AIReturn

Know what your AI is actually delivering

See if your AI is paying off — and fix what isn't.

AIReturn measures what your teams and AI agents actually produce, the rework they waste, and the return on every AI dollar — across engineering, product, support and sales — so you know where to spend more and what to fix.

See how it works
Illustrative sample data.

Return on AI

Last 90 days

3.1×

return on AI spend

Return on AI trend

AI under management

$1.2M

Wasted on rework

$140K

Recoverable

$92K

How we calculate this

Every team, one view.

Engineering isn't the only team spending on AI. AIReturn measures the return across product, support, sales, and ops — on one shared picture, not five disconnected tools.

EngineeringProductSupportSalesOperations

Built for the people who answer for AI spend — CFOs and Chief AI Officers.

Companies are pouring money into AI. Almost no one can prove it's working.

Only28%

of AI use cases hit their ROI target

— Gartner

74% → 20%

expected AI to grow revenue vs. those who saw it

— Deloitte

78%

of finance leaders can't tie AI spend to outcomes

— CloudZero

How it works

Which areas do you want to onboard?

EngineeringSupportSales
GitHubConnected · read-only

01 Connect — an agent sets you up.

Setup is a short conversation, not a rollout project. Tell the agent which areas to onboard, drop in your org chart — a screenshot works — and connect your tools with read-only OAuth. From first connect to a live baseline in days, not quarters.

Cost vs rework, assembling

02 Measure — against your own history.

We measure what each team produces, the rework it took, and what the AI cost — each team compared only to its own baseline, never ranked against another team.

Give agents a domain glossary + examples

Impact: HighEffort: Low

Name an owner for your AI setup

03 Act — on teams and their AI.

See where AI pays off, where it's burning money, and get the exact next fix — then watch the trend confirm it worked.

The work model

Built on a model of how your company actually works.

AIReturn maps your teams, the people on them, and the tools each person works in — including their AI — and learns each team's work cycle: the path a pull request, ticket, or deal follows from started to done. That's how it knows:

  • What counts as an outcome for every team — merged PR, solved ticket, closed-won deal.
  • Where rework hides — the loops: changes requested, reopened, sent back a stage.
  • Which AI spend belongs to which team — every AI dollar matched to the work it touched.

Set up once during onboarding. Kept current automatically. See the full methodology

How the work model maps a team to its tools and its work cycleLeft: a team node (Payments) connects to person dots, which connect to tool chips (GitHub, Jira, AI — AI highlighted in green). Right: the team's pipeline from Draft to Merged, with an amber rework loop from Changes requested back to In review.PaymentsteamGitHubJiraAIDraftIn reviewChanges requested ↩ApprovedMerged3.1 rounds

What you get

01 · The number · for the board

Your return on AI, in one number.

Value delivered vs. AI spend, by team.

See a sample board report
Illustrative sample data.

Return on AI

Last 90 days

3.1×

return on AI spend

Return on AI trend

AI under management

$1.2M

Wasted on rework

$140K

Recoverable

$92K

02 · The map · for the AI leader

A map of who's efficient and who's burning.

Cost of AI per result against rework — green is efficient, red is expensive with lots of redone work.

Cost vs Rework

By team · Last 90 days
Change in AI cost vs rework, each team against its own baselineSix teams across engineering, support and sales plotted by change in AI cost per result (x axis, −30% to +70%) and change in rework (y axis, −20% to +45%), each measured against its own baseline. Lines at zero split the chart into four quadrants: paying off, friction, costly, and burning. Bubble size is monthly AI spend.Paying off — scaleFriction — fix the setupCostly — optimizeBurning — intervenePlatformTier 2Mid-marketPayments−30%−10%+10%+30%+50%+70%Δ AI cost per result vs own baseline →−20%0+15%+30%+45%Δ rework vs own baseline ↑

Each team is measured against its own history — never ranked against another team.

Paying offCostly / frictionOn budgetBurning

03 · The fixes · for team leads

A prioritized list of what to fix.

For the teams and AI agents causing rework, the top moves to fix it — ranked by impact.

2 fixes in flight · re-checked against baseline in 2 weeks
  1. 01

    Give your AI agents a domain glossary + examples

    Impact: HighEffort: Low

    For: Payments, Mid-market

  2. 02

    Name an owner for how your AI is set up

    Impact: HighEffort: Low
  3. 03

    Add clear examples to your AI's instructions

    Impact: MediumEffort: Low

04 · The decision · for the CFO

A budget decision, not another dashboard.

Each team's AI spend against the budget you set — over or under — with a call you can act on: raise, hold, or review. Defend every AI dollar with what the work actually shows.

Illustrative sample data.

AI budget

This month
AI spend versus budget by team, with a recommended call for each.
TeamAI spend/moBudgetvs budgetCall
Platform (Engineering)$92K$85K+8% overRaise
Payments (Engineering)$118K$95K+24% overReview
Tier 1 (Support)$64K$65Kon budgetHold
Mid-market (Sales)$71K$60K+18% overReview

Apply all → $31K/mo shifts toward teams where AI pays off.

Everyone measures the inputs. AIReturn measures whether it worked.

AI cost / FinOps tools

  • AI cost
  • Model quality
  • Compliance
  • Did the work get better?

AI observability / monitoring

  • AI cost
  • Model quality
  • Compliance
  • Did the work get better?

AI governance tools

  • AI cost
  • Model quality
  • Compliance
  • Did the work get better?

AIReturn

  • AI cost
  • Model quality
  • Compliance
  • Did the work get better?

…and the only one that measures it across every team — not just engineering.

Connects to the tools your teams already use.

Engineering

GitHubGitLabJiraLinear

Support

ZendeskIntercomosTicket

Sales

HubSpotPipedrive

Docs

NotionConfluence

AI

OpenAIAnthropicGitHub CopilotCursor
+ moreSee all integrations

What changes for you

Defend your AI budget to the board with a real number.

Cut the rework quietly burning your AI spend.

Double down on the teams where AI actually pays off.

  • Read-only connections — we never write to your systems
  • Work items and activity logs only — never your documents, code, or conversations
  • Team-level, never individual — no scoreboards, no team-vs-team rankings
SOC 2 — in progressHow we handle your data

Priced to your results, not your headcount.

Pricing depends on your AI spend — you pay a small fraction of what you put under management, so our price only grows when your AI investment does. No per-seat games.

Stop guessing whether your AI is worth it.

Get a clear return-on-AI picture in weeks.

Questions, answered