Plan · ROI
Is your AI spend earning its keep?
Translate dollars into hours saved. We show the bull case, your case, and the bear case side by side — because boards ask for all three.
Inputs
Hours saved / engineer / week (your estimate)3h
GitHub's 2024 study: ~55% task speedup. METR 2025: experienced devs were 19% slower on familiar codebases. Reality varies wildly — survey your engineers.
Breakeven
0.59h
Per engineer, per week. Above this, you're net positive.
Bull case · 6h/wk saved
+$3.5M
net annual value
Hours saved/yr41,400
Value created$3.9M
ROI924%
$/$ spent10.2×
Payback1.2 mo
You case · 3h/wk saved
+$1.6M
net annual value
Hours saved/yr20,700
Value created$2M
ROI412%
$/$ spent5.1×
Payback2.3 mo
Bear case · -1h/wk saved
$-1M
net annual value
Hours saved/yr-6,900
Value created$-655.5K
ROI-271%
$/$ spent-1.7×
Paybacknever
How to use this with your board
- Survey your engineers — don't guess hours saved. Run a 3-question pulse: time saved this week, tasks accelerated, tasks slowed.
- Show all three cases — the bear case lends credibility. Boards trust people who name the downside.
- Pair ROI with the guardrails — "we're at $X net value, AND we've capped runaway spend at $Y" is the strongest position.
- Re-measure quarterly — productivity gains compound as workflows mature; spend can spike with new tools. Both deserve fresh numbers.
What we baked in
• Bull case uses 6h/wk saved — roughly consistent with GitHub's reported ~55% task speedup on greenfield code.
• Base case uses 3h/wk — a defensible middle, accounting for review overhead and context-switching.
• Bear case uses -1h/wk — reflecting METR's finding that AI can slow expert devs on familiar code by ~19%.
• None of these are universal. Use the slider to match your environment.