AI pilots often look successful because they operate in controlled environments with limited users, curated data, and close technical support.
Production changes the conditions.
Real-world AI systems must handle changing data, unreliable integrations, security controls, governance requirements, unpredictable user behavior, monitoring, escalation paths, and rising infrastructure or model costs.
For CTOs and CAIOs, production readiness means looking beyond model accuracy. Teams also need strong data pipelines, observability, clear ownership, evaluation frameworks, permission controls, operational processes, and measurable business outcomes.
A successful pilot proves that an idea can work.
A production-ready AI system proves that the organization can depend on it.
Read the full article:
https://famro-llc.com/blogs/why-ai-pilots-fail-when-they-reach-production.html
