Indian enterprises are accelerating their AI ambitions at a remarkable pace, but most lack the foundational readiness to scale safely and sustainably, according to a new study by the IBM Institute for Business Value (IBV). The report, AI Infrastructure That Endures, reveals a widening gap between investment momentum and enterprise preparedness—particularly around governance, architectural maturity, and talent capability.
The findings highlight a paradox: while 83% of Indian executives believe that effective governance is essential to successful AI infrastructure deployment, only 4% have embedded the required frameworks to manage AI-related risks and ensure responsible AI at scale. The study warns that without trust-by-design, rapid AI adoption could outpace an organization’s ability to manage security, compliance, and ethical implications.
Despite this, investment sentiment remains bullish. 58% of Indian organizations have increased infrastructure spending due to rising AI demand, with budgets expanding 19% year-over-year in 2025—in line with global trends. Yet only 1 in 10 business leaders feel their current IT infrastructure fully meets their AI requirements, underscoring an urgency to modernize hybrid environments and operational models.
“Indian enterprises are entering a pivotal phase in their AI journeys where ambition must now translate into sustainable impact,” said Subhathra Srinivasaraghavan, Vice President, IBM India Systems Development Lab. “To do that, organizations must intentionally build infrastructure that is agile, trusted and talent-led. By optimizing hybrid architectures, embedding robust governance frameworks and nurturing deeper AI skills, India can accelerate transformative outcomes across every sector, and move with confidence toward the vision of Viksit Bharat.”
Key areas where enterprises are focusing their AI readiness efforts
1. Hybrid Infrastructure Takes Center Stage: Indian organizations continue to integrate cloud, on-premises, and edge environments as they move toward AI-first operations.
- 65% say a “fit-for-purpose” hybrid strategy has improved cost efficiency and performance.
- 32% plan to expand hybrid architectures over the next three years to support more demanding AI workloads.
This shift reflects a growing recognition that generative AI and large-scale model deployment require specialized compute, flexible storage, and low-latency architectures—elements difficult to achieve through a single-cloud or legacy environment.
2. Trust-by-Design Emerges as the Missing Foundation: With AI adoption accelerating across sectors, governance is emerging as the weakest link.
- 83% agree governance is critical for AI success.
- But only 4% have robust frameworks for risk management, model monitoring, and ethical compliance.
This gap is particularly concerning as enterprises adopt more autonomous systems, bringing heightened risks related to bias, data privacy, and regulatory exposure. The report frames governance as a “non-negotiable,” not a bolt-on.
3. Talent Maturity Remains a Barrier to Scale: While investments are rising, workforce capabilities are still catching up.
- 83% of organizations are investing in AI skill development and recruitment.
- 75% remain in the early stages of AI talent maturity.
- 43% have set up dedicated AI Centers of Excellence to anchor capability building.
The study suggests that talent, not tools, will determine the speed at which enterprises can modernize infrastructure and operationalize AI responsibly.
The Bigger Picture: Ambition Meets Reality
India’s AI narrative is marked by optimism and aggressive investment, but the IBM findings point to a more nuanced reality. Enterprises are building the scaffolding for AI-driven transformation, yet structural challenges—governance deficits, fragmented architectures, and skill shortages—threaten to slow momentum.
For CIOs and technology leaders, the message is clear: the next phase of AI adoption will be defined not by model experimentation but by infrastructure modernization, trust engineering, and workforce readiness.
