Forecast organizational change load capacity

Forecast whether the organization's planned portfolio of migrations, launches, reorganizations, policy changes, and platform transitions exceeds aggregate operating capacity: select a saturating distributed-lag change-load model on pretest history, beat an autoregressive baseline on later periods, then simulate peak strain and limit-breach probability.

Forecasting & survival Forecast CTOs Tech CEOs IT managers Team leads Investors

What it's for

Lets a tech CEO, product owner, or investor see whether the company is attempting too many simultaneous changes—and which future periods carry the highest modeled operating-capacity risk.

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
half_life_candidates	array of number ≥ 2 items	Evidence	Optional
historical_periods	array of objects (5 fields) ≥ 60 items	Evidence	Yes
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_breach_probability	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_test_improvement	number ≥ -1, ≤ 1	Your calibration	Optional
planned_periods	array of objects (3 fields) ≥ 1 item	Evidence	Yes
saturation_scale_multipliers	array of number ≥ 2 items	Evidence	Optional
seed	integer	Numerical control	Optional
simulations	integer ≥ 500, ≤ 20000	Numerical control	Optional
strain_limit	number	Your calibration	Yes

Each `historical_periods` record

Field	Type	Required
change_load	number (≥ 0)	Yes
id	string (non-empty)	Yes
operating_strain	number	Yes
period	integer	Yes
split	one of "train", "test"	Yes

Example input

{
  "historical_periods": [
    {
      "change_load": 0,
      "id": "batch2-change-0",
      "operating_strain": 0.5,
      "period": 0,
      "split": "train"
    },
    {
      "change_load": 3,
      "id": "batch2-change-1",
      "operating_strain": 1.6441289955623646,
      "period": 1,
      "split": "train"
    },
    {
      "change_load": 6,
      "id": "batch2-change-2",
      "operating_strain": 2.7438652192628448,
      "period": 2,
      "split": "train"
    },
    {
      "change_load": 2,
      "id": "batch2-change-3",
      "operating_strain": 3.112494881145844,
      "period": 3,
      "split": "train"
    },
    {
      "change_load": 5,
      "id": "batch2-change-4",
      "operating_strain": 3.4072607743497247,
      "period": 4,
      "split": "train"
    },
    {
      "change_load": 1,
      "id": "batch2-change-5",
      "operating_strain": 3.4463562571748145,
      "period": 5,
      "split": "train"
    },

Truncated for display — the full payload is 469 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.

Example output

{
  "decision": "aggregate_change_load_capacity_risk_material",
  "executive_summary": {
    "maximum_accepted_breach_probability": 0.2,
    "peak_strain_median": 4.2084,
    "peak_strain_tail": 4.2261,
    "probability_strain_limit_breached": 1,
    "test_improvement_over_autoregressive_baseline": 0.8177
  },
  "guardrails": [
    "The fitted load-strain relationship is predictive and does not prove that a particular change caused strain.",
    "Change-load units, strain construction, cadence, and limit must be governed locally and revalidated after operating-model changes.",
    "Residual simulation conditions on the represented load range and does not cover omitted simultaneous shocks.",
    "Aggregate operating strain is not a named-person burnout, effort, competence, or employment score."
  ],
  "method": "nonlinear_distributed_lag_change_load_forecast",
  "model": {
    "candidate_models": 20,
    "intercept": 0.6695,
    "saturated_load_coefficient": 2.5857,
    "selected_change_load_half_life_periods": 4,
    "selected_saturation_scale": 6,
    "strain_persistence": 0.3119,
    "training_residual_sd": 0.0131
  },
  "period_forecast": [
    {
      "period": 60,
      "planned_change_load": 7,
      "probability_above_limit": 0,
      "strain_interval_lower": 3.9004,
      "strain_interval_upper": 3.9352,
      "strain_mean": 3.9153
    },
    {
      "period": 61,
      "planned_change_load": 7,
      "probability_above_limit": 0.34,
      "strain_interval_lower": 3.981,
      "strain_interval_upper": 4.0199,
      "strain_mean": 3.9964
    },
    {
      "period": 62,

Truncated for display — the full payload is 107 lines.

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1 Freeze an aggregate change-load unit, adverse operating-strain metric, cadence, strain limit, chronological training/test boundary, and complete historical/planned load ledger without using named-person health or behavior signals.
2 Transform change load through candidate exponential carryover half-lives and saturating response scales, select the nonlinear distributed-lag specification inside training history, and refit it with lagged strain on all training periods.
3 Evaluate the frozen load model on untouched later periods against an autoregressive strain baseline; treat failure to clear the minimum improvement as model abstention rather than evidence of spare capacity.
4 Recursively simulate the governed future change plan with training residuals, report peak-strain and period breach distributions, and gate the plan on the accountable owner's maximum accepted breach probability.

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

Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
Change-load weights have stable meaning and include launches, migrations, policy rollouts, reorgs, enablement, and overlapping initiatives that materially consume the same aggregate capacity.
The nonlinear carried-load relationship is sufficiently stable and represented by historical load ranges; major exogenous shocks and seasonal forces are absent or separately modeled.
Operating strain is an aggregate system outcome with a governed safe limit, not a proxy for individual emotion, effort, health, intent, or performance.
A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
The forecast is predictive; it does not prove that any particular change caused strain or that cancelling it creates the simulated benefit.
A low forecast breach probability is conditional on the submitted plan and represented environment, not a guarantee of organizational capacity.
Never translate aggregate strain into named-person burnout, attrition, competence, blame, hiring, firing, or security conclusions.

Minimum evidence

historical_periods: at least 60 rows/items
planned_periods: at least 1 rows/items
strain_limit: required and organization-defined

How to validate it

Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.

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

one complete cadence-aligned historical change-load and aggregate-strain panel with a frozen later test interval
future period load totals from the approved change portfolio, retaining overlapping and zero-load periods
change taxonomy and load weights, common capacity boundary, cadence, strain metric/version/direction, split, lag/saturation grids, validation improvement, strain limit, breach tolerance, horizon, and response owner

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": "forecast whether the organizations planned portfolio" }
  → finds "forecast_organizational_change_load_capacity"

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

gitrevio_capability_run
  { "capability_id": "forecast_organizational_change_load_capacity", "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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