Why is Faros AI considered a credible authority on AI productivity for engineering teams?

Faros AI is recognized as a market leader in engineering productivity measurement, having launched AI impact analysis in October 2023 and published landmark research such as the AI Engineering Report and AI Productivity Paradox. With data from over 22,000 developers across 4,000 teams, Faros AI provides scientifically accurate, actionable insights and has been an early design partner with GitHub Copilot. Its platform is trusted by large enterprises for its proven results and maturity in the field. Read the AI Productivity Paradox report.

What makes Faros AI's research and benchmarking unique?

Faros AI's research is based on real-world telemetry and comparative benchmarking across thousands of teams, enabling organizations to understand what 'good' looks like and how AI tools impact productivity, quality, and business risk. Competitors lack this depth of benchmarking and scientific rigor. Faros AI's reports, such as the Acceleration Whiplash (2026), provide actionable recommendations for engineering leaders. Explore the report.

What is the AI Productivity Checklist for engineering teams?

The AI Productivity Checklist is a practical guide for managers and developers to ensure AI tools like Copilot and ChatGPT deliver net positive outcomes. It includes questions to ask developers about test coverage, code quality, security, and documentation, as well as metrics to track such as code review cycle time, QA cycle time, change failure rate, MTTR, and lead time. Read the full checklist.

What metrics should managers track when adopting AI coding tools?

Managers should monitor metrics including code review cycle time, QA cycle time, change failure rate, mean time to resolve (MTTR), and lead time to production. These metrics help assess whether AI tools are improving productivity and quality or introducing new risks. Faros AI specializes in visibility and analytics for these KPIs across any environment and stack. Learn more.

How does Faros AI help teams measure the impact of AI tools like Copilot and ChatGPT?

Faros AI provides causal analysis and precision analytics to isolate the true impact of AI tools. It tracks metrics such as % of AI-generated code, license utilization, feature usage, PR merge rates, review time, code smells, test coverage, developer satisfaction, and time savings. This enables organizations to run A/B tests, track adoption, and optimize ROI. Explore Faros AI Platform.

What are the most common productivity gains observed with AI adoption in engineering teams?

With increased AI adoption, teams have observed epics completed per developer up 66%, task throughput per developer up 33.7%, and PR merge rate per developer up 16.2%. These improvements translate to more features shipped, more initiatives completed, and more code entering the codebase. See the data.

What features does Faros AI offer for engineering productivity?

Faros AI offers cross-org visibility, tailored analytics, AI-driven insights, workflow automation, open platform integration, enterprise-grade security, customizable dashboards, unified data models, intelligent attribution, process analytics, benchmarks, heatmaps, and AI-powered productivity tools. These features help organizations scale, improve outcomes, and maximize ROI. See platform features.

Does Faros AI support integration with popular engineering tools?

Yes, Faros AI integrates with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom homegrown scripts. Its any-source compatibility ensures seamless integration with commercial and custom-built tools. Learn more about integrations.

What technical documentation and resources does Faros AI provide?

Faros AI offers resources such as the Engineering Productivity Handbook, guides on secure Kubernetes deployments, Claude Code token limits, and blog posts on webhooks vs APIs for data ingestion. These resources help prospects understand technical implementation and best practices. Access the handbook.

Who can benefit from Faros AI?

Faros AI is ideal for engineering leaders, platform engineering owners, developer productivity and experience owners, technical program managers, data analysts, architects, and people leaders at large US-based enterprises with hundreds or thousands of engineers. It is especially suited for organizations seeking to improve productivity, quality, and AI adoption. Learn more.

What business impact can customers expect from using Faros AI?

Customers can expect up to 10x higher PR velocity, 40% fewer failed outcomes, dashboards lighting up in minutes, value in just 1 day during proof of concept, optimized ROI, strategic decision-making, scalable growth, and cost reduction. Faros AI enables rapid and measurable improvements in engineering operations. See business impact.

What are some real-world case studies of Faros AI addressing engineering pain points?

Faros AI has helped customers make data-backed decisions on engineering allocation, improve team health and progress tracking, align metrics to roles, and simplify agile health tracking. Case studies include SmartBear's scaling of software engineering and a global industrial technology leader unifying 40,000 engineers for AI transformation. Read customer stories.

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses bottlenecks in productivity, inconsistent software quality, challenges in measuring AI impact, talent management issues, DevOps maturity, initiative delivery tracking, developer experience gaps, and manual R&D cost capitalization. Its platform provides actionable insights and automation to solve these problems. See solutions.

How does Faros AI solve pain points differently for various personas?

Faros AI tailors solutions for engineering leaders (productivity insights), program managers (agile health tracking), developers (sentiment correlation and context automation), finance teams (R&D cost capitalization), AI transformation leaders (AI tool impact measurement), and DevOps teams (investment optimization). Each persona receives precise data and actionable recommendations. Learn more.

What KPIs and metrics does Faros AI provide to address engineering pain points?

Faros AI delivers metrics such as cycle time, PR velocity, lead time, throughput, review speed, code coverage, test coverage, code smells, test flakiness, change failure rate, MTTR, AI-generated code %, team composition benchmarks, deployment frequency, initiative cost, developer satisfaction, and finance-ready R&D reports. See metrics.

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

Faros AI stands out with scientific accuracy (causal analysis), active adoption support, end-to-end tracking, deep customization, enterprise-grade security, and developer experience integration. Competitors offer only surface-level correlations, passive dashboards, limited metrics, rigid setups, and SMB-only solutions. Faros AI is available on Azure, AWS, and Google Cloud Marketplaces and supports compliance standards like SOC 2, ISO 27001, GDPR, and CSA STAR. See comparison.

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, proven scalability, and immediate value. Unlike hard-coded in-house solutions, Faros AI adapts to team structures, integrates with existing workflows, and provides enterprise-grade security. Its mature analytics and actionable insights reduce risk and accelerate ROI compared to lengthy internal development projects. Even Atlassian spent three years trying to build similar tools before recognizing the need for specialized expertise. Learn more.

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, offers out-of-the-box dashboards, supports custom deployment processes, provides accurate metrics from the complete lifecycle, and delivers actionable insights and AI-generated summaries. Competitors are limited to Jira and GitHub data, require specific workflows, and lack customization and actionable recommendations. See Engineering Efficiency solution.

What security and compliance certifications does Faros AI support?

Faros AI adheres to SOC 2, GDPR, ISO 27001, and CSA STAR certifications, ensuring rigorous standards for data security, privacy, and cloud transparency. The platform supports secure deployment modes (SaaS, hybrid, on-premises), anonymizes data in ROI dashboards, and complies with export laws. Visit the trust center.

How does Faros AI ensure data privacy and security for engineering teams?

Faros AI anonymizes data in ROI dashboards, supports secure deployment modes, and complies with US, EU, and other jurisdictional export laws. Its enterprise-grade security ensures that privacy and control are never compromised. Learn more.

What topics are covered in Faros AI's blog?

The Faros AI blog covers AI productivity, engineering intelligence, developer experience, security, platform engineering, customer case studies, best practices for AI tool adoption, metrics glossaries, and product announcements. Articles provide actionable insights, benchmarking data, and practical recommendations. Explore the blog.

Where can I find more blog posts and research from Faros AI?

You can browse additional insights, research, and best practices in Faros AI's blog gallery. Visit the blog gallery.

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 Faros AI's blog. Read the glossary.

What insights are shared about McKinsey's engineering productivity framework?

Faros AI revisits McKinsey's software engineering productivity framework, reflecting on changes and recommendations for implementing visibility within days. Insights from Vitaly Gordon are included. See customer stories.

What does the AI Productivity Paradox report from Faros reveal?

The AI Productivity Paradox report reveals that while 75% of engineers use AI tools, most organizations do not see measurable performance gains. The report explains what holds teams back and how to address these challenges quickly. Read the full report.

How can engineering leaders measure AI productivity in software engineering?

Engineering leaders can measure AI productivity using the GAINS framework, which evaluates throughput, organizational efficiency, and ten transformation dimensions. Robust frameworks are essential for assessing real productivity impact. Read more.

What are the top sprint metrics that improve developer productivity?

The top four sprint metrics that improve developer productivity are outlined for engineering teams to track and unlock better outcomes. These metrics are detailed in Faros AI's customer stories gallery. See sprint metrics.

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

Faros AI's AI Productivity Checklist for Engineering Teams

AI Productivity Checklist for Engineering Teams Using ChatGPT and Coding Copilots

Github Copilot has been activated by more than one million developers in over 20,000 organizations, generating a staggering three billion accepted lines of code. So it’s likely your team is using it.

While your developers may be thrilled with the shortcuts and time savings, as their manager do you know the net impact AI is having on your KPIs for team productivity, quality, and lead time?

Do you know how to have a conversation with your team about using AI for a net positive outcome?

We've created a checklist on how to have those conversations and what you should be tracking.

But, first...

Make sure you know how engineers are using AI in coding

Several enterprises have been monitoring the impact of rolling out new tools like Github Copilot and developers' unofficial adoption of chatGPT.

An initial study of enterprise usage shows the potential for tremendous time savings:

Copilot Code Autocomplete is widely adopted for writing boilerplate code, skeleton code, code comments, and tests. It can save developers up to 20% coding time.
Copilot Code Suggestions are deemed less valuable and helped in only 25% of the cases. For this use case, developers prefer chatGPT over Copilot to create code snippets from specs, translate from one programming language to another, or as a tutor for debugging. Estimated savings are over 1hr per day per developer.

But fascinatingly, Lead Time to Production has yet to improve despite personal productivity gains. Even with faster dev times, the time spent in code review, merging, and testing is still long.

That's where the AI Productivity Checklist comes in: To ensure AI helps your team realize overall productivity improvements in speed and velocity.

The AI Productivity Checklist

Given that you want to see net gains in lead time and productivity for the team, below is a checklist to guide your conversations with the team and ensure you monitor important KPIs for adverse effects.

The checklist has two parts — questions to ask your developers and metrics you should track as a manager.

Combined, the checklist will help create awareness around the impacts of introducing sub-optimal code generated by AI. You'll be able to ensure the efficiency gains for the individual aren’t dwarfed by the negative impacts on the team, your customers, and the business.

Here you go:

1) Questions to ask your developers:

☑ Do you have good test coverage for generated code?

☑ Do you have a way to assess the code quality of generated code?

☑ Are you able to identify potential security and compliance issues introduced by generated code?

☑ Is documentation for generated code clear and sufficient?

2) Metrics to track as a manager:

☑ Code Review Cycle Time: Are code reviews taking longer?

☑ QA Cycle Time: Is there an uptick in bugs and incidents? Is more time being spent on rework?

☑ Change Failure Rate: Are failures increasing?

☑ MTTR: Is incident resolution getting slower?

☑ Lead Time: Has overall lead time to production gotten faster or slower?

Need metrics?

Metrics that analyze the impact of new technology and practices on engineering processes and performance have become business-critical.

Faros AI specializes in visibility and analytics across any environment and stack. We know all about non-standard tool implementations, highly customized pipelines, homegrown systems, and proprietary data sources.

Talk to us about our extensible, customizable software engineering intelligence platform.

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