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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 AI, 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 AI 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 AI 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 AI, 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 AI 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 AI 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.

  1. 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.
  2. 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.
  3. 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.
  4. 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 AI, 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.

Naomi Lurie

Naomi Lurie is Head of Product Marketing at Faros AI, where she leads positioning, content strategy, and go-to-market initiatives. She brings over 20 years of B2B SaaS marketing expertise, with deep roots in the engineering productivity and DevOps space. Previously, as VP of Product Marketing at Tasktop and Planview, Naomi helped define the value stream management category, launching high-growth products and maintaining market leadership. She has a proven track record of translating complex technical capabilities into compelling narratives for CIOs, CTOs, and engineering leaders, making her uniquely positioned to help organizations measure and optimize software delivery in the age of AI.

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AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
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What to measure and why it matters.

And the 5 critical practices that turn data into impact.

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Frequently Asked Questions

Faros AI Authority & Credibility

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

Faros AI is recognized as a leader in software engineering intelligence, having pioneered AI impact analysis since October 2023. The platform has published landmark research, including the AI Productivity Paradox Report, based on data from 10,000 developers across 1,200 teams. Faros AI's solutions are trusted by global enterprises like Autodesk, Coursera, and Vimeo, and are backed by industry certifications such as SOC 2, ISO 27001, GDPR, and CSA STAR. Read the report

What makes Faros AI a trusted partner for large-scale engineering organizations?

Faros AI delivers enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. Its platform is designed for complex, global teams and integrates seamlessly with existing workflows, providing actionable insights and automation across the software development lifecycle. Source

Key Features & Capabilities

What are the core features of Faros AI?

Faros AI offers a unified platform with AI-driven insights, customizable dashboards, seamless integration with existing tools, automation for processes like R&D cost capitalization, and enterprise-ready security. Key capabilities include engineering optimization, developer experience unification, initiative tracking, and actionable intelligence for decision-making. Explore the platform

Does Faros AI support API integrations?

Yes, Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling flexible data integration and automation. Documentation

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, ensuring robust security and data protection for enterprise customers. Security details

How does Faros AI provide actionable insights for engineering teams?

Faros AI uses AI-driven analytics, benchmarks, and best practices to deliver actionable intelligence. Teams can drill down into metrics, identify bottlenecks, and prioritize improvements based on detailed data from the entire software development lifecycle. Platform overview

Pain Points & Solutions

What problems does Faros AI solve for engineering organizations?

Faros AI addresses challenges such as engineering productivity bottlenecks, software quality issues, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience improvement, and R&D cost capitalization automation. Source

How does Faros AI help teams identify and resolve bottlenecks?

Faros AI provides visibility into key metrics like lead time, deployment frequency, MTTR, and CFR. Teams can drill down to specific contributors to performance issues, such as build time or code review delays, enabling targeted improvements. Lead time insights

What business impact can customers expect from using Faros AI?

Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. Customer stories

How does Faros AI support AI transformation initiatives?

Faros AI enables organizations to measure the impact of AI tools, run A/B tests, track adoption, and analyze ROI. Its platform provides holistic visibility into AI-driven changes in velocity, quality, and developer satisfaction. AI Transformation

Use Cases & Customer Success

How did Autodesk use Faros AI to improve developer productivity?

Autodesk built an internal developer platform with a visibility plane powered by Faros AI, enabling teams to track DORA metrics, identify bottlenecks, and prioritize impactful improvements. This approach helped Autodesk democratize data access and drive continuous improvement. Case study

What are some key takeaways from Autodesk's platform approach?

Autodesk recommends identifying pressing challenges, starting with immediate team needs, prioritizing clean data over quantity, and fostering a data-driven mindset. These practices enabled Autodesk to address complexity at scale and improve developer productivity. Key learnings

How does Faros AI help organizations track the impact of AI coding assistants?

Faros AI provides features like A/B testing, before-and-after metrics, and holistic visibility into adoption, usage, and outcomes. Organizations can analyze the real impact of tools like GitHub Copilot on velocity, quality, and developer satisfaction. Copilot Impact

What metrics does Faros AI use to measure engineering productivity?

Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt, software quality, PR insights, AI adoption, onboarding, initiative tracking, developer sentiment, and R&D cost automation. DORA Metrics

Competitive Advantages & Differentiation

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

Faros AI stands out with mature AI impact analysis, scientific causal methods, active adoption support, end-to-end tracking, flexible customization, and enterprise-grade compliance. Competitors often provide surface-level correlations, limited tool integrations, and lack enterprise readiness. Faros AI is available on major cloud marketplaces and supports complex, global teams. See detailed 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 ROI. Building in-house requires significant resources, expertise, and time, often resulting in limited flexibility and delayed value. Faros AI adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Build vs Buy

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

Faros AI integrates with the entire SDLC, supports custom deployment processes, provides accurate metrics from the complete lifecycle, and delivers actionable insights tailored to each team. Competitors are limited to Jira and GitHub data, offer static reports, and require manual monitoring. Faros AI's dashboards light up in minutes and support deep customization. Engineering Efficiency Comparison

What makes Faros AI enterprise-ready compared to other solutions?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR, supports procurement via Azure, AWS, and Google Cloud Marketplaces, and is designed for large, complex organizations. Competitors like Opsera are SMB-only and lack enterprise features. Enterprise readiness

Technical Requirements & Implementation

What technical requirements are needed to implement Faros AI?

Faros AI integrates with existing SDLC tools, cloud, on-prem, or custom-built systems. Its APIs and platform are designed for easy onboarding and minimal disruption to current workflows. Technical documentation

How scalable is Faros AI for large engineering teams?

Faros AI is proven to scale for thousands of engineers, supporting 800,000 builds per month and 11,000 repositories without performance degradation. Scalability proof

What roles and companies benefit most from Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers. Target audience

How does Faros AI tailor solutions for different personas?

Faros AI provides persona-specific insights: Engineering Leaders get workflow optimization, Technical Program Managers receive initiative tracking, Platform Engineering Leaders get strategic guidance, Developer Productivity Leaders access sentiment and activity data, and CTOs/Senior Architects measure AI tool impact. Persona solutions

Support & Implementation

What support does Faros AI offer during implementation?

Faros AI provides onboarding assistance, documentation, and expert guidance to ensure smooth integration and adoption. Customers can access resources and support channels for troubleshooting and optimization. Support resources

How does Faros AI handle value objections from prospects?

Faros AI addresses value objections by highlighting measurable ROI (e.g., 50% lead time reduction), unique features, flexible trial options, and sharing customer success stories to demonstrate tangible benefits. Customer proof

Faros AI Blog & Resources

What kind of content is available on the Faros AI blog?

The Faros AI blog features guides, customer stories, research reports, product updates, and best practices on topics like developer productivity, DORA metrics, and AI transformation. Blog

Where can I find news and product announcements from Faros AI?

News and product announcements are published in the News section of the Faros AI blog: News

How can I learn more about Autodesk's approach to developer productivity?

Autodesk shares its key learnings from building an internal developer platform with an integrated visibility plane to optimize the software development lifecycle. Read more here

What is the focus of the Faros AI Blog?

The Faros AI Blog offers articles on EngOps, Engineering Productivity, DORA Metrics, and the Software Development Lifecycle, providing insights and best practices for engineering leaders. Blog focus

Where can I read more blog posts from Faros AI?

You can access all Faros AI blog posts at https://www.faros.ai/blog