AI ENGINEERING REPORT 2026
acceleration whiplash
The definitive data on AI's engineering impact. What's working, what's breaking, and what leaders need to do next.
- Engineering throughput is up
- Bugs, incidents, and rework are rising faster
- Two years of data from 22,000 developers across 4,000 teams
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
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.