There is a gap at the center of the enterprise AI market that almost no one is naming precisely.
On the one hand, AI capabilities are advancing at a rate without historical precedent in enterprise software. Every quarter brings models that are materially more capable than the last. The raw computational power available to any organization — through API access, cloud deployment, and open-source models that can be run on-premises — is staggering and continues to accelerate.
On the other side, the ability of organizations to safely absorb, deploy, and extract value from that capability is advancing much more slowly. Gartner projects that over 40% of enterprise agentic AI projects will be canceled by 2027. Not because the models failed. Not because the use cases were wrong. Because the control infrastructure — as explored in the economics of AI compute infrastructure — wasn’t in place. The capability existed and was impressive. The harnessing did not.
This is the harnessing gap — the distance between what AI can do and what enterprises can safely deploy —, and it is the defining structural tension of the current AI market phase. It is widening before it narrows because model capabilities are advancing faster than governance, memory, and orchestration infrastructure can keep up.
Join 90,000+ strategists. Business model analysis, AI maps, and earnings deep dives — free.
