How high is ongoing maintenance cost for an automation, realistically?

Exactly. A figure that's completely missing from many first calculations.

Which missing factor accelerates an automation's value erosion the most?

Correct. Without clear ownership, a creeping malfunction often goes unnoticed for a long time.

Making sense of AI in your business

Estimating cost & ROI realistically

Q: What do most classic ROI calculations overlook?

Correct. Most calculations only show the day-one situation.

ROI math that only looks at day one is almost always too optimistic. Factoring in ongoing maintenance cost and the drivers behind it leads to a more realistic estimate – and fewer nasty surprises in year two.

One example per factor – to remember it

Try it yourself: when does it pay off?

Year 0

Cumulative cost

8,000 €

Cumulative savings

0 €

Not paid off yet (-8,000 €)

The typical back-of-envelope math

"Time saved per task × frequency × hourly rate = payback period" – that's how most business cases calculate it. The problem: that math only holds for the day of go-live, not for the time after.

The four factors of a realistic ROI estimate

Frequency & rule stability

How often does the process run, and how stable are the rules behind it? The less stable the rules, the more rework shows up later.

Exception rate

How many cases deviate from the standard case? A high exception rate eats into the planned time savings.

Ongoing maintenance cost

Realistically 10 to 40% of the original implementation cost per year – almost always missing from the first calculation.

A named owner

Without clear ownership, an automation's value demonstrably erodes faster – nobody notices in time when the underlying rules have changed.

Why this matters for you as a decision-maker

A realistic ROI estimate accounts for more than day one – it accounts for maintenance and value erosion over time. That's why Beyond Prompt's AI Opportunity Scan calculates with maintenance cost and a named owner from the start, instead of only showing the day-one number.

Key takeaways

ROI math that only looks at day one is almost always too optimistic.
Ongoing maintenance cost (realistically 10-40% of implementation cost per year) belongs in the calculation from the start.
Frequency, rule stability, exception rate, and a named owner together decide whether a process is really worth automating.
Without a named owner, an automation's value demonstrably erodes faster.
Realistic payback periods are often several years, not a few months.

→ Where does automation actually pay off? Why most ROI math is too optimistic

Quick check: did it land?

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What do most classic ROI calculations overlook?

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