METHOD
Where AI Transformation Fails
A task-level method for choosing who or what does the work
Know which tasks to give to people, which to AI, and which to both, before you spend.
For: Leaders running AI transformation
WHITE PAPERS
Measurement and evaluation, written down
The method behind the measures, in full: what we did, every parameter, and every limit, for the people who have to defend a decision about an AI system or a person.
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SIMULATION STUDY
The Error Bar You Buy Is the Error Bar You Get
Check that the error bar a vendor sells is the error bar you actually get.
For: AI evaluation and risk leaders
What is inside
- The problem. Judge severity varies across capability levels, and one average correction cannot represent a bias that changes sign. The parameterization is wrong, so no amount of data fixes it.
- This study. 15,000 simulated certification decisions over the measured severity curve from a judge probe we ran, with the method appendix, every parameter published, and an honest claim tier: model output at proposed prices, never measured field fact.
- The three-way contrast. No correction delivers a standard error of 1.16 against the 0.20 sold. The omnibus average leaves 1.34 points of leniency on weak low-band work and invents 0.73 to 1.44 points of harshness on strong work, the sign-flip an average cannot see. Capability-level correction delivers 0.21 against the 0.20 spec at zero marginal cost, with zero mean residual in every capability cell.
- The asymmetry a buyer should care about. A false pass admits a biased judge whose every later measure inherits the bias, a wave-scale contamination event. A false fail costs only the foregone judge-line saving. The risk sits orders of magnitude on the false-pass side, exactly where the average correction leaves 1.34 points of leniency.
- Traceability as the mechanism. How per-capability-level correction works, and why it makes every certification decision auditable after the fact, line by line.
METHOD
Job Graph Analysis
Find the constraint before you redesign the job
Start redesign at the bottleneck, then measure who or what should do each task there.
For: Leaders redesigning work around AI and job analysts
ARCHITECTURE
Calibrate the gate before the gate decides
How AIM puts every test item on one ruler before it judges a person, a model, or both
See which parts of AIM you can rely on today and which are still being built.
For: Evaluation teams and measurement scientists
METHOD
Selecting Talent for the AI Era
One estimate from many angles: how to combine what hiring science already knows with what AI can now measure
Combine proven hiring evidence with new AI measures into one estimate, and know how far to trust it.
For: Talent and HR leaders
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