Everyone quotes the statistic that 95% of GenAI pilots fail. Almost nobody reads the source. It measured attributable profit-and-loss impact inside a study window, not whether the feature worked, and the report was preliminary, its data unpublished, and criticised by academics who asked for release or retraction. The finding underneath survives anyway: pilots stall on integration and feedback, not on model capability. Gartner arrives at the same place, expecting over 40% of agentic AI projects to be cancelled by the end of 2027.<\/p>\n
The real gap is measurement. Conventional software tells you when it breaks. An AI feature returns something fluent, confident and wrong, with no exception and no alert, and it can degrade without anyone touching it. This paper supplies a Production Readiness Ladder of five gates: define what correct means, measure it automatically, contain what a bad output can do, instrument production, name an owner. It also maps the seven failures that actually occur to the controls that stop them. One deadline has already passed. EU transparency duties became enforceable on 2 August 2026. Read it before your next AI feature meets real users.<\/p>\n","protected":false},"excerpt":{"rendered":"
Your AI demo worked. That is not evidence it will ship. Everyone quotes the statistic that 95% of GenAI pilots fail. Almost nobody reads the source. It measured attributable profit-and-loss impact inside a study window, not whether the feature worked, and the report was preliminary, its data unpublished, and criticised by academics who asked for […]<\/p>\n","protected":false},"author":3,"featured_media":1820,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1816","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"yoast_head":"\n