> ## 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

> [card] [Value Outcome Units](https://docs.flowx.ai/6.x/observatory/roi/value-outcome-units.md)
>
> Group agents by business outcome to translate technical metrics into board-level language.

> [card] [Financial ROI](https://docs.flowx.ai/6.x/observatory/roi/financial-roi.md)
>
> Per-agent and per-project ROI: monthly savings, payback, annual ROI %, 5-year NPV.

> [card] [Compliance ROI](https://docs.flowx.ai/6.x/observatory/roi/compliance-roi.md)
>
> Quantify audit-labour savings and risk reduction from governance automation.

> [card] [Sensitivity analysis](https://docs.flowx.ai/6.x/observatory/roi/sensitivity-analysis.md)
>
> Monte Carlo simulations across ROI inputs — present a range, not a guess.

***

## 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={"dark"}
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](https://docs.flowx.ai/6.x/observatory/roi/financial-roi.md) — configure baselines per agent. |
| You have multiple agents per outcome | [Value Outcome Units](https://docs.flowx.ai/6.x/observatory/roi/value-outcome-units.md) — group them. |
| You're presenting to executives | [Sensitivity analysis](https://docs.flowx.ai/6.x/observatory/roi/sensitivity-analysis.md) — show the range. |
| You're including governance in the case | [Compliance ROI](https://docs.flowx.ai/6.x/observatory/roi/compliance-roi.md) — add audit savings. |

***

## Related resources

> [card] [AI Registry](https://docs.flowx.ai/6.x/observatory/governance/ai-registry.md)
>
> The inventory layer ROI rolls up against.

> [card] [Analytics](https://docs.flowx.ai/6.x/observatory/observability/analytics.md)
>
> Cost telemetry the ROI numbers consume.


## Related topics

- [Value Outcome Units](https://docs.flowx.ai/6.x/observatory/roi/value-outcome-units.md)
- [Observatory](https://docs.flowx.ai/6.x/observatory/overview.md)
- [Financial ROI](https://docs.flowx.ai/6.x/observatory/roi/financial-roi.md)
- [Compliance ROI](https://docs.flowx.ai/6.x/observatory/roi/compliance-roi.md)
- [Sensitivity analysis](https://docs.flowx.ai/6.x/observatory/roi/sensitivity-analysis.md)


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