How to Calculate ROI for AI Coding Assistants

The ROI Question Finance Teams Ask

You’ve rolled out GitHub Copilot or Claude to your engineering team. Initial feedback is positive—developers say it makes them faster. But when finance asks “What’s our ROI?” you’re stuck. The investment is clear ($20–39 per developer per month × team size), but the benefits feel abstract.

Without a clear ROI calculation, renewals become political. This guide walks through the exact formula top engineering teams use to quantify AI impact in financial terms that your CFO will understand.

The ROI Formula

ROI = (Time Saved Value - Total AI Cost) / Total AI Cost × 100%

Breaking this down:

• Time Saved Value: The dollar value of time developers save through AI assistance
• Total AI Cost: Sum of license fees, infrastructure, onboarding, training, and support
• Result: For every $1 spent on AI tools, how many cents do you save?

Step 1: Calculate Time Saved Value

The formula looks like this:

Time Saved Value = Hours Saved × Hourly Developer Rate

Calculating Hours Saved

Use this framework:

Hours Saved = (AI-Assisted Commits) × (Avg Time per Commit) × (% Time Saved per Commit)

• AI-Assisted Commits: Commits where a developer used GitHub Copilot or similar tool. Track via webhook analysis, commit metadata, or analytics platform.
• Avg Time per Commit: How long does a typical commit take your team? Consider:
  • Writing code: 15–40 minutes
  • Testing: 5–15 minutes
  • Code review iteration: 5–10 minutes
  • Debugging/fixes: 5–15 minutes
  • Total: ~45–90 minutes per commit (use 60 as a baseline)
• % Time Saved per Commit: How much faster does AI make each commit? Studies show:
  • Boilerplate/repetitive code: 40–60% faster
  • Feature implementation: 15–30% faster
  • Testing: 20–40% faster
  • Refactoring: 25–35% faster
  • Bug fixes: 10–25% faster
  • Average across all work: ~25% time savings per commit

Real-World Example

Let’s say your team has:

• 20 developers
• 500 commits/month total (25 per developer average)
• 50% of commits are AI-assisted = 250 AI-assisted commits/month
• Average 60 minutes per commit
• 25% time saved per commit on average

Hours Saved = 250 × 1 hour × 0.25 = 62.5 hours/month

Applying Developer Rate

Now multiply by your loaded developer cost (includes salary, benefits, taxes):

• Junior developer: $50–75/hour loaded
• Mid-level developer: $75–125/hour loaded
• Senior developer: $125–200+/hour loaded
• Use your team average. Example: $100/hour

Time Saved Value = 62.5 hours × $100/hour = $6,250/month or $75,000/year

Step 2: Calculate Total AI Cost

License Fees

Example (20 developers):

• GitHub Copilot: $19/developer/month × 20 = $380/month
• Annual cost: $4,560

Indirect Costs

Don’t forget:

• Onboarding/training: 2 hours per developer × 20 devs × $100/hour = $4,000 (one-time, spread over 12 months = $333/month)
• Infrastructure/analytics: If using a platform like GuageAI for tracking, $500–2,000/month
• Support and management: Engineering lead 4 hours/month × $150/hour = $600/month

Total Monthly Cost: $380 (licenses) + $333 (amortized training) + $500 (analytics) + $600 (support) = $1,813/month or $21,756/year

Step 3: Calculate ROI

Using our examples:

• Time Saved Value: $75,000/year
• Total AI Cost: $21,756/year

ROI = ($75,000 - $21,756) / $21,756 × 100%
ROI = $53,244 / $21,756 × 100%
ROI = 244%

Interpretation: For every $1 spent on AI tools, you save $3.44 in developer time. In the first year, you recoup your entire investment and gain $53k in net savings.

Industry Benchmarks

From surveys of 200+ engineering teams using GitHub Copilot:

Sensitivity Analysis: What If?

Here’s how ROI shifts with different assumptions (same 20-developer team):

Scenario 1: Lower Adoption (30% AI-assisted, 20% time savings)

Hours Saved = 150 commits × 1 hour × 0.20 = 30 hours/month
Time Saved Value = 30 × $100 = $3,000/month = $36,000/year
ROI = ($36,000 - $21,756) / $21,756 = 65% (breakeven in ~6 months)

Scenario 2: Higher Adoption (60% AI-assisted, 30% time savings)

Hours Saved = 300 commits × 1 hour × 0.30 = 90 hours/month
Time Saved Value = 90 × $100 = $9,000/month = $108,000/year
ROI = ($108,000 - $21,756) / $21,756 = 396% (payback in 2.4 months)

Beyond Financial ROI: Non-Financial Benefits

While financial ROI is compelling, consider additional benefits that aren’t easily quantified:

• Faster time-to-market: Ship features 30–50% faster, improving competitiveness
• Improved developer retention: Engineers love AI tools; reduces hiring costs
• Better onboarding: New developers ramp faster, reduce training time by 20–30%
• Reduced technical debt: More refactoring happens (AI makes it cheaper), code quality improves
• Knowledge democratization: Junior developers can solve problems senior devs would tackle, unlocking capacity

How to Present ROI to Leadership

For Finance/CFO: Lead with annual ROI percentage and payback period. Frame as cost reduction.

For Engineering Leaders: Emphasize developer satisfaction, faster feature delivery, and reduced toil.

For CEO/Board: Frame as competitive advantage—teams with AI move faster, ship more, win more deals.

Next Steps

1. Gather your team’s commit data and AI adoption rate for the past 3 months
2. Calculate average time per commit and time saved per commit
3. Plug numbers into the ROI formula
4. Run sensitivity analysis (conservative, typical, best-case scenarios)
5. Present to leadership with confidence