Applied AI deployment has moved beyond pilots and promises. A new inventory of 159 real-world AI implementations across 21 industries reveals which projects actually delivered value and which companies abandoned their efforts after launch.
The AI Use-Case Library catalogs working deployments alongside six cases where companies halted or reversed AI systems after deployment. Those reversals carry outsized weight. They document failure modes that matter more than success stories for teams planning their own rollouts. Companies learn faster from projects that failed after launch than from those that never shipped.
The library spans 77 implementations with reported outcomes, naming specific tools, vendors, and results. This specificity distinguishes it from generic AI benchmarking. Teams can search by industry, outcome type, or vendor rather than reading abstract case studies. The free, no-signup format removes friction from the research phase.
The timing reflects a maturation cycle. Early AI adoption ran on hype and pilot budgets. The market now separates implementations that survived real-world testing from those that looked promising in controlled environments. Reversals happened for concrete reasons: poor data quality, integration complexity, ROI that never materialized, or user adoption failures. These entries function as warnings.
What works tends to cluster in specific categories. Automation of high-volume, repetitive tasks succeeds most reliably. Diagnostic support systems in healthcare, fraud detection in finance, and content moderation show consistent results when AI augments human decision-making rather than replacing it outright. Generative AI deployments show slower traction than expected, with many organizations still testing rather than scaling.
The reversed cases reveal common patterns. Organizations underestimated implementation costs and timeline. They deployed models without accounting for ongoing maintenance and retraining. Some launched AI systems without sufficient change management, leading to employee resistance. Others discovered their data infrastructure couldn't support the tool they selected.
This library matters because applied AI adoption now