While 69% of Indian companies plan to boost AI investment, 97% currently lack the data infrastructure required for effective deployment. Investors should look beyond AI adoption announcements and evaluate the actual data readiness and governance of companies, as firms with mature systems are 6.3 times more likely to achieve significant returns on investment.
Corporate India is accelerating its investment in artificial intelligence, with 69% of firms planning to hike spending over the next year. However, a significant disconnect between capital expenditure and operational reality is emerging. According to the latest data from the Dun & Bradstreet AI Momentum Survey, 97% of Indian businesses do not have the complete data infrastructure needed to support full-scale AI adoption. This shortfall leaves local firms five percentage points behind the global benchmark for data readiness.
For investors, the primary concern is the potential for wasted capital. The data shows a direct link between the quality of a company's underlying data systems and its financial success. Organizations with fully prepared data sets are 6.3 times more likely to generate meaningful returns on their AI investments compared to their less-prepared peers. Currently, only 30% of domestic firms report strong returns from their AI initiatives, while nearly half of the businesses surveyed describe their internal infrastructure as only partially ready for advanced algorithmic integration.
Beyond technical hurdles, institutional maturity remains a bottleneck. The transition of AI from experimental pilot programs to core, profit-generating business functions is proving difficult. Only 35% of surveyed companies have successfully moved AI projects into production environments. Scaling these technologies requires more than just investment; it demands a capable workforce and strong internal governance. Currently, only 29% of firms express high confidence in their existing frameworks to monitor and mitigate AI-related operational risks.
As companies continue to integrate AI into their product and business models, the risk of cost overruns and operational failures remains elevated for those with fragmented systems. Investors tracking these developments should look past general announcements of AI adoption. Instead, the key monitorable is the company’s stated progress on data infrastructure, governance, and workforce upskilling as detailed in annual reports and management discussions. Companies that prioritize data hygiene are fundamentally better positioned to convert AI spending into sustainable financial growth, whereas those lacking foundational readiness may face pressure on their profit margins from unproductive capital deployment.