Most AI frameworks are a set of parallel domains you attend to simultaneously. Useful for organizing a conversation, useless for organizing a delivery. FRAME is five steps run in order, each producing an artifact the next one depends on.
Step 1
What is AI actually supposed to change here?
One business outcome. One named executive owner. One measurable baseline captured before anything is built. The baseline is the point, it's what makes measurement possible, what justifies the price of the build, and what we point at twelve months later.
Step 2
Can a machine read what we know?
Repository inventory. Content quality scoring. Ownership assignment. Retrieval testing against the real questions employees ask. This is the step every competitor skips and every failed pilot needed.
Step 3
Can you build one thing that actually works?
One agent, one calculation, one threshold, one escalation rule, one recurring artifact delivered into an existing weekly rhythm. Fixed scope, fixed price, ninety days.
Step 4
Is this a habit problem or a tooling problem?
Four measurement layers against the baseline from step F: input, throughput, output, outcome. Most partners report license counts and stop. The four-layer split tells you which remedy you actually need.
Step 5
How do we make the second agent cheap?
Reusable build standards, an MCP integration layer across Copilot, Claude and future approved platforms, an agent approval workflow, identity and permission model, data classification and security gates.
It tells a buyer what happens first and what happens next.
It makes scope boundaries obvious, which makes fixed-fee pricing possible.
It creates a natural expansion path instead of a one-time project.