peopleanalyst

Magazine · long-form

principal-issues.

A working magazine for the principal-issues thesis, AI–human-interaction, and the methodology spine underneath PeopleAnalyst research and development. 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.

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.

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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.

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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.

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Methodology · principia

people analytics · organization measurement & data science

DraftApril 27, 2026

Why People Analytics Is 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 →