> For the complete documentation index, see [llms.txt](https://docs.hivel.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hivel.ai/ai-adoption/hivel-surge-ai-roi-dashboard-overview.md).

# Hivel SURGE: AI ROI Dashboard Overview

**Overview**

Hivel's SURGE dashboard transforms raw AI spend and usage data into actionable insights across five critical dimensions. By connecting AI tool investments—from licensing costs to token consumption—with measurable engineering outcomes, SURGE helps teams understand whether their AI tooling is delivering real value.

**What you'll see**

**AI spend and efficiency metrics** at a glance with six core summary cards

* **Spend breakdown** by task type, model, and team to identify where costs concentrate
* **Utilization patterns** showing which seats are active, idle, or over-utilized
* **Recovery opportunities** surfaced by waste category—idle licenses, model misselection, zombie sessions, and stale code
* **Gain** measured against AI adoption, comparing AI-assisted output to traditional workflows
* **Efficiency** benchmarked across spend and output to spot high-performers and at-risk areas

**Why teams use SURGE**

Most AI dashboards show adoption in isolation—how much your team is using Claude, Copilot, or Cursor. SURGE connects that usage to what actually matters: ROI. It answers the questions that drive budget decisions:

* Are we getting productivity gains from our AI spend
* Where is money being wasted?
* Which teams are adopting effectively, and which need support?
* How do individual contributors using AI compare to those who don't?

**The five pillars**

SURGE organizes insights into five pillars you can explore individually or together:

* **Spend (S)** — Total AI costs, cost per PR, and token breakdown by intent
* **Utilization (U)** — License activation, token consumption, and seat health
* **Recovery (R)** — Quantified waste across four categories and cost-saving opportunities
* **Gains (G)** — AI-assisted output trends and developer productivity comparisons
* **Efficiency (E)** — Team-level ROI benchmarking and output-per-dollar analysis

Click any pillar button at the top of the dashboard to focus on that dimension. Select multiple pillars to see the combined view.

*The subsequent sub-articles present more information on each of these.* 

**Filtering and exploration**

All metrics respect two global filters:

* **Duration** — View trends over any time period
* **Teams** — Drill into specific teams or view org-wide aggregates

Use the configurable charts to slice data by team, model, or token type. Hover over summary cards to see trend sparklines. Click into treemaps and seat breakdowns to surface the user or team behind each metric.

**Common use cases**

* **Understand AI ROI** — Start with the Spend and Gains pillars to see whether increased token consumption and seat usage are translating into measurable improvements in cycle time, throughput, and output velocity.
* **Optimize licensing** — Use the Utilization and Recovery pillars to identify idle seats, over-utilized developers, and which models are generating the most waste through misselection or abandoned code.
* **Drive adoption** — Compare AI-active developers to non-AI users across coding time, review time, rework rate, and PR volume to make a data-backed case for AI tooling to skeptical teams.
* **Benchmark teams** — Plot teams on the efficiency quadrant to celebrate high performers, identify teams that need enablement, and spot underutilized resources.

**Conclusion**

SURGE translates AI spend into business outcomes—helping you invest with confidence, optimize costs with precision, and measure impact across your entire engineering organization.