India – 5 June 2026 — Veeam Software, the Data and AI Trust Company, unveiled new global research at VeeamON London, with Indian findings showing the most advanced agentic AI adoption in APJ and the strongest expectation of revenue upside from trusted data.
The Veeam study cites 93% of Indian organisations are already using or piloting AI agents — with 44% in active production — and 49% of executives believe trusted, secure, and compliant data could unlock more than 25% revenue growth. Yet 98% say data challenges have already slowed their AI progress, signalling that the bottleneck is not ambition but trust.
The Veeam study, based on a global survey of 600 senior executives across financial services, healthcare, manufacturing, retail, and technology, reveals that AI adoption is scaling dramatically faster than the governance structures designed to manage it. In India, despite strong executive investment and intent, the ability to control, monitor, and recover from AI failures are critically underdeveloped.
Key findings for India:
- 93% of Indian organisations are already using or piloting agentic AI systems (vs. 88% globally), with 44% already in production.
- 49% of Indian executives say trusted, secure data could unlock more than 25% revenue growth — and the average expected uplift is 31%, the highest of any market surveyed.
- 78% say the EU AI Act has already influenced their AI investment strategy in the last 12 months (vs. 61% globally) — reflecting India’s deep IT-services exposure to European clients.
- 42% cite ‘risks from autonomous agent behaviour or decision chaining’ as a top data challenge slowing AI progress — the highest reading in APJ.
- 53% rank ‘increased cyber risk’ as the top Shadow AI concern; 51% cite ‘ensuring personal data is not misused in AI systems’ as their biggest compliance worry.
- The figures show a clear trust gap between AI adoption and the governance, visibility, and control required to support it.
Revenue upside meets governance reality
Indian C-suites are unusually bullish on the revenue opportunity of trusted data — but the same respondents flag autonomous agent behaviour and decision chaining as the most acute risk to that opportunity. 64% report having a reliable inventory of AI deployed across their organisation, the highest in APJ, but 42% still cite agent-chaining risk as a brake on AI progress. The pattern points to a market that is moving fastest on adoption and most exposed to the governance gap if data trust isn’t operationalised.
With the Digital Personal Data Protection Act 2023 rules being finalised and the IndiaAI Mission’s ₹10,372 crore investment accelerating local compute and model development, Indian enterprises are operating in one of the most active AI-policy environments in APJ. Financial regulators are tightening expectations on responsible AI use, and India’s deep services links into Europe help explain why 78% of Indian executives say the EU AI Act has already influenced their AI strategy.
“Most organizations don’t have an AI adoption problem; they have an AI trust problem,” said Anand Eswaran, CEO of Veeam. “The first phase of AI was defined by infrastructure investment, experimentation, and acceleration. The next phase will be defined by trust. With the widespread adoption of autonomous AI agents operating at machine speed, the question transitions from whether you can use AI, to whether you can ensure all your data is secure, governed, compliant and resilient. And should something go wrong, can you recover with precision? That’s how you accelerate safe AI at scale without accelerating reputational and operational risk.”
This combination of rapid AI adoption, coupled with incomplete visibility and unclear accountability creates the conditions for failures that are difficult to detect, explain, and contain.
Executive Confidence Masks an Operational Reality Gap
The Veeam study uncovers a significant perception gap between the boardroom and the operational teams responsible for delivering AI outcomes. Progress frequently stalls between intent and execution: governance exists inconsistently, data is managed reactively, and ownership is assigned but fragmented.
- 65% of CEOs believe they have a full AI inventory, compared with just 48% of technical leaders.
- 52% of CEOs believe they actively lead on data, but only 41% of CISOs and 38% of CIOs agree.
- 48% of CEOs believe trusted, secure, and compliant data could unlock more than 25% revenue growth.
- 83% of CEOs feel pressure to accelerate their AI and data capabilities.
This combination of rapid AI adoption, coupled with incomplete visibility and unclear accountability creates the conditions for failures that are difficult to detect, explain, and contain.
When AI Fails, It Won’t Look Like Downtime
As AI systems become more autonomous, the nature of failure is shifting. Risk is moving away from traditional system outages toward data-level failures that are harder to detect, explain, and contain. The research warns that machine-speed mistakes can outpace detection, forcing resilience to evolve from broad recovery to precision – restoring only what is impacted, rather than rewinding entire environments.
Among organizations running AI today, only a minority could identify within minutes:
- 29% which systems it accessed.
- 25% what actions it took.
- 24% what decisions it influenced.
- 22% which data the system used.
Only 40% of leaders are very confident they can isolate and precisely reverse an agentic AI failure.
Inside-Out, Outside-In: Governance is Converging on Data
The governance challenge is converging on data from two directions: internal demand and external scrutiny.
Inside organizations, unauthorized AI use is now mainstream:
95% report unauthorized AI use within their organization and 93% view it as a significant risk.
- Yet only 25% offer approved alternatives, meaning most are trying to suppress demand rather than govern it effectively.
- 44% say increased cyber risk is the top “Shadow AI” risk.
At the same time, regulatory pressure outside the organization is intensifying. 61% of organizations say the EU AI Act has already influenced AI investment strategies in the last 12 months, while 47% cite maintaining audit trails for AI decisions as their biggest compliance concern.
Trust Requires Ownership, Not Shared Ambiguity
The new research shows that the core barriers to progress are fragmented ownership and misaligned operating disciplines – with data, AI, and governance responsibilities often spread across teams in ways that dilute accountability and slow execution. When “everyone owns it,” no one can decisively set policy, enforce controls, or prove outcomes.
Where ownership is clearly defined, outcomes improve significantly:
- 24% more likely to detect rogue AI behavior in organizations where CISOs own AI agent risk.
- 47% less likely to detect rogue AI behavior in organizations relying on shared ownership.
Data doesn’t need another champion – it needs accountable leadership strong enough to align governance, security, privacy, compliance, and resilience.
Trust is Becoming the Operating Foundation for Enterprise AI
A clear divide is emerging between organizations that can operationalize trust and those that cannot. Organizations that successfully align ambition, visibility and governance significantly outperform their peers.
Among organizations classified as fully AI-ready, 97% report measurable business benefits from data and AI investments, compared with 48% overall, demonstrating the value of operationalizing trust at enterprise scale.
Building the Data and AI Trust Layer
Veeam is addressing this challenge by combining data resilience, security and governance to help organizations see what data AI uses, govern how it’s accessed by humans and agents, and recover clean, trusted data with precision when incidents occur.
“The findings here leave no room for doubt. When 95% of executives say data challenges are already slowing their AI progress, the bottleneck isn’t the model – it’s trusted, governed, recoverable data,” added Eswaran. “Veeam is building the Data and AI Trust layer to give enterprises the visibility, control and precision recovery needed to scale AI safely and deliver real business value.”
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