India's AI Reality Check: Enterprises Race Beyond Pilots, But Readiness Gaps Remain, Says SAP study

For the past two years, enterprise AI conversations have largely revolved around pilots, proofs-of-concept, and experimentation. The question everyone asking now is whether organizations can move AI into day-to-day business operations without creating complex challenges around governance, data quality, and workforce readiness. According to new research from SAP and Oxford Economics, Indian enterprises appear increasingly willing to take that leap. The study suggests organizations are moving beyond testing AI tools and beginning to embed them into core business processes ranging from procurement and finance to supply chains and product development. Released at SAP NOW AI Tour India in Mumbai, the SAP Value of AI Report 2026 surveyed 2,600 business leaders across 13 countries, including 200 respondents from India. The findings show that Indian organizations plan to invest an average of US$25.9 million in AI, with spending expected to rise 45% over the next two years. AI currently supports 33% of business tasks, a figure projected to increase to 51% during the same period. The study also found that agentic AI, systems capable of autonomously executing tasks and coordinating workflows, is gaining traction. Nearly 67% of Indian businesses are already piloting agentic AI use cases, while 85% believe the technology has the potential to significantly transform business operations. At the same time, overall, AI return on investment is expected to rise from 22% today to 39% within the next two years. However, the survey suggests that this enthusiasm is running ahead of preparedness. While 63% of organizations now consider themselves data-ready for AI, up from 42% last year, challenges persist. Around 76% of respondents cited incomplete data as a barrier, while 67% pointed to concerns around data quality. Workforce readiness remains another concern, with only 11% of organizations believing they are fully prepared from a skills perspective. Just 14% believe they have the governance structures needed to scale AI effectively. From Digital Transformation to Autonomous Enterprises SAP executives framed the current AI wave as the latest chapter in India's broader technology transformation story. Speaking at the event, Manish Prasad, President and Managing Director, SAP Indian subcontinent, said enterprises have already moved through phases of foundational ERP adoption, industry-specific digitization, and data-driven modernization. The next phase, he argued, will be defined by AI-enabled business models and more autonomous ways of operating. "Today, we are entering a completely new phase. This isn't simply another technology cycle. Artificial intelligence is fundamentally changing the way organizations think about work, decision-making, and business models," Prasad said. He described SAP's vision of the "Autonomous Enterprise" as one built on three foundations: trusted business systems, high-quality and governed data, and the ability to turn intelligence into measurable business outcomes. Prasad also linked AI adoption to India's broader economic ambitions. As the country seeks to expand its economy over the coming decades, he said technology, human talent, and AI will increasingly need to work together to drive productivity and innovation. The broader context, according to SAP, is that enterprises are being forced to operate in an increasingly uncertain world. Manos Raptopoulos, Global President, Customer Success – Europe, APAC, Middle East & Africa, and Member of the Extended Board at SAP SE, pointed to geopolitical tensions, supply chain disruptions, cybersecurity risks, and rising technology costs as pressures that are reshaping how businesses operate. "Disruption is becoming the new normal," he said, arguing that enterprises need greater resilience and agility to respond to increasingly complex operating environments. Raptopoulos also drew a distinction between consumer AI and enterprise AI, suggesting that the requirements for business systems are fundamentally different from those of public-facing AI tools. "Consumer AI and enterprise AI are fundamentally different," he said. "If you're managing financial operations, supply chains, procurement, or manufacturing environments, ninety percent accuracy isn't good enough," he said. According to Raptopoulos, the challenge for enterprises is not simply deploying AI models but grounding them in trusted business data, operational context, and governance frameworks that can support critical decision-making. Calling AI as the backbone of the Autonomous Enterprise he said, "As this embedded intelligence becomes core to operations, organizations become more adaptive, resilient, and future-ready." Why Data, Governance and People Matter More Than Models One of the recurring themes throughout was that AI success increasingly depends on foundations rather than algorithms. Manoj Thamba of SAP argued that enterprises should avoid viewing AI as a collection of disconnected agents performing isolated tasks. Instead, organizations need systems where multiple AI agents can work together across finance, procurement, planning, and operations while remaining aligned to broader business goals. "The future is not about individual agents becoming heroes," he told the press during a private briefing. "The future is about orchestrating agents so they work together toward common business outcomes." Thamba said AI is also becoming a new interface for enterprise software, enabling users to access insights and execute tasks through natural language interactions rather than traditional dashboards and workflows. Yet despite growing interest in agentic AI, he acknowledged that readiness remains the industry's biggest challenge. SAP's research found that organizations continue to struggle with three key areas: data readiness, workforce readiness, and governance. The findings suggest that the next phase of enterprise AI adoption may depend less on model sophistication and more on whether organizations can prepare their data, processes, and people for large-scale deployment. Enterprises Shift Focus from Experimentation to Execution Customer discussions at the event reflected a similar shift in priorities with Pushkar Rege, Global CIO of UPL, the global agrochemicals company, said their AI initiatives are increasingly focused on improving decision-making across finance, HR, supply chain, and operational processes. Company leaders emphasized that building a strong data foundation proved just as important as adopting AI itself. The biscuit and packaged foods maker Parle Product’s CEO Sanjay Joshi, outlined how it is using AI to support forecasting, procurement planning, sales execution, and decision support while continuing its transition toward a cloud-first operating model. The company has also developed internal AI-powered tools to help employees access business insights more quickly. Meanwhile, Mayank Roy, CIO of PI Industries, an agrochemical and specialty chemicals manufacturer, described how AI initially demonstrated value in research and product discovery, helping accelerate development cycles before expanding into broader transformation initiatives. The company is also exploring agentic AI capabilities while leveraging SAP's AI tools during its modernization journey. What echoed throughout is that organizations making the most progress with AI are not necessarily those deploying the largest number of models. Instead, they are the ones that spent years modernizing business processes, standardizing data, and building cloud foundations before attempting large-scale AI adoption. The findings reveal that, enterprise AI is entering a different phase and having access to models is no longer the primary challenge as most organizations can experiment with AI today but the task is to operationalize it at scale. Despite rising investment levels and growing confidence in agentic AI, the study shows that only a small minority of organizations believe they are fully prepared from a governance or workforce perspective. Data quality remains a persistent obstacle. In other words, the next wave of enterprise AI adoption may be defined less by the sophistication of the technology and more by an organization's ability to align data, processes, and people around it. And for Indian enterprises, that shift is already underway but the question is if the enthusiasm for AI will translate into sustained business outcomes.