AI ROI Calculator
Estimate how much time and money your business can save by automating repetitive tasks with AI. Adjust the sliders below — your ROI, monthly savings, and payback period update instantly.
Inputs
Your business numbers
Result
Your automation payoff
ROI
679%
Payback
4 days
AI Automation ROI Calculator
Automating a task with AI isn't just cheaper or more expensive than doing it manually — the comparison changes with volume. A process that barely breaks even at low volume can become dramatically more cost-effective once it scales, because automation cost grows much slower than headcount cost does. Growfiy's calculator helps you model that FTE-equivalent savings and see where the automation actually pays for itself.
Built for teams deciding whether to automate a specific repetitive process — support triage, data entry, content tagging, report generation — versus continuing it manually or hiring for it.
The FTE-Equivalent Math
Converting automated work into an FTE-equivalent number makes the savings comparable to a hiring decision — "this automation frees up roughly 1.5 employees' worth of time" is easier to weigh against a hiring plan than a raw hours-saved figure.
Multiply the FTE-equivalent by your fully-loaded average salary for that role to get an annualized savings figure, then compare it against the automation's build and ongoing maintenance cost for the same period.
Automation vs Hiring: When Each Wins
- High-volume, repetitive, well-defined tasks: automation usually wins — cost per unit stays low and the fixed build cost amortizes quickly.
- Low-volume or highly variable tasks: hiring or keeping the process manual is often more cost-effective, since automation's fixed build cost may not be justified by the volume.
- Tasks requiring judgment or exceptions: a hybrid approach — automate the routine cases, route exceptions to a person — often outperforms trying to automate everything.
- Rapidly scaling volume: automation's advantage compounds here, since manual cost scales roughly linearly with headcount while automation's marginal cost per unit stays flat or falls.
Frequently Asked Questions
What does 'FTE-equivalent' mean in an automation ROI calculation?
It's the number of full-time employees a given amount of automated work volume would have required to handle manually. If automating a process saves 80 hours a week, that's roughly two full-time employees' worth of work (at ~40 hours each), which gives you a headcount-comparable figure for the business case.
How do I compare automating a task versus hiring someone to do it?
Compare the fully-loaded annual cost of a hire (salary, benefits, overhead, management time) against the annual cost of building and maintaining the automation (tooling, API/subscription cost, initial build time, ongoing upkeep). Automation tends to win as volume grows, while hiring can be more flexible for tasks with high variability or judgment calls.
Why does automation ROI improve as volume increases?
Most automation has a largely fixed build and maintenance cost, while the per-unit cost of processing each additional item stays low — so the same automation that looks marginal at low volume can show strong ROI once volume scales, unlike manual work where cost grows roughly linearly with headcount.
What's a realistic break-even volume for a task automation project?
It depends on the build cost and the manual cost per unit of work, but a useful way to frame it: divide your total automation build-and-maintenance cost by the labor cost saved per unit processed to find the volume at which the automation pays for itself.
Should every repetitive task be automated with AI?
Not necessarily — tasks with low volume, high judgment requirements, or frequent edge cases often aren't worth the build and maintenance cost relative to the savings. High-volume, well-defined, repetitive tasks are usually the strongest automation candidates.
What ongoing costs should I include for an automated process, not just the build?
Include API or subscription costs that scale with volume, occasional maintenance when the underlying process changes, monitoring for errors or edge cases the automation doesn't handle well, and periodic review time to keep it aligned with the current workflow.