Many enterprises are hitting roadblocks in deploying generative AI despite a surge in vendor investments. The primary challenge lies in fragmented and unstructured data, which is slowing down large-scale adoption. While technology providers continue to ramp up funding, organizations are cautious due to security risks, budget concerns, and a shortage of skilled AI talent.

“Enterprise data wasn’t up to the challenge,” Gartner Distinguished VP Analyst John-David Lovelock told CIO Dive earlier this year. Gartner projects that vendor spending will fuel a 76% increase in generative AI investments in 2025.

The pilot phase of AI revealed a significant mismatch between organizational ambitions and data maturity. Pluralsight’s March report, led by Chief Product and Technology Officer Chris McClellen, found that over 50% of companies lacked the readiness to meet AI’s technical and operational demands. Six months later, progress remains limited.

A Ponemon Institute survey showed that more than half of respondents still rank AI as a top priority. However, nearly one in three IT and security leaders cited budgetary constraints as a barrier.

“AI is mission-critical, but most organizations aren’t ready to support it,” said Shannon Bell, Chief Digital Officer at OpenText. “Without trusted, well-governed information, AI can’t deliver on its promise.”

The dual nature of AI poses both opportunities and risks for enterprises. Over 50% of organizations struggle to mitigate AI-related security and compliance risks, with 25% pointing to poor alignment between AI strategies and IT or security functions.

Despite this, AI is increasingly being integrated into cybersecurity strategies. Half of organizations already use AI in their security stack, and 39% report that generative AI enhances threat detection and alert analysis. Banking, in particular, is leveraging the technology—KPMG’s April survey of 200 executives found that one-third of banks are piloting generative AI-powered fraud detection and anomaly detection systems.