Gartner, Inc has highlighted a number of critical blind spots stemming from overlooked risks and unintended consequences of generative AI (GenAI) adoption.
While organizations often focus on immediate GenAI challenges such as business value, security, and data readiness, they may overlook critical blind spots because these are second- or third-order effects that are not always visible upfront.
Gartner predicts that these blind spots will create the dividing line between enterprises that scale AI safely and strategically and those that become locked in, outpaced, or disrupted from within.
To stay competitive and resilient, CIOs must tackle both visible challenges and hidden risks tied to GenAI adoption and prioritise addressing the following blind spots:
Explosion of shadow AI
A Gartner survey of 302 cybersecurity leaders conducted between March and May 2025 revealed that 69% of organizations suspect or have evidence that employees are using prohibited public GenAI tools. The rapid adoption of unsanctioned AI tools can lead to both visible and invisible impacts such as intellectual property loss, data exposure, and increased security risks.
Rising demand for data and AI sovereignty
Gartner predicts that by 2028, 65% of governments worldwide will introduce technological sovereignty requirements to improve independence and protect against extraterritorial regulatory interference.
Regulatory constraints on cross-border data or model sharing can slow enterprise-wide AI deployments, increase total cost of ownership (TCO), and result in suboptimal outcomes. To address these challenges, organizations need to build data sovereignty into their AI strategies from the start by engaging legal and compliance teams early and prioritising vendors that meet their data and AI sovereignty requirements.
Skills erosion
Over-reliance on AI can erode critical human expertise, judgement, and tacit knowledge. These are not easily codified or replaceable. This erosion occurs gradually and often goes unnoticed, so organizations may not recognise the risk until the enterprise struggles to function without AI or when AI fails in edge cases that require human intuition.
Ecosystem lock-in and interoperability
Enterprises eager to harness GenAI’s potential at scale often choose a single vendor for speed and simplicity. This deep dependency can impact an enterprise’s technical agility and future negotiating power on pricing, terms, or service levels. Many organizations underestimate how closely their data, models, or workflows become tied to vendor-specific APIs, data lakes, and platform tools.
More information about this area can be found at Gartner for AI Leaders
