Why 88% of AI projects never leave the pilot, and what the other 12% do differently

Описание и применение

88 % AI‑проектов не переходят из пилота в производство из‑за отсутствия готовности к эксплуатации, управления данными и чёткой ответственности. Решение – проводить воркшопы по готовности данных, фиксировать измеримые бизнес‑цели, обрабатывать переход от POC к MVP как инженерный этап и устанавливать человеческую ответственность заранее.

Задача

AI pilots often fail to scale because they lack production readiness, data governance, clear ownership, and measurable success criteria.

Результат

Adopting a structured, data‑ready, ownership‑centric approach turns pilots into production‑ready systems, reducing the 88% failure rate to about 46% as recent industry data shows.

Рабочий процесс

• Conduct a one-page workshop to capture problem, success metric, data sources, and owners.
• Validate data readiness and governance gaps before quoting.
• Time‑box the POC to 8‑10 weeks with fixed price and measurable business outcomes.
• Treat POC‑to‑MVP as an engineering phase with scripted scenarios, deterministic gates, and shared schema.
• Define human accountability and enforce boundaries in architecture.
• Create runbooks for wrong answers and error reversal.
• Iterate until all gates pass before production launch.

Доказательства

IDC, in a study with Lenovo, found that 88% of AI proofs of concept never reach large‑scale deployment.

Источник

The safety project’s model performed great during testing, but 19 of every 20 alerts were false positives in realistic footage.

Источник