AI Adoption Soars, but Data Reliability Concerns Persist
Enterprises are doubling down on artificial intelligence initiatives, with 69% of organizations integrating Generative AI into their business practices—a jump from 48% just a year ago. Nearly half (47%) have embraced agentic AI as well. But a new global survey, presented in Informatica's "CDO Insights 2026" report, reveals a revealing paradox: while enthusiasm for AI is at an all-time high, data quality and reliability are struggling to keep up.
The survey polled 600 data leaders across the U.S., UK/EU, and APAC. Over half (57%) named data reliability as their leading barrier to scaling AI projects from pilot testing into full production. Half of this group identified data quality as the primary challenge when implementing agentic AI. Despite this, roughly two-thirds (65%) of data leaders believe that most or nearly all employees trust the data being used for AI.
| Key Survey Findings | Percentage (%) |
|---|---|
| Companies with Integrated GenAI | 69 |
| Companies Using Agentic AI | 47 |
| Leaders Citing Data Reliability as Top Barrier | 57 |
| Leaders Report Employee Trust in Data | 65 |
Governance Not Keeping Pace with AI Usage
While AI adoption is surging, governance is lagging behind. A significant 76% of data leaders said their organization’s AI governance cannot fully keep up with the actual employee use of AI. This gap raises potential risks around privacy, security, ethics, and regulatory compliance. Without robust frameworks, organizations may find themselves exposed to vulnerabilities that could undermine both their reputation and legal standing.
Upskilling Is Essential for Responsible AI
The workforce faces a steep learning curve. Three-quarters (75%) of data leaders agree their teams need more upskilling in data literacy; a similar share (74%) report a need for increased AI literacy training to ensure responsible, effective AI use. As AI decision-making becomes more prevalent in operations, bridging these knowledge gaps grows even more critical.
| Top Priorities for Data Management Investment in 2026 | Percentage (%) |
|---|---|
| Improve Data Privacy & Security | 43 |
| Enhance Data & AI Governance | 41 |
| Upskill Employees for AI Fluency | 39 |
Investment in Data Management Set to Rise
In response to these gaps, 86% of surveyed companies report plans to increase investments in data management by 2026. The biggest motivators? Strengthening data privacy and security (43%), bolstering governance (41%), and enabling employees to become more fluent in data and AI practices (39%).
Expert Insight: Trust Is Crucial for AI ROI
As Amanda Fitzsimmons of RS Group puts it, “The risks of accelerating AI adoption without strong data governance and literacy are significant. Embedding governance and accountability into AI initiatives is essential to maximize opportunities while minimizing risks.”
Krish Vitaldevara, Informatica’s CPO, adds: “The promise of AI is immense, but so are the risks if you don’t have confidence in reliable data. Investing in rigorous governance and workforce upskilling is a must for trusted, effective AI.”
Key Takeaway: AI Is Only as Good as Its Data—and People
This global report underscores a central truth for any organization betting on AI: technology alone isn’t enough. Investing in strong data governance and upskilling employees are essential steps to avoid pitfalls and fully realize AI’s transformative potential. The message is clear—those who prioritize foundational data quality and workforce readiness will be best positioned to reap the rewards and manage the risks of AI-driven innovation.
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