The model, explained

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The thesis

AI agents absorb the execution layer of knowledge work — assembly, reconciliation, drafting, analysis. What they do not absorb is judgment: knowing what should exist, what is good enough, what to kill, what a market signal means, and what must never break. The empirical finding (Hitzig et al. 2026) is that domain judgment, not execution skill, is the multiplier on agent labor — and the redesign evidence (BCG 2026; Bain) says organizations capture that multiplier by redesigning processes, not by buying tools.

The unit of analysis is therefore the process / value stream, not the department. One canvas per process, with every functional seat in the room.

Five judgment types

Every process consumes some mix of these. Each one consumed must have a named human carrier — a person, never a committee, never an agent.

TASTE · PROTOTYPER

What should exist

The unscored new bet, the thing no scenario model proposes. Strongest pre-product-market-fit.

TRADE-OFF · BUILDER

Good enough / architecture

Fund which bet at what level; what quality bar clears; which structure holds.

SUBTRACTION · SWEEPER

Kill / simplify

What to unship, stop, or collapse. Grows in importance as products mature.

MARKET · GROWER

What the signal means

Reading demand, attribution, and segments — deciding what the numbers are telling you.

RISK · MAINTAINER

What must never break

Cash floors, compliance, committed spend, integrity constraints. Peaks at maturity and sunset.

The canvas — nine blocks, one process

Filled in a half-day workshop with all functional seats present. Three colors, three layers: human judgment, agent execution, audit / control.

#BlockKey questionOutput
1TriggerWhat starts the process today — and what should, once a continuous agent substrate exists?Trigger definition
2Accountability mapWhich function certifies what? Accountability never moves to agents.Certification register
3Execution layerWhich activities are agent-delegable now / in 12 months?Delegation backlog
4Judgment layerWhich of the five judgment types does this process consume, and which seat carries each?Judgment coverage matrix
5Encoded judgmentWhich judgments become agent rules? Every rule needs an owner and a review trigger.Rules-of-engagement registry
6Option architectureWhat option sets should agents pre-generate? Where are the human choice points?Session design
7Control planeSix checks: reconciliation · forecast drift · assumption freshness · override log · option-set hygiene · semantic consistency. Who audits?Control panel + audit owner
8Staffing shapeConsolidators to redeploy; stewards to create; judgment seats to protect.Role transition plan
9Learning loopWhere do juniors form judgment once execution is absorbed?Apprenticeship design

JDEA — the actor grid that replaces RACI

RACI assumed every actor was human. Inside a redesigned process, every task gets four seats:

J

Judges

Human. Owns the decision and its consequences. Exactly one J per task — a task with no J is an orphaned decision (bug B3).

D

Directs

Human steering agents: specifies, decomposes, recovers stuck runs.

E

Executes

Agent, human, or human-directing-agent. Where J and E are the same human, that task is a delegation candidate.

A

Audits

Control-plane owner. Must be independent of J and D — segregation of duties survives the agentic shift.

The coverage map — lifecycle × judgment intensity

Each lifecycle stage requires a different judgment mix. A required cell with no named carrier is a coverage gap — a hiring or assignment conclusion, not an observation. Headcount follows judgment gaps, not workload.

JUDGMENT TYPEPRE-PMFGROWTHMATURESUNSET
TastePrototyper
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Trade-offBuilder
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SubtractionSweeper
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MarketGrower
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RiskMaintainer
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Nine bugs, six twists

The framework distinguishes bugs — failure modes to detect and fix, each wired to a control-plane signal — from twists — expected role mutations to track and support. The classic failure is treating twists as bugs (resisting role change) while missing the real bugs.

BugDetection signal
B1Fossilization — encoded rules outlive their validityStale last-reviewed dates
B2Automation bias — humans certify agent output unreadOverride log ≈ zero
B3Orphaned judgment — a consumed judgment has no human ownerJDEA task with empty J
B4Apprenticeship gap — juniors never form judgmentDeclining exception-detection
B5Accountability drift — "the agent did it"Certifications lapse
B6Option monoculture — all scenarios are variants of one planNo unscored wildcard in sessions
B7Consolidator vacuum — analysts cut before stewards staffedRule registry unmaintained
B8Trust asymmetry — functions reject each other's pre-validationsDuplicate analyses reappear
B9Constraint conflict — two functions' rules contradict silentlyAgents deadlock or oscillate
Twist — role mutation to expect
T1Controller → harness auditor — certifies the machinery that produces the plan
T2Analyst → agent steward — maintains the function's agent model, rules, data hygiene
T3Middle manager → player-coach — models agent direction, coaches recovery
T4Specialist cluster → integrated lead — one full-stack judgment seat
T5Seniority inversion — judgment fit + agent fluency outvalue execution mastery
T6New seat: rules-of-engagement referee — owns cross-functional agent interfaces

The implementation sequence

Order matters: control before delegation, stewards before redeployment.

  1. Pick one process with a sharp before/after — quarterly budgeting is ideal.
  2. Run the canvas with all functional seats present; produce the JDEA grid.
  3. Pay down workflow debt first — stabilize and document before delegating.
  4. Stand up the control plane and name the auditor before switching execution to agents.
  5. Redesign sessions around option-choice points; protect the wildcard slot.
  6. Re-staff by judgment coverage — stewards before redeploying consolidators.
  7. Instrument from day one — decision log + override log are the early-warning system.
  8. Set the review cadence — constraints quarterly, bug scan monthly, twists each planning cycle.

Metrics that matter (enterprise level, not micro-productivity): decision-cycle time · forecast accuracy drift · override-pattern health · judgment-coverage gaps closed · share of leader time at choice points vs. assembly review.