What 300 Person-Hours Per Week Actually Costs — And How Automation Reclaims Them

Three hundred person-hours a week is roughly seven and a half full-time employees.

Not seven and a half people who could be made redundant — that framing is usually both wrong and unhelpful. Seven and a half people's worth of capacity currently spent on work that produces no analytical value: data entry, reconciliation, moving numbers between systems, generating reports somebody else will read.

That figure is real. It comes from a Fortune 500 commercial finance function where 50+ recurring manual tasks were automated to 99% accuracy, returning 300+ person-hours every week. What follows is how to work out the equivalent number for your own operation, and why the obvious calculation understates it.

The obvious calculation, and why it is too low

The instinct is hours multiplied by salary. That is the floor, not the answer.

Start with loaded cost, not salary. Salary plus employer taxes, benefits, equipment, software licences, management overhead, and facilities. Depending on the market and role this is typically 1.25 to 1.4 times base. An analyst at $75,000 costs somewhere near $100,000 to have.

At that rate, 300 hours a week is roughly $375,000 a year in direct capacity — assuming the work is being done by people you already employ, at ordinary rates, with no overtime.

But three other costs sit underneath, and in most operations they are larger.

The error cost

Manual repetitive work has an error rate. It is rarely measured, which is why it is rarely believed.

The costs of those errors are diffuse and land elsewhere: the reconciliation that takes an extra day, the customer credit, the restated figure, the compliance finding. Because they land in someone else's budget, they are almost never attributed back to the manual process that produced them.

The finance automation engagement targeted 99% accuracy with full error handling on every automation. The relevant comparison is not 99% against a hypothetical perfect human, but 99% against the actual measured human error rate on repetitive high-volume work — which, when anyone bothers to measure it, is often worse.

The scaling constraint

Manual work scales linearly with volume. Twenty percent more transactions means twenty percent more hours, which eventually means another hire.

This is the cost that matters most to a growing business, and it is invisible in a snapshot. The question is not what the process costs today. It is what it costs at the volume you are forecasting, and whether the answer requires headcount you would rather deploy elsewhere.

The opportunity cost

The most significant and least quantifiable. Analysts doing data entry are not doing analysis. The finance function in that engagement redirected capacity from reconciliation to actual financial analysis — the work those people were hired for and are evaluated on.

There is a retention dimension too. Skilled people who spend most of their week on mechanical work tend to leave, and replacing them costs a substantial fraction of a salary.

How to measure your own number

Do this before commissioning anything. It takes about two weeks and it determines whether a project is worth funding.

1. Inventory the recurring tasks. Ask each team to list what they do on a repeating schedule — daily, weekly, monthly. Not projects. The work that happens every cycle regardless.

2. Time them honestly. Self-reported estimates are unreliable in both directions. A week of actual tracking, even rough, beats a survey. Expect surprises: the task everyone complains about is often not the expensive one.

3. Apply loaded rates by role. A compliance officer's hour and a junior administrator's hour are not the same cost, and the mix matters.

4. Measure the error rate on a sample. Take a hundred completed items, check them properly, count the defects. This number is usually uncomfortable and always useful.

5. Model it at forecast volume. Take the growth number the business is planning around and recompute. The gap between today's cost and the eighteen-month cost is frequently what justifies the project.

What automation actually replaces

Being precise here matters, because overstating it is how projects lose credibility.

Replaced well: structured, repetitive, rule-governed work. Moving data between systems, reconciling records against a defined rule, generating a scheduled report, validating a form against criteria, routing by classification. The finance engagement's 50+ tasks were of this type.

Replaced partially: work requiring judgement on structured inputs. Systems can handle the routine majority and route exceptions to a person. This is where most current value sits — and it depends on defining the exception threshold well.

Not replaced: negotiation, relationship work, genuinely novel problems, and any judgement where the criteria are not articulable. Attempting these produces confident output that nobody should trust.

The honest framing is that automation removes the mechanical majority of a role, not the role. That is a better outcome for most teams than the alternative framing, and it is also what actually happens.

Frequently asked questions

How do I calculate the ROI of automating a manual process?

Loaded hourly cost multiplied by hours consumed, plus the measured cost of errors, plus the headcount the process will require at forecast volume. Compare that against build cost plus annual run cost. If the payback is longer than eighteen months on that basis, the use case is probably wrong — not the pricing.

Is 99% accuracy good enough?

It depends on what happens to the one percent, which is why error handling matters more than the headline figure. A system that is 99% accurate and routes its uncertain cases to a person is safer than one that is 99.5% accurate and fails silently. Measure the human baseline before deciding the bar, because on high-volume repetitive work it is often lower than assumed.

Will automation mean redundancies?

In the engagements described here it did not. Capacity was redirected — analysts moved from data entry to analysis, and the automation became the operational standard for the function. That said, the honest answer is that it depends on whether the business has more valuable work for that capacity. Companies that do redeploy; companies that do not will eventually face the question.

Which processes should be automated first?

High volume, low variance, clearly defined correct outcome, and a contained consequence when something goes wrong. Start where a mistake produces a caught error rather than a regulatory event, even if a higher-stakes process looks more valuable. The first deployment is the one most likely to surprise you.

How long before the hours actually come back?

For a scoped automation of well-defined tasks, eight to sixteen weeks to production, with the returned hours arriving incrementally as tasks go live rather than all at once. Sequencing several tasks so the first ones land early is what makes the programme visible while the rest is still being built.