Are Your Developers Actually Using AI Coding Tools?
You're spending $19-39/month per developer on GitHub Copilot, Cursor Pro, or Claude. Get real data on who's using it, how often, and what ROI you're actually getting.
Track Your AI Tool ROIThe AI Tool Blind Spot Every CTO Has
You approved the Copilot licenses. Your CFO asks "What's the ROI?" You have no idea.
Wasted Spend
40-60% of Copilot licenses go unused. That's $14K-21K/year wasted for a 20-person team.
No Adoption Tracking
You don't know which developers are AI power users vs. those who need training.
Can't Prove ROI
Your CFO asks "Is Copilot worth it?" You can't show productivity data tied to AI tool usage.
Real Story: Series B SaaS Company
"We had 30 Copilot licenses at $39/month. Our CTO assumed everyone was using it. When we finally audited, only 18 developers were active users. 12 licenses ($5,616/year) were completely unused."
"Worse: our two senior devs who WEREN'T using Copilot were also our slowest shippers. They needed training, not more tools. We reallocated licenses and ran training sessions. Output went up 22%."
— VP Engineering at 80-person startup
What RemoteBeat Tracks for AI Tools
Go beyond basic adoption metrics—track model usage, feature utilization, best practices adoption, and true cost-per-output ROI.
🎯 GitHub Copilot Metrics
- Adoption rate per developer (% of commits using Copilot)
- Active vs inactive license identification
- Acceptance rate trends over time
- Correlation with code review velocity
⚡ Cursor & Claude Code
- Cursor vs VS Code usage patterns
- AI-assisted vs manual development tracking
- Team-wide AI tool adoption curves
- Claude Code agent usage in PRs
🧠 Model & Feature Usage
- Which AI models are developers using (GPT-4, Claude Sonnet, etc.)
- Plan mode vs direct coding usage
- Debug mode and agent tool adoption
- Advanced features vs basic autocomplete
🔌 MCP Server Usage
- DevTools MCP for frontend developers
- Ticketing system connectors (Jira, Linear)
- Database MCP servers for schema understanding
- Custom internal MCP adoption rates
💰 Cost vs Output ROI
- Cost per PR shipped (AI-assisted vs manual)
- Feature velocity impact per AI license
- Code quality metrics (reviews, bugs) correlated with AI usage
- Developer efficiency scores by model choice
📈 Advanced Analytics
- Developer efficacy by model (Sonnet vs GPT-4 vs Haiku)
- Optimal tool stack recommendations per role
- Feature adoption lag identification (who's behind on new capabilities)
- Predictive ROI modeling for new AI tool investments
⚙️ Best Practices & Config
- Team/project hooks adoption (pre-commit, PR automation)
- Custom rules usage across team (linting, formatting, security)
- AI coding skills/agents deployment (who's using what)
- Standardization gaps (identify inconsistent setups)
🎯 Track Team-Wide Best Practices Adoption
See who's following your team's AI coding standards—and who needs onboarding to your hooks, rules, skills, and MCP server configurations.
🪝 Hooks & Automation
- • Pre-commit hooks (formatting, linting, security scans)
- • Post-merge automation (deploy previews, notifications)
- • PR automation hooks (auto-labeling, review assignment)
- • Identify developers missing critical hooks
📐 Rules & Standards
- • Team coding rules compliance (ESLint, Prettier configs)
- • Security policy enforcement (secret scanning, dependency checks)
- • Custom project rules adoption rates
- • Standardization across team members
🤹 Skills & Agents
- • Custom AI coding skills deployment (code review, testing, docs)
- • Specialized agent usage by team/project
- • Skill effectiveness metrics (time saved, quality improvement)
- • Skill sharing and adoption trends
🔌 MCP Server Configs
- • Team MCP server adoption (DevTools, DB, ticketing connectors)
- • Custom internal MCP server usage
- • Configuration drift detection (who's outdated)
- • Best-performing MCP stacks by role
💡 Example Insight: "3 junior developers aren't using the team's pre-commit hook for TypeScript linting. Their PRs average 12 review cycles vs 3 for developers with the hook configured."
Automatic ROI Calculation
How It Works
Connect Your Tools
Link GitHub (for Copilot metrics), commit metadata, and your project management tools in under 5 minutes.
Automatic Tracking
RemoteBeat analyzes commit patterns, code review data, and IDE telemetry to identify AI tool usage.
Actionable Insights
Get dashboards showing adoption rates, ROI calculations, and recommendations to optimize your AI tool investment.
Real Use Cases
Cut Wasteful Spending
"Discovered 8 developers weren't using their $39/mo Copilot licenses. Saved $3,744/year by reallocating to power users only."
— CTO, 40-person engineering team
Targeted Training
"Saw junior devs had 12% Copilot adoption vs 85% for seniors. Ran AI tool workshops. Junior productivity up 31% in 2 months."
— VP Engineering, Series B startup
Prove ROI to CFO
"CFO wanted to cut Copilot budget. Showed that devs using Copilot shipped 40% more PRs/week. Budget approved instantly."
— Engineering Director, Enterprise
Tool Migration Tracking
"Moving from Copilot to Cursor. RemoteBeat tracked adoption week-by-week. Hit 80% migration in 6 weeks vs 6-month estimate."
— CTO, AI-first company
Stop Guessing. Start Tracking Your AI Tool ROI.
Join engineering leaders who track what matters: real AI tool adoption, not hopes and assumptions.
14 days free. No credit card. See exactly how your team uses AI tools.
Track Your AI Tool ROI
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