Automation Hours Calculator

How many hours can you actually buy back?

Automation pays in hours, not stars. Add up the minutes per task, multiply by frequency, convert to dollars at your real hourly cost. The math tends to be embarrassing.

2,080
Hours per FTE per year
71%
Of AI users report productivity gains
$37.64
Employer cost per hour worked
Scoped
Engagement, sized to your operation

Sources: BLS OEWS (May 2025; 2,080-hour full-time year); Federal Reserve Banks, 2026 Report on Employer Firms (March 2026; small employer firms that use AI, 2025 Small Business Credit Survey); BLS Employer Costs for Employee Compensation (June 2026; private workplaces with 1 to 49 workers).

Annual Hours Saved by AI Automation
0hrs / year
$0 saved · 0 FTE equivalent · net of platform cost
0
Tasks Modeled
0 hrs
Weekly Hours Saved
... days
Platform Payback
$35

Your true labor cost per hour. Wages, taxes, benefits, overhead. BLS puts the mean receptionist wage at $18.97 an hour (May 2025) and total employer cost for private-industry office and administrative support staff at $36.73 per hour worked (June 2026).

BLS, 1 to 49 workers: $37.64 / hour
Default

Select an industry, then click the button to populate 6 common automatable tasks tuned to that vertical.

Repeatable Tasks (up to 8)
Each row is one process. The math runs live on every keystroke.
Tip: click the task name field for suggested phrasing · up to 8 rows
Total Tasks Modeled
0
Distinct repeatable processes in your stack right now.
Weekly Hours Saved
0
Hours of human work AI absorbs per week, across all tasks.
Annual Dollars Saved
$0
Annual hours saved x your fully-loaded hourly cost.
FTE Equivalent
0.00
Annual hours saved / 2,080. Translates AI to headcount.
Time Savings Curve (biggest time-eaters)
Annual hours saved Total task hours
Add at least one task above to see the time savings breakdown.
Sensitivity: which assumption swings the number?
Modeled against your current inputs
Operator Playbook: what to automate first, what to never automate
1 Start with what you do daily, not weekly. The compound math comes from frequency. A 4-minute task done 5 times a day eats more hours per year than a 40-minute task done once a week. Frequency wins.
2 Automate the message templating, not the message strategy. AI is excellent at drafting, scheduling, and triaging. It is dangerous at deciding "do we offer this customer a refund." Keep the judgment calls human.
3 Phone answering, intake, and confirmations are the highest ROI starting points. They run during business hours, after hours, and weekends. Coverage gaps disappear overnight when an AI voice agent picks up.
4 Measure in hours back, not "AI conversations." Vanity metrics like calls handled per day do not prove ROI. The real test is whether a human stopped doing that task. If a human is still backstopping every output, automation has not landed yet.
5 NEVER automate final-stage refunds, hiring decisions, legal correspondence, or medical guidance. These are jobs for licensed humans with accountability. AI drafts. Humans sign.

What happens next

You just counted the hours. Now meet the employee that gives them back.

Nirvani installs AI employees that answer every call, text back every missed one and book the work straight into your calendar. Everything below is free and live today.

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Every input and result you set above, sent to your inbox with a reference number, plus one confirmation text. No drip sequence: your run lands with a principal, not in a funnel.

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How this calculator works

What formula is this using under the hood?
For each task: weekly_hours = (minutes_per_task x tasks_per_week) / 60, then annual_hours = weekly_hours x 52, then annual_hours_saved = annual_hours x (automation_percent / 100), and finally annual_dollars_saved = annual_hours_saved x hourly_cost. Totals sum across all tasks. FTE equivalent is annual hours saved divided by 2,080 (one full-time human-year).
Why the 90% default automation coverage per task?
For well-scoped repeatable workflows (intake calls, confirmations, status updates, FAQ answering, triage), 90% is a modeling assumption, not a measured result. The remaining 10% stands in for true edge cases that route to a human. Tasks involving judgment, escalation, or unhappy customers should be modeled lower, around 40 to 60%. Drag the per-task slider to be conservative.
Should I use a low or high hourly cost?
Use the fully-loaded number. That means wage plus benefits and payroll taxes, plus overhead (workspace, tools, training). At private workplaces with 1 to 49 workers, BLS puts benefits, including legally required ones such as payroll taxes, at 25.9% of total compensation (June 2026), about 35% on top of wages. At that rate a $20 per hour wage costs about $27 before overhead. If a real human stops doing the task, the ROI math should reflect what that human actually costs your business per hour, not their take-home rate.
Does this assume I fire someone?
No. The smartest deploys redeploy the hours back into higher-leverage work: outbound, retention, hospitality, upsell, training, back-office cleanup. The calculator measures hours bought back. What you do with the hours is the operator's call. Reinvesting the time into revenue work that compounds is where automation pays for itself.
How is platform investment determined?
Engagements are scoped to your operation: call volume, channels, locations, and the outcomes you need. There is no public package ladder. On a strategy call we size the system to the business and put a clear number on the work.
What is in the detailed PDF report?
Five pages: (1) Your task-by-task savings table with weekly, monthly, and annual hours. (2) Your hourly cost compared with the model defaults. (3) The three highest-ROI tasks to automate first, ranked by hours-per-dollar. (4) A "never automate this" guardrail list tuned to your industry. (5) Implementation roadmap, week 1 through day 90. We'll email it after you submit. Delivery time can vary.