Capacity Calculator: Hours Saved Per Week
Estimate how many hours AI automation could give back to your team each month, the full-time-equivalent capacity that unlocks, and the GBP equivalent for the board. Personalised results in about 2 minutes.
How do I calculate the capacity automation could unlock? Start with the hours your team currently loses to the target workflow each month, estimate the percentage that could be removed by automation, and translate the result into hours per year and full-time-equivalent capacity across the team. The calculator below walks you through it and returns the GBP equivalent for the board.
Hours Saved
Monthly and annual hours recovered across the team
Capacity Unlocked
Full-time-equivalent capacity the team gets back
GBP Equivalent
Translation of capacity into board-ready GBP figures
What You'll See in Your Results
Answer a few quick questions about your team and the workflow you'd like to automate. We'll show you:
- ✓Hours saved per week across your team
- ✓Full-time-equivalent capacity unlocked
- ✓GBP equivalent for the board paper
- ✓Payback window in months, net of maintenance
Privacy Note: everything runs in your browser. Nothing is collected or stored, and there is no sign-up to see your results.
Tell us about your team
We need some basic information about your business
This helps us size the capacity automation could unlock across the team.
Think about tasks like data entry, market research, report generation, email responses, and manual processing.
Used to translate hours saved into a GBP equivalent for the board.
Don't worry, this is just an estimate. We'll refine it together during your consultation.
Frequently asked questions
Start with the hours your team currently loses to the target workflow each week. Estimate the percentage that could be removed by automation, multiply by 52, and divide by 1,820 (UK full-time hours per year) to get the FTE-equivalent capacity unlocked. For mid-market businesses, that capacity figure is the most defensible number on a board paper, because it ties directly to avoided hire or bandwidth recovered for higher-value work.
Foundation-phase automations for Agenticise clients have recovered between 20 and 40 hours per month per workflow. A construction platform partner recovered 25 hours every month from partner onboarding inside 8 weeks. A commercial flooring client recovered 25+ hours of project paperwork every month. A global biometrics company cut lead qualification time by 95%. Anything that does not save a measurable block of hours within the first quarter usually signals the scope was too narrow or the wrong workflow was chosen first.
Most well-scoped first automations show measurable hours saved within 6 to 10 weeks from scoping to stable production. The build itself can take 2 to 5 weeks once the scope is signed off (the construction platform partner onboarding automation took 2 weeks to build). The recovered capacity compounds week on week from go-live, so the payback window stated in months is usually conservative once the team is using the new workflow consistently.
Five line items belong in the business case: discovery and process mapping (typically the largest pre-build cost), build and integration (2 to 5 weeks of effort), human-in-the-loop and change management design, ongoing maintenance (10 to 20% of build cost annually), and model or API usage (usually less than 10% of total for automations at this scale). Most boards underestimate discovery and change management, and overestimate the model usage. The AI model itself is rarely the most expensive part.
Capture a baseline before any build starts. Three methods work: team self-report (each person estimates time spent on the workflow last week), time-tracking tools if you already have them, or process observation over 1 to 2 weeks. Sample the same number again 4 to 6 weeks after the automation is in production, with at least two consecutive weeks of stable operation. The difference is your hours-saved figure. Avoid measuring inside the first month of go-live, because the team is still adjusting to the new pattern.
For a worked example with your own numbers, read how to build an AI automation strategy.
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