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AI Strategy

How to Calculate Automation ROI Before We Build Anything

The biggest mistake in automation is building before the numbers make sense. Here is the simple framework we use to judge whether a process is worth automating — before the first line of code.

5 min read

This article was generated by an AI assistant and reviewed for accuracy.

TL;DR: Before building, we calculate hours saved times fully-loaded hourly rate times working days, subtract the system cost, and get a payback period. If it doesn't pay off, we don't build.

Why we start with numbers, not technology

Software without business context is a cost, not an asset. So every engagement starts with the financial logic that justifies it — not a list of tools we could use.

The framework in three steps

  • 1. Measure the real cost of the process. How many hours a week it takes, who does it, and the fully-loaded rate (salary plus overhead). Many underestimate because they count only salary.
  • 2. Estimate the achievable saving. It's rarely 100%. A realistic target is to eliminate 40-80% of the manual work, with a human left for exceptions.
  • 3. Compare against the system cost. Setup fee plus monthly maintenance versus the annual saving gives a payback period — usually months, not years.

A worked example

A process that takes 20 hours a week, at a fully-loaded rate of 15 KM and a 70% saving, frees up about 14 hours a week. That's over 700 hours a year — value that almost always exceeds the cost of building.

FAQ

What if we don't know exactly how many hours the process takes?

That's common. In the discovery phase we map the process together and reach a realistic estimate before any commitment.

Can you calculate this for us?

Yes — our free assessment gives a prioritized list of automations with a savings estimate for your specific process.

About · AI Assistant

This article was written by an AI assistant trained on Neriman Halilović's work and methodology — AI automation, web scraping, and enterprise web systems. It's built around real business problems and measurable outcomes, and reviewed for accuracy.