How to Calculate AI Coding ROI for a 5-Person Engineering Team (2026 Worksheet)

Why a 5-Person Team Is the Right Unit

Five engineers is the smallest team where AI coding ROI gets real. With one or two engineers, the cost is small enough that ROI doesn't matter much; with 50+ engineers, the variation across people drowns out any single team-level calculation. At 5 people, AI tool costs are non-trivial ($1.5K-$5K/month), and individual savings aggregate cleanly into a number you can defend to leadership.

This worksheet walks through the calculation with concrete numbers from 2026 conditions. Plug in your own values to land on your team's break-even point.

Step 1: Total Tool Spend

Sum the monthly cost of every AI coding tool your team uses. For a typical 5-person engineering team in 2026:

• Cursor Teams (5 × $40-$80): $200-$400
• Claude Code or Claude.ai Team (5 × $25-$200): $125-$1,000
• GitHub Copilot Business (5 × $19): $95
• API tokens for direct use (Anthropic/OpenAI/Google): $500-$2,500
• Specialty tools (Aider, CodeRabbit, Bugbot, MCP gateway): $0-$500

Typical total: $1,500-$4,500/month. Heavy users land at the top end; teams sharing a single mid-tier subscription land near the bottom.

Step 2: Engineer Fully-Loaded Cost

ROI math only works when you compare AI cost against real labor cost. Use fully-loaded cost (salary + benefits + overhead), not raw salary. Typical 2026 fully-loaded hourly rates:

• Junior engineer: $50-$100/hour
• Mid-level engineer: $100-$180/hour
• Senior engineer: $180-$300/hour
• Staff/principal engineer: $300-$500/hour

For a typical 5-person mid-level team, blended fully-loaded rate is roughly $150/hour. Annual cost per engineer: ~$300K. Team annual cost: ~$1.5M.

Step 3: Hours Saved Per Engineer

This is the hardest input to estimate honestly. Avoid surveying engineers in the abstract — the answers tend toward 30-50% which is unrealistic. Use a more rigorous approach:

Method A — Activity sampling. For one week, have engineers log activities in 30-min blocks and note which involved AI. Estimate time saved per AI-assisted task vs the time it would have taken unassisted. Sum and project to monthly.

Method B — Pre/post comparison. Compare your team's ticket throughput, PR throughput, or commit volume from 3 months pre-AI to 3 months post-AI. Adjust for headcount and project mix. The delta is your AI-attributable productivity.

Realistic 2026 results for well-run teams:

• 5-10 hours/engineer/month saved (modest adoption, routine tasks)
• 15-25 hours/engineer/month saved (good adoption, mixed tasks)
• 30-50 hours/engineer/month saved (heavy adoption, agent-style workflows)

Numbers higher than 50 hours/month are usually self-reporting bias. Numbers lower than 5 hours suggest the team isn't really using the tools.

Step 4: Compute ROI

Plug into the formula:

Monthly value = team size × hours saved per engineer × hourly rate

For a 5-person mid-level team saving 20 hours/engineer/month at $150/hour:

• Monthly value: 5 × 20 × $150 = $15,000
• Monthly tool spend: $3,000 (mid-range)
• Monthly ROI: $15,000 / $3,000 = 5×
• Net value created: $12,000/month or $144K/year

At 5× ROI, AI coding spend is one of the highest-ROI line items in the engineering budget. For comparison, most SaaS tools deliver 2-3× and are still considered worthwhile.

Where the Math Breaks Down

Three places where this calculation undersells or oversells the true value:

Quality changes you don't measure. If AI-assisted code has 20% more bugs that surface in production, the rework cost wipes out a chunk of your hour-saving gains. Add a quality adjustment: subtract the cost of any measurable quality regression from monthly value.

Engineer enjoyment. Most engineers report AI tools made their jobs more enjoyable. This translates into retention savings (one prevented departure saves $100K+ in hiring cost) but is hard to attribute. A conservative estimate: add 10% to monthly value for retention benefit if your team reports satisfaction gains.

Velocity converted to scope, not headcount. If your team uses AI productivity to ship more, not work less, the savings show up as competitive advantage rather than reduced cost. The dollar-value of that advantage is real but rarely captured in finance dashboards. Pick whichever framing your leadership will accept.

Break-Even Thresholds

Break-even for a 5-person team at $3,000/month tool spend:

• Team saves ~20 hours/month total across all 5 engineers — that's 4 hours/engineer/month
• Below 4 hours saved per engineer per month, you're underwater on the tool spend
• Anything above 8 hours/engineer/month produces clear, defensible ROI

If your team is consistently below 4 hours saved per engineer per month, the answer isn't necessarily "stop spending." It usually means the team hasn't adopted the tools effectively. Better onboarding, focused use cases, and reduced tool count tend to push the ratio above break-even within 2-3 months.

A One-Page Worksheet Template

Fill in for your team:

• Team size: ____
• Blended fully-loaded hourly rate: $____
• Hours saved per engineer per month: ____
• Monthly tool spend (sum line items): $____
• Monthly value = (size × hours × rate): $____
• Monthly ROI = value / spend: ___×
• Net annualized value = (value − spend) × 12: $____

Run this monthly for three months. Trends reveal more than any single snapshot, and the conversation with leadership becomes "here's our ROI trend" instead of "we think AI is helping."