AI Readiness Before Project Commitments

AI projects often fail before build work starts because organisations commit to automation before assessing process maturity, data quality, governance, infrastructure, and user readiness. The article explains six recurring failure patterns: undocumented workflows, fragmented knowledge, missing KPI baselines, pilot scope expansion, late security and compliance involvement, and low user trust. It argues that formal readiness assessment is not needed for low-risk experiments, but becomes critical when production data, regulated processes, or material budgets are involved. Technology leaders should treat AI readiness as a pre-investment control, not a post-pilot review.