A Dun & Bradstreet survey shows all surveyed Indian companies are active in AI, with 73% reporting measurable returns. However, only 4% of businesses feel their data infrastructure is fully ready for scaling AI across operations. For investors, this highlights a gap between AI spending and the operational efficiency gains needed for long-term profit margin improvement.
Indian enterprises are shifting from experimental AI projects to broader operational integration, according to the recent Dun & Bradstreet AI Momentum Survey. While every organization surveyed has an active AI initiative, the level of maturity varies significantly. About 44% of companies are still in the testing or planning phase, while 30% have started using AI in their day-to-day operations. A smaller group, roughly 26%, has moved toward more advanced stages, such as embedding AI into core business functions or utilizing agentic AI, which involves systems capable of executing complex tasks with minimal human intervention.
AI Investment Trends and Profitability
Businesses are backing this technology push with capital. Over two-thirds of the surveyed companies expect to increase their AI spending, with 13% planning a significant rise and 56% anticipating a moderate increase. Notably, no surveyed firm reported plans to decrease AI investment, signaling a long-term commitment to digital transformation. This trend toward higher capital spending on technology is intended to drive future efficiency, but investors should monitor whether these expenses translate into sustainable cost savings or revenue growth over time.
The Data Quality Bottleneck
While 73% of companies reported measurable returns from their current AI initiatives, these gains are largely limited to isolated projects or specific departments. Scaling these successes across an entire enterprise remains difficult due to poor data infrastructure. Only 4% of respondents stated that their data is fully ready to support enterprise-wide AI deployment. The majority, 58%, indicated that their data is only partially ready, while 30% consider it mostly ready.
For businesses, this creates an execution risk. If underlying data is inconsistent or poor in quality, the effectiveness of AI systems can be severely limited, potentially resulting in lower-than-expected returns on investment. Investors tracking companies with high AI spending should look for management commentary on data modernization efforts, as firms that successfully clean and organize their data are likely to see a better return on their technology expenditure. The ability to bridge the gap between pilot projects and company-wide scaling will be a key factor in determining which firms gain a competitive advantage in the coming years.