Teams miss dates less often because the work was harder than expected, and more often because the people were less available than assumed.
- ›Why capacity is systematically overstated
- ›What AI computes that a spreadsheet cannot
- ›Reading utilization without burning people
- ›A practical planning rhythm
Why capacity is overstated
Three consistent errors:
Counting people, not availability. Five people is not five people-weeks. Subtract leave, meetings, support duty, and the two who are half-allocated to another project.
Ignoring unplanned work. Every team absorbs interruptions. A team that historically loses 30% to unplanned work and plans at 100% is planning to fail, arithmetically.
Assuming a person is fungible. Two people with capacity does not mean the specific work can be done by either of them.
What AI computes that a spreadsheet cannot
A spreadsheet holds numbers you type. The advantage of computing capacity inside a connected system is that the numbers are current and complete:
- ›Approved leave flows in automatically, so next month's capacity reflects the holiday nobody remembered.
- ›Cross-project allocation is visible, which catches the person committed to three teams at 50% each.
- ›Historical unplanned-work rates per team, so the buffer is measured rather than guessed.
- ›Skill matching, so capacity is assessed against who can actually do the work.
- ›Forward view against pipeline, so a deal closing next month meets a staffing plan rather than a surprise.
That last one is the argument for capacity planning that spans sales and delivery. A pipeline you cannot staff is not a forecast, it is a risk.
Reading utilization carefully
Utilization is the most misused number in resource planning.
Pushed toward 100%, it eliminates the slack that absorbs variability, and everything slows down. Queueing behavior is unforgiving here: systems near full utilization develop long waits from small disruptions.
Sustained high utilization also predicts turnover, which costs far more than the idle time it was meant to eliminate. Target something in the 70-85% range for knowledge work and treat sustained 95% as a warning rather than an achievement.
Important: Utilization measured alone drives teams to fill hours with low-value billable work. Pair it with margin or outcome, or it will optimize itself into something you did not want.
A practical rhythm
Weekly: check next week's commitments against next week's real availability. This catches most problems while they are still cheap.
Monthly: look at the next quarter's pipeline against the team you will have, including hiring that has not closed yet.
Quarterly: review unplanned-work rates and adjust the buffer. Teams change; the buffer that was right last year is often wrong now.
FAQ
How much buffer should we plan?
Measure your unplanned-work rate over the last few months and use that. Most teams land between 15% and 30%, and are surprised by their own number.
Does this need time tracking?
It helps and is not required. Assignments, leave, and commitments give a workable capacity picture; time tracking makes it precise and is worth it if you bill hourly. See business software for agencies.
What about hiring plans?
Model them with start dates and a ramp period. Counting a new hire at full capacity from day one is a common and expensive planning error.
Brainis computes capacity from assignments, approved leave, and cross-project allocation, against a pipeline in the same system. See pricing.
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