In February 2026, India hosted the largest AI summit ever convened with 300,000 participants, over 100 country delegations, and heads of state from across the world, all gathered in New Delhi. Prime Minister Modi described AI as a “transformative power” that becomes a solution when given the right direction, and a disruption when left directionless. It was a moment of genuine global leadership.
Now the harder question lands on the desks of every CIO, CTO, and CEO in India: what comes next? Hosting the world’s AI conversation is one thing. Building the internal infrastructure to actually deliver on it is another and for most Indian enterprises, that infrastructure has a critical gap. The data behind their AI systems cannot be trusted.
This is not a technology shortage. Indian enterprises have access to world-class cloud platforms, capable vendors, and deep technical talent. The bottleneck is foundational poor data quality, fragmented lineage, and governance frameworks designed for a slower era. Until that changes, AI will keep producing inconsistent results, and business leaders will keep second-guessing its outputs.
For CIOs, CTOs, and CEOs, this is now a strategic issue, not an IT one.
The governance model is broken and most organisations know it
Ask any CIO in India whether their organisation has a data governance policy and the answer is almost always yes. Ask whether that policy is enforced in real time, across cloud and on-prem environments, at the speed AI systems actually operate and the conversation changes.
Traditional governance was built for periodic audits, static data environments, and quarterly reviews. It was never designed for a world where data moves continuously across hybrid infrastructure, where AI models are retrained on live data, and where a single pipeline failure can corrupt the outputs of dozens of downstream systems. Layering compliance on after deployment, the way most organisations still do it is becoming structurally untenable.
India’s regulatory environment is tightening this further. The Digital Personal Data Protection (DPDP) Rules were notified in November 2025, and organisations have entered the execution phase the question has shifted from “what is DPDP?” to “how do we operationalise it?” And that is only the beginning. Organisations operating in BFSI are navigating RBI and SEBI frameworks simultaneously. Those in insurance face IRDAI scrutiny. Penalties under the DPDP Act can reach ₹250 crore per contravention. The idea that a single governance review cycle can keep pace with all of this is no longer realistic.
The shift: From governance as checkpoint to governance as infrastructure
The organisations making real progress with AI in India share a common trait: they stopped treating governance as a downstream activity. Instead, they have embedded it into the data pipeline itself making it operational, automated, and continuous.
What does that actually look like? It means consent and classification are enforced at the point of ingestion, not checked after the fact. It means data lineage is tracked end-to-end, so when an AI model produces a result that doesn’t make sense, teams can trace it back to its source in minutes rather than days. It means pipelines are not just moving data, they are enforcing policy.
This matters especially in India’s enterprise context, where many large organisations still run significant workloads on-premises, alongside cloud deployments. The governance layer has to work across that complexity, not assume a clean cloud-native environment.
The agentic AI problem that no one is talking about enough
Most governance conversations focus on reporting and analytics systems that inform decisions. That challenge is hard enough. But the frontier has moved.
Agentic AI systems do not just surface insights. They act initiating transactions, triggering workflows, making procurement decisions, routing customer cases. In this context, the question for a CEO or CTO is not “is our data good enough for analysis?” It is “would we trust this data to take actions on our behalf, automatically, at scale?”
For most Indian enterprises, the honest answer today is no. Not because the AI is wrong, but because the data it is acting on has not been verified, traced, or governed with that level of autonomy in mind. The gap between AI capability and data readiness is where the real risk lives.
Organisations that are ahead of this are investing in explainability as a design requirement, not an afterthought. They want to know how an AI output was generated, what data it depended on, and whether that chain of logic can withstand scrutiny from regulators, from auditors, or from a board asking hard questions after something goes wrong.
What business leaders should be asking right now
This is not a problem that resolves itself with more AI investment. It requires deliberate decisions at the leadership level. Three questions worth pressure-testing in your organisation:
Can you trace any AI output back to its source data, in under an hour?
If the answer is no, your teams are flying blind when something goes wrong and something always goes wrong.
Is your governance model keeping pace with your data volume?
Manual review processes that worked at 10 million records do not scale to 10 billion. If governance hasn’t grown with the data estate, it is already a liability.
Does your AI strategy account for regulatory evolution?
DPDP is the beginning of a longer arc. Compliance obligations are expected to be fully enforced by late 2026. The organisations building adaptive governance frameworks today will spend far less time and money reacting to each new requirement as it arrives.
The window to build this right is narrowing
India’s AI adoption curve is steep. Organisations that invest now in data quality, lineage, and operational governance will be compounding that advantage over the next three to five years. Those that continue to treat governance as a compliance checkbox will face a harder problem later, not just regulatory, but competitive. AI systems built on poor data foundations produce inconsistent outputs, erode internal trust, and slow the very transformation they were meant to accelerate.
The conversation in India’s boardrooms is shifting from “are we doing AI?” to “can we trust what our AI is doing?” That is the right question. The answer depends on what is happening three layers below the model in the pipelines, the lineage, and the governance infrastructure that most organisations have not yet built.
India stood up and led the world’s most ambitious AI summit. The next act – the harder one, is building the data infrastructure worthy of that ambition. In the era of agentic AI, trusted data is not a technical nicety. It is the foundation on which every other ambition depends.
The author is Varun Babbar, VP and Managing Director, India — Qlik.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
By commenting, you agree to the Prohibited Content Policy
By commenting, you agree to the Prohibited Content Policy
News 
See whats happening in Indian IT industry right now
Exclusive
Read and get insights from specially curated unique stories from editorial
Leaders Speak
Business leaders sharing their insights
Events
Explore and discuss challenges & trends in India’s leading B2B events
Awards
Recognise work that not only stood out but was also purposeful
Webinars
Join leaders & experts for roundtables, conferences, panels and discussions
Subscribe to our Daily Newsletter
By continuing you agree to our Privacy Policy & Terms & Conditions
Advertise With Us
We have various options to advertise with us including Events, Advertorials, Banners, Mailers, etc.
Download ETCIO App
Save your favourite articles with seamless reading experience
Get updates on your preferred social platform
Follow us for the latest news, insider access to events and more.
About Us
Contact Us
Newsletters
Leave a Reply