Gartner IDs Gen AI Blind Spots
Generative AI technologies are rapidly evolving. And while companies stand to benefit from the advancement, they also run the risk of unintended consequences due to critical blind spots, according to Gartner VP analyst Arun Chandrasekaran.
“Gen AI 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,” Chandrasekaran said in statements shared from the company's IT Symposium/Xpo.
Gartner identified key areas that CIOs should urgently address to avoid issues in the future.
[Also: Gartner reveals top strategic AI predictions for 2026]
Shadow AI
Sixty-nine percent of organizations suspect that employees are using prohibited public generative AI, according to a Gartner survey of 302 cybersecurity leaders from March.
Unsanctioned AI tools can result in IP loss, data exposure and increased security risks. By 2030, more than 40% of enterprises will experience security or compliance incidents linked to unauthorized shadow AI, Gartner predicts.
“To address these risks, CIOs should define clear enterprise-wide policies for AI tool usage, conduct regular audits for shadow AI activity and incorporate Gen AI risk evaluation into their SaaS assessment processes,” said Chandrasekaran.
AI Technical Debt
By 2030, 50% of enterprises will face delayed AI upgrades along with increasing maintenance costs due to unmanaged generative technical debt, suggests Gartner.
Chandrasekaran said constant fixes to code, content and design can erode generative AI's ROI.
“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."
Data and AI Sovereignty
By 2028, 65% of governments across the globe will introduce some technological sovereignty requirements to protect from external regulatory interference and promote independence, Gartner predicts.
To overcome slowdowns due to constraints on cross-border data or model sharing, CIOs should build data sovereignty into their AI strategies from the start.
Skills Erosion
CIOs should be careful not to over-rely on AI, especially for functions that require human intuition.
“To prevent the gradual loss of enterprise memory and capability, organizations should identify where human judgment and craftsmanship are essential, designing AI solutions to complement, not replace, these skills,” said Chandrasekaran.
Ecosystem Interoperability
If CIOs put all AI efforts into a relationship with a single vendor, it can often impact their power in future negotiations.
“Prioritizing open standards, open APIs and modular architectures in AI stack design, help enterprises avoid vendor lock-ins,” said Chandrasekaran. “In addition, CIOs must make interoperability a standard in Gen AI pilots and assessments.”
