Artificial intelligence (AI) is racing into the mainstream, but organizations are struggling to keep pace with the governance and training needed to use it responsibly, according to a new report from Informatica Inc.
The CDO Insights 2026 study surveyed 600 global data leaders and reveals a striking disconnect. While AI adoption is exploding, the foundational systems required to support it are lagging dangerously behind.
Generative AI adoption has jumped to 69% of companies today from just 48% a year ago. Nearly half of organizations have already moved beyond basic GenAI to deploy agentic AI systems capable of autonomous decision-making.
But this breakneck pace is creating serious problems. Moving AI projects from pilot programs to full production remains a major challenge, with 57% of data leaders citing poor data quality as a primary barrier. Half point to data reliability issues as their top obstacle in deploying agentic AI specifically.
The report identifies what Informatica calls a “trust paradox” at the heart of these struggles. While 65% of data leaders believe their employees trust the data being used for AI applications, the reality appears more complicated. Three-quarters of leaders acknowledge their workforce needs better training in data literacy, and 74% say employees require improved AI literacy to use these tools responsibly.
Perhaps most concerning, 76% of survey participants admit their company’s AI governance frameworks can’t keep up with how quickly employees are adopting AI technology. This gap exposes organizations to mounting risks around privacy violations, security breaches, ethical concerns, and potential regulatory compliance failures.
“The promise of AI is immense, but so are the risks if you don’t have confidence in a reliable data foundation,” Informatica Chief Product Officer Krish Vitaldevara said. He emphasized that without proper governance structures and workforce capabilities, organizations face “significant risk exposure” that undermines confidence in AI initiatives.
The challenges extend beyond governance. Leaders cite lack of expertise, insufficient tools for managing AI agents, and inadequate guardrails as top obstacles to successful AI implementation.
Organizations are responding with their wallets: 86% plan to increase data management spending in 2026. Top investment priorities include improving data privacy and security (43%), enhancing data and AI governance (41%), and upskilling employees (39%).
The study also examined regional variations, vendor strategies, and specific AI use cases organizations are pursuing, offering a comprehensive snapshot of AI’s growing pains as it transitions from experimental technology to business-critical infrastructure.
OpenAI Chief Financial Officer Sarah Friar has dubbed 2026 the year of “practical adoption” for AI, even as new research reveals most corporate leaders have yet to see tangible results from their AI investments.
According to recent data from PwC, more than half of CEOs report they have experienced neither revenue growth nor cost savings from AI implementations despite pouring significant resources into the technology.
This disconnect between investment and returns is shaping the next phase of AI development. As organizations move beyond experimental pilot programs, the industry is reaching a critical inflection point where technical sophistication alone no longer guarantees success.
