Insight
Why Most Enterprises Are Not Ready for AI at Scale
Enterprise AI momentum is high, but most operating environments are not yet built for sustained, production-scale AI workloads.
February 15, 2026
By Danny McGinniss
The Readiness Gap Is Operational, Not Conceptual
Many organizations have aligned on AI ambition, but core operating models are still tuned for traditional enterprise workloads.
Infrastructure Baselines Often Lag AI Demands
- Compute planning cycles are too slow for rapidly changing model requirements.
- Power and cooling assumptions were built for legacy utilization profiles.
- Data movement patterns are not optimized for AI-intensive pipelines.
Governance and Decision Velocity Need to Improve
Leadership teams need clearer cross-functional governance between technology, finance, operations, and risk.
What Executive Teams Should Prioritize Now
- Establish enterprise AI readiness criteria across infrastructure, talent, and controls.
- Build scenario-based capacity planning tied to business priorities.
- Define operating guardrails before scaling pilots into production.
Closing Perspective
Readiness for AI at scale is not a single technology decision. It is a coordinated enterprise operating decision.