Scaling agentic AI requires more than deploying new models, it requires trusted data foundations. A new survey of 600 chief data officers (CDOs) from Informatica, Wakefield Research, and Deloitte shows that while generative AI adoption is accelerating, data readiness and governance remain major obstacles.
According to the report, 69% of companies with revenues above $500 million are already using generative AI in their operations, up from 48% in 2025. However, many organizations struggle to operationalize AI systems at scale. Half of agentic AI adopters say data quality and retrieval issues are the biggest barriers to deployment. This highlights how infrastructure limitations can slow enterprise AI initiatives.
Workforce readiness is another challenge. About 75% of CDOs say their employees need improved data literacy, while 74% report a need for stronger AI literacy to ensure responsible use of AI outputs in daily operations. Without these skills, employees may struggle to detect poor data quality or misuse AI-generated insights.
Governance gaps further complicate scaling efforts. Nearly 76% of data leaders say governance frameworks have not kept pace with the rapid expansion of AI usage inside their organizations. As more employees experiment with AI tools, maintaining visibility, security, and compliance becomes increasingly difficult.
To address these risks, data leaders are significantly increasing investments. The survey shows that 86% of organizations plan to boost spending on data management in 2026. Priority areas include improving data quality, strengthening data privacy protections, managing unstructured data, and ensuring regulatory compliance.
Technology partnerships also play an important role. On average, companies now work with seven vendors for data management and eight vendors for AI systems. While multiple partners can expand capabilities, they also introduce integration complexity that can slow scalability.
The report’s central conclusion is clear: scaling agentic AI depends on trust in enterprise data. Organizations that prioritize governance, literacy, and high-quality data management will be better positioned to unlock the full value of autonomous AI systems.
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