Magazine · long-form
principal-issues.
Long-form on one idea worth defending: behavioral science — the discipline of measuring people and how they change — is the missing methodology for AI, not its casualty. Each essay takes one place the AI field is reinventing something psychometrics or diffusion science already settled, and shows the older answer. Measurement-first, source-anchored, claims defended rather than asserted — and an experiment in bi-directional adaptive learning.
Currently a small set of pieces, growing as the program does. Borrowing infrastructure (and editorial discipline) from Vela's magazine, oriented to a different topic domain.
Read as a set · 8 principles
The Principles — how we think about measuring people, and AI
The handful of essays that articulate our philosophy and how we build — gathered so they don't crowd the magazine. Each also stands alone as a piece of method you can use whether or not you ever work with us.
Read the principles →
Read as a set · 7 pieces · field craft
The ways a dashboard lies — and how to read it honestly
The recurring traps that turn a confident chart into a wrong decision — causation, composition, small samples, survivorship, multiple comparisons. Pure method, free to use. (These also appear in the feed below.)
Read field craft →
Read as a set · 4 pieces · what the tools miss
What the tools miss — performance, one setting at a time
A cited essay per context — engineering, the support floor, the hospital, the school — each leading with what performance actually means there, and what generic, off-the-shelf tools miss. (These also appear in the feed below.)
Read what the tools miss →
Corpus analysis · the four-S thesis
people analytics · organization measurement & data science · AI–human interaction
People Analytics Is Not Data Science for HR
I reduced two libraries to their underlying models — 25 books on people analytics, 25 on data science. They are not the same discipline, and the gap between them is the whole point.
"Just hire a data scientist and point them at the HRIS" assumes people analytics is data science with an HR dataset. It isn't. I reduced two libraries — 25 books on people analytics, 25 on data science and business intelligence — to the models they argue, and the two maps barely overlap: people analytics is a deep theory of people with a thin coat of analytics; data science is a deep theory of method with no subject underneath it. The four-S thesis falls out of the data — data science supplies Statistics and Systems, people analytics supplies behavioral Science and Strategy — and each library's blind spot is the other's core. The one thing both literatures agree on, independently, is that the binding constraint is organizational, not technical. People analytics isn't data science for HR; it's the merge, and the merge is the work.
Read · by Mike West →
Thesis · the economy of work
organization measurement & data science · people analytics · AI–human interaction
A Number, Not an Argument
Comparing two jobs is still an expert's opinion. Pantone turned “match this color” from an argument into a number — and the same move is sitting unclaimed in the economy of work.
Ask three comp pros whether one company's Senior Engineer equals another's Staff Engineer and you get three defended, uncheckable answers — leveling is an argument, every time, because work has no shared reference. Pantone fixed exactly this for color in 1963: own the reference everyone maps into, not the ink, and give it coordinates so difference becomes a computed number. Jobs can carry coordinates too — kept in separate spaces, honest enough to say “no confident match,” frozen in editions. The irony that opens the market: the survey houses copyright and enforce their structures, which fences them permanently out of the neutral position none of them can occupy — and universal math locates their jobs without ever storing their codes. We own the universal structure; licensed crosswalks stay client-side. Never pull an Adobe.
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Methodology · the registry of record
organization measurement & data science · people analytics · AI–human interaction
You Can't Compute With a PDF
The research you need exists — frozen in a table you can read but can't use. A catalog tells you what's known; a calculator hands you a number you can compute with.
The most-studied figure in a field can sit in a results table on page nineteen and still be unusable — you can read it, you can't drop it into a model, so the decision gets made on a half-remembered guess. The reference shelf was built for a reading animal, not a computing one. And the number you do retype is frozen, naked (stripped of its uncertainty), and biased by the file drawer. The fix isn't a better-organized shelf — it's to stop cataloging the literature and start computing it: synthesize it into source-graded, queryable priors that carry their uncertainty, trace to the page, and keep updating. That's the difference between a catalog and a calculator.
Read · by Mike West →
Thesis · the economy of work
people analytics · organization measurement & data science · AI–human interaction
The Join Key
Every decision about work joins on one field — the job — and almost no one has bothered to standardize it. That's not a gap. It's the most undervalued position in the data economy.
The job is the primary key of the economy of work: you compare pay, plan hiring, audit representation, and map careers by job — yet in nearly every dataset it's free text two companies fill in differently and no one reconciles. The market chased skills (the attribute) and skipped the job (the entity they attach to). Governments built the codes coarse-but-open; the survey houses built them rich-but-locked; nobody built the canonical architecture that's both. Get the job right — function × level, synthesized to map every source — and pay, skills, supply/demand, representation, and careers all compose onto it. The hilltop went unclaimed because the path up was labeled boring.
Read · by Mike West →
Methodology · selection science
people analytics · organization measurement & data science · AI–human interaction
Borrowed Validity
You adopted the predictor because it works on average, somewhere — and never checked whether it works here. The meta-analytic number is a prior, not a verdict.
An assessment scores candidates for three years and nobody checks whether it works — because it was bought on a real validity number, measured on other people at other companies. That's borrowed validity, and it's the most common measurement mistake in hiring. Validity generalization earned the borrowing; a 2022 reanalysis just reordered the famous predictor table from under everyone; range restriction hides your own validity from you. The fix isn't borrow-vs-validate-locally — it's the Bayesian both/and: anchor to the literature prior, update to your own posterior, prune what doesn't earn its place, and flag what's decaying.
Read · by Mike West →
Methodology · AI × people analytics
AI–human interaction · people analytics · organization measurement & data science
Themes Aren't Evidence
Every tool reads your open text for themes. None of them tells you whether your theory is right — and that's a confirmatory question with a century-old answer.
An engagement survey returns eleven thousand comments; the dashboard renders themes, sentiment, a word cloud — and goes silent on the only question that drives a budget: is it actually pay? Description tells you what people said; it can't tell you whether you're right. That gap is the line between exploratory and confirmatory analysis, drawn sixty years ago and skipped daily. The fix isn't a smarter word cloud — it's coding open text to the constructs the science already validated, then testing the stated theory against the published prior, so the text is finally allowed to disagree with you.
Read · by Mike West →
What the tools miss · R&D / engineering
organization measurement & data science · people analytics
What the tools miss in engineering
Engineering performance isn't a number — and the dashboards that pretend it is measure the cost of the work, not the work.
Commit counts, lines of code, velocity points: the engineering dashboard is seductive and wrong. Performance here is shipped, adopted capability under real constraints — and the four conditions that bind it look nothing like they do on a sales floor.
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What the tools miss · customer service
organization measurement & data science · people analytics
What the tools miss on the support floor
Your scorecard measures how fast you closed the ticket, not whether the customer was served — and the gap between those two is where service quality and your best agents quietly go.
Average handle time, service level, productivity rate: you can hit every number on the board and still lose customers and burn out the people who serve them. Service performance is quality under continuous emotional load — and the conditions that bind it are invisible to a speed dashboard.
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What the tools miss · hospital / clinical
organization measurement & data science · people analytics
What the tools miss in a hospital
Medicine is the field that looked at the standard team surveys, refused them, and built its own by the dozen — and then discovered that even 'hospital' is too coarse: the OR, the ward, and the clinic each need a different tool.
The hospital is the least deniable case for the whole thesis. Its sharpest room, the OR, shows what generic team surveys miss; the deeper lesson is that the setting keeps subdividing under you — the off-the-shelf battery was the wrong altitude from the start.
Read · by Mike West →
What the tools miss · school / district
organization measurement & data science · people analytics
What the tools miss in a school
The beloved, inspiring principal whose test scores never move isn't a paradox — it's the field's central finding, and the leadership survey is blind to the half of the job that actually moves learning.
Schools run on instructional leadership — a style almost no general leadership instrument measures. Inspiration is necessary and not sufficient; grade a principal on an off-the-shelf survey and you grade them on the visible half and stay silent on the half that moves student outcomes.
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Reliability · AI × measurement
AI–human interaction · people analytics · organization measurement & data science
The Reliability Problem
What a century of measuring people teaches us about trusting AI — and why "put a human in the loop" is the wrong lesson.
We asked four AI readers to model the same book and got four different answers (32 / 22 / 40 / 16). The reflex is to fix it with more machine. But how to trust a noisy rater was solved a century ago by the people who measured human judgment — and the uncomfortable finding is that single human raters were never reliable either. AI doesn't fail because it's AI; it fails the way we fail. The disease is single-rater; the cure — standardize, anchor to a criterion, train, multi-rater panels — is already written.
Read · by Mike West →
Compensation · Value equation
people analytics · organization measurement & data science
What Are You Paying For?
Pay is a reconciliation of three kinds of value — external, internal, and personal. Get it wrong and the bill arrives as an attraction, activation, and attrition problem you spend a year misdiagnosing.
Ten people, one job, ten different numbers. The reflex is to hunt for the real market value. There is no real market value. The editorial companion to the value-equation framework — why "we pay market" is usually gobbledegook, why the fairness people feel beats the fairness you can prove, and how an errant value equation quietly drains the roles your strategy depends on.
Read · by Mike West →
Field guide · AI failures
AI–human interaction · people analytics
The Failure Record
Seven enterprise AI deployments that didn't work — and the structural discipline that would have caught each one before it shipped.
The trade-press AI-failure piece runs as a parade of executive embarrassment. The deployment record argues something else: the failure modes are not idiosyncratic — they recur because the methodology gap recurs. Seven cases drawn from the public record, each rendered as the category of failure it instantiates and mapped to the structural correction the literature has already named.
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Adoption · Network-readiness
AI–human interaction · people analytics
Twelve Conditions for the Crown Fire
An executive primer on reading network readiness — three of twelve dimensions in the wildfire register that the rest of the framework inherits.
The twelve-factor AI-readiness self-assessment is not a pass/fail gate before deploying AI. It is a fuel-readiness map for where the rollout will catastrophically fail when it meets the early majority. Three dimensions reframed — management buy-in, change fatigue, workflow mapping — to teach the move; the remaining nine work the same way.
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CAMS
people analytics · organization measurement & data science
Why CAMS
The activation framework, the conjunction problem, and the eight items that make it executable without a PhD.
Capability, Alignment, Motivation, Support — four conditions for consistent above-expectation performance, every one of them required. The case for the conjunction, the eight-item survey, and the thresholds that turn the index into action at the team level.
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NAV · Methodology
people analytics · organization measurement & data science
Net Activated Value
The C-suite metric that puts human capital and dollar outcomes on the same axis — what it does, what it doesn't, and why most people analytics functions don't have one.
A CHRO walks into a capital-allocation meeting without a number. NAV is the number — the single indexable KPI tying human-capital state to dollar outcomes, segment by segment. The math is simple; the discipline is in what NAV deliberately doesn't try to be.
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Case study · Compensation
organization measurement & data science · people analytics
Before the Ratings
A compensation cycle in three movements — and why distribution and payout are not the independent cost drivers most models treat them as.
Senior leaders need to set incentive-plan parameters before performance ratings exist. The naïve framing — multiply headcount by rate, compare to budget — misses the structural non-linearity. This is the case for scenario modeling against a fixed-vs-variable trade-off, Monte Carlo simulation that surfaces the dispersion the deterministic model can't, and regression surrogate calculators with explicit interaction terms.
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Architecture · Cross-portfolio
AI–human interaction · organization measurement & data science
You Don't Pick the Architecture. You Catch It.
Architectural decisions for solo-operator portfolios are recognition moves, not menu-picks.
Most architectural-decision writing reads like menu-picking — multi-tenant vs. monorepo vs. shared-package vs. API service. That framing imports an evaluation done at team scale. The right move at solo cadence is to derive the architecture from objectives and constraints specific to your situation — and to recognize the pattern your products are already trying to converge on.
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Methodology · principia
people analytics · organization measurement & data science
When People Analytics Gets Stuck — And How to Unstick It
Rapid Collaborative Impact (RCI), the load-bearing set, and why most organizations cannot do people analytics — even though it has been demonstrably valuable for two decades.
Most organizations cannot do people analytics — not for lack of data, but for lack of methodology. The field has stayed stuck at a few elite organizations because most attempts copy Google's *outputs* without the underlying four-S capability that produced them. The principal-issues thesis names the load-bearing set, fast.
Read · by Mike West →