peopleanalyst

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.

Methodology · measuring leadership

people analytics · organization measurement & data science · AI–human interaction

June 17, 2026

Measured Against Reality

We judge leaders by how they're perceived — 360s, reputation, room presence — not by the measurable state of the systems they're responsible for. The second is harder, slower, and the only one that should decide who leads.

A board spends an hour on the leader and never looks at the team. The 360 measures the leader's image — and rater idiosyncrasy, not the leader's behavior, is the largest thing it captures. The romance of leadership over-attributes outcomes to a single protagonist, rewarding good stories over good states. The alternative is harder: measure the state of the system a leader produces and its trajectory — variance shrinking, alignment tightening, resources flowing to value, forecasts coming true, the conditions improving — against a baseline, honest about attribution and the lag. Measured properly against reality, leadership matters enormously; measured by perception, we're scoring the myth.

Read · by Mike West →

Methodology · value of information

people analytics · organization measurement & data science · AI–human interaction

June 17, 2026

The Error Bar Is the Product

Analytics sells answers, but a point estimate hides the only thing a decision-maker actually needs: how much to trust it — and what tightening it would cost.

A slide shows a single confident effect; the data only said "about" so, on a sample thin enough to cover both transformative and noise — and the decision gets made on a number a good week of data from reversing. The answer was never the product; the error bar is. Decision theory settled it sixty years ago: information is worth only what it changes about the decision in front of you (value of information), so the question isn't "what's the answer" but "how much certainty does this decision deserve, and what would it cost to get it." Certainty is a dial with a posted price — and telling a customer when more analysis isn't worth buying is what proves it isn't a sales tactic.

Read · by Mike West →

Thesis · the adaptive magazine

AI–human interaction · people analytics · organization measurement & data science

June 17, 2026

A Poverty of Attention

The problem was never a shortage of content — it's that more content floods the scarce resource. Simon named it in 1971. The cure isn't a bigger library; it's one that spends your attention on the right next thing.

A reader who wants to actually do the thing finds abundance — hundreds of articles, a search box ranked by nothing she cares about — and leaves with twelve tabs and no first move. Availability stopped being the bottleneck; attention is. The feed already allocates attention at scale, but toward engagement, not advancement. A guide that recommends toward progress needs what a feed doesn't: a model of where the reader is, a connected corpus (a graph, not a heap), and a direction (learning has an order). Point that machinery at the reader's progress instead of the platform's, and the same library spends your attention instead of drowning it. More content was never the answer; the right next thing was.

Read · by Mike West →

Thesis · the economy of work

organization measurement & data science · people analytics · AI–human interaction

June 17, 2026

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.

Read · by Mike West →

Methodology · the registry of record

organization measurement & data science · people analytics · AI–human interaction

June 17, 2026

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

June 16, 2026

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

June 16, 2026

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

June 16, 2026

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 →

Transparency · AI × people analytics

AI–human interaction · people analytics · organization measurement & data science

June 5, 2026

Show Your Work

The contest over AI in organizations will be won on legibility, not accuracy — and a century of procedural-justice research already settled why.

Every public argument about AI in the workplace is conducted in the currency of accuracy. But the evidence says humans judge a decision by whether they can see how it was made — hardest of all when they can't. A black box fails that test by construction; it manufactures the information vacuum that suspicion fills. The decisive move isn't a more accurate model — it's a legible one, transparent by construction. And it's the moat a black-box incumbent can't retrofit.

Read · by Mike West →

Reliability · AI × measurement

AI–human interaction · people analytics · organization measurement & data science

DraftJune 3, 2026

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

June 1, 2026

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

May 22, 2026

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.

Read · by Mike West →

4S · Science

people analytics

May 15, 2026

Science: Why The Math of People is Different Than Machines (And Why Statistics Alone Won't Save You)

The Science S in the four-S synthesis — behavioral science as load-bearing infrastructure, not a sticker on the vendor deck.

People analytics fails predictably when statistics and systems ship without the layer that defines constructs, defends labels, and names what can go wrong when you intervene. This is the long case for the Science S: the machine fantasy, science vs statistics, failure modes, and a usable minimum that does not require Google's headcount.

Read · by Mike West →

4S · Statistics

people analytics

May 15, 2026

Statistics: Signal-from-Noise as Discipline (And Why Statistical Significance Isn't Meaning)

The Statistics S in the four-S synthesis — decision-grade reasoning under uncertainty, not the toolkit you already have because someone on the team knows Python.

Every people-analytics team claims statistics. Most are bluffing — running tests without owning the protocol, reading p-values as findings, rolling up to N=12 cohorts and calling them comparable. This is the long case for the Statistics S: inferential honesty, protocol discipline, decision-grade reasoning, the five failure modes that show up across the deployment record, and a usable minimum that does not require NIH-scale infrastructure.

Read · by Mike West →

4S · Systems

people analytics

May 15, 2026

Systems: People Analytics as a Software Stack (And Why the Reporting Team Will Never Catch Up)

The Systems S in the four-S synthesis — the analytical function as a software stack with six composing layers, not a reporting team that ships dashboards.

Most people-analytics functions ship dashboards from a reporting-team configuration and wonder why their work doesn't compound. The structural alternative is the function as a software stack — substrate, normalization, privacy, statistical, decision-support, surface — composed into capabilities rather than rebuilt per request. This is the long case for the Systems S, with the People Analytics Toolbox as the reference implementation.

Read · by Mike West →

4S · Strategy

people analytics

May 15, 2026

Strategy: HR Strategy as a Decision-Support Substrate (And Why the Twelve-Initiative People Strategy Will Never Land)

The Strategy S in the four-S synthesis — strategy as decision-support apparatus, not the twelve-initiative deck that gets retired at the end of the year.

Most HR strategy in 2026 is built as a deliverable rather than as decision-support substrate. The deck has twelve initiatives, four pillars, a north-star statement — and no named alternatives, no characterized uncertainty, no financial translation. This is the long case for the Strategy S: decision-shape clarity, Three A's lifecycle measurement, NAV financial translation, and the difference between the strategy that survives the budget cycle and the strategy that gets retired.

Read · by Mike West →

Adoption · Network-readiness

AI–human interaction · people analytics

DraftMay 13, 2026

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.

Read · by Mike West →

NAV · Methodology

people analytics · organization measurement & data science

May 10, 2026

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.

Read · by Mike West →

Case study · Compensation

organization measurement & data science · people analytics

May 9, 2026

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.

Read · by Mike West →

Architecture · Cross-portfolio

AI–human interaction · organization measurement & data science

May 9, 2026

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.

Read · by Mike West →

Methodology · principia

people analytics · organization measurement & data science

DraftApril 27, 2026

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 →