> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flowx.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ROI & value

> Risk-adjusted financial ROI, Value Outcome Units, and Monte Carlo sensitivity — board-grade numbers for AI investment.

ROI & value is the layer that turns telemetry into the kind of numbers a CFO will sign off on. Per-agent, per-project, risk-adjusted, with payback and NPV — and confidence ranges instead of single-point estimates.

***

## What's inside

<CardGroup cols={2}>
  <Card title="Value Outcome Units" icon="objects-column" href="./value-outcome-units">
    Group agents by business outcome to translate technical metrics into board-level language.
  </Card>

  <Card title="Financial ROI" icon="chart-line" href="./financial-roi">
    Per-agent and per-project ROI: monthly savings, payback, annual ROI %, 5-year NPV.
  </Card>

  <Card title="Compliance ROI" icon="clipboard-check" href="./compliance-roi">
    Quantify audit-labour savings and risk reduction from governance automation.
  </Card>

  <Card title="Sensitivity analysis" icon="sliders" href="./sensitivity-analysis">
    Monte Carlo simulations across ROI inputs — present a range, not a guess.
  </Card>
</CardGroup>

***

## Why this exists

LLM ops dashboards report cost and latency. They don't answer the question executives actually ask: **is this AI investment worth it?**

Observatory's ROI module exists because:

* Per-call cost is meaningless without a baseline ("what would humans cost?")
* A favourable point estimate hides the downside — what if adoption is half what we expected?
* Compliance work has a real cost that needs to live in the same calculation
* "We saved time" is rejected by finance unless it ties to a Value Outcome Unit with revenue or cost attached

***

## The ROI calculation

```mermaid theme={"system"}
flowchart LR
    Baselines["Agent baselines<br/>(hours, rate, volume)"] --> Savings[Labour savings]
    Telemetry["Telemetry<br/>(actual cost, volume)"] --> LLMCost[LLM cost]
    Savings --> Net[Net contribution]
    LLMCost --> Net
    Risk[Compliance risk] -->|adjust| Net
    Net --> Payback[Payback period]
    Net --> NPV[5-year NPV]
    Net --> Monte[Monte Carlo bands]
```

Each agent has a **baseline** — what manual effort it replaces (hours, rate, volume). Observatory combines the baseline with actual telemetry-derived cost to compute net contribution. Compliance risk modifies the contribution. The numbers roll up through Value Outcome Units to per-project ROI.

***

## Where to start

| Maturity                                | Start with                                                        |
| --------------------------------------- | ----------------------------------------------------------------- |
| You have agents but no baselines        | [Financial ROI](./financial-roi) — configure baselines per agent. |
| You have multiple agents per outcome    | [Value Outcome Units](./value-outcome-units) — group them.        |
| You're presenting to executives         | [Sensitivity analysis](./sensitivity-analysis) — show the range.  |
| You're including governance in the case | [Compliance ROI](./compliance-roi) — add audit savings.           |

***

## Related resources

<CardGroup cols={2}>
  <Card title="AI Registry" icon="server" href="../governance/ai-registry">
    The inventory layer ROI rolls up against.
  </Card>

  <Card title="Analytics" icon="chart-line" href="../observability/analytics">
    Cost telemetry the ROI numbers consume.
  </Card>
</CardGroup>