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Operations Tax Calculator
Estimate the value of time AI could free up in a recurring workflow.
What is the operations tax?
The operations tax is our name for the labor cost of recurring work that AI could help reduce. It may include reviewing documents, routing requests, or resolving routine exceptions. Estimating it starts with the time people spend on a specific workflow.
Enter the annual hours, the fully loaded hourly cost, and the share of work you expect AI to handle. The result estimates the value of that time before implementation and ongoing costs. Freeing time creates capacity; it reduces spending only if you also change how the work is staffed or funded.
Use the estimate to decide what deserves a closer look. Check the assumptions with the people doing the work, and test the proposed change before treating the result as a forecast.
- Pick a workflow you suspect carries hidden cost. Start with one. Pattern-rich, high-volume work is the best first candidate.
- Plug in the three numbers. Hover the question marks for help on each one. Numbers stay in your browser.
- Add more workflows to see the cumulative tax across a function or business unit. Try the industry presets below to see what typical mid-market scenarios look like.
Run your numbers
| Workflow | Annual hours | Loaded $/hour | AI-replaceable % | Annual tax |
|---|
About the inputs
The methodology behind the formula
The formula makes the assumptions visible so you can discuss and test them. It does not measure model quality or decide whether a workflow is suitable for automation.
On annual hours. Use time-tracking or staffing data for the people doing the work. If several roles contribute, use their respective hours and costs. Include time spent checking outputs and handling exceptions when estimating the benefit.
On loaded hourly cost. Include salary, benefits, and overhead. A base wage can understate this cost by 30-40%. Our sample rates range from $50/hr for early-career analyst work to $90-110/hr for senior knowledge work. Replace them with your own figures where available.
On AI-replaceable %. Joust diagnostics have found ranges of 30-50% for high-volume, repeatable workflows and 5-15% for work requiring more individual judgment. These are starting points for comparison. A trial on representative work should establish the rate for your workflow, including human review and corrections.
On the order of magnitude. The output is intended to be accurate to one significant figure. If the tool says $416,000, treat it as "around four hundred thousand." The deeper modeling we do in the roadmap engagement refines the number; the rough cut is for triage.
How your numbers compare to typical roadmap findings
Aggregated annual operations-tax ranges per workflow, drawn from Joust mid-market roadmap findings. Use these as sanity checks, not benchmarks.
Ranges are per workflow. Joust diagnostics commonly identify 3-5 workflows in these bands. Estimate each one separately and check for overlapping hours before adding them together.
Costs to include next
- Implementation cost (one-time, amortize over three years). Includes diagnostic, integration, model selection, change management.
- Recurring AI tooling cost (annual). Model API spend, vector store, observability, governance overhead.
- Workflow redesign cost (often the largest line, and often missed). The work to actually change how the team operates around the new tool.
Compare the value of time freed up with these costs, then decide how to test it. The estimate above is not a forecast of cash savings.
For an AI Operations Roadmap, we examine three to seven workflows over five weeks. The work includes checking the estimates, comparing implementation options, and setting out costs, responsibilities, and a delivery plan.
References and where this approach comes from
Joust internal sources
- The cost of recurring manual work (Joust, 2026), the long-form companion to this calculator. joustagency.com/blog/six-figures-hiding/
- Joust AI Operations Roadmap findings, aggregated patterns across mid-market engagements (2024-2026).
External research informing the inputs
- Brynjolfsson, Li, Raymond. Generative AI at Work (2023 working paper). Reported a 14% average increase in issues resolved per hour in a customer-support setting. This is a finding about that setting, not a replacement rate for other workflows.
- McKinsey & Company. The state of AI in early 2024. A survey of adoption, reported benefits, and risk-management practices.
- GitHub. Quantifying GitHub Copilot’s impact on developer productivity (2022). A vendor-run experiment reported roughly 55% faster completion of a defined programming task. It does not establish an annual savings rate.
What we deliberately exclude
- Unmeasured vendor productivity claims. Controlled task results are useful context, but are not direct inputs to your estimate.
- Studies that aggregate hours saved across knowledge work generally; they hide the variance that matters at the workflow level.
- "AI will replace X% of jobs" macro projections; they do not help operators size next-quarter investment decisions.