AI investment is up across India, So is the pressure to prove it works
By Chris Arrasmith, Executive Vice President and Chief Operating Officer, Unisys
Indian businesses are investing real money in artificial intelligence, and they are doing so faster than in any other region. According to a recent Autodesk report, 91% of Indian organisations increased their AI investment over the past year, well ahead of peers in APAC and globally. Adoption is broadening just as quickly, with 63% of Indian companies now using AI assistants and chatbots, the highest rate among the APAC markets surveyed.
While this momentum is promising, leaders across every industry are facing a growing challenge: confidence in AI is rising faster than the results it is producing. While 90% of global organisations now believe they have the right architecture in place to support large-scale, data-driven decisions, only 65% report that operational efficiency exceeds expectations, down from 80% the year before, according to the Unisys AI & Cloud Insights report.
At a time when enterprises are facing growing demands from their boards to demonstrate the real benefits of AI, changes in how AI is planned, deployed and managed are needed. In India, where the pace of adoption is accelerating, the need to change is even more pressing, to ensure the region stands out as an innovation hub and doesn’t fall behind competitively.
The real test for India’s businesses
In the past year, 95% of enterprise AI deployments failed to deliver measurable financial returns globally. This shows that deployment and adoption are not the hard part, proving meaningful impact is what makes a difference.
India is not behind on AI. In fact, in many ways, the opposite is true: 40% of Indian companies report significant or full AI usage, well ahead of the global average of roughly 28%. However, since India’s adoption curve is much steeper than the global norm, skipping important steps such as integration, governance, and workforce readiness can harm business operations for years to come.
Why the gap between investments and outcomes exists
Businesses face two persistent challenges when converting AI-driven promises into outcomes: a shortage of skilled labour and immature data governance and security. Both challenges are seen at the global scale and are especially prevalent in India.
India has one of the largest technology workforces in the world. Still, much of that talent has built its expertise on traditional software development rather than the specific discipline of deploying, scaling and maintaining production-grade AI systems. This is a different skillset, and one that will require attention from both government and industry to solve. The Indian government is already taking steps to prioritise AI skilling as part of its broader AI strategy announced earlier this year, which includes robust plans to prepare the country’s youth and future workforce and to establish the region as a global innovation hub.
But upskilling talent alone won’t fix organisations’ AI ROI problem. Data management and security guardrails remain major hurdles; if not addressed, even companies with the most advanced AI architectures will struggle to deliver at the pace boards expect.
This goes beyond ensuring organisations have access to the right data and that it’s stored securely. With many organisations still working with data scattered across legacy systems, siloed by function or geography, closing this gap means treating data integration as a part of the infrastructure rather than an afterthought bolted on after an AI initiative is already underway.
Data integration cannot be a one-time project, it needs the same lifecycle management as any other critical system: ongoing monitoring, clear ownership and sustained investment as data sources, formats and business needs evolve. Without that support, a data environment will decline, and any AI built on top of it will inherit the same blind spots organisations set out to fix.
What India’s businesses must do differently
Delivering business results and proving the usefulness of AI starts with a shift in mindset in how it is used. This requires moving from pilots to scaled, measurable deployment and not treating proof of concept as the finish line. It also means aligning AI investment directly with business outcomes rather than technical capability alone. Every deployment should be tied to a specific business challenge, with employees brought along as part of the change rather than presented with new tools after the fact.
Organisations must treat security and governance as enablers and not deterrents of progress. This matters more than ever as cybersecurity incidents in India increase rapidly due to AI-powered attacks. When done well, strong governance frameworks give organisations the confidence to scale faster, not slower.
Finally, organisations should establish clear, board-level metrics for AI return on investment before scaling further. Only then can AI tools be deployed selectively where they clearly create value, rather than being treated as a default that fails to demonstrate measurable ROI.
Architecture readiness was always just the first milestone, not the finish line. India has already shown it can adopt AI faster than almost any market in the world. Now, it must focus on what matters to boards, employees and clients alike, showing what that investment delivers.