Predictive PlanningInstitute
The Book

Predictive Planning: How AI Made Strategy Continuous.

The book that defines predictive planning as a leadership discipline — not a software feature, not a forecasting upgrade, not a futurist narrative. A practitioner-grade framework for the executive layer of an AI-native operating environment.

Predictive Planning Institute
Predictive
Planning
How AI Made Strategy Continuous
Tim Woodring
Forthcoming, Spring 2027

Your competitors are buying predictive planning software. Nobody has written the strategy that goes with it.

Scenario planning was invented for a pre-AI world — slow-moving, expert-dependent, episodic. The tools have changed radically. The methodology has not caught up. Meanwhile, enterprise software vendors are seeding "predictive planning" as a vocabulary into every CFO and COO in the Fortune 500, with no strategic framework behind the term. This book defines predictive planning as a leadership discipline — a practitioner-grade framework executives can actually use in the operating environment they actually have.

Sample figures from the print interior
Figure 1A · Facing-Page Spread, Left

Sources & Ingestion

External and internal sources flow through adapters, a governance gate, and an extraction layer before they reach the math contract on the facing page.

Sources · the worldIngestion + governanceExternalMacro feedsFRED · BLS · BISMarketsFX · oil · equitiesPrediction marketsKalshi · PolymarketNews + regulatoryfilings · comment ltrsTalent + patentjob posts · filingsInternalERP / GLNetSuite · SAP · QBOCap tableCarta · PulleyWork surfacesM365 · Drive · SlackProject toolsLinear · GitHub · NotionCalendarsOutlook · GoogleSelf-reportweekly check-insAdaptersOAuth · API · scrapeRSS · webhook · streamGovernance gateper-channel opt-inper-account scopingaudit log every readExtractionsignal-class normalizeprovenance taggingno raw content storedInference routecustomer-keyed inferenceyour keys, your modelExit posturestructured signal recordshanded to the lattice as P-gradedclasses with ±-weighted edges→ continued on facing pageIngestion cadence (bounded)Daily · ingestion automated, signal queue refreshedContinuous · governance gate applied to every readAudit-grade · provenance + timestamp on every recordpage L
Figure 1B · Facing-Page Spread, Right

Lattice, Modules & Decisions

The math contract composes signals into interpretive surfaces and drives the four decision types that close the Loop.

← from ingestion (facing page)The lattice — one mathModules + agentDecisionsTHE LATTICEone math · one weightingMath contractP1 — P5 priority±1 — ±5 edge weightscoring → all surfacesSignal classes (received)Externalmacro · mkt · newsInternalOperationalKPIsNarrativetone · driftFinancialplan conf.CapitalwindowsPeopleagent-onlyInterpretationComposite indicatorsnamed, methodology pageInterpretive lensesindustry · risk · scenarioComposition canvassignals → typed edges →generated scenarioGovernanceconsent · audit · customer-keyedread-only, alwaysRead surfacesScan Briefranked, contextual newsScenariosprobability-scoredBack-Plan5-yr back-planAdvisory Councilhuman advisory tierInternal readsOperationalstrategy → KPI signalsFinancialforecast-as-scenarioCapitalwindows · refi · raisePeopleteam patterns (agent-only)Synthesisone read, many decisionssynthesis · personalizationrole + disposition tunedStakesizing matrixreversibility checkSteerconvergence reviewadjust / hold / killReforecastresource movecapex envelope shiftBoard briefquarterly scenariorunning postureSteer learnings → next Scanpage R
Figure 4A · Facing-Page Spread, Left

Raw Signals & Composites

The upstream half of the composition surface — raw signals fan into named composites and into the steward's own.

Composition surface · upstreamRaw signals fan into named composites — and into the steward's own.Each typed edge is a relationship the scoring engine respects. The steward node on the right is the artifact that crosses to the facing page.Raw signalsCompositesSteward-builtExternal · P4Capital TightnessIndex0.34 · easingExternal · P3Sector M&A vol.90-day rolling↑ 22%Internal · P4Outbound velocityCadence read24% of targetInternal · P5Exec memocoherence↓ 0.42Composite · P5Refi WindowIndex0.74 · openingmethodology page →Composite · P4Plan CoherenceIndex0.42 · watchfuldrift detected ↑Steward-built · P5Capital DecisionPosture (this org)composite of left ←+ inverse memo coherence+ dampened by outboundsaved as first-class signal→ continued on facing pageHow to read this pageNodes on the left are raw signals — typed, P-graded, ±-weighted, fed in by the lattice.Nodes in the middle are composite indicators — named, methodology-page-documented, recomputed daily.The node on the right is steward-built — the operator's own composite, treated as a first-class signal by the rest of the system.The math primitive is the same at every level — what changes is the author.Edge types on this pageleads-to · forward-positiveamplifies · multiplicativedampens · inverse-multiinverse · forward-negativecontingent · gated on thresholdgenerated · see facing pagepage L
Figure 4B · Facing-Page Spread, Right

Steward-Built → Generated Scenario

The synthesis half — the same scoring engine that powers ranking, probability, and personalization writes the scenario.

← from upstream (facing page)Composition surface · synthesisPress Generate. The same engine writes the scenario.No new math, no new model, no new vocabulary — the steward's composite is fed into the scoring engine and emerges as a named, scored, reversibility-tagged scenario.Steward-built (received)Generated · scenarioSteward-built · P5Capital DecisionPosture (this org)composite of upstream inputs+ inverse memo coherence+ dampened by outboundtreated as first-class signal→ feeds the scoring enginepress Generatesame engine, same mathGenerated · scenario"Refi First,Raise Second"Probability · scored0.71Time horizonQ4 — Q1Implied stakerefi senior, hold raiseReversibilitytwo-way doorWatch signals5 linkedWhat the scenario does next→ Scan BriefScenario summary rankedinto the morning brieffor affected roles.→ Stake reviewSurfaces at the nextmonthly review withconviction + size suggestion.→ Steer watch5 watch signals monitored;scenario convergence /collapse flagged on drift.Edge types · full referenceleads-toforward-positive couplinginverseforward-negative couplingamplifiesmultiplicativedampensinverse-multiplicativecontingentgated on thresholdgeneratedscenario synthesispage R
Contents

Fifteen passes through the discipline.

An introduction, thirteen chapters, and a conclusion. Readable straight through for the framework, or referenced by chapter as the discipline is installed.

  1. 00
    Introduction — The planning crisis

    Why the cadence of the world has outpaced the cadence of strategy.

  2. 01
    The end of the planning calendar

    Why the quarterly cycle no longer matches the cadence of the operating environment.

  3. 02
    What predictive planning is — and isn't

    The discipline as distinct from forecasting, scenario planning alone, and software.

  4. 03
    The prediction-market era

    What public probability infrastructure tells us about the new shape of foresight.

  5. 04
    Scan — the live signal layer

    Building the operating intake of system data, paid feeds, and human signal.

  6. 05
    Story — scenarios as decisions

    Three scenarios, named distinctly, with indicator sets that make them accountable.

  7. 06
    Stake — sizing the commitment

    Probe, Hedge, Build, Bet. Reversibility and conviction as the only two axes that matter.

  8. 07
    Steer — adjustment without restart

    Continuous correction as the alternative to periodic relitigation.

  9. 08
    AI as planning partner

    Where the model adds leverage, where it actively misleads, and how to keep both straight.

  10. 09
    Tacit knowledge and the operator

    Why the senior operator's chair is the most underweighted in the modern executive suite.

  11. 10
    Installing the discipline

    Ninety days from cold start to first stake decision in a single operating unit.

  12. 11
    The prepared organization

    What it looks like when the discipline becomes the operating system.

  13. 12
    The category and the categorist

    Naming the discipline as a precondition for teaching it — and a hazard if mistaken for the work itself.

  14. 13
    The next ten years

    What an AI-native operating environment asks of executive judgment between now and the late 2030s.

  15. 14
    Conclusion — A working discipline

    What the prepared organization actually feels like from the inside on a Tuesday morning.

The book also names a new vocation — the Predictionist — formalized in Appendix B. The Predictive Planning Institute is building the certification at /certification.

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