Applied AI deployments are finally moving beyond the hype cycle. AI Weekly published a comprehensive library documenting 159 real-world AI implementations across 21 industries, with detailed information on tools, vendors, and measured outcomes for 77 of them. The database is freely accessible and searchable, requiring no signup.

The most telling entries are the six deployments that were halted or reversed. These failures provide concrete lessons that boardrooms and engineering teams need before committing budgets. They show where AI integration stumbled in practice, not theory.

What's working spans concrete applications. Companies are deploying AI for customer service automation, supply chain optimization, document processing, and predictive maintenance. The outcomes vary by industry and implementation depth, but the pattern is clear: narrow, well-defined tasks with clean data show measurable returns. Broader, more speculative applications struggle.

What got pulled back reveals the gap between pilot results and production reality. Some deployments encountered quality issues at scale. Others faced workforce resistance or discovered that the promised efficiency gains didn't materialize after accounting for data preparation and model maintenance costs. A few hit regulatory friction or discovered the technology couldn't handle edge cases in live environments.

Why this matters now sits in timing. The industry is transitioning from proof-of-concept theater to actual deployment. Boards are asking harder questions. Teams building or buying AI need reference points beyond vendor case studies. A searchable library of named companies, specific failures, and actual outcomes becomes a baseline for decision-making.

The library format matters too. It's not a ranked list of winners and losers. It's raw data on what got attempted, who attempted it, and what happened. That transparency helps teams calibrate expectations. It reduces the risk of repeating known failures or overselling capability internally.

This represents a maturation signal in applied AI. When industries publish their mistakes alongside successes, the field moves toward honest