Open Dataset: 3,023 Enterprise AI Implementations With Analysis

I analyzed 3,023 enterprise AI use cases to understand what’s actually being deployed vs. vendor claims.

Key findings:

Technology maturity:

• Copilots: 352 cases (production-ready)
• Multimodal: 288 cases (vision + voice + text)
• Reasoning models (e.g. o1/o3): 26 cases
• Agentic AI: 224 cases (growing)

Vendor landscape:

Google published 996 cases (33% of dataset), Microsoft 755 (25%). These reflect marketing budgets, not market share.

OpenAI published only 151 cases but appears in 500 implementations (3.3x multiplier through Azure).

Breakthrough applications:

• 4-hour bacterial diagnosis vs 5 days (Biofy)
• 60x faster code review (cubic)
• 200K gig workers filed taxes (ClearTax)

Limitations:

This shows what vendors publish, not:

• Success rates (failures aren’t documented)
• Total cost of ownership
• Pilot vs production ratios

My take: Reasoning models show capability breakthroughs but minimal adoption. Multimodal is becoming table stakes. Stop chasing hype, look for measurable production deployments.

Full analysis on Substack.
Dataset (open source) on GitHub.