The named model
The Emerging Advantage Framework
How I turn emerging technology into operating advantage.
Most AI projects start with tools. Mine start with the money: where you’re leaking time, margin, or opportunity. They end with a system that proves it pays.
The 3 × 3
Three layers, nine moves
From ground truth (Map), to working systems (Make), to compounding advantage (Multiply).
Map
See clearly before you build.
- 01
Process
Map where the real work happens — workflows, handoffs, exceptions, judgment calls — and find the friction: the delays, rework, and bottlenecks that mark the highest-value openings.
- 02
Data
Establish what context and knowledge already exists and where it lives — the data, documents, systems, and institutional knowledge AI will need to draw on.
- 03
People
Assess readiness: the skills, capacity, and buy-in for change. AI succeeds or fails on whether people will actually adopt it.
Make
Turn opportunity into working systems.
- 04
Prioritize
Pick the highest-ROI use cases — where impact is high and effort is justified — and sequence them.
- 05
Design
Architect the solution: agents, automation, and human-in-the-loop — deciding where AI observes, reasons, drafts, and executes, and where humans still own the call.
- 06
Pilot
Ship a working proof fast enough to learn from but serious enough to test the real workflow — validated against reality, not a demo.
Multiply
Compound value across the org.
- 07
Adopt
Embed the system into the daily workflow and train the humans — the difference between a clever build and a capability the business actually runs on.
- 08
Measure
Instrument ROI and iterate: track business impact, adoption, and quality, and improve on the evidence.
- 09
Scale
Roll out across the org, govern it — guardrails, ownership, security — and surface the next opportunity, restarting the cycle at a higher altitude.
Map · Make · Multiply
Buy it a rung at a time: the Audit is Map · the build is Make · the retainer is Multiply.
The second named model
The Adoption Ladder
Three stages of AI readiness. Every library scenario is graded by the minimum stage it requires, and the free Score places your business on the same ladder — so the money you see is money you’re ready for.
Assist
Copilots and simple automations. Works on the data you have; a human stays in the loop.
Augment
Department brains. Needs organized knowledge and connected systems.
Autonomous
Agents end-to-end. Needs clean data, guardrails, and earned trust.
Our three stages map to Microsoft's Agentic AI Adoption Maturity Model (Assist ≈ L100–200 · Augment ≈ L300 · Autonomous ≈ L400–500) and Gartner's AI maturity levels.
The free Score places your business on this ladder; the library shows you the money at your stage.
Why it’s different
Diagnosis first. Evidence throughout.
Diagnosis before prescription
The tool is chosen last, after the leak is found and costed.
Build-vs-buy honesty
Three verdicts, and one of them is 'do nothing.'
Evidence over clever demos
Every number labeled: modelled, projected, or measured.
An operating rhythm, not deck-and-leave
Embedded until the system runs — and kept running.
Adoption designed in, not hoped for
People is a third of Map; Adopt opens Multiply. A system the team doesn’t use pays back nothing.
