Maxim Salnikov’s Post

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6047Qgl25 Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6043QgzeW Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6043QgbmW Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6040QYH3E Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6040Qgm8O Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6044Q9TVy Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6046QcI8j Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6046Q9Q1k Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate

Are you measuring GitHub Copilot usage… or real impact? 🤔💡 Many teams proudly track seats, active users, and suggestion acceptance in GitHub Copilot. That’s adoption. But leadership conversations are shifting toward something else: 👉 Is AI actually improving engineering performance? This is where gh-devlake changes the game. Built as a GitHub CLI extension, gh-devlake connects Copilot usage data with real delivery metrics across your engineering systems. More details and how to start: https://msft.it/6046Qcnlp Instead of looking at AI stats in isolation, you can correlate: 📊 Copilot usage ⏱️ PR cycle time 🚀 Deployment frequency 🛠️ Change failure rate 🔁 Mean time to recovery Now we’re talking about impact. What I like about this approach is that it moves the conversation from opinion to evidence. You’re no longer asking whether AI “feels” productive. You’re analyzing how AI adoption aligns with actual delivery outcomes in your organization. For engineering leaders, this is critical. AI investments need defensible ROI narratives. DevLake Copilot gives you the foundation to build them using your own DevOps data. We are entering the phase where AI in engineering is not about experimentation anymore. It is about measurable value. Are you already correlating Copilot adoption with delivery metrics? Or still tracking usage alone? #GitHubCopilot #EngineeringLeadership #DeveloperProductivity #SoftwareEngineering #msftadvocate