{
  "objectName": "Incentive Alignment Checklist",
  "objectType": "LPM Knowledge Object",
  "version": "1.0",
  "lastReviewedDate": "2026-06-23",
  "ownerOfArtifact": "LPM / Lapemo",
  "lpmLayer": "Identity & Incentives",
  "purpose": "Identify whether goals, metrics, rewards, recognition, budgets, career signals, and AI adoption behaviors are aligned to the desired operating model.",
  "intendedAudience": [
    "Executives",
    "Transformation leaders",
    "HR leaders",
    "Finance leaders",
    "Product leaders",
    "Operations leaders",
    "Technology leaders",
    "AI program owners"
  ],
  "requiredFields": [
    {
      "name": "incentiveDomain",
      "type": "string",
      "required": true
    },
    {
      "name": "companyProfile",
      "type": "enum",
      "values": [
        "500_plus",
        "5000_plus",
        "10000_plus"
      ],
      "required": true
    },
    {
      "name": "desiredBehavior",
      "type": "string",
      "required": true
    },
    {
      "name": "currentIncentive",
      "type": "string",
      "required": true
    },
    {
      "name": "affectedPopulation",
      "type": "role_or_group[]",
      "required": true
    },
    {
      "name": "incentiveOwner",
      "type": "role",
      "required": true
    },
    {
      "name": "connectedOperatingLayer",
      "type": "enum",
      "values": [
        "identity_incentives",
        "decision_architecture",
        "communication_architecture",
        "information_ecology",
        "platform_structure",
        "governance_architecture",
        "ai_amplification"
      ],
      "required": true
    },
    {
      "name": "evidenceRequired",
      "type": "string[]",
      "required": true
    },
    {
      "name": "misalignmentSignal",
      "type": "string",
      "required": true
    },
    {
      "name": "aiAmplificationRisk",
      "type": "string",
      "required": true
    },
    {
      "name": "correctionAction",
      "type": "string",
      "required": true
    },
    {
      "name": "systemOfRecord",
      "type": "string",
      "required": true
    },
    {
      "name": "reviewCadence",
      "type": "enum",
      "values": [
        "monthly",
        "quarterly",
        "semiannual",
        "annual",
        "event_driven"
      ],
      "required": true
    },
    {
      "name": "version",
      "type": "string",
      "required": true
    }
  ],
  "optionalFields": [
    "compensationImpact",
    "promotionImpact",
    "recognitionMechanism",
    "budgetSignal",
    "okr",
    "kpi",
    "riskTier",
    "customerImpact",
    "regulatoryImpact",
    "aiUseCase",
    "changeLog"
  ],
  "scoringLogic": {
    "dimensions": [
      "behaviorClarity",
      "metricFit",
      "ownerFit",
      "crossFunctionalFit",
      "evidenceQuality",
      "aiAmplificationSafety"
    ],
    "scale": {
      "0": "unknown or actively conflicting",
      "1": "visible but informal or local",
      "2": "partially aligned with tradeoff risk",
      "3": "documented, owned, measured, and aligned",
      "4": "governed, reviewed, outcome-linked, and AI-ready"
    },
    "alignmentScore": "average(behaviorClarity, metricFit, ownerFit, crossFunctionalFit, evidenceQuality, aiAmplificationSafety)",
    "riskFlagRules": [
      "flag high_ai_incentive_risk when alignmentScore < 3 and aiAmplificationRisk is not empty",
      "flag adoption_theater when currentIncentive includes usage and evidenceRequired excludes outcome, quality, risk, or cycle time",
      "flag silo_optimization when affectedPopulation is one function and desiredBehavior depends on multiple functions",
      "flag missing_owner when incentiveOwner is empty",
      "flag stale_review when lastReviewedDate exceeds reviewCadence"
    ]
  },
  "profiles": {
    "500_plus": {
      "designPrinciple": "Reward scalable behavior before adding heavy governance.",
      "primaryRisk": "Heroics, founder preference, informal recognition, and speed-over-quality incentives.",
      "recommendedPattern": "Start with top desired behaviors, compare to what is actually rewarded, assign owners, and review monthly."
    },
    "5000_plus": {
      "designPrinciple": "Align functional incentives to shared operating outcomes.",
      "primaryRisk": "Silo scorecards, adoption theater, invisible data work, and late risk engagement.",
      "recommendedPattern": "Map scorecards to LPM layers, add shared metrics, and require incentive review during AI and transformation intake."
    },
    "10000_plus": {
      "designPrinciple": "Centralize incentive standards where risk and scale matter, while allowing local tuning where context matters.",
      "primaryRisk": "Local P&L optimization, platform duplication, governance bypass, and AI framed only as labor reduction.",
      "recommendedPattern": "Connect incentives to capital allocation, platform reuse, data quality, risk appetite, workforce evolution, and AI governance."
    }
  },
  "aiPrompts": [
    "Classify the following goals, metrics, rewards, and recognition patterns into LPM incentive domains and company profile.",
    "Identify where current incentives conflict with the desired operating behavior.",
    "Score each incentive area from 0 to 4 across behavior clarity, metric fit, owner fit, cross-functional fit, evidence quality, and AI amplification safety.",
    "Detect AI adoption theater where usage is rewarded without value, quality, risk, or cycle-time evidence.",
    "Recommend corrections, owners, evidence, and review cadence for each incentive misalignment."
  ],
  "validationRules": [
    {
      "rule": "Every incentive area must state the desired behavior.",
      "severity": "critical"
    },
    {
      "rule": "Every incentive area must capture the current incentive shaping behavior.",
      "severity": "critical"
    },
    {
      "rule": "Each incentive must have an accountable owner.",
      "severity": "critical"
    },
    {
      "rule": "Evidence required must include outcome, quality, risk, cycle time, or operating health where applicable.",
      "severity": "warning"
    },
    {
      "rule": "AI amplification risk must be assessed for AI, automation, data, customer, risk, or employee-performance incentives.",
      "severity": "critical"
    },
    {
      "rule": "Correction action must have owner and review cadence.",
      "severity": "warning"
    }
  ],
  "mappingRules": [
    {
      "sourceField": "incentiveDomain",
      "targetModule": "Identity & Incentives"
    },
    {
      "sourceField": "desiredBehavior",
      "targetModule": "Operating Model Target State"
    },
    {
      "sourceField": "incentiveOwner",
      "targetModule": "Ownership Map"
    },
    {
      "sourceField": "connectedOperatingLayer",
      "targetModule": "LPM Layer Graph"
    },
    {
      "sourceField": "evidenceRequired",
      "targetModule": "Information Ecology"
    },
    {
      "sourceField": "systemOfRecord",
      "targetModule": "Platform Structure"
    },
    {
      "sourceField": "aiAmplificationRisk",
      "targetModule": "AI Amplification"
    },
    {
      "sourceField": "correctionAction",
      "targetModule": "Governance Architecture"
    },
    {
      "sourceField": "alignmentScore",
      "targetModule": "Readiness, Risk, and Lineage Dashboards"
    }
  ],
  "renderTargets": [
    "docx",
    "pdf",
    "markdown",
    "json",
    "csv",
    "website_page",
    "lapemo_in_app_workflow"
  ],
  "changeLog": [
    {
      "version": "1.0",
      "date": "2026-06-23",
      "owner": "LPM / Lapemo",
      "change": "Initial reusable Incentive Alignment Checklist knowledge object."
    }
  ]
}