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AI systems become dependable enough to act on only when they can use trustworthy data in the right context—and when governance controls what they may see and do. For Indian enterprises moving AI beyond pilots, that context includes data provenance and quality, business rules, the user’s role and intent, and enforceable access and use policies. Accurate data alone is not enough.
What “trusted context” means for AI
In an article published by ETCIO on September 23, 2026, Sumeet Agrawal, Vice President of Product Management at Informatica from Salesforce, argues that organizations need reliable context and governance to move AI from pilots toward scaled or agentic use. That is the author’s thesis, not proof that any single data architecture guarantees successful AI.
Agrawal defines trust as information that has been verified, is reliable, and aligns with the business’s rules and policies. In practice, this means giving an AI system more than a set of apparently accurate records: it needs to know where data came from, how it should be used, who is asking, and what actions are permitted. The article notes that the author’s views are his own and do not necessarily represent ETCIO’s position. Read the ETCIO article.
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- Data context: the source, format, lineage, freshness, and quality of the information.
- Business context: operating rules and workflows that determine how information should affect a decision.
- User context: who is making the request and why, including role and intent.
- Governance context: policies, security, compliance, and permissions governing who may see or act on the information.
These layers answer different questions. A record can be accurate but stale; current but outside an approved workflow; relevant to one role but restricted for another. Trust depends on the system being able to distinguish those cases.
Why accurate inputs can still produce a bad decision
ETCIO illustrates the problem with a procurement agent that selects the lowest supplier bid while overlooking a previous quality flag and an approved-supplier restriction. This is an illustrative scenario, not a reported incident. The bid amount may be correct, yet the recommendation is inappropriate because the agent lacks relevant business and governance context.
The example points to a practical distinction: data quality checks whether information is usable and dependable; business rules determine what it means for a particular decision; governance determines whether the system and user may use it in that way. Treating all three as one generic “data problem” makes failures harder to prevent and diagnose.
Capabilities that can make context usable
Agrawal identifies five enterprise capabilities as building blocks. They are a proposed set of capabilities, not an exhaustive standard or a comparison of software products.
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- Current data integration: connect relevant information across systems and keep it sufficiently current for the decision at hand.
- Continuous data-quality monitoring: detect quality problems as data changes, rather than relying only on one-time checks.
- Master data management: keep core records—such as customer, product, and supplier identities—consistent across systems.
- Governance that travels with data: make applicable rules and controls operative where information is accessed or used, rather than leaving them only in policy documents.
For an implementation review, these capabilities can be assessed against six practical questions: Can the system expose provenance and lineage? Are freshness and quality controls visible? Can it apply business rules? Does access reflect the user’s role and purpose? Can decisions and interventions be audited? Does the approach work with existing systems? These questions help turn “trust” into verifiable design requirements without assuming that a catalogue or model alone solves it.
What the India-specific figures do—and do not—show
ETCIO reports several survey findings that indicate concerns about AI deployment, business context, governance, and employee trust. The original survey reports were not independently retrieved for this article, so the figures below should be read as ETCIO’s account of those reports, not as independently verified measurements. Different surveys and questions are not directly comparable.
| Figure reported by ETCIO | Attribution in ETCIO’s account | Qualification |
|---|---|---|
| Nearly 40% of Indian business and technology leaders, versus 28% globally | Deloitte’s State of AI in the Enterprise | As reported in ETCIO’s September 23, 2026 article; the underlying survey was not independently checked. |
| 38% of AI pilots in India unsuccessful, versus 28% globally | Salesforce’s Agentic Workplace Study | As reported in ETCIO; the underlying study was not independently checked. |
| 34% of Indian respondents cited lack of business context as the largest reason pilots fell short, versus 22% globally | Salesforce’s Agentic Workplace Study | As reported in ETCIO; the underlying study was not independently checked. |
| 64.5% of Indian business leaders described data governance and security as a very severe obstacle to scaling AI | EY’s AIdea of India | As reported in ETCIO; the underlying study was not independently checked. |
| 65% of employees trust the data behind their AI tools | Informatica’s CDO Insights 2026 | As reported in ETCIO; the underlying study was not independently checked. |
| 75% of data leaders say employees need more data-literacy upskilling | Informatica’s CDO Insights 2026 | As reported in ETCIO; the underlying study was not independently checked. |
These figures suggest that organizations face both technical and organizational obstacles, but they do not establish that a particular context layer caused a given pilot outcome. They also do not prove that the proposed capabilities will produce a specific success rate.
India’s digital public infrastructure is relevant, but not a substitute
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 trusted data exchange, including Aadhaar, UPI, and DigiLocker. It presents digital public infrastructure as an interoperable public foundation that leaves room for private-sector applications, and describes maturity in terms of implementation, adoption, and leverage. View the report.
That infrastructure is national context, not a replacement for an enterprise’s own controls. An organization still needs to define its purposes for using data, set boundaries, protect personal information, assign accountability, and address risks such as discrimination. The OECD’s 2019 public-sector data guidance offers that governance lens, including integrity, transparency, individual control over personal data, and inclusion; it is international guidance, not Indian law. Read the OECD recommendation.
At the Internet Governance Forum in 2025, Abhishek Singh, identified as 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. This is a broader national AI frame; it does not discharge an individual enterprise’s governance responsibilities. Read the IGF event page.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Regulatory claims need an official-text check
ETCIO 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 includes expectations concerning board-approved policies, audit trails, explainability, and meaningful human oversight. The article’s account is not a substitute for the official rules, commencement notifications, or RBI framework, which were not independently checked here. Organizations should consult the applicable official Government of India and RBI materials before relying on a legal duty, scope, or implementation date.
How to translate trust into an enterprise decision
- Define the decision and its boundaries. Specify what the AI may recommend or do, what data it needs, and which actions require human approval.
- Map the context required. Identify the relevant sources and quality signals, business rules, user roles and purposes, and applicable access or use restrictions.
- Make controls observable. Confirm that teams can inspect provenance, freshness, rule application, permissions, and audit records for a decision.
- Test realistic exceptions. Use cases such as a low bid from a supplier with a quality flag to check whether the system can follow business rules rather than optimize a single field.
- Assign ongoing ownership. Decide who maintains data definitions, monitors quality, updates business rules, reviews access, and responds when an agent acts outside expectations.
India’s public digital infrastructure and national AI initiatives may expand the foundations available to organizations, but dependable enterprise AI still depends on making context and governance operational at the point of use.
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