Hitachi report shows AI adoption in India straining data infrastructure

AI adoption in India is progressing faster than global averages, with enterprises deploying artificial intelligence at scale and extracting measurable business value, according to the 2025 State of Data Infrastructure Report released by Hitachi Vantara, the data storage, infrastructure, and hybrid cloud management subsidiary of Hitachi Ltd.

The report is based on a global survey of 1,244 business and IT leaders across 15 countries, including 104 respondents from India. It found that 89 percent of Indian organisations have either widely adopted AI or made it critical to their operations, compared with a global average of 69 percent.

Indian enterprises move beyond AI pilots

The findings indicate that Indian enterprises have largely moved past experimentation. Nearly two-thirds of organisations in India, or 63 percent, rate themselves as strong or established, with some gaps, in achieving return on investment from AI initiatives.

This exceeds the global benchmark and suggests that AI deployments in India are increasingly enterprise-wide, with a focus on delivering tangible business outcomes rather than isolated pilots.

Data infrastructure complexity rises rapidly

As AI adoption in India accelerates, data infrastructure complexity is increasing at a faster pace. The report shows that 87 percent of Indian enterprises say their data infrastructure complexity is rising rapidly or faster, compared with 80 percent globally.

AI investment in India is expected to grow by 75.6 percent over the next two years, outpacing the global projection of 70.3 percent. Over the same period, data storage requirements are projected to rise by 73.9 percent in India, compared with 68.9 percent globally.

Four in ten Indian organisations now manage between 50 and 200 petabytes of data, compared with 31 percent globally. This range represents a critical growth band where infrastructure strain becomes increasingly acute.

Hybrid environments add governance pressure

The expansion of AI workloads is taking place across increasingly fragmented environments. In India, 46 percent of organisations surveyed store operational data in public cloud environments, with even higher usage levels for general business data.

This multi-environment reality creates governance, visibility, and control challenges, which intensify as AI workloads scale across hybrid data infrastructures.

Security emerges as the dominant concern

Security has become the primary challenge associated with AI adoption in India. Two-thirds of Indian organisations, or 67 percent, cite data security as a leading concern when implementing AI, placing India among the most security-conscious markets globally.

At the same time, Indian enterprises report stronger governance frameworks and leadership alignment than many global peers. The report shows that 81 percent of organisations have executive teams with clearly defined AI visions, compared with 71 percent globally.

In addition, 79 percent have dedicated AI or machine learning teams, compared with 70 percent globally, and 77 percent have defined KPIs and expected business outcomes for AI initiatives, compared with 67 percent globally.

Operational discipline supports AI outcomes

Indian enterprises also lead in operational maturity. Seventy-one percent rate their MLOps capabilities as strong and established, compared with 63 percent globally. Sixty-three percent report having advanced governance models, compared with 49 percent globally.

Monitoring of AI performance is widespread, with 89 percent of Indian organisations tracking performance, compared with 85 percent globally. Sustainability is also more embedded, with 54 percent incorporating sustainability into infrastructure strategies, compared with 42 percent globally.

Overall, 75 percent of Indian organisations report successful AI outcomes, with virtually no respondents reporting outright failures. The primary use cases driving value include automating workflows at 29 percent, generating insights at 29 percent, and improving productivity and decision-making at 26 percent.

Success factors cited include high-quality data at 65 percent, robust monitoring at 61 percent, employee adoption at 61 percent, and skilled teams at 59 percent.

Readiness gap threatens long-term scalability

Despite strong adoption and governance alignment, the report identifies a clear AI readiness divide. While 55 percent of Indian organisations have reached managed or optimised data infrastructure maturity stages, the remaining 45 percent continue to operate with less mature foundations.

Only 32 percent of organisations report having predictive, automated, and cost-efficient infrastructure scaling capabilities in place, limiting their ability to sustain AI ROI as data volumes and workloads grow.

Talent shortages increase partner reliance

Talent availability is emerging as one of the most significant constraints on AI growth in India. More than half of organisations, or 54 percent, cite hiring skilled workers as a top AI implementation challenge.

To bridge these gaps, 76 percent of Indian enterprises report working with partners or outsourcing key areas of their AI and data initiatives. This figure is higher than the global average and reflects increasing reliance on external expertise as internal capabilities struggle to keep pace.

Executive views on sustaining AI momentum

Commenting on the findings, Hemant Tiwari, Managing Director, Hitachi Vantara, India & SAARC, said Indian enterprises have moved beyond experimenting with AI and are now focused on delivering measurable business outcomes at scale. He noted that infrastructure complexity is rising alongside adoption and emphasised the need to modernise data foundations early to prevent AI momentum from being slowed by risk or operational strain.

Sanjay Agrawal, CTO, Head Pre-sales, India and SAARC region, Hitachi Vantara, said the report highlights how complexity and security risks increase as organisations operate at higher data volumes across hybrid environments. He said organisations investing early in automation, governance, and data quality are better positioned to scale AI predictably.

Sustaining leadership in AI adoption

The report concludes that while AI adoption in India continues to lead globally, long-term success will depend on addressing data infrastructure complexity, security pressures, and talent shortages. Early investment in automation, governance, and data quality is identified as critical to ensuring AI remains scalable and controllable as adoption expands.

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