Cloud readiness for AI is the ability to adapt data, compute, controls, and economics together as workloads change. This matters now because model demand changes rapidly while legacy migration, data quality, and governance move on slower schedules.
Accenture reports that 59 percent of workloads in its research remain on-premises or in legacy environments. The report also says only 8 percent are dedicated to experimenting with advanced technology and proposes three cloud-maturity pathways. The mechanism is a composable foundation that exposes reliable data, portable services, workload observability, policy controls, and FinOps signals to AI applications and agents.
Technology leaders should make no-regret improvements that increase visibility and portability before committing every workload to one AI stack. The percentages are attributed to Accenture's research and do not independently establish the best cloud strategy for each organization. Watch whether modernization plans improve deployment speed, reliability, cost attribution, and the ability to change models without major rework.