1 hr ago
Indian Enterprises Rebuild Data Foundations for Scalable, Trusted AI
Many Indian companies have already tried artificial intelligence in small experiments.
Now they want AI to produce clear benefits, such as higher productivity or lower costs.
Experts say companies first need clean and well-organised data.
They also need to record what their data means so AI can use it correctly.
Delhivery is using AI to spot damaged packages and predict delivery delays.
It is also using AI systems to answer some customer questions from start to finish.
Usha International began with a simple chatbot for employee approvals, and many workers adopted it.
Banks and other regulated companies want to understand how AI reaches its decisions.
The main lesson is that trusted data, careful rules and suitable infrastructure matter as much as the AI tools themselves.
Indian companies are shifting from AI experimentation toward measurable business outcomes and scalable deployment.
Executives say clean, annotated data and organisational knowledge are essential foundations for reliable AI.
Delhivery is using computer vision, predictive models and agentic systems in live logistics operations.
Usha International reports nearly 90% employee adoption of a chatbot for leave and reimbursement approvals.
Regulation and cost concerns are making explainability, governance and AI-focused cloud architecture more important.
- Who
- Indian enterprises, technology leaders and data executives, including representatives from Oracle India, Delhivery, Usha International and Srei Infrastructure Finance.
- What
- Businesses are moving from AI pilots toward outcome-driven, scalable and governed AI systems.
- Where
- Across Indian enterprises and sectors, with examples from logistics, consumer durables and financial services.
- When
- The shift is described as taking place as Indian enterprises enter a second chapter of AI adoption; the discussion occurred at the Oracle × LiveMint Executive Roundtable.
- Why
- Companies want AI to deliver measurable benefits while managing data quality, costs, employee trust, explainability and regulatory responsibilities.
Scale AI Adoption
Build Trust and Foundations First
Deployment strategy
Scale AI Adoption
Companies can move beyond isolated pilots by putting AI and agentic processes directly into live operations, as Delhivery has done.
Build Trust and Foundations First
Executives argue that organisations should first establish clean, annotated data, clear outcomes and safeguards before expanding AI into higher-stakes processes.
Employee adoption
Scale AI Adoption
Starting with visible, familiar applications can encourage broad usage; Usha International reports nearly 90% adoption of its internal chatbot.
Build Trust and Foundations First
Adoption should grow incrementally because employees, especially outside technology functions, need to build trust before AI is used in more consequential work.
Infrastructure choices
Scale AI Adoption
Scalable cloud resources can support expanding AI workloads and improve operational efficiency.
Build Trust and Foundations First
Not every problem requires the most powerful technology; organisations should evaluate costs, data-egress charges and the appropriate level of infrastructure.
Key facts
- Global AI adoption
- The article cites the 2026 Stanford AI Index as saying nearly 88% of organisations globally use some form of AI.
- Agentic AI adoption
- Deployment of autonomous AI agents remains in the single digits across almost every business function.
- Delhivery applications
- Delhivery uses vision-based cameras to flag loading and unloading damage, predictive models to anticipate delays and agentic systems to resolve some shipment queries.
- Usha International adoption
- Usha International reports chatbot adoption close to 90% for leave and reimbursement approvals.
- Financial-services requirement
- Srei Infrastructure Finance says explainability is as important as the answer in regulated industries.
- Infrastructure change
- Srei Infrastructure Finance reported lower overall computing costs and data-egress charges after moving core workloads to Oracle Cloud Infrastructure.
- Governance pressures
- The Digital Personal Data Protection Act and sector-regulator expectations are pushing governance toward a prerequisite for AI deployment.
Quotes
Sanjeev Chauhan
Vice President of Cloud Engineering at Oracle India
“The discussion is quickly moving away from doing AI for the sake of AI. Companies have started defining a clear outcome. Is it adding to the top line? The bottom line? Productivity? AI is no longer a GPU problem. It’s an outcome problem, and only then does it become an engineering problem, one where guardrails matter enormously, especially in regulated sectors”
livemint.com
“If you don’t annotate your data at the source, you end up spending enormous time later digging through what is essentially a data landfill just to figure out what it means”
livemint.com
Sources
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