AI Readiness is not a technology problem. It’s an operating-model problem and most enterprises are solving for the wrong one
Leadership ambition is outpacing governance, skills, and accountability. The gap isn’t access to models; it’s the discipline to convert them into outcomes.
The technical stack is the easy part: Our research with 200+ companies (1,600+ respondents) shows tech stack readiness at 2.25/5 and cybersecurity at 2.15/5 — the relatively strong end of the maturity curve. The weak end is where value actually gets created or lost: formal governance (1.06/5), operating model (1.04/5), GenAI policy (1.03/5), decision rights (1.15/5), and prompting skills (1.15/5).
What enterprises are mistaking for a solution
- Launching more pilots
- Writing policies that can’t be executed
- Buying an enterprise copilot
- Standing up a small, centralized AI team
None of these, on their own, produces readiness. AI readiness is a system -governance, delivery, platforms, talent, and adoption working together.
Our point of view
AI readiness does not come from more pilots, more tools, or isolated AI teams. It comes from building the operating model needed to govern, prioritize, deploy, and scale AI responsibly. Organizations need both strong governance and repeatable delivery capabilities to turn AI ambition into measurable enterprise value.
