Value AI assistant rollout ROI
Value an aggregate AI-assistant rollout from aligned joint causal-effect draws, preserving delivery/time/defect/incident dependence while enforcing identification, out-of-time, overlap, metric-integrity, effective-sample, quality-harm, NPV, ROI, and payback gates.
What it's for
Turns the AI Impact promise into an executive investment decision with causal-evidence refusal, correlated outcome economics, quality guardrails, licenses, enablement, NPV, ROI, and payback.
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.
| Field | Type | Role | Required |
|---|---|---|---|
| confidence_level | number ≥ 0.8, ≤ 0.99 | Your calibration | Optional |
| defect_cost | number ≥ 0 | Your calibration | Yes |
| delivery_value_per_unit | number ≥ 0 | Your calibration | Yes |
| discount_rate_per_period | number ≥ 0, ≤ 1 | Your calibration | Optional |
| effect_draws | array of objects (5 fields) ≥ 200 items | Evidence | Yes |
| evidence_gate | object | Evidence | Yes |
| horizon_periods | integer ≥ 1, ≤ 60 | Your calibration | Yes |
| incident_cost | number ≥ 0 | Your calibration | Yes |
| incident_quality_weight | number ≥ 0 | Your calibration | Optional |
| loaded_cost_per_engineering_hour | number ≥ 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_quality_harm_probability | number ≥ 0, ≤ 1 | Your calibration | Optional |
| maximum_quality_harm_units | number ≥ 0 | Your calibration | Optional |
| minimum_median_roi | number | Your calibration | Optional |
| minimum_positive_npv_probability | number ≥ 0, ≤ 1 | Your calibration | Optional |
| rollout_plan | array of objects (6 fields) ≥ 1 item | Evidence | Yes |
Each evidence_gate
record
| Field | Type | Required |
|---|---|---|
| design | string (non-empty) | Yes |
| effective_sample_size | number (≥ 0) | Yes |
| identification_passed | boolean | Yes |
| metric_integrity_passed | boolean | Yes |
| minimum_effective_sample_size | number (≥ 1) | Yes |
| out_of_time_validation_passed | boolean | Yes |
| overlap_passed | boolean | Yes |
{
"defect_cost": 500,
"delivery_value_per_unit": 200,
"effect_draws": [
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.2,
"engineering_hours_saved_per_active_seat": 1.1,
"id": "ai-effect-draw-0",
"incidents_delta_per_active_seat": 0
},
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.21682941969615793,
"engineering_hours_saved_per_active_seat": 1.0540302305868139,
"id": "ai-effect-draw-1",
"incidents_delta_per_active_seat": 0
},
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.21818594853651366,
"engineering_hours_saved_per_active_seat": 0.9583853163452858,
"id": "ai-effect-draw-2",
"incidents_delta_per_active_seat": 0
},
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.20282240016119735,
"engineering_hours_saved_per_active_seat": 0.9010007503399554,
"id": "ai-effect-draw-3",
"incidents_delta_per_active_seat": 0
},
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.18486395009384143,
"engineering_hours_saved_per_active_seat": 0.9346356379136388,
"id": "ai-effect-draw-4",
"incidents_delta_per_active_seat": 0
},
{
"defects_delta_per_active_seat": -0.02,
"delivery_units_delta_per_active_seat": 0.18082151450673725,
"engineering_hours_saved_per_active_seat": 1.0283662185463227,
"id": "ai-effect-draw-5",Truncated for display — the full payload is 1452 lines.
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.
{
"assumptions": [
"Each row is one aligned joint draw from an identified and validated aggregate causal effect model; outcome correlations are preserved within rows.",
"Delivery value, loaded labor cost, quality costs, licenses, enablement, adoption, and harm tolerance are governed locally rather than inferred from activity volume.",
"Hours saved are economically useful only if the organization can redeploy them; the value bridge is a scenario, not booked accounting revenue."
],
"configuration": {
"discount_rate_per_period": 0.01,
"maximum_quality_harm_probability": 0.1,
"maximum_quality_harm_units": 0,
"minimum_median_roi": 0,
"minimum_positive_npv_probability": 0.8
},
"decision": "ai_rollout_economically_supported",
"evidence_gate": {
"checks": {
"effective_sample_size_passed": true,
"identification_passed": true,
"metric_integrity_passed": true,
"out_of_time_validation_passed": true,
"overlap_passed": true
},
"design": "randomized-switchback",
"effective_sample_size": 120,
"failed_checks": []
},
"executive_summary": {
"expected_npv": 3840.7007,
"median_roi": 4.3126,
"npv_confidence_interval": [
3507.2113,
4175.2987
],
"npv_p10": 3520.2423,
"npv_p50": 3830.4827,
"npv_p90": 4161.6268,
"payback_period_p50": 0,
"probability_payback_within_horizon": 1,
"probability_positive_npv": 1,
"probability_quality_harm_exceeds_limit": 0
},
"expected_value_bridge": [
{
"component": "delivery_value",Truncated for display — the full payload is 108 lines.
How it works
Causal inference & experiment design — Separate what a change caused from what merely moved alongside it.
- 1 Refuse economic interpretation unless the upstream aggregate causal design clears identification, out-of-time validation, overlap, metric integrity, and effective-sample gates.
- 2 Preserve each aligned joint effect draw across delivery, engineering time, defects, and incidents instead of multiplying independent marginal percentiles.
- 3 Apply the governed seat/adoption schedule and discount delivery value, redeployable time value, quality losses, licenses, and enablement into an NPV and ROI distribution.
- 4 Block a financially attractive rollout when quality-harm probability exceeds tolerance, and report payback only for draws that actually cross zero within the horizon.
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.
Assumptions & guardrails
- Assignment or adoption timing, interference policy, overlap, attrition, and outcome availability match the estimand encoded by the method.
- Effect rows come from one identified and externally validated aggregate causal model with correlations preserved within draw index.
- Saved hours have a credible redeployment path and economic values are finance-owned scenarios rather than reconstructed revenue.
- A causal label is warranted only when design and diagnostic requirements pass; otherwise treat the result as descriptive or abstain.
- The result values an aggregate rollout; it does not infer AI authorship, attribute individual performance, or convert observational AI-user differences into causal ROI.
Minimum evidence
- effect_draws: at least 200 rows/items
- rollout_plan: at least 1 rows/items
- evidence_gate: required and organization-defined
- horizon_periods: required and organization-defined
- delivery_value_per_unit: required and organization-defined
- loaded_cost_per_engineering_hour: required and organization-defined
- defect_cost: required and organization-defined
- incident_cost: required and organization-defined
How to validate it
Preserve the assignment/adoption design and validate overlap, pre-period or placebo diagnostics, attrition, interference policy, and cluster-level uncertainty; never tune on the estimated effect.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- aligned joint causal-effect draws per active seat across all outcomes
- out-of-time identification, overlap, metric-integrity, and effective-sample diagnostics
- period rollout plan with seats and adoption fraction
- delivery value, redeployable-hour value, defect and incident losses
- license and enablement costs, quality-harm definition and tolerance
- NPV probability, ROI threshold, horizon, and discount rate
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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": "value an aggregate aiassistant rollout from" }
→ finds "value_ai_assistant_rollout_roi"
gitrevio_capability_describe
{ "capability_id": "value_ai_assistant_rollout_roi" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "value_ai_assistant_rollout_roi", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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