The problem isn't the technology. It's that no one has done the math.
When a COO or CFO evaluates whether automating operational processes makes sense, the conversation tends to stall at the same point: "We don't know exactly how much time we're losing on that."
That's an honest answer. It's also an avoidable obstacle.
A three-month diagnostic isn't required to produce a useful estimate. With the data your team already generates — task logs, close timelines, request volumes, report frequency — you can build a working hypothesis in under half an hour. A hypothesis solid enough to decide whether it's worth going further.
This article explains how.
What "automatable task" means in this context
Before counting hours, it helps to define the term. A task is automatable when it meets at least two of the following three conditions:
- It recurs on a predictable schedule — daily, weekly, or monthly, with little variation in structure.
- It follows a defined logic — however complex, a clear criterion exists for executing it correctly.
- It consumes time from people with judgment — meaning someone with analytical capacity is doing work that doesn't actually require their judgment.
Typical examples in mid-size industrial companies or companies running an ERP: data consolidation across systems, financial close report generation, purchase order tracking, reconciliations, budget variance alerts, responses to recurring internal inquiries.
None of these processes require creativity. All of them consume real time from real people.
The exercise: how to estimate in 20 minutes
The goal is not accounting-level precision. It's having a reference number that allows you to prioritize. Follow these four steps.
Step 1 — List the five most frequent operational tasks your team performs (5 minutes)
Think about the recurring tasks your team executes that generate the most friction or complaints. You don't need to be exhaustive. Five representative tasks are sufficient.
For each one, note: the task name, its weekly or monthly frequency, and who performs it (role, not name).
Step 2 — Estimate the actual time per execution (5 minutes)
For each task, estimate how long a complete execution takes. If you're not certain, use a conservative range. It's better to underestimate than to inflate.
Multiply: time per execution × monthly frequency = monthly hours per task.
Step 3 — Apply an automatability factor (5 minutes)
Not all hours are recoverable. Some tasks include components that require human judgment. Apply an estimated percentage of what could be automated:
- Fully structured and repetitive task: 70–90%
- Task with a review or validation component: 40–60%
- Task with frequent discretionary decisions: 20–35%
Multiply the monthly hours by that percentage. The result is your estimated recoverable hours per task.
Step 4 — Add up and translate to cost (5 minutes)
Sum the recoverable hours across the five tasks. Multiply by the average hourly cost of the profile performing them. If you don't have that figure, use a conservative market range for your country.
The result is an estimate of the monthly cost of those tasks in their current state.
A concrete example with range-based assumptions
A mid-size manufacturing company — between 80 and 150 employees, running an ERP, with a four-person operations team — completes this exercise and arrives at the following result:
| Task | Hours/month | Automatability factor | Recoverable hours |
|---|
| Production data consolidation for weekly report | 12 | 80% | 9.6 |
| Manual tracking of open purchase orders | 8 | 70% | 5.6 |
| Budget variance report generation | 6 | 75% | 4.5 |
| Responses to internal inquiries about order status | 10 | 85% | 8.5 |
| Reconciliation between ERP and inventory spreadsheet | 9 | 65% | 5.9 |
Total estimate: 34 recoverable hours per month.
At an hourly cost of between 25 and 40 euros for that profile, the monthly cost of those tasks ranges from EUR 850 to EUR 1,360. Annualized: between EUR 10,200 and EUR 16,320 in work that could cease to exist.
That figure excludes the cost of errors produced in those tasks and the time the team is not spending on higher-value work.
This is not a published real-world case. It is a hypothesis built from parameters typical of this segment. Ranges vary by company. But the structure of the exercise applies to any similar situation.
Why this exercise matters before any technology conversation
The conversation about automation usually starts in the wrong place: which tool to use, which vendor to engage, how much implementation costs.
Those are valid questions — but only after you have clarity on what will be automated and how much recovering that time is worth.
The exercise above does not replace a technical diagnostic. It does allow you to enter that conversation with a concrete number in hand, which improves the quality of the analysis and accelerates decision-making.
At OuroAI, we work with mid-size companies that arrive with estimates like this one, and we use them as the starting point for designing an automation roadmap with clear priorities. The prioritization criterion is not what is easiest to automate — it's what generates the most return in the least time.
What to do with your estimate
If your exercise yields more than 20 recoverable hours per month, you have sufficient basis to evaluate an implementation. If it exceeds 40 hours, you are likely already paying a meaningful opportunity cost every month that passes.
The next step is not to engage anyone. It's to compare your estimate with someone who has seen similar cases and can tell you whether the numbers hold up, which tasks other COOs in your industry prioritize, and what implementation timelines are realistic to expect.
That's what our free diagnostic is for. No engagement commitment. No sales presentation. Just a 15-minute conversation to review your estimate and provide context.
If you completed the exercise and want to compare notes, fill out the form below.
