Calculate human AI decision system value

What it's for

Shows CEOs, CTOs and investors whether a human–AI operating model pays after the costs most AI business cases omit: expert review, delay, implementation, operation and downside.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "scenarios": [
    {
      "id": "base",
      "probability": 0.5
    },
    {
      "id": "growth",
      "probability": 0.3
    },
    {
      "id": "stress",
      "probability": 0.2
    }
  ],
  "systems": [
    {
      "ai_operating_cost_scenarios": [
        30,
        30,
        30
      ],
      "baseline_loss_per_decision_scenarios": [
        10,
        10,
        10
      ],
      "combined_loss_per_decision_scenarios": [
        4,
        4,
        4
      ],
      "complementarity_supported": true,
      "decision_volume_scenarios": [
        100,
        100,
        100
      ],
      "delay_cost_per_decision_scenarios": [
        0.5,
        0.5,
        0.5
      ],
      "fixed_cost_scenarios": [

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "configuration": {
    "maximum_cvar_downside": null,
    "minimum_expected_net_value": 0,
    "minimum_probability_positive_net_value": 0.7,
    "tail_probability": 0.2
  },
  "decision": "valuable_human_ai_systems_available",
  "guardrails": [
    "Only systems that passed a version-matched prospective complementarity audit are economically eligible; a cheaper system that lacks complementarity evidence is not promoted.",
    "All loss, cost and volume arrays must preserve coherent joint scenarios on one currency and horizon basis. Complete cost includes implementation, AI operation, human review and decision delay.",
    "Scenario value is not a causal estimate unless the baseline-to-combined loss contrast is prospectively identified. ROI never authorizes automated decisions or removal of required human accountability."
  ],
  "method": "coherent_scenario_human_ai_decision_system_value_v1",
  "summary": {
    "best_expected_net_value": 400,
    "best_system_id": "delivery-risk-triage",
    "system_count": 1,
    "valuable_system_count": 1
  },
  "system_diagnostics": [
    {
      "complementarity_supported": true,
      "decision": "human_ai_system_value_supported",
      "expected_gross_avoided_decision_loss": 600,
      "expected_human_review_cost": 100,
      "expected_net_value": 400,
      "expected_return_on_cost": 2,
      "expected_total_cost": 200,
      "failed_gates": [],
      "probability_positive_net_value": 1,
      "system_id": "delivery-risk-triage",
      "tail_cvar_downside": 0
    }
  ],
  "truncation": {
    "systems_omitted": 0
  }
}

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Admit only a version-matched system whose prospective complementarity audit cleared; align decision volume, standalone-baseline loss and combined-policy loss in coherent finance-owned scenarios.
2. 2 Within every scenario calculate gross avoided decision loss, human review cost, implementation and AI operating cost, delay cost, total cost and net value without mixing marginal futures.
3. 3 Aggregate expected value, return on cost, probability of positive value and downside CVaR; recommend only when complementarity, expected-value, probability and optional tail gates all clear.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "calculate the complete economic value of" }
  → finds "calculate_human_ai_decision_system_value"

gitrevio_capability_describe
  { "capability_id": "calculate_human_ai_decision_system_value" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_human_ai_decision_system_value", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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