Rapid AI and platform adoption can outpace governance, architecture, and finance. Greg Keith’s Scaling Instability Curve describes how delivery velocity exceeds maturity, producing recurring failures, rising costs, and blurred ownership — and he says the first question for any change is: “Why are you doing this?”
Adoption is ahead of organizational change: McKinsey finds enterprise AI scaling rising, while an MIT study reports 95% of generative AI pilots show no measurable bottom-line impact.
Watch operational signals early: increased failure frequency, longer recovery times, and unexpected cloud economics are concrete indicators you’ve crossed the instability point.
Greg Keith's Scaling Instability Curve framework marks the point where delivery velocity outpaces architectural maturity. He argues organizations become unstable when governance fails to scale alongside technology, and that the first question before any change should be "Why are you doing this?" AI has crossed a decisive threshold in the enterprise. The question facing technology leaders then is whether every organization is ready for the speed AI makes possible.
The pattern, Keith argues, often begins with a reasonable response to an emerging problem. Keith has watched organizations encounter a new tool at a conference or through industry enthusiasm and quickly decide it will solve their problems. A more serious signal appears when failures become more frequent, or recovery takes progressively longer.
Cloud economics adds another layer of pressure. AI spending was already being managed by 63% of respondents. The technology itself is rarely the entire problem.
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