Why AI Projects Fail in Production [pdf] (amethix.com)

Amethix’s briefing "Why AI Projects Fail in Production" distills why most enterprise AI pilots never deliver value and warns against replacing developers with off-the-shelf generative tools. The note identifies five recurring failure modes: unclear business objectives that leave teams modeling without measurable success criteria; poor data quality and governance (inconsistent schemas, missing values, siloed systems, unclear ownership); overengineering and stakeholder misalignment that favors complex researchy architectures over pragmatic solutions; fragile MLOps and deployment processes (manual steps, brittle pipelines, lack of monitoring and handoffs); and organizational resistance plus missing domain and engineering talent that blocks operational integration. Technically, the implications are stark: without solid data pipelines, governance and monitoring, even state‑of‑the‑art models will be unreliable or unsafe at scale; chasing novelty increases cost and fragility; and inadequate MLOps prevents prototypes from becoming production services. The brief also argues that mass layoffs of engineers in favor of AI-generated code will create undertrained developers, brittle, insecure systems, and a future scarcity of senior systems engineers — driving up remediation costs and regulatory risk. Bottom line: successful AI requires measurable objectives, disciplined data and MLOps practices, and investment in human expertise, not wholesale replacement of developers with models.