Indian enterprises are rapidly transitioning from AI experimentation to large-scale deployment, with investment in agentic AI projected to increase fivefold over the next two years, according to SAP’s latest Value of AI Report 2026.
The study, conducted by SAP in collaboration with Oxford Economics, surveyed 2,600 business leaders across 13 countries, including 200 respondents from India. The findings suggest that Indian organisations are increasingly treating AI as a strategic business priority rather than a standalone technology initiative.
According to the report, Indian enterprises plan to invest US$25.9 million in AI, with overall spending expected to increase by 45 per cent over the next two years. The study also projects returns from AI investments to rise from 22 per cent today to 39 per cent within the same period.
India emerges as a global AI adoption leader
The findings indicate that India ranks among the most proactive markets globally in terms of structured AI adoption.
Nearly three-quarters of surveyed organisations reported having an AI strategy aligned with broader business objectives, while AI currently supports approximately one-third of business tasks. That figure is expected to rise to more than half of all business activities over the next two years.
The study also found that 55 per cent of organisations have appointed dedicated AI leaders to drive adoption, the highest proportion among all surveyed markets.
In addition, end-to-end AI deployment across business functions is expected to more than double, highlighting a shift from isolated pilot projects to enterprise-wide operational integration.
Agentic AI gains momentum
One of the most notable findings relates to the rise of agentic AI, where autonomous AI systems are increasingly being deployed to execute tasks and support business decision-making.
The report found that 67 per cent of Indian organisations are already piloting agentic AI use cases, while 85 per cent believe the technology has the potential to significantly transform business operations.
SAP estimates that spending on agentic AI initiatives could increase fivefold to US$14.4 million as enterprises seek to embed intelligence directly into business processes.
Speaking about the trend, Varun Thamba, Head of AI, Asia Pacific at SAP, noted that organisations are increasingly looking beyond standalone AI applications towards systems that are natively integrated into enterprise workflows.
According to Thamba, the next phase of AI adoption will involve reimagining core business processes such as procurement, supply chain management and finance, enabling AI systems to make contextual recommendations and automate decisions based on business data, policies and operational requirements.
Data readiness improves, but challenges persist
The study found significant progress in AI data readiness across Indian organisations.
Sixty-three per cent of respondents reported being prepared from a data perspective for AI adoption, up from 42 per cent last year, representing the highest year-on-year increase among surveyed countries.
However, challenges remain. More than three-quarters of organisations reported difficulties related to incomplete data, while 67 per cent cited concerns around data quality.
Thamba highlighted that data remains one of the most significant barriers to successful AI deployment. He noted that while organisations expect AI to support a growing proportion of business activities, relatively few believe they currently possess the data quality required to maximise AI outcomes.
Industry experts increasingly view data governance and data quality as critical factors in determining whether AI initiatives successfully transition from pilot programmes to production environments.
Governance emerges as a critical requirement
The report also highlights governance as a major concern among organisations scaling AI deployments.
While businesses remain optimistic about AI’s potential, many acknowledge that governance frameworks, policies and organisational readiness have not yet fully matured.
According to the findings, only a minority of organisations believe they are fully prepared in terms of AI-related skills and governance processes.
Thamba emphasised that enterprise AI requires far more than access to advanced language models. Organisations must ensure that AI systems understand user permissions, data access policies and regulatory requirements before being deployed at scale.
He noted that enterprises are often hesitant to move AI projects into production environments until they are confident that appropriate governance controls are in place.
Workforce transformation becomes strategic priority
The study suggests that AI adoption will significantly reshape workforce requirements over the coming years.
Approximately 80 per cent of respondents believe that realising AI’s full value will require broader workforce transformation initiatives rather than traditional upskilling alone. Meanwhile, 66 per cent identified soft skills as an increasingly important requirement in AI-enabled workplaces.
Despite continued investment in training programmes, nearly eight in ten organisations expressed concern that workforce development efforts may struggle to keep pace with the rapid evolution of AI technologies.
Building the autonomous enterprise
SAP positioned the findings within its broader vision of the autonomous enterprise, where AI, business processes and enterprise data are tightly integrated to support decision-making and operational efficiency.
Manos Raptopoulos, Global President, Customer Success – Europe, APAC, Middle East and Africa at SAP, said AI is increasingly becoming embedded within business operations rather than functioning as a separate technology layer.
The company argues that organisations that successfully combine relevant, reliable and responsibly governed AI with high-quality business data will be best positioned to achieve measurable business outcomes.
The report suggests that Indian enterprises are entering a new phase of AI maturity, characterised by larger investments, stronger governance requirements and deeper integration of AI into business processes. As organisations move from experimentation to execution, factors such as data quality, workforce readiness and operational governance are expected to play a decisive role in determining long-term success.