Applied AI deployments are moving from pilot phase to production, but the path forward remains uneven. A new AI Use-Case Library documents 159 real deployments across 21 industries, offering a rare look at what works and what doesn't.

The library catalogs actual named AI implementations with specific tools, vendors, and reported outcomes for 77 cases. Six deployments were halted or reversed, making those failures particularly instructive. Companies can search the database before committing budget or building internal pitches, removing guesswork from vendor evaluation.

The timing matters. Most AI case studies emphasize success. This library inverts that focus by treating reversals as data points. When Walmart scaled back certain AI hiring tools or retail chains paused recommendation engines due to poor results, those decisions reveal constraints that generic ROI claims obscure. The database captures what actually persisted versus what got killed quietly.

The deployments span customer service automation, supply chain optimization, document processing, and demand forecasting. Some saw measurable efficiency gains. Others faced adoption friction or failed to justify implementation costs. A few encountered unexpected side effects that warranted rollback.

What emerges is a map of the AI adoption frontier in 2024. The technology works best on narrow, well-defined tasks with clean data and clear metrics. Customer churn prediction, invoice processing, and equipment maintenance scheduling report consistent wins. Broader applications like AI-driven hiring or content moderation face harder scaling problems.

The reversals carry lessons. One deployment halted because the AI model drift required constant retraining that consumed more resources than the manual process it replaced. Another was pulled after stakeholder resistance emerged once workers understood the implications. A third simply underdelivered on the vendor's promised accuracy threshold.

The library's open access model removes a key friction point in enterprise AI adoption. Typically, companies evaluate vendors through vendor-provided case studies. This independent collection