How to Calculate ROI on Your AI Coding Investment

Your team is spending $800-$3,000/month on AI coding tools. Leadership wants to know if it is working. Here is a framework that produces a defensible number, not vibes.

The ROI equation

ROI = (Productivity Gains - AI Costs) / AI Costs × 100%

Both sides require measurement. Most teams can estimate productivity gains but dramatically undercount costs. Let us fix both.

Measuring productivity gains

Pick metrics you already track. Do not invent new ones for this exercise.

PRs merged per developer per week. Compare a 4-week baseline (pre-AI or AI-disabled) against the current 4-week window. A team that went from 4.2 to 5.8 PRs/dev/week has a 38% throughput increase.

Time-to-first-commit on new tasks. Measure from ticket assignment to first pushed commit. AI tools typically compress the exploration and scaffolding phase. A drop from 6 hours to 3.5 hours is meaningful.

Code review turnaround. If developers use AI to explain unfamiliar code during review, review cycle time should shrink. Measure time from review-requested to approved.

Incident MTTR. AI-assisted debugging often shortens mean time to resolution. Compare the last 20 incidents against the prior 20.

Convert these into dollar value using your team's blended hourly cost (salary + benefits + overhead, typically $80-$150/hr for mid-senior engineers in the US).

Measuring AI costs (the part people get wrong)

Most teams count subscriptions and stop. The real cost stack:

API spend: Claude, GPT-5.5, etc. Check your billing dashboard.
Subscription fees: Cursor Pro, GitHub Copilot seats, etc.
Context overhead: Wasted tokens from bloated prompts, unused tool schemas, stale instructions. Don't guess a percentage — measure it.
Model misrouting: Using Opus-class models ($5/M input on Claude Opus 4.8) for tasks Haiku 4.5 ($1/M) could handle — a 5× rate difference on every misrouted token.

TokenCheat's audit quantifies the last two categories. Run it against a week of session logs and you get a breakdown of productive vs. wasted spend.

Worked example

All inputs are stated assumptions — swap in your own:

Input	Value
Team	10 developers, mid-senior, $120/hr blended cost
PRs merged, before → after	4.0 → 5.4 per dev/week (+35%)
Average effort per PR	6 hours
Hours saved	14 extra PRs/week × 6 hrs = 84 hrs/week
Productivity gain	84 × $120 = $10,080/week ≈ $43,680/month
API spend (Claude)	$800/mo
Subscriptions	$200/mo
Context waste (measured via TokenCheat audit)	$320/mo
Total AI cost	$1,320/month

ROI: ($43,680 − $1,320) / $1,320 = 3,209%.

Even if you discount the productivity gain by 50% (some of that improvement might be attributable to other factors), you are still at 1,554% ROI. This is the kind of number that makes budget conversations easy.

Presenting to leadership

Lead with the ROI number. Then show the methodology. Three slides:

ROI headline + cost vs. gain summary
Productivity metrics with before/after data (use your actual sprint data)
Cost breakdown showing you are actively managing waste (this is where TokenCheat's audit report becomes a visual)

The third slide matters. It tells leadership you are not just spending — you are optimizing. That distinction is the difference between "approved" and "let us revisit next quarter."

What good looks like

Sanity-check targets to aim for — not industry measurements:

ROI comfortably in the hundreds of percent (the worked example above survives a 50% haircut on the gains and stays there)
Context waste in the low single digits as a share of total spend
A written model-routing policy that keeps flagship models off grunt work

If your numbers fall short, the fix is usually cost-side (waste reduction) rather than productivity-side (more tool usage). Put your own per-PR economics on a page with the cost-per-PR calculator.