AI Agent ROI & TCO Calculator
The agent conversation is shifting from capability hype to cost and ROI. Enter your workflow parameters and get a conservative Ship / Skip / Needs Proof verdict — with a shareable result you can send to your team.
All calculations run client-side. No data is sent to a server. Assumptions are conservative by default — see the note below each result.
Value inputs
What the agent produces
Cost inputs
What the agent costs to run
Monthly net value of $2240 at $18.24 cost per successful run — 88% cheaper than human-only. Economics support shipping.
Monthly net value
+$2240
$2550 value − $310 cost
Cost / successful run
$18.24
vs. $150 human labor
Break-even
1
successful runs/month needed
Payback period
<1 mo
to cover fixed overhead
Conservative assumptions
- Failure rate applies to all runs — costs are incurred even when the agent produces no output.
- Human review time is charged at the same hourly rate as time saved.
- Setup & maintenance cost is treated as a monthly fixed overhead, not a one-time amortization.
- No compounding or scale effects — value and cost are linear with run count.
Your result — copy & share
✅ Ship — My AI agent saves $2240/month at $18.24/successful run. Break-even at 1 runs/month. Calculated with the Ship or Skip ROI Calculator: shiporskip.io/ai-agent-roi-calculator
About the verdict methodology
Ship— Monthly net value is positive, cost per successful run is meaningfully below human labor equivalent (<90%), and payback period is under 6 months. Economics support deploying this workflow.
Needs Proof — Positive net value but thin margins, long payback, or high failure rate. Run a controlled proof period (30+ days, 20+ representative tasks) before committing to this workflow at scale. This is the most common real-world outcome for early-stage agent deployments.
Skip — Costs exceed value at current parameters. The agent is burning more than it saves. Reduce token cost, failure rate, or review overhead — or increase the value of the task being automated — before deploying.
This calculator uses conservative, first-principles math. It does not account for compounding value, cross-run learning, or scale discounts. If the conservative model says Ship, you have a solid case. If it says Skip, optimize inputs first — don't rationalize your way into deployment.