Engineering Productivity Insights

Insights — Full visibility into your engineering workflow, from code to delivery.

By Myra Magpantay

• 6 articles

⚙️ Engineering Productivity Insights

💡 What is Insights? Insights is Optimal AI’s productivity analytics layer. It gives engineering leaders a unified, data-driven view of team performance, bottlenecks, and delivery efficiency — directly connected to their GitHub and Jira activity. By surfacing cycle times, code review velocity, allocations, and goals, Insights transforms scattered development data into meaningful metrics that drive better decisions and higher performance. 🚧 The Problem Engineering productivity has always been hard to quantify. Teams rely on anecdotal updates or manual spreadsheets, and leaders struggle to identify what’s slowing projects down. Traditional tools focus on tasks — not the flow of engineering work. Without proper visibility, it’s difficult to: - Spot delivery bottlenecks - Measure review and merge velocity - Track time in status or cycle times - Balance team workloads Insights solves this by creating clarity out of complexity — automatically. 🚀 The Solution Insights integrates directly with your repositories and project management tools to build a real-time performance dashboard for your engineering organization. It combines DORA metrics, AI-based analysis, and custom productivity views so you can see exactly how your teams are working — and where to improve. With Insights, you can: - Track velocity and throughput across teams - Measure review and merge times - Identify where PRs or tasks get stuck - Align engineering goals with company OKRs - Drive data-backed process improvements 🔑 Key Capabilities 📈 Cycle Time Tracking Insights visualizes the complete development lifecycle — from first commit to deploy — highlighting delays between coding, review, and deployment. It helps you pinpoint stages where work slows down, whether that’s during PR reviews or QA, and provides recommendations to optimize your delivery pipeline. 🎯 Allocations & Goals Understand how your team’s time is distributed. With Allocations and Goals, you can define strategic focus areas (e.g., “60% feature work, 20% tech debt”) and measure actual time spent based on GitHub and Jira activity. This ensures your engineering effort is aligned with business priorities, not lost in reactive work. 💬 Review Load & Velocity Review health is a key indicator of productivity. Insights tracks how many PRs are being reviewed, who’s performing reviews, and average time to merge. You can quickly see if certain reviewers are overloaded or if feedback loops are too long — allowing you to rebalance and maintain flow. 🧩 GitHub & Jira Integrations Insights connects seamlessly with your existing tools — no manual setup required. It continuously syncs data from your repositories and project management systems, transforming raw activity into actionable insights. All integrations are designed with zero data retention and secure API access, ensuring privacy while maintaining visibility. 🤖 AI-Powered Trend Detection Beyond static charts, Insights uses AI to detect patterns and anomalies — such as sudden dips in engagement, PR spikes, or delivery delays. It surfaces these as proactive notifications, helping leaders make adjustments before problems grow. 🧭 Get Started - Insights for GitHub - Insights for Jira - Jira Integration Setup

Last updated on Oct 08, 2025

Setting Up Jira Integration in Insights

Connecting Jira with Insights allows you to track engineering productivity, including cycle times, issue progress, and delivery metrics, all in one place. This guide walks you through the full setup process. 1. Navigate to Settings To begin, click the Settings icon in the bottom left of your sidebar. 2. Add Team Members Inside the Settings page, navigate to the Members tab. - Click Add a new member to add the engineers whose cycle times and issue data you want to track. - Once members are added, you’ll be able to link them with their Jira accounts later in the process. 3. Open Jira Integration Next, go to the Integrations tab and select Jira Integration. 4. Enter Jira Connection Details Fill in the required fields for the Jira integration: - Domain Name – Use the domain of your Jira Cloud or Jira Server instance (e.g., https://yourcompany.atlassian.net/). - Service Account – (Recommended) Create a dedicated service account in Jira with access to the projects you want Insights to analyze. - Alternatively, you may use a personal access token linked to your Jira user account. - API Token – Generate and paste the API token associated with your service account or personal account. 💡 Tip: Using a service account is recommended for reliability and centralized control. 5. Configure Import & Webhooks Enable the following options for a complete setup: - Select to Import users from Jira → Ensures your Jira users are synced into Insights. - Select to automatically integrate with Jira webhooks → Allows Insights to update metrics automatically as activity occurs in Jira. Alternatively, you can copy and paste the webhook link manually into Jira if you prefer. 6. Save Your Integration Once all details are entered, click Save. - The initial connection may take a few minutes while Jira validates the service account or access token. - Once complete, Insights will begin importing data from your Jira projects. 7. Link Users to Jira After setup, return to the Members tab to ensure users are correctly mapped to their Jira accounts. - Click the Jira icon beside each user. - Link the Insights member profile with the correct Jira user account. This ensures that productivity and delivery metrics are accurately attributed to each engineer. ✅ Jira Integration Complete Your Insights dashboard is now connected to Jira. You’ll start seeing data populate in areas like PR Cycle Time, Story Points, and Time in Status, giving you visibility into your team’s delivery flow.

Last updated on Oct 08, 2025

Insights for GitHub

Optimal AI Insights for GitHub turns repository data into actionable engineering metrics. By connecting your GitHub repos, you’ll see clear dashboards for pull requests, deployments, activity, and contributors — with AI-powered summaries that make trends easy to understand. Getting Started with GitHub Insights It only takes a few steps to start seeing GitHub metrics in Insights: 1. Sign up for Insights – Create your account and log in. 2. Connect GitHub – Set up GitHub Integration to link your repositories. 3. Configure your views – Select which repos, branches, or teams you want to monitor. 4. View your dashboards – Within minutes, Insights will begin populating PR cycle times, deployment frequency, and activity data from GitHub. PR Cycle Time The PR Cycle Time view tracks how long pull requests take from the first commit to merge. It helps teams identify bottlenecks in the review process and optimize delivery speed. Key Metrics - Time to Open – Time from the first commit to when the PR is opened. - In Review – Duration until the first review is received (Optibot often reduces this to under a minute). - Time to Merge – Overall time from PR open to merge. - Reworks – Measures how much of the PR modifies previously shipped code. - Check Failure Rate – Percentage of failed CI/CD checks. - Comments & Discussions – Tracks collaboration activity per PR. Deployment Frequency The Deployment Frequency view measures how often code is shipped from GitHub to your environments. Key Metrics - Deployments per Day – Average daily deployment rate. - Environment Breakdown – Deployments to development, staging, and production. - Comparisons Over Time – See if deployment frequency is increasing or slowing down. - Deployment Graphs – Visualize peaks, gaps, and release rhythms. Activity The Activity view shows GitHub events by contributor or team across a selected time period. Key Features - Timeline View – PRs opened, merged, commits, and comments. - Team vs. Individual – Drill down into specific contributors or roll up to teams. - Custom Filters – Show or hide activity types (e.g., only reviews). Contributors The Contributors view provides a detailed report on individual engineers based on GitHub activity. Key Metrics - Highlights – AI-generated summary of GitHub activity (e.g., “Cycle time decreased by 62.5% this week”). - Efficiency Score – Composite measure of commits, PRs, reviews, and coding days. - Team Benchmarking – Compare an engineer’s performance against their team. - Coding Days – How many days a contributor committed code. - PR Statistics – Cycle time, reviews completed, merges. - Commit Contribution – Graph of commits across months. - Review Collaboration – See which teammates reviewed their PRs. AI Insights AI-generated summaries automatically explain trends across GitHub data views (PR Cycle Time, Activity, Contributors). Example Summaries - “Time to Open decreased by 83.55% compared to last week.” - “PR sizes vary widely, with larger PRs merging faster than smaller ones.” - “This contributor increased coding days and reduced cycle time by 62.5%.” Why Insights for GitHub Matters - Direct GitHub integration – No extra setup, just connect your repos. - Beyond commits – Track reviews, reworks, CI failures, and collaboration. - Team + individual views – Zoom in on contributors or zoom out on whole teams. - AI-powered summaries – Insights highlights trends so you don’t have to dig through graphs. With Insights for GitHub, engineering leaders get a transparent view of delivery speed and collaboration — making it easier to spot bottlenecks, celebrate wins, and continuously improve

Last updated on Oct 08, 2025

Insights for Jira

Bring clarity to your Jira data with Optimal AI Insights. Optimal AI Insights integrates directly with your Jira workspace to surface actionable engineering metrics. Instead of manually piecing together reports, Insights transforms your Jira issues, story points, and sprints into easy-to-read dashboards that highlight progress, bottlenecks, and delivery trends. ✨ Sign up for Insights 🔗 Jira Setup & Integration Guide Issues Progress Track the progress of every issue in Jira, broken down by team members. - Visualize tasks by status (To Do, In Progress, Done). - Drill down into issue-level details, including Epics and subtasks. - Understand how workload is distributed across your team. - Quickly see which tasks are blocking progress and where bottlenecks may form. 📊 Example: Jimmy Jimmels has 13 tasks in “To Do” and 6 in “In Progress,” while others have none assigned — making it easy to rebalance workload before sprint deadlines. Story Points Measure sprint commitments against actual delivery. - Committed Story Points – the total points estimated before a sprint begins. - Planned Complete – points from committed tasks that were finished. - Unplanned Complete – tasks added mid-sprint that were still completed. - Incomplete – points that carried over or remained unfinished. Use this to: - Compare planned vs. actual delivery across sprints. - Identify consistent carryovers or underestimations. - Improve sprint planning accuracy and velocity forecasting. 📊 Each sprint’s story points are color-coded — committed, carried over, unplanned, complete, and incomplete — giving you a clear picture of team execution at a glance. Time in Status Find bottlenecks by analyzing how long issues sit in each Jira status. - Customize your workflow mapping (e.g., To Do → In Progress → QA → Done). - View average and median time per status to understand flow efficiency. - Highlight the biggest bottleneck (e.g., issues spending 67 days in “In Progress”). - Detects outlier issues that significantly exceed typical cycle times. Metrics provided: - Average Time in Status – how long issues spend before moving forward. - Flow Rate – average issues created per day. - Lead Time – total time from creation to Done. 📊 Outlier detection flags issues that are “stuck,” such as tickets spending 931 days in To Do, so teams can take immediate corrective action. Key Benefits of Insights for Jira ✅ Real-time visibility into sprint and issue progress. ✅ Identify bottlenecks before they delay releases. ✅ Balance workloads across team members. ✅ Track planned vs. actual commitments with story points. ✅ Improve forecasting accuracy and delivery outcomes.

Last updated on Oct 08, 2025

📊 Investment: Allocations, Goals & Distribution

The Investment module in Optimal Insights helps leaders track where engineering effort is being invested. You can set goals for initiatives, measure allocations, and analyze the distribution of work across GitHub and Jira. Goals: Setting Strategic Targets Goals let you define expectations for how much work should go into each initiative. Step 1 — Choose a Platform When creating a goal, Insights asks which platform to track: - GitHub → Uses labels (e.g., bug, documentation) - Jira → Uses issue types or epics Step 2 — Select Team or Project - In GitHub, select a team. - In Jira, select a project. Step 3 — Define Initiatives Create at least two initiatives by linking to labels (GitHub) or issue types (Jira). Example: - Bugs → bug label - Docs → documentation label Step 4 — Set Goals Allocate percentages (total = 100%) for how much work should go to each initiative. Example: - Bugs → 80% - Docs → 20% Step 5 — Track Results The dashboard displays actual vs. expected allocation. Example outcome: - Bugs: 100% (Exceeded 80% target) - Docs: 0% (Below 20% target) Distribution: Measuring Reality Distribution shows the actual breakdown of work, regardless of goals. - Charts: Breakdown of PRs/issues across labels/types - Overlap: Totals can exceed 100% (multiple labels per R/issue) - Timeline: Trends over days/weeks Example: - Bugs: 40% - Dependencies: 20% - JavaScript: 20% - Time in Status: 60% Distribution works the same across GitHub PRs and Jira issues. 👉 Sign up for Insights to start setting up allocations.

Last updated on Oct 08, 2025

AI Adoption

Understand how your engineering team is adopting and engaging with AI copilots across your stack. Overview The AI Adoption dashboard provides a clear, data-driven view of how engineers are interacting with AI copilots such as GitHub Copilot, Cursor, and Claude. It surfaces adoption metrics, engagement trends, and model performance insights — all in one place. These insights are not available natively in GitHub today. Optimal AI built a custom layer on top of API data to visualize and store AI usage metrics over time, enabling teams to measure the real impact of AI tools in their development workflow. Summary Metrics At the top of the dashboard, you’ll find key adoption metrics: - Overall Code Acceptance Rate: Percentage of copilot or cursor suggestions accepted by your team. - Average Chat Interactions per Day: Number of back-and-forth AI chat sessions (not just auto-suggestions). - Top Performing Model by Acceptance: The AI model with the highest code acceptance rate (e.g., Claude-3.7). - Highest Acceptance by Language: Programming language with the most accepted AI-generated code (e.g., Python). - Average Daily Engagement Rate: Portion of active users engaging with copilots across editors or CLI. These metrics provide a quick snapshot of how well AI assistants are being integrated into daily engineering work. Example dashboard: Copilot User Engagement The Copilot User Engagement chart tracks both active and engaged copilot users over time. - Active Users: Developers who have access to copilot or AI tooling. - Engaged Users: Those who actively use the AI assistant during their coding sessions. This data helps quantify adoption health and engagement depth. In the example below, on April 23, there were 50 active users and 43 engaged users — an engagement rate of 86%. The dashboard automatically averages engagement across the selected date range and supports 28-day historical backfill after connection to GitHub. Code Generation Efficiency The Code Generation Efficiency panel visualizes total lines of code suggested vs. accepted, allowing you to assess the quality and usefulness of AI-generated code. You can filter and compare results by: - Language (e.g., Go, Java, JavaScript, Python) - Model (e.g., Claude-3.7, GPT-4, Gemini-1.5) - Editor (e.g., VS Code, Visual Studio) Hovering over the bars reveals acceptances and suggestions per category, so you can easily compare performance across tools and technologies. For example, the data may show that Go and JavaScript lead in code acceptances while Python suggestions remain under review. Filtering and Time Range The AI Adoption dashboard dynamically updates over any 14-day window you select. Use the date picker to track trends over time or analyze shorter bursts of copilot activity. You can also compare AI adoption across models, editors, or teams to identify where AI is having the biggest impact. Why It Matters By surfacing previously hidden metrics, AI Adoption helps engineering leaders: - Understand where copilots add value and where they’re under-utilized. - Compare model performance objectively across languages and editors. - Measure real AI ROI and inform adoption strategies. - Encourage consistent, data-driven AI usage across the engineering organization.

Last updated on Oct 13, 2025