The hidden cost of manual QA in companies with between 50 and 200 employees

The invisible cost of manual QA

There's one line in the budget that many companies don't have: the real cost of manual QA.

Not because it doesn't exist, but because it's distributed in other games.
The hours that a developer spends on regression appear as development.
The time that the CTO spends reviewing bugs is considered management.
And when a release is delayed due to issues detected late, it's simply labeled as “project delay”.

In no case is it reflected as what it really is: the cost of not having an automated QA process.

In agile teams, this cost can be significant.
It is estimated that manual QA generates between $45,000 and $62,000 annually per developer in rework, regression and error correction tasks that reach production.

In a company with 50 to 200 employees, with a technical team of between 5 and 15 people, this impact can represent a significant part of the annual technological budget, without anyone being directly measuring it.

Where the cost really hides

The regression that slows down each release

Every time you make a change to an application, you need to check that everything above is still working.

Without automation, this involves manually going through critical flows:
forms, integrations, key processes or checkout in different browsers.

Not only is this process time consuming, but it gets slower as the application grows.

An application with 20 critical flows can be validated in two days.
When it reaches 80, that same process can take up to a week.

The problem isn't just time, but growth: validation doesn't scale at the same rate as the product.

The cost of bugs in production

Fixing an error in production is considerably more expensive than correcting it during development.

Not only because of the technical time, but because of everything that involves:

• customer support
• internal management of the incident
• reputational impact
• and, in some cases, direct loss of income

Every bug that reaches production because it was not detected in time has a real cost, even if it is not reflected in any QA metric.

The time of the equipment that is not counted

In many teams, QA isn't a dedicated function.

The developer himself tests his code, the CTO reviews before each release and, ultimately, it is the users who end up detecting the errors.

This model generates an obvious bias and, in addition, consumes time from profiles that should be focused on development or strategic decisions.

That time is not recorded as QA, but it directly impacts the speed and quality of the product.

Debt in the tests

Over time, manual validation processes degrade.

Outdated checklists, spreadsheets that no longer cover all cases, new flows that are not documented...

Test coverage is growing slower than the application, and keeping it up to date is no longer a priority over new features.

The result is a silent debt: less and less is being tested than it really should be.

The real impact

Manual QA isn't free.

It has a cost of:

• Team time
• Launch speed
• errors in production
• and operational maintenance

The difference between that cost and that of automating is what determines whether automation makes sense.

In many cases, even a complex analysis is not necessary.

A simple question is enough: How many hours does the team spend each week manually validating?

This is where a cost that, until then, was completely hidden, begins to become visible.

References

1. Capgemini/ OpenText. (2025). World Quality Report 2025-26. https://www.capgemini.com/insights/research-library/world-quality-report-2025-26/ — 43% of organizations experiment with generative AI in QA, but only 15% have scaled it to the enterprise level. 60% struggle with secure and scalable test data. 58% cite challenges in adopting AI-driven tools, which explains why automation maturity remains limited.

2. AtestLab. (2024). AI In Test Automation: From Costs To Benefits. https://blog.qatestlab.com/2024/11/27/test-automation-and-ai-benefits/ — Test automation platforms with AI report reductions in test cycle time of up to 60%. Maintaining manual tests in agile environments generates hidden costs that many organizations do not correctly attribute to the QA budget: time to rewrite tests after UI changes, coordination of test sessions and management of defects fed back to development.

3. Tricentis. (2024). The State of Test Automation 2024. Tricentis Research. https://www.tricentis.com/resources/state-of-test-automation — In agile teams, manual QA generates hidden annual costs of between $45,000 and $62,000 per developer in rework, regression tests and bug fixes that went into production undetected. Manual regression slows down launches by up to 60% in organizations without test automation.

4. ITIC. (2024). 2024 Hourly Cost of Downtime Survey. ITIC. — Companies that spend more than 40% of their IT budget on repetitive QA and bug correction tasks see reduced resources available for innovation. The correlation between lack of QA automation and slow release cycles is one of the most consistent patterns in ITIC data.

5. Gartner. (2024). Market Guide for AI-Augmented Software Testing Tools. Gartner Research. — Gartner estimates that the cost of fixing a bug in production is around 100 times higher than correcting it during development. For companies with short release cycles, the accumulation of technical debt resulting from insufficient QA generates a cost spiral that increases with each release.

6. Erwood Group. (2025). The True Costs of Downtime in 2025. https://www.erwoodgroup.com/blog/the-true-costs-of-downtime-in-2025-a-deep-dive-by-business-size-and-industry/ — Manual testing and QA debt can cost companies around $2.4 million annually to maintain slow tests and regressions, in addition to slowing down launches by up to 60%.