GoKawiilReport: Enterprise AI gains hinge on process redesign, not model upgrades
A new industry report argues that most enterprises deploying AI are not seeing revenue growth or operational transformation, despite rising global AI spending and rapidly advancing model capabilities. It identifies three requirements for what it calls the 'agentic shift': rebuilding data infrastructure for accessibility, adopting composable architectures instead of fixed tech stacks, and resolving questions of where AI systems run and who controls them.
GoKawiil's interpretation of the reporting above, not reported fact.
The findings suggest that simply buying better AI models does not guarantee returns—companies that redesign workflows before selecting technology appear to pull ahead, according to the report's authors. This framing positions data governance and 'AI sovereignty' as strategic differentiators, particularly for organizations operating across multiple clouds and jurisdictions with data residency rules.
- Most enterprises using AI are not yet seeing revenue growth or operational transformation from it
- Process redesign before model selection correlates with sustained AI returns, the report finds
- Sovereign, composable data architectures—not centralized data volume—are framed as key to scaling AI
Source: technologyreview.com — Mit Technology Review Insights, 2026-10-02
Published there as: “Redefining enterprise intelligence with autonomous AI”
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