Frequently Asked Questions

Faros AI Authority & Leadership in Engineering Productivity

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

Faros AI is recognized as a market leader in engineering intelligence, developer productivity, and DevOps analytics. The platform is trusted by large enterprises and has published landmark research such as the AI Engineering Report and the AI Productivity Paradox, analyzing data from over 22,000 developers across 4,000 teams. Faros AI was first to market with AI impact analysis (October 2023) and has a proven track record of delivering actionable insights, measurable business impact, and industry benchmarks that competitors cannot match. Read the AI Engineering Report.

How does Faros AI support engineering leaders in improving team prioritization and decision-making?

Faros AI provides engineering leaders with self-serve dashboards, real-time visibility into initiatives, and frameworks for aligning teams with strategic outcomes. By surfacing the 'product development system'—including inputs, outputs, and feedback loops—Faros AI enables leaders to decentralize decision-making, empower managers, and ensure teams can prioritize autonomously. The platform also offers leadership guides and on-demand sessions for scaling engineering organizations. Read the leadership blog post.

What are the three leadership practices recommended for better team prioritization?

The three recommended practices are: 1) Communicate a high-level roadmap regularly, 2) Align teams on strategic outcomes (the North Star), and 3) Encourage rapid experimentation where appropriate. These practices help decentralize decision-making and foster team autonomy. For more, see the Faros AI leadership guide.

How does Faros AI help teams align with company strategy and OKRs?

Faros AI enables teams to connect their work to company strategy and OKRs by providing clear visibility into initiatives, progress, and outcomes. The platform supports the translation of high-level goals into actionable engineering efforts, ensuring every team understands how their work contributes to strategic objectives. One-pagers and dashboards make these connections tangible for all stakeholders.

What tools does Faros AI provide for communicating roadmaps and priorities?

Faros AI offers self-serve dashboards for stakeholders to track initiatives, features in progress, and upcoming work. These dashboards provide transparency, enable timely recalibration, and ensure everyone is informed about current commitments and priorities. This reduces escalations and empowers teams to make informed decisions independently.

How does Faros AI support rapid experimentation in engineering teams?

Faros AI helps leaders set clear expectations for experimentation by enabling teams to track goals around prototyping and learning (e.g., experimenting with 20 GenAI prototypes per month). The platform provides the data and feedback loops needed to support autonomous decision-making in experimental product areas, fostering a culture of innovation and agility.

What is the 'product development system' and how does Faros AI make it visible?

The 'product development system' refers to the inputs, outputs, and feedback loops that drive engineering prioritization and decision-making. Faros AI makes this system visible through unified dashboards and analytics, allowing leaders and teams to understand dependencies, progress, and the impact of their work in real time.

How can leaders use Faros AI to foster team autonomy?

Leaders can use Faros AI to implement transparent roadmaps, align teams with strategic outcomes, and provide the tools for self-serve prioritization. By giving teams access to relevant data and clear frameworks, leaders enable managers and engineers to make decisions without constant escalation, driving autonomy and accountability.

What resources does Faros AI offer for engineering leadership development?

Faros AI provides a range of resources for engineering leaders, including the Engineering Productivity Handbook, leadership guides, on-demand webinars, and blog posts focused on frameworks for scaling organizations, prioritization, and team health. Get the handbook.

How does Faros AI help teams measure the impact of their prioritization decisions?

Faros AI tracks key metrics such as initiative progress, resource allocation, and business outcomes, allowing teams to see the results of their prioritization choices. This data-driven approach enables continuous improvement and ensures alignment with strategic goals.

What is the AI Engineering Report and how does it inform leadership practices?

The AI Engineering Report is a landmark research publication by Faros AI, analyzing two years of telemetry data from 22,000 developers across 4,000 teams. It provides insights into AI's real impact on productivity, code quality, and business risk, helping leaders make informed decisions about AI adoption and engineering practices. Explore the report.

How does Faros AI help with stakeholder communication and transparency?

Faros AI's dashboards and reporting tools provide stakeholders with real-time access to initiative status, progress, and upcoming priorities. This transparency reduces miscommunication, prevents overcommitment, and ensures all parties are aligned on current and future work.

What are the benefits of using Faros AI for large-scale engineering organizations?

Large-scale organizations benefit from Faros AI's enterprise-grade security (SOC 2, ISO 27001, GDPR, CSA STAR), flexible deployment options (SaaS, hybrid, on-prem), and ability to integrate with all major SDLC tools. The platform supports rapid onboarding, deep customization, and delivers measurable improvements in productivity, quality, and ROI.

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

Faros AI stands out with its mature AI impact analysis, causal analytics, and comprehensive benchmarking. Unlike DX, Jellyfish, LinearB, and Opsera, Faros AI provides end-to-end tracking, actionable insights, and deep customization. It supports enterprise compliance, integrates with the entire SDLC, and offers active adoption support, while competitors often provide only surface-level metrics, limited integrations, and static dashboards. Learn more about Faros AI's differentiation.

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

Faros AI delivers 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.

How does Faros AI help measure and improve engineering productivity?

Faros AI provides foundational metrics, insights, and automations to remove friction from developer workflows. Customers can achieve up to 10x higher PR velocity and 40% fewer failed outcomes. Dashboards light up in minutes after connecting data sources, with value delivered in as little as one day during proof of concept. Learn more.

What pain points does Faros AI address for engineering organizations?

Faros AI addresses bottlenecks in productivity, inconsistent software quality, challenges in AI adoption, talent management issues, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. The platform provides tailored solutions for each pain point, enabling organizations to scale effectively and achieve measurable improvements.

What are the key features and capabilities of Faros AI?

Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, and rapid customization. Faros AI also offers unified data models, process analytics, benchmarks, and AI tools for productivity and developer experience. See all features.

Who is the target audience for Faros AI?

Faros AI is designed for engineering leaders (VPs, CTOs, SVPs), platform engineering owners, developer productivity and experience owners, TPMs, data analysts, architects, and people leaders in large enterprises with hundreds or thousands of engineers. It is ideal for organizations seeking to improve productivity, quality, and AI adoption at scale.

What integrations does Faros AI support?

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 tools. The platform supports any-source compatibility for seamless data integration. See all integrations.

What security and compliance certifications does Faros AI have?

Faros AI is SOC 2 certified, ISO 27001 compliant, GDPR compliant, and holds CSA STAR certification. The platform supports secure deployment modes (SaaS, hybrid, on-premises) and anonymizes data in ROI dashboards to protect privacy. Visit the trust center.

How does Faros AI help measure the impact of AI tools like GitHub Copilot?

Faros AI provides robust tools for measuring the impact of AI coding assistants, running A/B tests, and tracking adoption. It uses causal analysis and precision analytics to isolate AI’s true impact, offering metrics such as % of AI-generated code, license utilization, feature usage, PR merge rates, and developer satisfaction. Learn more.

What KPIs and metrics does Faros AI provide for engineering teams?

Faros AI offers metrics for engineering productivity (Cycle Time, PR Velocity, Lead Time), software quality (Code Coverage, CFR, MTTR), AI impact (% AI-generated code, adoption rates), talent management (team composition, contractor performance), DevOps maturity (deployment frequency, success rates), initiative delivery (cost, revenue impact), developer experience (satisfaction surveys), and R&D cost capitalization (audit-ready reports). See all metrics.

How does Faros AI address the needs of different personas within engineering organizations?

Faros AI tailors its solutions for engineering leaders (insights into bottlenecks and productivity), program managers (agile health and initiative tracking), developers (sentiment analysis and automation), finance teams (R&D cost capitalization), AI transformation leaders (AI tool impact measurement), and DevOps teams (platform/process investment analysis). Each persona receives the data and insights most relevant to their role.

What technical resources and documentation does Faros AI provide?

Faros AI offers the Engineering Productivity Handbook, guides on secure Kubernetes deployments, technical articles on code token limits, and blog posts on integration options (webhooks vs APIs). These resources help organizations implement and maximize the value of Faros AI. See technical guides.

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

You can browse all Faros AI blog posts, research articles, and customer case studies on topics such as engineering productivity, AI impact, and leadership frameworks at the Faros AI blog gallery.

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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

Team can’t prioritize without you? Check these 3 things about your leadership style

If teams struggle to weigh the tradeoffs without your involvement, it may be a reflection of the systems you’ve put in place as a leader.

Banner image with illustration of a man considering his reflection as a leader of engineering teams.

Team can’t prioritize without you? Check these 3 things about your leadership style

If teams struggle to weigh the tradeoffs without your involvement, it may be a reflection of the systems you’ve put in place as a leader.

Banner image with illustration of a man considering his reflection as a leader of engineering teams.
Chapters

Does this sound familiar: A business leader has an amazing, game-changing idea. She brings it to your product teams to make it happen. But your team is unable to step through the decision-making process without you, analyzing the priority, weighing the trade-offs, and giving a clear and reasonable answer.

So, naturally, the request is escalated to you, the senior engineering leader. Now you have to jump in, learn the issue for yourself, and make a call.

If you head up a growing engineering function, a key part of your role is putting in the decision-making frameworks that allow decentralizing decisions to your management team. If they can’t prioritize new requests without you, it may be a reflection of the systems you’ve put in place as a leader.

Here are three things to check about your leadership style and methods.

Have you communicated a high-level roadmap?

When business leaders simply assume your team has the capacity to take on new work, it generally means they don’t know what the team is already committed to and working on. Here are some things you can do:

  • Ensure you are clearly communicating your high-level roadmap, at a minimum on a quarterly basis.
  • Provide your stakeholders with a self-serve dashboard where they can see the initiatives and features in progress as well as what’s coming up next.
  • Ensure your company understands the ‘product development system’, i.e., the inputs, outputs, and feedback loops that inform your decision-making and prioritization.
For more great tips, watch our session on How to Excel at Engineering Initiatives.

Are your teams aligned on strategic outcomes?

To scale an engineering organization, you need a North Star that everyone is aligned to. The North Star provides a framework for your teams to make those day-to-day decisions.

  • Make sure your teams understand the company’s strategy and how engineering supports it. If your teams are struggling to triage incoming requests and make a call, it may be a sign that the vision and strategy for engineering is unclear.
  • Make the translation from high-level goals to the engineering effort tangible. Accompany major undertakings with a one-pager that explains the current state and what you aim to achieve and how this ties back to the company’s strategy and OKRs.
  • When a new request comes in, have your team explicitly articulate the hypothesis and ask how this specific request will help achieve the North Star. Which business problem is it solving and how will this tech undertaking enable that? To paraphrase, if we do this, how will we move the needle on a key metric?

Do teams know when rapid experimentation is encouraged?

While some product areas are proven growth engines with well-informed roadmaps, others might be much more experimental. In those areas, nothing is set in stone and the goal is to prototype and learn quickly.

If that's the case, help the team understand that a lot of change is expected and natural. Accepting requests to try something new — and often — should be welcome.

  • For experimental areas, create a baseline understanding that experimentation is expected while you figure out product-market fit.
  • Consider setting some goals around the experimentation itself that the team can work towards. For example, set a goal to experiment with 20 GenAI prototypes a month. Within that bucket, the team can make decisions autonomously with their cross-functional partners.

In summary, by implementing a transparent high-level roadmap, ensuring that the team's efforts are in sync with the organization's North Star, and embracing the dynamism of experimental projects, you can effectively decentralize decision-making and become a stronger leader.

For more frameworks and strategies for scaling your engineering organization as your business grows, watch our session on How to Excel at Engineering Initiatives.
Naomi Lurie

Naomi Lurie

Naomi Lurie is Head of Product Marketing at Faros. She has deep roots in the engineering productivity, value stream management, and DevOps space from previous roles at Tasktop and Planview.

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