AI investment in India set to surge nearly 76 per cent over next two years, as per Hitachi Vantara’s report

According to a report by Hitachi Vantara, Indian enterprises are adopting artificial intelligence (AI) faster than their global counterparts and are also seeing stronger returns, but growing data infrastructure complexity and security challenges are beginning to act as constraints. The report is based on a global survey of 1,244 business and IT leaders across 15 countries, including 104 respondents from India.

The survey shows that 89 per cent of Indian organisations have either widely adopted AI or made it critical to their operations, compared with a global average of 69 per cent. Nearly 63 per cent of Indian respondents consider themselves strong or established, albeit with some gaps, in achieving returns on AI investments, indicating that many have moved beyond pilot projects to broader deployment. At the same time, 87 per cent of Indian enterprises said data infrastructure complexity is increasing rapidly or faster, versus 80 per cent globally, highlighting the strain of scaling AI systems.

The report points to further pressure from rising investment and expanding data volumes. AI investment in India is expected to increase by 75.6 per cent over the next two years, while data storage requirements are projected to grow by 73.9 per cent. Around 40 per cent of Indian organisations reported managing between 50 and 200 petabytes of data, compared with 31 per cent globally, a scale at which operational complexity typically intensifies. Nearly 46 per cent of respondents store operational data in public cloud environments, reflecting multi-environment setups that can complicate governance, visibility and control as AI workloads expand.

Further, security has emerged as a key limiting factor, with 67 per cent of Indian organisations identifying data security as a major challenge in implementing AI.

Additionally, many firms reported increasing formalisation of AI programmes, including clearly defined executive-level AI strategies (81 per cent), dedicated AI and machine learning teams (79 per cent), and well-defined KPIs and expected outcomes (77 per cent). Respondents also highlighted stronger maturity in MLOps practices (71 per cent), more advanced governance models (63 per cent), and widespread monitoring of AI performance (89 per cent).