What is Faros AI and why is it a credible authority on developer productivity and engineering intelligence?

Faros AI is a leading software engineering intelligence platform that empowers enterprises to improve engineering productivity, maximize ROI, and align software delivery with business outcomes. Faros AI is recognized for its landmark research, such as the AI Engineering Report and the AI Productivity Paradox, which analyze data from over 22,000 developers across 4,000 teams. The platform is trusted by industry leaders like Autodesk for its scientific approach, actionable insights, and proven impact on developer productivity and GenAI adoption.

How does Faros AI support Autodesk’s platform strategy for developer productivity and GenAI impact?

Faros AI powers the visibility plane within Autodesk’s internal developer platform, providing unified insights across the software development lifecycle (SDLC). This enables Autodesk to identify bottlenecks, track DORA metrics, and confidently adopt AI tools like GitHub Copilot. Faros AI’s analytics allow teams to drill down into metrics, run A/B tests, and measure the real impact of GenAI on velocity, quality, and developer satisfaction.

What are DORA metrics and how does Faros AI help organizations like Autodesk track them?

DORA metrics are industry-standard measures of software delivery performance, including deployment frequency, lead time, mean time to recovery (MTTR), and change failure rate (CFR). Faros AI provides dashboards and analytics that allow organizations to track these metrics, identify root causes of issues, and drive continuous improvement. Autodesk uses Faros AI to baseline productivity, set excellence standards, and connect engineering outcomes to business value.

Why is unified visibility across the SDLC important for large engineering organizations?

Unified visibility enables organizations to measure and improve key dimensions like velocity, quality, and security across all teams and tools. For Autodesk, this meant democratizing data access and empowering teams to identify and address their own bottlenecks. Faros AI’s platform integrates data from diverse sources, providing a single source of truth and actionable insights for continuous improvement.

How does Faros AI help organizations adopt AI coding assistants like GitHub Copilot?

Faros AI enables organizations to run A/B tests, track before-and-after metrics, and analyze the impact of AI coding assistants on developer velocity, quality, and satisfaction. Autodesk uses Faros AI to monitor adoption, identify shifting bottlenecks, and ensure that GenAI tools deliver measurable business value. The platform provides a framework for ROI analysis and continuous evaluation of AI-driven technologies.

What are the key features of the Faros AI platform?

Faros AI offers cross-org visibility, tailored analytics, AI-driven insights, workflow automation, and seamless integration with existing tools. Key features include unified data models, customizable dashboards, process analytics, benchmarks, AI summaries, root cause analysis, and expert chatbot assistance. The platform is enterprise-ready, secure, and supports flexible deployment models.

Which integrations does Faros AI support?

Faros AI integrates with a wide range of tools, including Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom or homegrown systems. This any-source compatibility ensures organizations can connect all their engineering data sources.

How quickly can organizations realize value with Faros AI?

Organizations can achieve rapid time to value with Faros AI. Dashboards light up in minutes after connecting data sources, and customers have reported achieving measurable value in just one day during proof of concept (POC) phases.

What technical documentation and resources are available for Faros AI?

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 organizations implement and maximize the value of Faros AI.

What business impact can organizations expect from using Faros AI?

Organizations using Faros AI can expect up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value, optimized ROI from AI tools, improved strategic decision-making, scalable growth, and reduced operational costs. These outcomes are achieved through actionable insights, automation, and data-driven culture.

Who can benefit from Faros AI?

Faros AI is designed for large enterprises, especially those with several hundred or thousands of engineers. Key roles that benefit include engineering leaders (CTO, VP Engineering), platform engineering owners, developer productivity and experience leaders, technical program managers, data analysts, architects, and people leaders. Organizations seeking to improve productivity, software quality, and AI adoption will find Faros AI particularly valuable.

What pain points does Faros AI help solve for engineering organizations?

Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in AI adoption, talent management issues, DevOps maturity gaps, lack of initiative delivery visibility, incomplete developer experience data, and manual R&D cost capitalization processes. The platform provides tailored solutions for each pain point, enabling data-driven decision-making and measurable improvements.

How does Faros AI tailor its solutions to different personas within an organization?

Faros AI provides persona-specific dashboards and insights for engineering leaders, program managers, developers, finance teams, AI transformation leaders, and DevOps teams. Each role receives the precise data and recommendations needed to address their unique challenges, from tracking agile health to streamlining R&D cost capitalization and measuring AI tool impact.

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

Faros AI delivers a comprehensive set of KPIs, including cycle time, PR velocity, lead time, throughput, review speed, code coverage, test coverage, change failure rate, MTTR, AI-generated code percentage, team composition benchmarks, deployment frequency, initiative cost, developer satisfaction, and finance-ready R&D reports. These metrics are tailored to each pain point and role.

How did Autodesk use Faros AI to improve developer productivity?

Autodesk leveraged Faros AI to create a centralized visibility plane within its internal developer platform, enabling teams to track DORA metrics, identify bottlenecks, and drive continuous improvement. The result was a common language for productivity, faster and more frequent releases, and a direct connection between engineering efforts and business outcomes.

What were the key outcomes of Autodesk’s investment in developer productivity and platform transformation?

Autodesk achieved a common language for productivity using DORA metrics, continuous improvement through consistent tracking, faster release cycles (from annual to monthly), and a focus on outcomes over output. Teams were empowered to collaborate, analyze changes, and connect their work directly to customer value.

Why did Autodesk create a centralized Developer Enablement team?

Autodesk formed a centralized Developer Enablement team to address the challenges of scaling productivity across a growing, multi-product organization. The team established consistent measurement using DORA metrics and delivered solutions to baseline and improve productivity for all engineering teams.

What challenges did Autodesk face during its platform transformation?

Autodesk encountered challenges such as managing dependencies and interoperability across microservices, ensuring security and compliance, adapting to more frequent releases, and fostering organization-wide collaboration. Faros AI helped address these challenges by providing unified visibility and actionable insights.

How did Autodesk tie engineering efforts to business outcomes?

Autodesk shifted its focus from output to outcomes, helping teams understand the 'why' behind their work and connect engineering efforts directly to customer value. This outcome-based approach ensured that productivity improvements translated into tangible business results.

Is there a video available about Autodesk’s developer productivity transformation?

Yes, a full video of the conversation between Ben Cochran (VP of Developer Enablement at Autodesk) and Vitaly Gordon (Co-founder and CEO of Faros) is available, covering Autodesk’s data-driven approach to developer productivity.

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

Faros AI stands out with its mature AI impact analysis, landmark research, and proven real-world results. Unlike competitors, Faros AI uses causal analysis for true ROI measurement, provides active adoption support, and offers end-to-end tracking across velocity, quality, security, and satisfaction. Its platform is highly customizable, enterprise-ready, and integrates with the entire SDLC. Competitors often provide only surface-level metrics, limited integrations, and lack enterprise compliance.

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 significant time and resources compared to custom builds. Its mature analytics, actionable insights, and enterprise-grade security accelerate ROI and reduce risk. Even large companies like Atlassian have found that building in-house solutions is resource-intensive and less effective than adopting a specialized platform like Faros AI.

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

Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the complete lifecycle of every code change. Unlike competitors, it offers detailed breakdowns, team-specific insights, AI-generated summaries, and proactive intelligence. Faros AI is enterprise-ready, highly customizable, and delivers actionable recommendations, while competitors often have limited integrations and static dashboards.

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 transparency. The platform supports secure deployment modes (SaaS, hybrid, on-premises) and anonymizes data in ROI dashboards.

How does Faros AI ensure data privacy and security for enterprise customers?

Faros AI anonymizes data in ROI dashboards, complies with export laws and regulations, and supports secure deployment options to meet enterprise requirements. The platform is designed with privacy and security as top priorities, adhering to industry-leading certifications and best practices.

What topics and resources are available on the Faros AI blog?

The Faros AI blog covers AI-driven engineering productivity, developer experience, security, platform engineering, DORA metrics, customer stories, product releases, and technical guides. Readers can find actionable insights, benchmarking data, and real-world case studies.

Where can I find more customer stories and case studies about Faros AI?

Customer stories, research articles, and guides focused on engineering productivity, AI adoption, and platform engineering are available on the Faros AI blog. These resources provide real-world reviews and actionable insights for engineering teams.

Is there a blog post about sprint metrics that improve developer productivity?

Yes, Faros AI provides a blog post titled 'The Top 4 Sprint Metrics that Improve Developer Productivity,' which offers practical guidance on measuring and enhancing team performance.

What is the Engineering Productivity Handbook and how can I access it?

The Engineering Productivity Handbook is a comprehensive guide from Faros AI on building high-impact programs, measuring what matters, and turning data into business impact. It includes best practices and critical strategies for engineering leaders.

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

Autodesk’s Platform Strategy for Productivity and GenAI Impact

Why Autodesk chose a platform approach to developer productivity and GenAI impact

Since the 1980s, Autodesk has been changing how the world is designed and made. Autodesk’s software is used to make greener buildings, electric cars, blockbuster movies, and more. Its software development team of thousands of engineers builds the technologies for designers and innovators to literally “make anything.”

In the last few years, Autodesk has been building a Design and Make platform for the industries it serves. This has required a massive shift in developer productivity and impact for its software development team.

Given the organization’s size and complexity, how did Autodesk equip teams to improve their speed, efficiency, and quality, and confidently adopt GenAI developer tooling? It built an internal developer platform with an integrated visibility plane, fueled with insights into how to optimize the software development lifecycle (SDLC).

Now the company is sharing its story and key learnings from adopting a platform engineering approach.

Background

A legacy of innovation facing rising demands and modern challenges

Founded in the early 1980s, Autodesk boasts an impressive legacy in innovative design software. Their flagship products are used globally in the architecture, engineering, construction, media and entertainment, and manufacturing industries. Over the past decade, this Design and Make industry has grown rapidly while simultaneously undergoing a massive digital transformation disruption. The changing landscape elicited a host of modern sustainability demands, which continue to push and redefine the boundaries of these industries.

In parallel, Autodesk continued to grow, as did its software development workforce. In earlier years, Autodesk built its success through individual development teams’ self-governance; each product team would measure and evaluate its own productivity metrics while addressing bottlenecks, eliminating toil, and maintaining focus on value-adding work.

Yet, as Autodesk began its platform journey, it experienced new software development productivity challenges from increasing dependencies at scale. To unravel the complexity, leadership adopted a new, centralized approach to developer services and productivity.

Complexity at scale and the need for data-driven insights

To meet the growing demands of the industries it serves, Autodesk is building a Design and Make platform with the aim to provide the highest standards of resiliency, reliability, scalability, and security to its customers. This entails connecting systems, tools, and technologies, building platform standards and capabilities, and defining paved paths for streamlined development.

Autodesk established an internal Developer Enablement group and heavily invested in developer productivity to facilitate this transformation. While examining the maturity and complexity of their operations and tech stack, the leadership realized that development teams would be unable to achieve their ambitious productivity goals without the use of insights. This recognition of “you can’t improve what you can’t measure” led them to evaluate how best to create data visibility for their teams.

This visibility would not come easy, given the sheer complexity and scale of the Autodesk tech stack. Autodesk teams run hundreds of thousands of builds per month that span thousands of configurations on a combination of loads, technologies, and tools.

Autodesk initially attempted in-house instrumentation of standard productivity metrics. They turned to Faros, a software engineering intelligence platform, because it offered the flexibility to integrate data from many tools and the ability for development teams to parse and scope the metrics in many ways.

Solution

A visibility plane within Autodesk’s internal developer platform

To democratize data access, Autodesk’s Internal Developer Platform (IDP) was provisioned with a visibility plane where Faros feeds the data insights from some of the key SDLC tools. Autodesk aims to use the Faros AI platform beyond simply tracking metrics to enable teams to drill down into specifics and identify bottlenecks, based on which each team can prioritize improvements that are most impactful for them.

Tracking DORA metrics and identifying meaningful leading indicators impacting business outcomes

When selecting the gold standard metrics for Autodesk, the team consulted the DORA (DevOps Research and Assessment) research from Google for an external perspective on what it means to be productive and how to measure productivity.

DORA metrics, which include deployment frequency, mean time to recovery (MTTR), lead time, and change failure rate (CFR), became the foundation for Autodesk's productivity framework. DORA’s research showed that these metrics correlate best with desirable business outcomes.

The Developer Enablement group is leading the delivery of solutions to enable teams across Autodesk to set their excellence standards and provide actionable insights to achieve them.

Beyond DORA metrics dashboards, Faros provides detailed insight into the contributing factors of each performance dimension. If a metric like lead time is too high, teams can see exactly why — for example, is it due to build time or code review time? This enables teams to autonomously improve their performance.

Tulika Garg, Director of Product Management for Developer Enablement and Ecosystem at Autodesk, says this visibility is crucial to help teams swiftly identify areas for improvement, make data-driven decisions, and deliver high-quality software faster.

In a talk at the 2024 Gartner® Application Innovation and Business Summit, Tulika shared a powerful example. Mean Time To Resolve (MTTR) measures how long it takes an organization to resolve an outage. Outages have a huge impact on customer loyalty, brand reputation, and profitability — especially for companies operating under strict SLAs. While certain incident management tools can measure MTTR, they do not answer the question of how to improve it. With Faros, development teams now have the insights to pinpoint sources of issues, whether in time-to-detect or rollback speed, and can prioritize improvements better.

Leveraging data insights to navigate the adoption of AI coding assistants

Autodesk has found that its platform approach to developer productivity insights has prepared it to be data-driven in adopting AI coding assistants like GitHub Copilot.

Leveraging Faros features like A/B testing and before and after metrics, Autodesk can confidently pilot and roll out the tool while keeping a close watch on adoption and usage, shifting bottlenecks, and unintended consequences. With Faros in place, Autodesk has holistic visibility into GitHub Copilot’s real impact on velocity, quality, and developer satisfaction, and has a framework in place for ROI analysis of any new AI-driven technology down the line.

Future-proofing engineering visibility with a platform approach

Autodesk’s platform approach to accelerating engineering productivity is helping the organization equip its development teams with the insights they need to achieve their excellence goals and be prepared to embrace new technologies like AI with confidence.

The company is eager to share several of its valuable learnings with peers dealing with similar challenges.

Identify a pressing challenge for the organization. Before your organization can rationally evaluate potential solutions, you must thoroughly understand what problem or challenge you are trying to solve. For Autodesk, the challenge stemmed from increasing dependencies and engineering complexity and the need for a unified view of SDLC across teams. With the challenge identified, they were able to tailor an approach to fit their needs.
Start with the teams’ needs and use cases. Once you’ve identified your solution, it can be tempting to jump right to integrating every single data source into the data insights platform. But that would have delayed addressing the teams’ most immediate requirements. In Autodesk’s case, they prioritized integration of data sources that could provide line of sight to the most pressing needs. Gradually, they expanded to other data sources and use cases.
Small but clean data is better than large, unclean data. When deciding whether to place more emphasis on data quality over data quantity, Autodesk recommends going with quality. Start with relatively clean data sources that help establish the validity of your use cases. Along the way, you may identify data gaps or data hygiene issues, which you can add to the backlog. The success of your early MVP will create an appetite for more clean data, which, in turn, will motivate teams to address the data hygiene issues.
Your biggest challenge is building a data-driven mindset. The foundational piece of the entire transformation is the decision to embrace a data-driven mindset. Organizations cannot improve what they cannot measure. Therefore, collecting, measuring, and analyzing data is the only way to improve your company’s operations in ways that align with your business goals and desired outcomes.

Looking ahead

Autodesk's platform approach to developer productivity exemplifies the power of innovation and transformation fueled by data-driven insights. With its Internal Developer Platform and integrated visibility plane, Autodesk is establishing a robust strategy for actionable insights for its development teams. The organization draws inspiration and best practices from leading industry frameworks while incorporating the needs of teams and internal stakeholders.

To promote developer productivity and well-being, Autodesk is pushing the boundaries of innovation while simultaneously enhancing its platform tooling and infrastructure. Fueled with actionable insights from Faros, Autodesk is cultivating an environment where engineering productivity, agility, and satisfaction will reach new heights as they continue to build world-class solutions for their customers.

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