AI Productivity Impact
Maximize AI CODING Impact & ROI
Move from pilots to enterprise-scale programs with the only platform built to prove AI coding ROI using cause-and-effect analysis. Understand impact. Optimize adoption. Scale what delivers.
Centralized AI coding impact analysis and insight
Adoption metrics
Granular
Coarse
Usage tracking
Full data history
Partial
Downstream impact metrics
Cause-and-effect analysis on velocity, quality, security, and satisfaction with data from 100+ tools
Limited
Team and Power User views
A/B and Before/After analysis
Out-of-the-box dashboards for tracking adoption, impact, risk and value
Out-of-the-box developer surveys
Team-tailored alerts and recommendations to address new bottlenecks
Comprehensive visibility into the impact of AI coding tools
AI is everywhere. Impact isn't.
Copilot, Claude, Codex, Cursor—teams are using multiple AI tools every day. But enterprise leaders are still grappling with what's working, what's not, and whether spend is actually translating into results.
Faros provides defensible impact measurement by tying AI adoption and usage to real engineering outcomes. Establish baselines, quantify lift, and communicate ROI across speed, throughput, quality, and risk—by team, repo, and workflow.
Unlock 40% higher ROI from your AI coding tools
New AI coding tools are announced daily. Improve your return on investment with a full measurement framework that guides you from trial to rollout to optimization.
Optimize tooling mix
Identify the most effective AI tools and models, best-suited to your code and favored by your developers.
Prove business value
Quantify and improve ROI with cause-and-effect analysis tied to delivery, quality, and risk.
Scale with control
Scale AI adoption strategically and safely with governance, guardrails, and end-to-end monitoring.
“While AI coding tools have the potential to increase productivity, having a way to evaluate their impact scientifically will help build the business case for the investment.”


Insights to guide your AI investment
Track adoption and usage over time
Without usage, there can be no ROI. Optimize your rollout for higher impact with insights into adoption and usage.
- Measure daily, weekly, and monthly adoption.
- Track acceptance rates and lines of code generated, by language and editor.
- Measure the percentage of AI-generated code by repo.
- Identify unused licenses and power users who can train others.
Measure downstream impacts across the entire SDLC
Separate hype from reality and set the right expectations for your organization.
- Identify emerging bottlenecks in code review, deployment, or QA.
- Maintain visibility into quality, reliability, and tech debt.
- Identify potential security and compliance issues in AI-augmented code.
- Present clear cost/benefit metrics to executive leadership and finance.
Understand time savings and developer satisfaction
See how accelerated coding is changing development patterns.
- Measure time savings and where they’re being reinvested (velocity, quality, tech debt, upskilling, etc.).
- Capture developer sentiment with out-of-the-box surveys.
- Understand which teams and roles are adopting and benefiting most.
- Improve training and mentoring to optimize your rollout.
Frequently asked questions
Faros provides a wealth of metrics to understand what coding assistants like GitHub Copilot, Cursor, Windsurf, Claude, Devin and others does to productivity, velocity, quality, security, developer satisfaction, and more. Sample metrics include PR Merge Rate, PR Merge Time, Review Time, PR Cycle Time, PR Size, Test Coverage, Code Smells, Number of Bugs, and Number of Incidents. We also capture developer satisfaction from out-of-the-box or custom surveys.
We make it easy to compare different cohorts to contrast adoption and ROI for different AI coding assistants. For example, one client used Faros to compare GitHub Copilot with Amazon Q Developer. Productivity metrics, developer preference, and programming language support helped determine the best tool for them.
Faros helps increase adoption, whether adoption is mandated top-down or growing bottom-up.
- Identifies power users or ‘power user teams’ within the organization, who can be converted to champions, trainers, and mentors for others.
- Illustrates the impact of enablement and training through cohort comparison, to show whether it’s beneficial and for whom.
- Compares adoption among junior, mid-level, and senior engineers to identify which communities need the most facilitation and encouragement.
Not a problem. Faros can capture historical data, such that the full pilot duration will be covered. In addition, the baseline prior to using the AI coding tool will also be available to measure the benefits.
Yes. The data displayed in our dashboards is at the team level. Optionally, you can turn on Power User identification to study their outcomes closely.
Your security and privacy are assured. Faros is SOC 2, ISO 27001, and GDPR-compliant. Visit our Trust Center at security.faros.ai.
maximize your coding roi
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Context for the work you do every day
Engineering context powers the use cases that help your organization transform as fast as AI evolves.