The rules of enterprise AI are changing as rising costs, governance demands, and sovereignty requirements reshape how and where organisations run AI, according to a new report from Cloudera.
The company’s The State of Enterprise AI 2026: The New Rules of Scale report surveyed 1 500 global IT leaders and decision-makers – including 525 across EMEA – focusing how enterprises are navigating the cost, control and flexibility required to sustain AI at scale.
In EMEA, 85% of respondents have already limited or redesigned AI use cases because of rising token consumption – and 82% of EMEA decision-makers say data residency, sovereignty, or jurisdiction-specific requirements influence where AI workloads are deployed, underscoring the need for greater flexibility and control as enterprises scale AI.
Among the report’s key findings in EMEA:
- Globally, 98% of organisations have been forced to pull back an AI deployment due to hallucinations, errors, or unexpected model behaviour in production.
- Three-quarters (75%) of EMEA IT leaders identify governance and compliance as their primary barrier to scaling AI, while 49% report that compliance expenses grew faster than anticipated.
- Unpredictable inference and storage inflation have driven 47% of EMEA enterprises to relocate AI workloads across environments, while 25% have paused or cancelled projects outright.
- While data residency requirements dictate AI placement for 82% of EMEA decision-makers, 58% admit that moving workloads between clouds or on-premises platforms requires costly application redesigns.
Francisco Mateo-Sidron, head of EMEA at Cloudera, says: “Scale is exposing a new reality for enterprise AI: what works today may not make economic or operational sense tomorrow. Costs change, models evolve, regulations shift, and the right place to run a workload changes with them. Leaders need an AI strategy built for that reality, one that gives them the freedom to adapt without giving up control of their data, governance, or spend.”
The findings also reveal a growing focus on the foundations required for long-term AI success.
If given the opportunity to restart their AI journey, 49% of respondents globally say they would strengthen governance from the beginning, 46% would invest in data quality earlier, and 45% would modernise infrastructure sooner. Another 45% would invest more in employee training.
This need for adaptability is making flexibility across environments increasingly important. Organisations in EMEA cite resilience (42%), sovereignty and residency requirements (41%), cost reduction (38%), and proximity to data (37%) among the leading reasons for operating AI workloads across multiple environments.