Gartner projects enterprise AI spending will exceed $200 billion in 2026. But here is the uncomfortable truth: most of that money is being spent on infrastructure and tooling that will never reach production. DGT's analysis of 40+ enterprise AI programs reveals a consistent pattern — 60-70% of AI budgets go to platform licensing and data engineering, while less than 15% goes to the change management and integration work that actually determines whether AI creates business value.
The Budget Allocation That Actually Works
Based on our most successful engagements, here is the allocation that consistently delivers ROI within 6 months:
- 30% on data readiness and governance (not just engineering — governance)
- 25% on use case identification and business process integration
- 20% on model development and deployment infrastructure
- 15% on change management, training, and adoption programs
- 10% on monitoring, optimization, and scaling frameworks
The enterprises getting the best returns from AI are spending less on technology and more on organizational readiness.
Three Questions for Your Next Budget Cycle
Before you approve your next AI investment, ask: What percentage of our current AI spend has reached production? What is the measurable business outcome we expect in the first 6 months? And who owns the adoption — not the technology, the adoption — of each AI initiative?
If those questions are hard to answer, your budget structure may be the problem, not your technology choices.