Enterprise AI is surging, data problems persist

Jan 27, 2026

2:00pm UTC

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Despite fears of an AI bubble fueled by weak demand, a new report shows enterprise adoption is booming.

Informatica, the Salesforce-owned cloud data management company, published its CDO Insights 2026 global study, which surveyed 600 data leaders across the US, UK/EU, and Asia-Pacific (APAC). Headline numbers show that AI adoption is thriving, with 69% of companies having integrated generative AI (GenAI) into their business practices, a significant increase from 48% the year prior.

The growth doesn’t stop there: 25% expect to incorporate generative AI into their practice in the next 12 months. Even more advanced use cases of AI, such as agentic AI assistance, are being incorporated, with nearly half of the respondents reporting that they have already “embraced agentic AI.” However, this isn’t without its challenges, especially when data is at the core.

Data reliability has consistently been identified as a top barrier to moving projects from pilot to production, cited by 56% of respondents last year and 57% this year. Half of the surveyed leaders also specifically pointed to data quality as their primary challenge in deploying agentic AI. A majority of data leaders (75%) also think their workforce needs upskilling in data literacy.

To address this issue, leaders are taking several key actions: improving workflows around data and AI systems, increasing investments in data quality, and strengthening data and metadata collection and management, according to the report. A majority of these leaders are also increasing their data management investment in 2026.

AI usage is outpacing guidelines in many of these organizations, with 76% of the leaders reporting that their company’s AI governance does not “completely keep pace with employee use of AI technology.” The result is opportunities for things to go wrong, exposing employees to vulnerabilities in security, safety, and privacy.

Our Deeper View

While AI demand continues to accelerate, data remains the perceived biggest challenge in bringing AI projects to fruition. As soon as AI exploded in popularity three years ago, the importance of data became immediately apparent, as organizations struggled to apply generative AI tools to their workflows because of a lack of structured data. While organizing data is timely and sometimes costly, it remains critical for business leaders to prioritize to unlock successful AI deployments.