AI Weekly released a comprehensive database tracking 159 real-world AI deployments across 21 industries, offering rare insight into which applications actually deliver results and which fail. The library documents tools, vendors, and outcomes for 77 deployments, making it freely available without registration.

The most revealing entries are six projects that companies halted or reversed. These failures matter more than successes because they expose what doesn't work and why organizations abandon AI initiatives after investment. Understanding reversals prevents repeating expensive mistakes across sectors.

The database serves as a reference file for teams planning AI adoption. Rather than relying on vendor claims or hype cycles, companies can examine named deployments in their industry, compare technical stacks, and assess realistic outcomes before committing budget or staff time. This ground-level data replaces speculation with precedent.

The timing reflects a shift in AI adoption. Early 2024 focused on large language models and generative AI potential. By late 2024, organizations moved beyond experimentation toward production systems. That transition demands evidence of what actually works in specific contexts, not broad capabilities.

The library organizes deployments by industry, allowing teams to find comparable use cases. A financial services company can see which AI tools other banks deployed successfully. A healthcare organization reviews medical AI implementations with documented results. This specificity beats generic best practices.

Failures deserve equal attention to successes. When companies pull back AI projects, reasons include poor data quality, integration complexity, regulatory friction, or insufficient ROI. These obstacles repeat across industries. A manufacturer that abandoned an AI quality-control system because of legacy equipment incompatibility helps another manufacturer avoid the same path.

The free access removes barriers to exploration. Teams can search the database before presenting business cases internally, strengthening proposals with real examples. Executives see actual deployments rather than consultant projections.

This shift from hype to evidence marks applied AI maturation. The industry