What 3,000 AI Case Studies Actually Tell Us (And What They Don’t)
artificial
What 3,000 AI Case Studies Actually Tell Us (And What They Don’t)
I analyzed 3,023 enterprise AI use cases to understand what's actually being deployed vs. vendor claims. Google published 996 cases (33% of dataset), Microsoft 755 (25%). These reflect marketing budgets, not market share. OpenAI published only 151 ...
artificial

What 3,000 AI Case Studies Actually Tell Us (And What They Don’t)

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

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).

This shows what vendors publish, not:

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

Those looking to deploy AI should stop chasing hype, and instead look for measurable production deployments.

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

submitted by /u/abbas_ai
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