Overconfident Data Strategies Jeopardize Enterprise AI Success

Executives overestimate their data maturity by 12%, while 90% of professionals face challenges managing data complexity. Fixing data issues is crucial for successful AI deployment.

Published on: Aug 21, 2025
Overconfident Data Strategies Jeopardize Enterprise AI Success

Immature Data Strategies Threaten Enterprise AI Plans

Executives consistently overestimate their organizations' data maturity, rating it 12% higher than professionals who interact with data daily. This insight comes from the Actian State of Data Governance Maturity 2025 report, which surveyed over 600 enterprise data experts.

Business Leaders Overconfident in Data Readiness

Despite senior leadership’s confidence, enterprises face significant hurdles in scaling and managing data complexity. Nearly 90% of data professionals report challenges in these areas, while over 80% highlight governance and compliance as ongoing issues.

Additional challenges include securing data access, maintaining data quality, building trust, and addressing skills shortages. Recognizing these problems, more than half of surveyed organizations expect that strengthening data governance will improve AI implementation and increase trust in business decisions.

Urgency for CIOs to Address Data Challenges

With AI adoption accelerating, CIOs are under pressure to fix data issues that could otherwise derail AI initiatives. AI both drives the need for better data governance and exposes weaknesses in current data practices.

Data ownership is evolving rapidly. Over 80% of decision-makers report shifts in ownership as AI projects grow. This trend will likely continue as organizations adopt agentic AI tools that require integrating both structured and unstructured data.

Unstructured data — which makes up roughly 80% of company information — presents a major obstacle. It often resides in isolated locations like Google Drive or PDFs, making extraction difficult. Without tackling these data fragmentation issues, AI agents cannot function effectively.

Consequences of Poor Data Hygiene

Weak data frameworks increase costs, erode trust, and threaten enterprise ambitions. CIOs who accurately assess and improve their data strategy’s sustainability will be better positioned to lead successful AI deployments.

As one expert put it, fixing data problems is essential for AI agents to operate properly, especially given the growing complexity of data environments.

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