# **DevSat Survey: Measuring Developer Experience Beyond System Metrics**

The fastest way to slow down innovation is **hidden friction in your engineering workflows**. At GitHub, we know system data tells us only part of the story. To build an environment where developers can thrive, we also need to understand **how developers experience their day-to-day work**.

That’s why we run the Developer Satisfaction (DevSat) survey, which reveals perception-based signals and pain points that our tools can’t yet see. These allow us to address friction in engineering workflows *before* it slows down innovation.

## **Why developer experience needs both system and human signals**

System metrics — like deployment frequency or change failure rate — are essential. But they don’t always reveal *why* developers feel slowed down or feel stressed. Further, to continuously improve our developer experience, we need a clear picture of where engineers feel bottlenecks so we can prioritize improvements with the **highest impact**.

To balance the picture, our [Engineering System Success Playbook (ESSP)](https://assets.ctfassets.net/wfutmusr1t3h/us6AUuwawrtNGTlwlT9Ac/f0fce86712054fc87f10db28b20f303b/GitHub-ESSP.pdf?utm_source=chatgpt.com) emphasizes two complementary data types:

* **System-derived data**: e.g. build times, release frequency

* **Perception-derived data**: e.g. developer tooling satisfaction, ease of debugging

Both are necessary to understand engineering productivity at scale and with this in mind, we recently switched our Developer Satisfaction (DevSat) survey tooling, which allowed us to change our approach of how we send out the survey: by sending it as a direct message (DM) integrating it into our engineers daily workflows rather than an email. The result: a **95% participation rate**, giving us statistically significant input from almost every engineer at GitHub. That level of engagement gives us confidence that the results don’t just reflect isolated opinions, but reliable insight about where friction exists across our engineering organization.

“It’s actually when quality, velocity, and developer happiness are working in unison that organizations see their best results.” [ESSP](https://assets.ctfassets.net/wfutmusr1t3h/us6AUuwawrtNGTlwlT9Ac/f0fce86712054fc87f10db28b20f303b/GitHub-ESSP.pdf?utm_source=chatgpt.com)

## **How we run DevSat**

To establish a baseline, we run our **Developer Satisfaction (DevSat) survey** quarterly. It is intentionally lightweight, with just a few rotating questions to keep the duration between five to 10 minutes. This and embedding the survey into engineers’ workflows helped us achieve a **95% participation rate**, giving us one of the clearest, most representative pictures of developer experience across the company.

The survey covers end-to-end developer workflows, including:

* Build and test experience  
* Ease of release  
* Development environment setup  
* Debugging workflows  
* Balance of deep work vs. meeting-heavy days

Because engineers work across very different domains — from AI research to database infrastructure — we also ask questions about their focus areas. This helps us uncover where friction happens across tools, teams, and handoffs.

## **Turning data into insights**

Now that we’ve gathered both system and human signals through our 2025 DevSat survey, the next step is **analyzing this information to understand** where **our biggest pain points** lie. By looking at perception data alongside system metrics, we can make informed decisions about where to focus our investments to improve developer experience. Before deciding on specific actions, we use the following approaches to turn raw responses into actionable insights. 

* **Industry benchmarking:** We expected meeting load to be a major issue for a remote, global company. But our most recent survey showed GitHub engineers reported **19% fewer meeting-heavy days** than the industry benchmark, freeing us to focus on bigger challenges. 

* **Differentiating systemic patterns from team variations:** In DevSat we saw Platform teams maintaining older tooling that carries higher **Keep the Lights On (KTLO)** burden than feature teams, which often spend around 66.6% of their **time on new capabilities** compared to an industry P90 of 65.5%. 

* **Crisis scenarios:** Occasionally, the survey uncovers extreme outliers, such as teams performing significantly below or above industry benchmarks (e.g., \-10 or \+22 points). These situations give us a chance to dig deeper and understand the underlying causes. Sometimes, lower scores reflect the complexity of the systems a team maintains, while higher scores can highlight strong practices—like effective incident handling or cross-team collaboration—which often rely on clear playbooks, supportive peers, and easy access to the right information. By investigating these outliers, we can make informed decisions about where to prioritize investments and improvements across the organization.

![devsat](//images.ctfassets.net/wfutmusr1t3h/3gykCb8Q5j6qa15AocyR72/388dc9f540998eee6d51c32eada4b1ac/Screenshot_2025-11-18_at_4.25.54â__PM.png)

### **Beyond benchmarks: Contextualizing impact**

Not all friction comes from everyday workflows — sometimes, major company investments reshape the developer experience in unexpected ways. Running DevSat quarterly helps us track how these shifts affect engineers, and whether we’re moving in the right direction, so we can distinguish between the unavoidable “cost of doing business” and the areas where strategic investment lets us rebalance workloads toward innovation.

For example, while building [GHEC with data residency](https://github.blog/engineering/engineering-principles/github-enterprise-cloud-with-data-residency/), we needed to support a customer experience that spans multiple regions, without slowing down our ability to ship. That meant re-architecting both our tooling and our developer workflows.

To solve this, we built a **Developer Portal** where engineers could manage feature flags across all regions through a single UI, rather than juggling multiple user experiences per region. This design ensured compliance with data residency guidelines while keeping the developer workflow simple and fast. The result was a __CSAT score of 80–83__ for those tools, showing that a careful UX investment turned what could have been extra overhead into a positive developer experience.

![devsat2](//images.ctfassets.net/wfutmusr1t3h/7zxmPxbnwWmx0XJmKyVvFv/3f64c412bf0f1d11d3f62b4462b10006/Screenshot_2025-11-18_at_4.28.16â__PM.png)

## **Avoiding anti-patterns**

One of the most important principles in using DevSat is avoiding direct score comparisons across teams, because it can create the wrong incentives leading to so called Anti-patterns (see [ESSP for common anti patterns](https://assets.ctfassets.net/wfutmusr1t3h/us6AUuwawrtNGTlwlT9Ac/f0fce86712054fc87f10db28b20f303b/GitHub-ESSP.pdf?utm_source=chatgpt.com)). For example, an AI R\&D team’s release process is completely different from a database infrastructure team’s, and both are valid. Instead of raw comparisons, we use DevSat to:

* **Identify systemic challenges** that affect multiple teams or the organization as a whole.

* **Spot outliers** within a team or workflow that signal localized friction.

* **Enable context-driven conversations** to surface root causes and determine where interventions will have real impact. 

By pairing these insights with lightweight experiments and MVPs, we can check whether interventions actually move the needle on key outcomes.

## **Lessons learned**

Running DevSat quarterly has reinforced a few guiding principles:

| Insight | Why it matters |
| :---- | :---- |
| Use both system and human-derived data | A holistic picture of organizational health to derive the most impactful actions |
| Embed surveys in existing workflows to drive higher participation rates | Increases signal quality and nuanced understanding |
|  Benchmark for context | Helps set priorities beyond internal norms |
| Avoid cross-team score comparisons | Preserves meaningful work variance |
| Revisit metrics regularly | Keeps you aligned with business shifts and AI acceleration |

Following the ESSP to redesign how we measure success allowed us to identify not only where friction exists, but also where targeted interventions will have the greatest impact. DevSat now continues to **serve as a baseline for continuous improvement**, helping us invest in automation, optimize workflows, and enable engineers to spend more time delivering value rather than managing overhead.

## **Looking forward**

Developer experience is never static. By running DevSat alongside system telemetry and using the ESSP framework, we’ve built a new baseline for continuous DX improvement.

Next, we’ll:

* Track how our investments shift metrics over time, as we automate more work  
* Ensure new tools and regions scale sustainably without adding friction  
* Keep rebalancing workloads toward innovation as the pace of AI acceleration continues

The combination of system data and human insight gives us a clearer picture of engineering health — and helps us focus on improvements that matter most to developers.

---

👉 Want to learn more about the Engineering System Success Playbook? You can read the [GitHub ESSP here](https://assets.ctfassets.net/wfutmusr1t3h/us6AUuwawrtNGTlwlT9Ac/f0fce86712054fc87f10db28b20f303b/GitHub-ESSP.pdf?utm_source=chatgpt.com).

System metrics show what is slow, but a deep-dive developer survey reveals why, turning hidden friction into actionable engineering insights.

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Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Matt Nigh](https://github.com/mattnigh), Program Manager Director of AI for Everyone

Choosing the right AI tools is one of the biggest challenges for leaders today. The market is overwhelming, and common pitfalls can paralyze your organization.

In our [Playbook for building an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we introduced the core principles for building an AI-native organization. This guide dives deeper into one of the most critical components: building a "right-fit" AI tooling strategy. We'll show you how to move beyond not knowing where to begin and create a curated portfolio of vetted, high-impact tools that empower your teams and accelerate your company's AI vision.

## A portfolio approach to AI tools

Your tooling strategy should be guided by a simple principle: make it easy for employees to do the right thing. This means providing a core set of powerful, vetted, and well-supported tools that can help them get their work done, whether they’re engineers, designers, operations, HR, etc. This curated portfolio becomes the default, safe choice for everyone.

At GitHub, we think about our toolset in three key categories:

* **Core enterprise tools:** These are the "green light" tools that are fully vetted, have enterprise-grade security, and are approved for a wide range of data types. They are the foundation of your AI-powered workforce. Think of tools like GitHub Copilot, Microsoft 365 Copilot, or Google's Gemini for Workspace. They provide a secure, default starting point for everyone.  
* **First-party tools:** These are the AI-powered products and features you build yourself. They create a powerful feedback loop between your employees and your product teams, accelerating development and ensuring your products meet real-world needs. For us these tools are primarily the Github Copilot portfolio of products and VS Code.  
* **Department-specific tools:** Not every need will be met by your core tools. A sales team might need a specialized AI-powered CRM feature, while a marketing team might benefit from an advanced analytics tool. Empowering departments to select these tools is key, but it requires a lightweight yet rigorous approval process to manage risk.

This model doesn’t require a full security review for every single tool, but it should build confidence that you are creating a solid foundation —and that you can enable smart, decentralized decisions to scale without compromising safety.

## Evaluating new tools

When a new AI tool shows up on your radar, you need a fast, structured way to decide whether it’s worth exploring, adopting, or investing in. Here’s a simple framework you can use.

### Tips for evaluating AI tools

When a new tool is submitted, evaluate it against a consistent set of criteria. Frame them as questions to guide the review:

1. **What problem does it solve? (Functionality):** Is it a niche tool or a broad platform? Does its core function address a high-value business need?  
2. **Is it a standalone solution or part of a platform? (Ecosystem):** Does the tool integrate with your existing systems? A tool that fits into your current tech stack is often more valuable than a standalone product that creates another data silo.  
3. **Where does our data go? (Data sensitivity & flow):** This is the most critical question. What data does the tool access? Where is it stored? Is it used to train public models? You must have a clear understanding of the data lifecycle.  
4. **Does it meet our security standards? (Security & compliance):** Does the tool support single sign-on (SSO)? What are its data retention policies? Does it comply with relevant regulations like GDPR or CCPA?  
5. **What is the expected benefit and/or return on investment? (Value & ROI):** How will this tool impact the business? While direct revenue attribution can be difficult, consider proxies for value. Will it significantly speed up a common workflow? Does it reduce a specific type of risk? A tool with a high price tag may be well worth it if it delivers substantial time savings for a large team.

### The evaluation workflow

1. **Submission form:** Create a simple intake form where employees can submit new tools, answering the five questions above.  
2. **Triage workflow:** Designate a small review committee (e.g., from IT, Security, and a business unit lead) to triage submissions weekly or bi-weekly.  
3. **Security and compliance review:** For tools that pass triage, conduct a lightweight review using predefined risk checklists to speed up this step.  
4. **Pilot and feedback:** For promising tools, set up a short pilot with clear success criteria. Collect feedback and security signals before a full rollout.  
5. **Decision and documentation:** Publish all decisions, rationales, and tier placements in a central tool registry to maintain visibility and accountability.

## Running a pilot

A successful pilot is more than just giving people access to a new tool; it's a structured experiment designed to answer key questions about a tool's value, usability, and risks before making a long-term commitment.

### 1\. Assemble your pilot group

The right testers provide the right feedback.

* **For broad, enterprise-wide tools:** Lean on your established group of "AI advocates" or internal champions. These enthusiastic early adopters are often more resilient to the rough edges of new technology and can provide insightful, high-signal feedback.  
* **For department-specific tools:** The pilot group should be the team that will actually use the tool daily. A new AI feature for a CRM should be tested by the sales team, not IT. This ensures the feedback is grounded in real-world workflows.

### 2\. Structure for success

A well-defined structure is key to getting clear results.

* **Define success metrics:** What does success look like? Is it saving three hours per week per user, increasing lead conversion by 5%, or achieving an 80% user satisfaction score? Set clear, measurable goals from the start.  
* **Time-box the pilot:** A typical pilot should run for 2-4 weeks. This is long enough for users to form habits and provide meaningful feedback, but short enough to maintain momentum.  
* **Provide lightweight training:** Host a quick kickoff session to demonstrate the tool's core functionality and explain the pilot's goals. Record the session to serve as an initial training resource. Don't assume users will figure it out on their own.

### 3\. Gather actionable feedback

Good feedback is specific and contextual.

* **Use a mix of channels:** Combine a dedicated chat channel for real-time questions, short weekly surveys to track sentiment, and a final wrap-up session or detailed survey to capture comprehensive thoughts.  
* **Ask specific questions:** Go beyond whether or not someone liked using the tool. Ask questions like, "Describe a specific task where this tool saved you time," "Where did the tool get in your way or slow you down?" and "What's the one thing you wish this tool could do that it can't?"  
* **Look for qualitative and quantitative signals:** A satisfaction score of 7/10 is useful, but a direct quote about how the tool streamlined a frustrating, manual process is often more powerful when making a final decision.

## From reactive to empowerment

Implementing a structured evaluation and piloting process isn't just about managing risk; it's about building a culture of intentional innovation. By empowering your employees with the *right* tools, not just the *most* tools, you create a resilient AI strategy that can adapt as quickly as the market itself.

This process keeps evaluation fast and scalable while maintaining the rigor needed to protect the business. The goal is to move from reactive decision-making to a proactive state of empowerment, accelerating AI adoption responsibly and unlocking your team's full potential.

---

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more thought leadership on the future of technology and business.

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Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Matt Nigh](https://github.com/mattnigh), Program Manager Director of AI for Everyone

In our [journey to build an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we’re discussing the foundation of the entire structure: **active, visible, and unwavering executive support.**

An AI adoption program without leaders may have a budget, but it will never have the strategic alignment, organizational authority, and grit to navigate the inevitable challenges of a massive technological shift. It will become a series of tactical projects, not a true transformation.

This post is a blueprint for leaders who want to be more than just sponsors. It’s for those who want to be the engine of their company's AI transformation.

## Beyond sponsorship: Setting a new standard

Effective executive support isn’t about passively signing checks; it’s about actively setting a new standard. It’s about fundamentally reframing AI from a "nice-to-have" tool into a core competency for every single employee. Communicating that this isn't a suggestion, it's the new reality of work.

This requires two bold actions:

* **Make AI fluency a foundational expectation:** The most powerful message a leader can send is that learning to use AI is not optional. It’s as fundamental as email or using a spreadsheet. You have to be direct: in the age of AI, standing still means falling behind. The expectation must be crystal clear that everyone, in every role, is responsible for building their AI skills.  
* **Operationalize the expectation:** A declaration is just words without a system to back it up. To make it real, leaders must weave this new expectation into the company’s core operating rhythms.

You can operationalize the expectation by:

* **Connecting it to performance:** Explicitly include the effective use of AI as a factor in performance reviews, promotion decisions, and reward cycles. This sends an unmistakable signal that AI proficiency is a valued and required competency.  
* **Challenging resource allocation:** Before approving requests for more headcount or budget, challenge your teams. Ask them, "How have you tried to solve this with AI first?" This forces a crucial mindset shift, from "we need more people" to "how can our team, augmented by AI, achieve this goal?"

## A cascading strategy

So you've set the new standard. How do you make sure it sticks? A single all-hands announcement from the CEO is a powerful catalyst, but it's just the first domino. A truly successful communications plan ensures the executive message is not just heard, but understood, translated, and acted upon at every level. It’s a deliberate cascade that turns a single moment into a sustained rhythm.

### 1\. The executive mandate: Setting and reinforcing the "why"

The cascade begins with a clear, high-level mandate from the top. This is the initial, non-negotiable standard: *AI is now a core part of how we work, and proficiency is expected.* But the most effective leaders know this isn't a one-and-done memo. They treat the announcement as the first beat in a continuous drumbeat, reinforcing the message in meetings, Q\&As, and internal posts. More importantly, they make choices in strategy, in organization, and others that make the commitment to AI tangible and impactful..

### 2\. Leadership translation: Defining the "how"

Next, VPs and directors must translate this "why" into a "how" for their specific functions. This is more than just forwarding an email. It’s about actively creating opportunities for their teams to apply the new mandate. At GitHub, we’ve seen our VPs push teams to use AI heavily for maintenance tasks, internal apps, triaging help requests, writing tests, and building proofs-of-concept. Our leaders have even organized department-specific AI learning weeks and hackathons. This step makes the high-level vision relevant, actionable, and a genuine learning opportunity.

### 3\. Front-line rhythms: Executing the "what"

Finally, the message cascade transforms into the daily, practical work led by managers and senior individual contributors. This is where the strategy becomes reality. The communication becomes a continuous, peer-to-peer rhythm in team meetings, 1:1s, and async channels. The focus shifts to:

* **Sharing specific wins:** "Check out this prompt that saved me an hour."  
* **Troubleshooting challenges together.**  
* **Celebrating peers** who are finding clever ways to use AI.

This is the grassroots momentum that turns a top-down directive into a living, breathing part of the culture.

## Enabling your executives

The relationship between the AI program and its executive sponsors is a two-way street. While leaders provide top-down support, the AI program must, in turn, provide bottom-up enablement to the executive team. A key function of the program's DRI is to make it easy for leaders to be effective and consistent champions.

This "Executive Enablement Loop" involves proactively equipping leaders with the content and context they need, including:

* **Providing curated talking points:** Supplying executives with a steady stream of the most compelling internal use cases, success stories, and data points. This arms them with fresh, relevant material for board meetings, investor calls, and public appearances.  
* **Preparing high-impact demos:** Assisting leaders in preparing for all-hands meetings or customer events by building powerful, tailored demos that showcase the company's AI capabilities in action.  
* **Drafting internal communications:** Partnering with the executive team and internal comms to ghostwrite or consult on company-wide memos and posts. This ensures the AI narrative is authentic, consistent, and lands with maximum impact.

This reciprocal support ensures that the executive message is always grounded in the reality of how the company is using AI, making their sponsorship more credible and effective.

### The executive's public learning journey

Beyond the memos and the mandates, the most powerful way for a leader to signal commitment is to model the behavior they expect. This means learning in public. When executives share their own journey: the wins, the struggles, the "aha\!" moments, they demystify the process and make the call to action feel human and achievable.

This doesn't need to be polished. In fact, unscripted moments are often the most powerful:

* **Sharing a win in a public channel:** A leader posting a prompt they used to summarize a long report.  
* **Recording a quick, unpolished demo:** A short screen recording of an executive brainstorming presentation ideas with an AI tool.  
* **Admitting a fail:** Openly talking about a time they struggled to get the right result. This normalizes the learning curve and shows that it’s okay not to be an expert overnight.  
* **Celebrating team wins:** Publicly amplifying a specific team or individual's success, highlighting not just the outcome but the cleverness of the solution.

This vulnerability and transparency is incredibly powerful. It transforms the AI mandate from a top-down directive into a shared, company-wide endeavor.

Ultimately, executive support is not a line item on a project plan. It is a continuous commitment: the steady, visible force that gives the entire organization the confidence and motivation to build the future of work, together.

## Communications plan checklist

A project or communications manager can use this checklist to operationalize the cascading strategy:

**Executive mandate (The "why")**

* \[ \] Draft the CEO's memo or talking points in partnership with the AI program DRI and communications team.  
* \[ \] Secure final review and approval from key stakeholders (e.g., legal, HR).  
* \[ \] Schedule the delivery of the message (e.g., all-hands meeting, internal blog post publication).  
* \[ \] Plan 2-3 follow-up communication beats for the executive to reinforce the message in the following month.

**Leadership translation (The "how")**

* \[ \] Prepare a briefing packet for VPs and directors with key messages, talking points, and an FAQ document.  
* \[ \] Schedule department-level meetings for the week immediately following the CEO's announcement.  
* \[ \] Provide a simple slide deck template that leaders can adapt for their team discussions.

**Front-line rhythms (The "what")**

* \[ \] Create a resource guide for managers on how to discuss AI proficiency in 1:1s and team meetings.  
* \[ \] Partner with AI advocates to seed internal communication channels with conversation starters and examples of early wins.  
* \[ \] Establish a simple process for employees to share their AI success stories for potential amplification.

---

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more thought leadership on the future of technology and business.

For AI to successfully transform an organization, leaders must be actively involved, not just approving budgets. This playbook is a blueprint for executives who want to drive, and not merely sponsor, their company's shift into an AI era.

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