Artificial intelligence is becoming a significant source of operational disruption across enterprises, with AI-related incidents now accounting for more than one in 10 publicly reported technology failures. A new analysis from StackGen reveals that AI incidents have risen sharply over the past three years, jumping from 1.7% of all disclosed technology incidents in 2023 to 10.7% in 2026, representing an approximate six-fold increase. The findings underscore a critical gap: organizations are investing rapidly in AI adoption while their ability to manage the risks it creates continues to lag behind.

Why Are AI Incidents Increasing So Rapidly?

The surge in AI-related failures reflects a fundamental shift in how businesses operate. As AI becomes embedded in everyday business operations, leaders are no longer simply managing technology adoption. They are also being asked to manage new risks around governance, resilience, third-party dependencies, and autonomous AI systems. The research reinforces a growing body of evidence showing that employees are adopting AI faster than employers can govern it, and that many organizations remain focused on preparing employees to use today's AI tools rather than preparing for the new operational challenges AI creates.

The StackGen report notes that the 10.7% figure likely underestimates AI's wider impact because it only includes incidents involving AI providers and AI application companies. It does not capture AI-related failures occurring within organizations using AI as part of their own operations, meaning the true scope of AI-related disruption is probably much larger.

What New Categories of Risk Is AI Creating?

AI is introducing operational challenges that traditional incident response approaches were never designed to handle. Researchers identify three broad categories of emerging risk that organizations must now contend with:

• Third-Party Dependencies: Greater reliance on external AI providers means outages can rapidly affect multiple downstream organizations, creating cascading failures across entire ecosystems.
• AI-Generated Outputs: AI systems can produce information that appears normal but contains inaccurate or misleading content, creating quality and accuracy challenges that are difficult to detect.
• Autonomous AI Agents: AI systems capable of taking actions inside live environments can create unintended operational disruption without human intervention or oversight.

The impact of third-party failures is particularly severe. More than one in four incidents analyzed originated outside the affected organization, at suppliers or technology providers beyond the company's direct control. When incidents do occur at external providers, they take significantly longer to resolve. Third-party incidents took a median of 247 minutes to resolve, compared to just 96 minutes for internally caused configuration issues. The October 2025 AWS outage serves as a stark example of how disruption at a single provider can cascade across hundreds of organizations, affecting digital services, customer experience, and revenue.

How Should Organizations Prepare for AI-Related Risks?

Despite AI introducing new categories of operational risk, many organizations continue relying on familiar approaches such as restarting systems, rolling back deployments, or waiting for upstream providers to restore services. Unless operational practices evolve alongside AI adoption, incident volumes may continue increasing faster than organizations' ability to respond. To address this gap, organizations should treat AI governance as a strategic business capability rather than solely an IT responsibility. Key steps include:

• Embed AI Governance: Integrate AI governance within enterprise risk management frameworks rather than treating it as a separate technology concern.
• Review External Dependencies: Assess reliance on third-party AI and cloud providers as part of business continuity planning to understand cascade risks.
• Establish Autonomous AI Controls: Create clear governance for autonomous AI systems, including permissions, approvals, and mandatory human oversight mechanisms.
• Strengthen Monitoring: Develop AI-specific monitoring, incident response, and resilience planning tailored to the unique characteristics of AI failures.
• Train Leadership: Provide leaders and managers with practical training on AI governance and organizational risk management.
• Develop Clear Policies: Create policies covering both enterprise AI platforms and employee use of consumer AI tools outside approved systems.
• Measure Holistically: Measure AI success through resilience, trust, governance, and business outcomes, not productivity alone.

The research also highlights a broader workforce challenge. Two-thirds of employees spend up to six hours each week correcting poor-quality AI-generated work, demonstrating that AI can introduce new operational and quality challenges alongside productivity gains. This suggests that governance challenges extend beyond technology infrastructure to the way AI is adopted across the workforce.

What Does This Mean for AI Investment and ROI?

The findings carry important implications for how organizations measure AI success. Earlier research cited in the report found that seven in 10 organizations could reduce AI spending if returns fail to meet expectations, suggesting that resilience and reliability may become increasingly important measures of AI success. As organizations face pressure to demonstrate measurable returns from AI investment, the ability to deploy AI reliably and govern it effectively will likely become a competitive differentiator.

The next phase of AI adoption will be defined not simply by how quickly organizations innovate, but by how effectively they govern AI, build resilient systems, and equip their people to manage an increasingly AI-enabled world. For employers, the organizations most likely to succeed will be those that invest in governance, leadership, and resilience with the same urgency as they invest in the technology itself.