Gartner Inc., a business and technology insights company, said organizations are missing several risks tied to generative AI (GenAI) adoption, creating blind spots that could affect long-term plans.
CIOs are facing a fast-moving GenAI landscape, with constant changes in tools and techniques.
“GenAI technologies and techniques are evolving at an unprecedented pace, matched only by the surrounding hype, which makes it challenging for CIOs to navigate this dynamic landscape,” said Arun Chandrasekaran, analyst at Gartner.
Gartner said many organizations focus on immediate issues around business value, security, and data readiness, but may not notice risks that appear later. These include shadow AI, technical debt, skills erosion, data sovereignty concerns, interoperability gaps, and vendor lock-in. According to the company’s outlook, these risks may shape the divide between enterprises that scale AI responsibly and those that fall behind by 2030.
Shadow AI remains one of the most pressing risks. A Gartner survey of 302 cybersecurity leaders from March to May 2025 found that 69% of organizations suspect or have evidence that employees use prohibited public GenAI tools. Gartner expects that more than 40% of enterprises will encounter security or compliance issues tied to unauthorized AI use by 2030.
“To address these risks, CIOs should define clear enterprise-wide policies for AI tool usage, conduct regular audits for shadow AI activity and incorporate GenAI risk evaluation into their SaaS assessment processes,” Chandrasekaran said.
Gartner also noted that unmanaged GenAI technical debt can slow down projects and increase costs. The company predicts that by 2030, half of enterprises will encounter delays in AI upgrades or face rising maintenance expenses because of poor oversight of GenAI-generated assets.
Chandrasekaran said enterprises must manage the cost of reviewing or maintaining AI-generated code, content, and design.
“By establishing clear standards for reviewing and documenting AI-generated assets and tracking technical debt metrics in IT dashboards, enterprises can take proactive steps to prevent costly disruptions,” he said.
Data sovereignty is another emerging concern as governments introduce rules on technology and cross-border data flows. Gartner predicts that by 2028, 65% of governments will issue requirements linked to technological sovereignty. These rules may increase operating costs and slow down enterprise-level AI rollouts.
Gartner said CIOs should factor sovereignty requirements into AI strategies from the start. This includes working closely with legal and compliance teams and choosing vendors that support sovereignty expectations.
The firm also warned about skills erosion as teams rely too much on AI. When employees depend on AI for tasks that require human judgment or experience, organizations may lose institutional knowledge.
Chandrasekaran said companies should identify areas where human expertise must remain at the center and design AI systems that support, rather than replace, these skills.
Vendor lock-in is another risk as enterprises often work with a single AI provider for convenience. Gartner said this can limit flexibility, tie workflows to specific platforms, and weaken a company’s ability to negotiate contract terms.