Scoopfeeds — Intelligent news, curated.
AI was supposed to prevent downtime. Instead, it’s creating new kinds of outages
business

AI was supposed to prevent downtime. Instead, it’s creating new kinds of outages

Fast Company · Jun 1, 2026, 11:10 AM · Also reported by 2 other sources

Enterprise AI promised executives something close to operational certainty: fewer outages, less human error, and systems capable of catching problems before customers ever noticed. But a new report from the software company Splunk on AI-related downtime suggests those promises are colliding with a messier reality. For businesses, downtime—unexpected interruptions to the software systems and applications that keep operations running—can trigger everything from lost sales to frozen logistics networks and customer backlash. For years, companies treated the problem as fundamentally solvable: Automate enough of the right processes, and human error could largely be engineered out. Acting on that logic, companies spent a median of $24.5 million annually on artificial intelligence systems designed to prevent downtime, per the report from Splunk, a unit of Cisco. But many now report that AI itself is becoming part of the outage problem, quietly introducing several new failure modes in the process. Half of surveyed organizations experienced downtime tied to incorrect AI automation or model drift. Nearly one-third blamed bugs introduced by embedding AI into production systems. Conducted with Oxford Economics across 2,000 executives of Global 2000 companies, the survey report estimates that unplanned downtime now costs businesses $600 billion annually, up 50% in just two years. Every minute of downtime costs roughly $15,000, and businesses lose an average of $300 million annually before anyone formally calls it a crisis. Splunk calls it the reliability paradox: The more aggressively companies deploy AI to eliminate operational risk, the more they find themselves managing a newer, less predictable category of it. “Organizations are deploying AI into mission-critical systems without clearly defined escalation paths,” says Kamal Hathi, senior VP and general manager of Splunk. “They lack monitoring tuned to detect model drift, and there’s no clear ownership when things go wro

Article preview — originally published by Fast Company. Full story at the source.
Read full story on Fast Company → More top stories

Also covered by

Aggregated and edited by the Scoop newsroom. We surface news from Fast Company alongside other reporting so you can compare coverage in one place. Editorial policy · Corrections · About Scoop