Methodology
Honest, defensible, provable
The point of r.Potential is to remove guesswork from AI investment. That only works if the method underneath is worth trusting.
01
Evidence over adoption
Shipping an AI tool is not the same as creating value with it. Every configuration is measured against outcomes — value created, work changed, confidence earned — not seat counts or usage curves.
02
Independence over sponsorship
Recommendations are governed independently. No vendor can pay for placement, and nothing rises in a portfolio except on evidence. When the board asks why, the reasoning is defensible and auditable.
03
Humans as primary actors
Configurations are combinations of humans and agents — designed to automate work without dehumanizing workers. Workforce impact is stated up front, never discovered after the fact.
04
Living rankings
Business conditions change and deployments succeed or fail. Rankings re-score continuously as deployment data flows back in, so what you see is what is true now.
What feeds the graph
Three classes of data, one coherent picture
Proprietary employment data
Decades of workforce data — how work is actually structured, staffed, and paid across industries.
Public market signals
Deployment announcements, vendor performance, labor market movement, and financial outcomes across 7M+ companies.
Your company data
Org structure, workflows, and systems — placing your organization precisely within the global picture.
From data to decision
01
Observe
The graph watches how AI meets work across the market.
02
Configure
Candidate human + agent configurations are generated for your org.
03
Score
Each gets value, workforce impact, and a confidence rating.
04
Re-rank
Deployment evidence flows back and the portfolio re-scores.