AI Weekly released a comprehensive inventory of 159 real-world AI deployments across 21 industries, cataloging which projects succeed, which fail, and why the distinction matters for organizations planning their own AI strategies.
The library documents actual implementations with named companies, specific tools, vendors, and reported outcomes for 77 deployments. Six projects were halted or reversed, and these failures form the most instructive entries. The dataset spans industries from healthcare to manufacturing, providing practitioners with concrete data rather than vendor marketing claims.
The timing reflects a shift in AI conversation. Early 2024 focused on capability benchmarks and model releases. Now companies face pressure to show actual returns on generative AI investments made in 2023. Budget committees demand use-case justification. Some organizations are pulling back projects that looked promising in planning stages but underperformed in practice. Others are scaling what works.
The library's search-first design lets teams check existing deployments before committing resources to similar problems. This prevents redundant exploration and reveals which vendors and tools appear most frequently in successful implementations. It also highlights the specific tools chosen for different tasks, not just broad categories like "machine learning" or "large language models."
The six halted projects carry particular weight. Studying reversals exposes common failure patterns: scope creep, data quality issues, integration complexity, poor change management, or misaligned ROI expectations. These cases show the gap between theoretical AI applications and real operational constraints.
Publishing this without signup barriers matters. Information asymmetry has favored vendors and consultants who control use-case narratives. Open access to this library shifts power back to practitioners making decisions with incomplete information.
The release reflects market maturation. Applied AI moved from "what's possible" to "what pays for itself." Organizations now benchmark against peers facing similar problems. The precedent file becomes a practical tool for due diligence before deployment, not