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The article argues that AI projects fail not because of poor model performance but due to lack of trust and adoption, emphasizing that improving trust and boring infrastructure is more critical than model accuracy.
Argues that most AI projects fail because organizations treat LLMs as simple SaaS products rather than complex infrastructure requiring technical rigor.
This article highlights that many AI agent projects fail in production not because of model quality, but because teams launch without clearly defining what constitutes failure, missing critical edge cases that lead to confident incorrect outputs.
The article warns against using a code repository as an organization's memory for decisions and knowledge, advocating for a separate knowledge management system to avoid noise and buried information.
A RAND study of 2,400 AI projects found only 19.7% succeeded, with 77% of failures due to strategy and governance issues rather than technology. Companies with strong data foundations achieved 10.3x ROI versus 3.7x for weak data, and sustained executive sponsorship was critical to success.
A 4-hour course on using Claude Code to build products, automate workflows, and generate income from AI projects, created by Michele Torti.
This post shares a curated GitHub repository containing over 30 practical AI projects, covering domains from regression to generative AI, with many end-to-end examples, suitable for learners and developers.