> For the complete documentation index, see [llms.txt](https://docs.harmony.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.harmony.io/dashboard-and-analytics/understanding-estimated-savings-metrics.md).

# Understanding Estimated Savings Metrics

Harmony's estimated savings metrics give you a clear, quantifiable picture of the value your AI-powered support is delivering. Rather than tracking only conversation volume or resolution rates, these metrics translate automated resolutions into two practical measures: time recovered for your team and money saved for your organization.

Savings data is available directly within Harmony's reporting and analytics, so you can monitor impact over time and share results with stakeholders without needing to build separate calculations.

***

### What estimated savings measure

When Harmony resolves a conversation without requiring a human agent to step in, that resolution has a real effect on your team's workload and your operational budget. Estimated savings metrics quantify that effect across two dimensions.

#### Time saved

Time saved represents the estimated number of seconds recovered from AI-resolved conversations. This figure is based on the average handling time that a human agent would have spent on a comparable conversation. As your AI resolution volume grows, the cumulative time saved gives you a concrete sense of capacity that has been returned to your team.

#### Cost savings

Cost savings translate those resolved conversations into estimated financial impact. Because support team costs vary significantly across organizations, Harmony provides three pricing scenarios rather than a single figure:

| Scenario         | What it reflects                                                                    |
| ---------------- | ----------------------------------------------------------------------------------- |
| **Conservative** | Lower-cost team structures, useful as a floor estimate                              |
| **Typical**      | A mid-range benchmark representing common support team costs                        |
| **Enterprise**   | Higher-cost team structures, useful for larger or specialized support organizations |

Presenting three scenarios lets you identify the estimate that best fits your context, or use the range to frame a minimum-to-maximum impact story for internal reporting.

***

### Where to find savings metrics

Estimated savings data appears within Harmony's reporting and analytics section. You can view it alongside your other key metrics such as resolution rate and conversation volume, giving you a unified view of both operational performance and business value.

***

### How to use savings data effectively

**Reporting to stakeholders** - Use the cost savings range to build a business case or demonstrate ROI. The conservative figure sets a defensible floor, while the enterprise figure illustrates upside potential.

**Tracking trends over time** - Monitor time saved on a weekly or monthly basis to understand whether your AI's impact is growing as you expand coverage, refine your knowledge base, or adjust routing rules.

**Sizing capacity decisions** - Time saved is a useful input when your team is planning headcount. If the AI is consistently recovering dozens or hundreds of agent-hours per month, that context helps frame hiring decisions or reallocation of existing capacity.

**Benchmarking improvements** - When you make changes to your AI configuration, compare savings metrics before and after to assess whether those changes are increasing automated resolution value.

***

### Notes on estimation methodology

Estimated savings are calculated based on AI-resolved conversations - specifically, conversations that were handled to resolution without escalation to a human agent. The time and cost figures use standardized assumptions about average handling time and agent cost rates, mapped to the three pricing tiers described above.

These are estimates intended to give you a directional, evidence-based view of impact. They are most useful when tracked consistently over time and compared against your own internal benchmarks where available.


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