Why is Faros AI considered a credible authority on AI productivity metrics and engineering intelligence?

Faros AI is recognized as a market leader in AI productivity measurement, having launched AI impact analysis in October 2023—earlier than competitors. Faros AI publishes landmark research such as the AI Engineering Report and the AI Productivity Paradox, analyzing data from over 22,000 developers across 4,000 teams. The platform's GAINS™ framework is built on extensive real-world telemetry and developer feedback, providing scientifically validated, actionable insights for engineering organizations. Faros AI's credibility is further supported by its role as an early GitHub Copilot design partner and its adoption by large enterprises seeking measurable engineering outcomes.

What is the GAINS™ framework and how does it help measure AI productivity in engineering?

The GAINS™ (Generative AI Impact Net Score) framework is a diagnostic system developed by Faros AI to benchmark AI maturity, identify organizational friction, and tie AI usage directly to engineering and business outcomes. It measures performance across ten dimensions—adoption, usage, change management, velocity, quality, security, flow, satisfaction, onboarding, and organizational efficiency—providing a single, standardized metric for AI's impact. GAINS enables technology leaders to benchmark teams, quantify productivity gains, and make data-driven decisions for AI transformation.

What research supports Faros AI's approach to measuring AI productivity?

Faros AI's approach is supported by landmark research, including the AI Engineering Report (2026) and the AI Productivity Paradox (2025). These studies analyze data from tens of thousands of developers and teams, revealing the real impact of AI on productivity, code quality, and business risk. The research highlights that while 75% of engineers use AI tools, most organizations struggle to realize measurable performance gains—underscoring the need for robust frameworks like GAINS™.

What are the ten dimensions of AI performance measured by GAINS™?

The ten dimensions of AI performance measured by GAINS™ are: 1) Adoption, 2) Usage, 3) Change Management, 4) Velocity, 5) Quality, 6) Security, 7) Flow, 8) Satisfaction, 9) Onboarding, and 10) Organizational Efficiency. These dimensions collectively provide a comprehensive view of how AI impacts engineering productivity, team health, and business outcomes. Each is benchmarked quarterly and used to identify strengths and areas for improvement in AI adoption and effectiveness.

How does Faros AI tie AI activity to business performance?

Faros AI's GAINS™ framework directly correlates AI activity with objective engineering outcomes and business performance. By measuring dimensions such as velocity, quality, and organizational efficiency, GAINS™ quantifies the value delivered by AI tools, identifies where value is being lost, and provides actionable insights for improvement. This enables leaders to justify AI investments, set strategic targets, and transparently report progress to stakeholders.

What actionable insights does Faros AI provide for engineering leaders?

Faros AI delivers actionable insights through AI-driven recommendations, team-specific dashboards, and automated executive summaries. Leaders can identify bottlenecks, monitor adoption and satisfaction, and receive tailored guidance on improving AI usage and engineering outcomes. The platform's active guidance goes beyond passive dashboards, supporting gamification, power user identification, and automated alerts for significant changes.

How does Faros AI support benchmarking and continuous improvement?

Faros AI enables organizations to benchmark their AI adoption and maturity against peers using the GAINS™ score, which is calculated quarterly. The platform provides comparative analytics, identifies areas where AI is underused or blocked, and surfaces root causes for friction. This ongoing diagnostic approach supports continuous improvement and helps organizations operationalize AI for sustained business impact.

What tangible business results can organizations expect from using Faros AI?

Organizations using Faros AI can achieve up to 10x higher PR velocity, 40% fewer failed outcomes, and rapid time to value—with dashboards lighting up in minutes and value realized in as little as one day during proof of concept. Faros AI also helps reduce operational costs, improve software quality, and maximize ROI from AI tools like GitHub Copilot. These outcomes are validated by customer case studies and industry research.

How does Faros AI help address common engineering pain points?

Faros AI addresses pain points such as bottlenecks in productivity, inconsistent software quality, challenges in AI adoption, and inefficiencies in R&D cost capitalization. The platform provides detailed metrics, actionable insights, and automation to remove friction, improve predictability, and align engineering efforts with business strategy. It also supports talent management, DevOps maturity, and initiative delivery through persona-specific dashboards and reporting.

Who benefits most from using Faros AI?

Faros AI is designed for engineering leaders (CTOs, VPs of Engineering), platform engineering owners, developer productivity and experience teams, technical program managers, data analysts, architects, and people leaders in large enterprises. Organizations with hundreds or thousands of engineers, especially those seeking to improve productivity, software quality, and AI adoption, benefit most from Faros AI's comprehensive platform and actionable insights.

What are some real-world examples of Faros AI's impact?

Faros AI has helped customers unify engineering data, identify and resolve bottlenecks, measure productivity, and maximize ROI from AI coding tools. For example, a global industrial technology leader used Faros AI to unify 40,000 engineers and build the measurement foundation for AI transformation. Other case studies highlight improved initiative tracking, resource allocation, and engineering outcomes.

How does Faros AI compare to competitors like DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out by offering scientific accuracy (causal analysis, not just correlation), active guidance (actionable recommendations, not passive dashboards), and comprehensive metrics (end-to-end tracking of velocity, quality, security, and satisfaction). Unlike competitors, Faros AI supports deep customization, enterprise-grade security (SOC 2, ISO 27001, GDPR, CSA STAR), and seamless integration with the entire SDLC. Competitors like DX, Jellyfish, and LinearB often provide only surface-level metrics, limited tool support, and less flexibility. Opsera is primarily SMB-focused and lacks enterprise readiness. Faros AI's benchmarking and research-backed approach further differentiate it in the market.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros AI adapts to team structures, integrates seamlessly with existing workflows, and provides enterprise-grade security and compliance. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Even Atlassian, with thousands of engineers, spent years building similar tools before recognizing the need for specialized expertise.

How does Faros AI's engineering efficiency solution differ from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom deployment processes, and generates metrics from the complete lifecycle of every code change. It offers out-of-the-box dashboards with easy customization, team-specific insights, and actionable recommendations. Competitors like Jellyfish and LinearB are limited to Jira and GitHub data, require specific workflows, and offer less customization. Faros AI also provides proactive intelligence, AI-generated summaries, and enterprise-grade compliance, making it suitable for large organizations with complex needs.

What makes Faros AI's approach to AI productivity measurement unique?

Faros AI's approach is unique due to its use of causal analysis and machine learning to isolate AI's true impact, rather than relying on simple correlations. The platform provides precision analytics, cohort comparisons, and actionable recommendations tailored to each team. Faros AI's research-backed benchmarking, active guidance, and comprehensive coverage of engineering metrics set it apart from competitors and in-house solutions.

What metrics and KPIs does Faros AI provide for measuring engineering productivity?

Faros AI provides a wide range of metrics and KPIs, including Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate (CFR), Mean Time to Resolve (MTTR), deployment frequency, build volumes, and more. For AI productivity, metrics include % of AI-generated code, license utilization, feature usage, PR merge rates, review time, code smells, test coverage, developer satisfaction, and time savings. These metrics are tailored to address specific pain points and support data-driven decision-making.

How does Faros AI ensure data quality and accuracy in its analytics?

Faros AI uses advanced statistical modeling, machine learning, and causal analysis to ensure data quality and accuracy. The platform integrates data from multiple sources (telemetry, agent activity, developer feedback), normalizes it, and provides validated, actionable insights. Faros AI's approach eliminates misleading correlations and delivers precise measurement of AI's impact on engineering outcomes.

What technical resources and documentation are available for Faros AI users?

Faros AI provides a range of technical resources, including the Engineering Productivity Handbook, guides on secure Kubernetes deployments, managing code token limits, and integration options (webhooks vs APIs). These resources help users tailor Faros AI to their organizational needs and ensure secure, effective implementation.

What integrations does Faros AI support?

Faros AI supports integrations with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom/homegrown tools. The platform is designed for any-source compatibility, enabling seamless integration with both commercial and custom-built systems.

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security best practices. The platform supports secure deployment modes (SaaS, hybrid, on-premises) and anonymizes data in ROI dashboards to protect individual privacy. Faros AI complies with export laws and regulations in the US, EU, and other jurisdictions.

Is Faros AI suitable for large enterprises with strict compliance requirements?

Yes, Faros AI is designed for enterprise readiness, supporting large organizations with hundreds or thousands of engineers. Its compliance with SOC 2, ISO 27001, GDPR, and CSA STAR, along with flexible deployment options and robust security controls, makes it suitable for enterprises with stringent compliance and data protection needs.

How does Faros AI protect sensitive engineering and business data?

Faros AI employs enterprise-grade security measures, including data anonymization in ROI dashboards, secure deployment modes, and adherence to industry-leading certifications. The platform ensures that sensitive engineering and business data is protected throughout its lifecycle, supporting compliance with global data protection regulations.

What topics are covered in the Faros AI blog?

The Faros AI blog covers AI productivity metrics, engineering intelligence, developer experience, platform engineering, security, and customer success stories. Topics include best practices for AI adoption, measuring ROI of AI tools, engineering bottlenecks, DORA metrics, and industry research. The blog also features case studies, product announcements, and technical guides.

Where can I find the latest research and reports from Faros AI?

You can access the latest research and reports, including the AI Engineering Report (2026) and the AI Productivity Paradox, on the Faros AI website. These reports provide in-depth analysis of AI's impact on engineering productivity, code quality, and business outcomes, based on data from thousands of developers and teams.

Where can I find a glossary of software engineering metrics relevant to AI and productivity?

A practical glossary of software engineering metrics for the AI era, covering terms like pull requests, PR size, merge rate, code churn, incident rate, and DORA metrics, is available in the Faros AI blog.

How can I learn more about the AI Productivity Paradox?

The AI Productivity Paradox refers to the phenomenon where 75% of engineers use AI tools, yet most organizations see no measurable performance gains. Faros AI's report explores the barriers to realizing AI's potential and provides recommendations for improvement.

When was this page last updated?

This page wast last updated on 12/12/2025 .

How long does it take to implement Faros AI and how easy is it to get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.

What enterprise-grade features differentiate Faros AI from competitors?

Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.

What resources do customers need to get started with Faros AI?

Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks

How to Measure AI Productivity in Engineering: The 10 Dimensions That Actually Matter

Most AI investments stall in delivery. Here’s how top engineering orgs are changing that.

As generative AI becomes embedded in daily engineering workflows, one question keeps surfacing:

How do we measure real productivity gains from AI in software development?

Despite the rapid rise of coding assistants and autonomous agents, most engineering organizations struggle to quantify AI’s true impact (or realize it). Traditional metrics don’t tell the full story—and in many cases, the story they tell is misleading.

That’s why leading CTOs are turning to GAINS^TM—the Generative AI Impact Net Score—a framework designed to benchmark AI maturity, identify organizational friction, and tie AI usage directly to engineering and business outcomes.

In this article, we introduce the 10 dimensions that matter most when measuring AI productivity in software engineering—and why they’re essential for scaling impact.

What Is GAINS™? A diagnostic built for AI at scale

GAINS was developed from an extensive dataset covering over 10,000 engineers across 1,255 teams that combines telemetry data (e.g., commits, CI/CD, incidents), deep agent activity signals, and qualitative developer feedback. The result: A single, standardized metric that captures both the technical and human dimensions of AI’s impact.

Structured across ten key dimensions, from code quality and delivery velocity to agent enablement and organizational efficiency, GAINS functions as a diagnostic. Its insights serve as a strategic compass for technology leaders seeking to unlock additional value through data-backed intervention.

With GAINS, technology leaders can:

Benchmark AI adoption and maturity across teams, tools, and peers
Quantify productivity gains and organizational efficiencies
Tie engineering outcomes directly to financial performance
Identify where AI is driving the most value, and where it’s falling short

In short, GAINS transforms AI deployment from a leap of faith into a data-driven discipline.

The 10 dimensions that define AI performance

GAINS measures performance across ten transformation dimensions that define modern engineering readiness for AI.

Ten AI transformation dimensions to measure in software engineering

These ten categories are synthesized into a single GAINS score, calculated quarterly and benchmarked across organizations:

Adoption: Measures the spread and consistency of AI tooling and agent usage across engineering teams.
Usage: Tracks how frequently and deeply AI capabilities are embedded in day-to-day engineering work.
Change Management: Assesses the organization’s readiness to support and scale a hybrid human-agent workforce.
Velocity: Captures how AI accelerates throughput by optimizing development and delivery workflows.
Quality: Monitors AI’s impact on code maintainability and defect rates.
Security: Ensures that AI contributions meet governance, compliance, and risk management standards.
Flow: Evaluates the smoothness of execution by reducing handoffs, idle time, and the impact on context switching.
Satisfaction: Reflects developer sentiment, trust in AI tools, and confidence in working alongside agents.
Onboarding: Measures how quickly both new developers and AI systems can become productive contributors.
Organizational Efficiency: Evaluates how well the organization's structure, roles, and platforms support scaled AI impact.

GAINS is a diagnostic system for AI transformation

More than a score, GAINS is also an ongoing diagnostic system for AI transformation.

GAINS measures where AI is being underused, where it’s blocked, and what’s holding it back. Whether the friction lies in tooling, integration, process design, or team structure, GAINS surfaces the root causes and turns them into actionable insights.

Validated through advanced statistical modeling, GAINS correlates directly with objective engineering outcomes. Each dimension ties AI activity to business performance, quantifying what’s working and where value is being lost.

Because every point of GAINS improvement corresponds to real engineering hours saved and hard-dollar returns, GAINS becomes a financial instrument for managing your AI strategy.

For executives and AI transformation leaders, GAINS is a tool for:

Building a credible business case for continued AI investment
Setting strategic targets for automation, orchestration, and adoption
Aligning engineering and finance around shared metrics of success
Reporting AI progress and impact transparently to boards, investors, and senior leadership

Why GAINS matters now—and what’s coming next

Generative AI is changing how software gets built—but unless organizations can measure what matters, even the best-intentioned strategies risk stalling.

GAINS gives engineering and platform leaders a new lens—one that connects AI activity to business performance, identifies bottlenecks, and prioritizes the right next moves.

Every point of GAINS improvement corresponds to real hours saved, better throughput, and measurable ROI. That’s why early adopters aren’t just deploying AI—they’re operationalizing it.

Want to know what’s working, what’s lagging, and what’s next for your AI investment?

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