India’s AI landscape is expanding fast-with strong adoption and expected investment growth of 75.6% over the next two years. But as companies scale, they’re running into real technical challenges around data infrastructure and security that deserve attention.
From AI Experiments to Real-World Use
Most Indian enterprises (nearly 9 in 10) have integrated AI into key operations, and many are seeing measurable ROI. This suggests India is moving beyond experimentation toward large-scale production use cases.
Rapid Investment Growth
Projected 75.6% AI investment growth indicates that companies are committing serious budgets for tools, platforms, and talent. Survey data also suggests significant increases in data volumes and storage needs - pushing teams to rethink architecture.
Infrastructure Complexity - A Key Constraint
A major share of organizations reports rising data infrastructure complexity, driven by:
- Hybrid and multi-cloud setups
- Large, disparate datasets
- Scaling bottlenecks without automated tooling
Infrastructure complexity can slow down data pipelines, AI model deployment, and observability - making operations fragile.
Key technical takeaway: Teams should invest in data governance frameworks, automated scaling systems, and unified data catalogs early to avoid expensive rework later.
Security & Talent Pressure
- 67% of firms see data security as a top challenge in AI deployment.
- 54% report hiring skilled AI professionals as difficult.
Security and talent constraints mean that development teams must emphasize secure coding practices, threat modeling, and modular architectures to support long-term AI growth.
What’s Working
Despite challenges:
- 75% of teams report AI success outcomes
- Most rely on outcomes like automation and enhanced decision support.
This highlights that pragmatic use cases - not just cutting-edge experiments - are driving real value.
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