CDOs Ramp Up Data Management To Power Agentic AI

Agentic AI may be the next big accelerator in enterprise automation, but it only scales when organizations can trust their data. A new survey of 600 chief data officers from Informatica, Wakefield Research, and Deloitte shows leaders are moving fast to fix the basics: 69% of companies with $500M+ in revenue now use generative AI, and 47% have already adopted agentic AI. The catch is clear—half of would-be adopters cite data quality and retrieval as the main barriers, pushing CDOs to invest heavily in data foundations.

Why Agentic AI Rises Or Falls On Data Trust

Agentic systems plan, call tools, retrieve context, and take actions—meaning any gap in data quality or access reverberates across every step. According to the survey, 50% flag data quality and access as the top blockers, and 57% say data reliability is the key barrier to graduating projects from pilot to production. In other words, scaling agents is a data problem first, an AI problem second.

That urgency is underscored by academic findings. A recent MIT-led analysis of autonomous agents observed that they can behave “fast and loose,” especially when context is incomplete or retrieval is noisy. Enterprises are responding by hardening retrieval pipelines, enforcing permission-aware access, and adding guardrails that prevent agents from acting on stale, biased, or off-policy data.

What CDOs Are Buying Now To Enable Trusted Agentic AI

Data leaders are opening their wallets: 86% plan to increase data management investments in the coming years. Their top priorities are improving data privacy and security (43%), strengthening data and AI governance (41%), and upskilling the workforce in data and AI literacy (39%).

On the shopping list: data observability to detect drift and anomalies before agents consume bad inputs; unified metadata catalogs and lineage to trace every answer back to its sources; master data and identity resolution to reduce duplication; PII scanning and policy-as-code to prevent sensitive leaks; and retrieval quality monitoring to benchmark RAG precision and recall. Many are also piloting synthetic data for safer prototyping and rolling out vector database governance to control embeddings, retention, and access.

Agentic and Generative AI Adoption Trends Snapshot

The survey shows the center of gravity shifting from experiments to scale: 69% report using generative AI, up markedly from the prior survey, and 47% have adopted agentic AI with another 31% planning adoption within 12 months. Larger organizations are ahead (54% adoption) versus smaller peers (44%).

Why push forward? Leaders point to enhanced customer experience (29%), better business intelligence and decision-making (28%), improved regulatory compliance (27%), and streamlined collaboration and workflows (26%). Importantly, 61% of CDOs say better data makes AI adoption easier—evidence that trustable data is the lever for faster, safer rollout.

Governance And Literacy Close The Confidence Gap

Trust is high but fragile. Overall, 65% of data leaders believe employees trust the data used for AI, rising to 74% among companies already running agentic systems. Yet most CDOs also see a skills gap: 75% say staff need data literacy upskilling and 74% need AI literacy to responsibly use AI outputs. High trust without the know-how to spot data quality issues is a risk.

Nearly three-quarters acknowledge governance and visibility have not kept pace with AI usage. Corrective actions now in motion include role-based access and least-privilege design, data lineage for auditability, agent and model registries, approval workflows for prompts and tools, and standardized evaluation reports that make AI behavior explainable to risk, legal, and compliance teams.

From Pilot To Production: Operationalizing Agentic AI

The leaders moving fastest are treating agentic AI like a data product. They define “golden” datasets, establish ground-truth labels, and stand up offline evaluation harnesses to track retrieval precision/recall, answer accuracy, and hallucination rates by domain. They measure data freshness, set SLAs for upstream sources, and run regular PII and policy compliance tests.

Operationally, they adopt human-in-the-loop checkpoints for high-impact actions, implement rollbacks for bad agent states, and maintain immutable logs for forensics. Many align their controls to frameworks such as the NIST AI Risk Management Framework and prepare for AI governance requirements emerging in global regulations, applying the same rigor used for financial reporting and cybersecurity.

The Vendor Juggle Across Data And AI Management Stacks

Data leaders expect to work with multiple partners—on average seven for data management and eight for AI management—to hit their goals. That breadth offers flexibility but adds complexity. Integration patterns like open metadata, data contracts, and shared lineage are becoming nonnegotiable to keep platforms interoperable and audits efficient.

Approaches to AI governance tooling are split: nearly half are adapting existing platforms, 30% are buying discrete tools, and 22% are building new. Organizations expanding their existing governance stacks are further along—75% of them have already adopted generative AI, compared with 65% among teams developing new tools—suggesting that meeting AI where data lives speeds adoption.

The Bottom Line On Scaling Agentic AI With Trusted Data

Agentic AI will not scale on model prowess alone. It scales on clean, governed, observable, and well-understood data. That is why CDOs are channeling budget into privacy and security controls, governance and lineage, workforce literacy, and retrieval quality. The next wave of winners will be the teams that make data trust a first-class product—and give their agents a solid ground to stand on.