lines up with the business’s own rules and policies.” This is the author’s thesis, not proof that any single technology guarantees trustworthy AI.
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The distinction matters because an input can be factually accurate while still being insufficient for a safe or useful decision. Context supplies the additional signals that tell an AI system how to interpret information and what actions are appropriate.
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Data context
Data context describes a record’s source, format, lineage, and quality. It helps answer whether information is current, how it was produced, and whether it is reliable enough for the intended task.
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Business context
Business context captures the organization’s operating rules and workflows. These can determine what counts as an acceptable choice even when the underlying figures are correct.
User context
User context identifies who is asking and why. A person’s role and intent can affect which information is relevant and what action they are authorized to request.
Governance context
Governance context applies policy, compliance, and security requirements to access and use. For an AI system, governance needs to shape what it can see and do at the point of operation, not exist only in a policy document.
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Why correct data can still lead to a bad decision
Agrawal illustrates the problem with a procurement agent that selects the lowest supplier bid. The price may be accurate, but the decision can be wrong if the supplier has a previous quality flag or is not on the approved-supplier list. This is an illustrative scenario, not a reported incident. It shows why a system must apply business rules and governance alongside data values.
The same principle applies beyond procurement: a useful answer depends on whether the information is fresh, whether the user may access it, and whether the proposed action fits the organization’s process.
Practical building blocks for trusted context
Agrawal proposes five enterprise capabilities. They are implementation building blocks, not a complete standard or a comparison of products.
- Metadata catalogue: Make data origin and reliability visible so users and systems can understand what a record represents.
- Current data integration: Connect relevant sources and keep information sufficiently up to date for the task.
- Continuous data-quality monitoring: Detect quality problems as data changes rather than relying only on occasional checks.
- Master data management: Keep core records—such as customers, products, and suppliers—consistent across systems.
- Governance that travels with data: Carry access and use rules into the systems and workflows where AI operates.
Together, these capabilities address different failure points. A catalogue can expose provenance, but it does not by itself enforce a purchasing rule. Clean master records can reduce ambiguity, but they do not decide whether a user is authorized to act. Reliable deployment therefore depends on connecting data controls to business workflows, user roles, and governance.
What the India figures do—and do not—show
ETCIO’s September 2026 article reports several India-specific survey figures as evidence of concern about AI readiness and trust. Because the underlying survey reports were not retrieved for independent verification, these should be read as figures reported by ETCIO, not as independently confirmed measurements. The surveys use different questions and populations, so their percentages should not be treated as directly comparable.
- Nearly 40% of Indian business and technology leaders reportedly identified a challenge in Deloitte’s State of AI in the Enterprise, compared with a 28% global average, according to ETCIO.
- Salesforce’s Agentic Workplace Study is reported as finding 38% unsuccessful AI pilots in India versus 28% globally; 34% of Indian respondents reportedly cited lack of business context as the largest reason pilots fell short, compared with 22% globally.
- ETCIO reports that 64.5% of Indian business leaders described data governance and security as a very severe obstacle to scaling AI, citing EY’s AIdea of India.
- Informatica’s CDO Insights 2026 is reported as finding that 65% of employees trust the data behind their AI tools, while 75% of data leaders say employees need more data-literacy upskilling.
These figures point to issues organizations may need to address, but they do not establish that a particular data platform or governance model will solve them.
India’s digital infrastructure is relevant, not a substitute for enterprise controls
India’s digital public infrastructure offers a broader context for thinking about trusted data exchange. The 2025 State of DPI in India report, credited to IIM Bangalore’s Center for Digital Public Goods, describes an ecosystem built around identity, payments, and data exchange, including Aadhaar, UPI, and DigiLocker. It presents interoperability and public infrastructure as foundations that can also leave room for private-sector applications, and identifies implementation, adoption, and leverage as stages of maturity.
That national infrastructure does not settle how an individual enterprise should govern its own AI systems, data access, or business decisions. At IGF 2025, Abhishek Singh, identified as an Additional Secretary at India’s Ministry of Electronics and Information Technology and CEO of the IndiaAI Mission, highlighted publicly supported shared compute, community-driven data collection, AI skills, and shareable use cases as pillars of inclusive and sustainable AI. These priorities concern the wider AI ecosystem; organizations still need controls appropriate to their own data and operations.
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Read regulatory timelines carefully
ETCIO’s article says the Digital Personal Data Protection Rules 2025 point to a May 2027 deadline for substantive data-fiduciary obligations. It also says the RBI FREE-AI framework was released in August 2025 and sets expectations around board-approved policies, audit trails, explainability, and meaningful human oversight. The official rules, commencement notifications, and RBI framework were not independently reviewed for this article, so treat those dates and descriptions as the article’s account rather than definitive legal guidance. Organizations should consult the applicable official texts and implementation timelines before making compliance decisions.
A practical way to assess an AI workflow
Before allowing an AI system to inform or take an operational action, an organization can examine the workflow through these questions:
- Can the system show where the information came from, how current it is, and what quality checks apply?
- Are the relevant business rules represented in the workflow, including exceptions and approval requirements?
- Does the system account for the user’s role and purpose when retrieving information or proposing an action?
- Are access limits and governance policies enforced where the system operates?
- Can people review consequential actions, and can the organization trace what information and rules informed a decision?
These questions do not prescribe a vendor or guarantee a particular result. They help reveal whether an AI workflow has the context needed to make its outputs appropriate for real organizational use.
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