White Paper · 2026
The AI Maturity
The AI Maturity
Framework
Defining, governing, and assessing applied AI.
Most teams are focused on shipping AI, yet few are turning that into real value. What separates the organizations that do is discipline: defining the problem before they build, measuring against criteria set in advance, and running AI systems with the same engineering rigor as everything else in production.
This paper lays out how to build that discipline into practice, and helps you self-assess where you stand on your path to AI maturity.
What's inside
- The maturity gap. Why most AI programs stall in pilots, and what sets the teams that break through apart.
- A working delivery framework. A structured way to define what you're building and for whom, how to evaluate it against criteria fixed in advance, and how to run it in production.
- Production rigor. What it takes to contain a probabilistic system once it's live: security, reliability, and cost.
- Human oversight. The accountability layer that keeps a system trustworthy with real users.
- Your place on the curve. How to assess where you stand and what the next stage of maturity takes.
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