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, from workflows and handoffs to exceptions and 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 the effort is justified, and sequence them.
- 05
Design
Architect the solution across agents, automation, and human review. Decide 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, and validate it against reality rather than a demo.
Multiply
Compound value across the org.
- 07
Adopt
Embed the system into the daily workflow and train the people who use it. That is 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 organization and govern it with clear guardrails, ownership and security. Then surface the next opportunity and start the cycle again from a stronger position.
Map · Make · Multiply
Buy it one step 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. I use it at the start of every Audit to place a business on the ladder, so the opportunities we pick are ones 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.
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 after that.
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.