Whenever engineering teams evaluate AI tools, the first question is usually:
"How much faster will developers write code?"
I think that's the wrong question.
A better question is:
How much engineering time are we wasting investigating production incidents?
Because that's where a surprising amount of money quietly disappears.
Let's do a simple calculation.
No complicated ROI formulas.
No marketing buzzwords.
Just numbers.
Imagine Your Engineering Team Looks Like This
- Team size: 50 engineers
- Production incidents: Around 180 per year
- Engineers involved in each incident: 5
- Average investigation time per engineer: 2.5 hours
- Average engineering cost: $80/hour
Nothing unusual.
In fact, these numbers are fairly common for many growing SaaS companies.
Engineering Hours Lost Every Year
Each production incident requires:
5 engineers × 2.5 hours = 12.5 engineering hours
Across 180 incidents:
180 × 12.5 = 2,250 engineering hours every year
Read that again.
2,250 engineering hours.
That's time spent:
- Understanding production failures
- Jumping between logs
- Looking at dashboards
- Checking traces
- Reading deployment history
Instead of:
- Shipping new features
- Improving reliability
- Reducing technical debt
- Building products customers love
Now Translate Those Hours Into Money
If engineering costs average $80/hour:
2,250 × $80 = $180,000/year
That's $180,000 spent just investigating incidents.
And for many organizations, nobody actually measures this cost.
Maybe We're Measuring AI Incorrectly
Most AI discussions focus on:
- Writing code faster
- Autocomplete
- Pull request generation
Those are valuable.
But once software reaches production, another problem begins.
Finding the root cause.
And that's often where engineering teams lose the most time.
Why We Built FixBugs
This problem is exactly why we're building FixBugs.
Instead of making engineers jump across logs, traces, code, deployments, alerts, and documentation, FixBugs automatically gathers the debugging context and helps teams reach the root cause much faster.
Learn more:
What If Investigation Time Drops By 50%?
Current effort:
2,250 engineering hours/year
With a 50% reduction:
1,125 engineering hours saved
Financial impact:
1,125 × $80 = $90,000/year
More importantly...
Those hours go back to engineering teams.
Not by replacing engineers.
But by helping them spend more time building instead of investigating.
Four Questions Every Engineering Leader Should Ask
Instead of asking:
"Does this AI tool generate code?"
Ask:
- How many production incidents do we investigate every year?
- How many engineering hours do they consume?
- What is one engineering hour worth?
- How much of that time could realistically be recovered?
Those answers build a much stronger business case than any marketing claim.
Final Thoughts
AI ROI shouldn't be measured by how many lines of code it generates.
It should be measured by how much engineering time it gives back.
For many organizations, the biggest opportunity isn't writing software faster.
It's spending less time figuring out why production broke.
Sometimes the highest ROI doesn't come from hiring more engineers.
It comes from helping the engineers you already have spend more time building—and less time investigating.
Discussion
How does your engineering team currently investigate production incidents?
Do you actually measure the engineering hours spent on incident investigation?
I'd love to hear how other teams approach this.
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