Nearly seven in 10 Indian businesses plan to increase their AI spending over the next 12 months, but only 3% say their enterprise data is fully prepared to support AI at scale, according to the Dun & Bradstreet’s Q4 2026 AI Momentum Survey report published on November 2.

The survey covers 10,000 businesses across 32 countries, providing a global benchmark for the Indian findings.

While 69% of Indian businesses expect to raise AI-related expenditure, 44% described their enterprise data as partially ready and 42% as mostly ready. Only 3% reported full readiness, compared with 8% globally.

The survey found that businesses with fully AI-ready data were 6.3 times more likely to report broad or strong returns on investment than those whose data was only partially ready. Overall, 30% of respondents reported broad or strong returns from AI initiatives, while another 46% said they were seeing “pockets of ROI.”

AI deployment is also moving beyond experimentation. About 35% of businesses said their AI pilots were entering production, while 26% reported that AI had already been embedded across multiple core business processes.

Workforce and Risk Readiness Lag

Only 25% of Indian businesses reported strong or very strong workforce readiness for AI, while nearly half described employee capabilities as mixed.

On risk management, just 29% expressed strong or high confidence in their ability to identify, monitor and mitigate AI-related risks. Another 53% reported moderate confidence.

“As enterprises embed AI more deeply across operations and decision-making, the quality of the underlying foundation will determine the quality of the outcomes,” said Dun & Bradstreet Executive Vice President Julian Prower. “Trusted, connected and well-governed data, combined with an AI-ready workforce, will be critical to scaling AI responsibly and effectively.” 

Data Readiness Becomes a Priority for AI Leaders

The findings put data governance and quality at the center of the enterprise AI scaling challenge. 

For chief data officers (CDOs), the task is to ensure that data is accurate, connected and consistent across systems before it is used by models and AI agents.

The gap between AI investment and data preparedness also has governance implications.

Enterprises need clear data ownership, reliable metadata, consistent definitions and processes for monitoring the information used in AI-driven decisions. Those foundations become more important as AI moves into core business processes.

The results reinforce the need to align data governance with AI governance. CDOs can play a central role by working with technology, security and business teams to establish the data foundations required for responsible deployment.