Changelog

Follow up on the latest improvements andΒ updates.

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Captura de pantalla 2026-04-02 a la(s) 10
Leanmote now supports
Azure DevOps
as a first-class integration β€” bringing the same depth of engineering analytics to teams using Microsoft's DevOps platform as we've long offered for GitHub, GitLab, and Bitbucket.
## What Gets Connected
With a single OAuth connection, Leanmote syncs data from two Azure DevOps surfaces:
  • Azure Repos:
    Pull requests, commits, branches, and repository activity β€” the same data that powers Cycle Time, Software Delivery Performance, DORA metrics, and AI Impact for GitHub users is now available for Azure DevOps repositories.
  • Azure Boards:
    Tasks, work items, and team activity from your Azure Boards projects β€” feeding into workflow metrics, throughput analysis, and investment tracking in Leanmote.
## How to Connect
  1. Go to
    Productivity Tools
    in Leanmote
  2. Find the
    Azure DevOps & Boards
    card
  3. Click
    Connect
    and authorize via OAuth β€” no manual token management required
  4. Leanmote will begin syncing your repositories and boards automatically
Once connected, your Azure DevOps data flows into all the same dashboards your team already uses: Performance Delivery, Project Financials, Strategic Overview, and more.
## Who This Is For
Any engineering team that uses Azure DevOps as their primary platform β€” whether for repositories, project tracking, or both β€” can now get the full Leanmote experience without needing to migrate tools or maintain manual exports.
If your organization uses a mix of Azure DevOps and GitHub (or other platforms), Leanmote handles multi-source setups automatically, combining data across all connected integrations into a single unified view.
## Why It Matters
Azure DevOps is widely adopted across enterprise engineering teams, particularly in organizations with Microsoft-centric infrastructure. This integration removes the last barrier for those teams to adopt Leanmote's engineering intelligence platform without changing their existing toolchain.
Leanmote now includes
AI Impact
β€” a dedicated dashboard available in both
Performance Delivery
and
Project Financials
that answers the question every engineering leader is asking:
"Are our AI tools actually making the team deliver better and faster?"
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## AI Impact in Performance Delivery
The AI Impact section in Performance Delivery is split into two views:
### Delivery Correlation
This view connects AI usage data directly to your team's delivery metrics, surfacing three key signals:
  • AI Impact Trend:
    Tracks whether days with higher AI intensity also produce better delivery outcomes β€” higher throughput and more frequent deployments. The signal is labeled clearly:
    Positive
    ,
    Mixed
    , or
    Negative
    , so you know at a glance if AI is helping or not.
  • Adoption Effect:
    Compares throughput per user-day between engineers with low and high AI adoption. A positive adoption effect means your heavier AI users are consistently shipping more.
  • Assisted vs Delegated LOC:
    Breaks down AI-generated code into two categories:
-
Assisted
β€” lines produced via tab-style completions (e.g., Copilot suggestions accepted by the developer)
-
Delegated
β€” lines written directly by an AI agent flow, with minimal manual editing
This distinction matters: delegation is a stronger signal of AI reliance than assistance. The dashboard shows the split as a percentage and tracks how it evolves over time.
  • AI vs Bugs Correlation:
    A risk signal that checks whether increased AI usage is moving together with a higher bug rate or change failure rate β€” helping you catch quality regressions early.
### Adoption & Usage (AI Impact - Others)
This view gives you the raw adoption data behind the signal:
  • Active users, interactions, code generations, acceptances, and acceptance rate
    β€” all in one place
  • LOC Suggested
    β€” total lines of code AI offered across the team
  • Breakdowns by:
    top features used, programming languages, AI models, and IDEs
  • Per-user table:
    see individual stats for each engineer β€” generations, acceptance rate, primary IDE, and top models
## AI Impact in Project Financials
The AI Impact tab in Project Financials adds a financial lens to AI usage:
  • AI Cost vs Deployments:
    A weekly chart that plots your AI tool spend against deployment output, surfacing a clear ROI signal β€”
    Positive
    ,
    Mixed
    , or
    Negative
    .
  • Spend breakdown:
    Total AI spend for the period, monthly cost across all seats, and β€” importantly β€” the portion of AI spend attributable to bugs (spend that went toward work that introduced or fixed defects).
  • Adoption Overview:
    Classifies your engineers into segments β€”
    Power
    ,
    Casual
    ,
    Idle
    ,
    New
    , and
    Unlicensed
    β€” so you can understand the health of your AI rollout and identify who might need more support or training.
## How to Connect AI Data
AI Impact works with
GitHub Copilot
(with Copilot telemetry enabled) and
Claude Code Teams
. Once connected via Productivity Tools, metrics appear automatically β€” no additional setup needed.
## Why It Matters
AI tools represent a significant and growing investment for engineering teams. AI Impact gives you the data to move beyond anecdotal evidence and measure β€” with real delivery and financial data β€” whether that investment is paying off.
We're launching
Bottleneck Insights
β€” a new AI-powered module that automatically detects where your engineering workflows are slowing down, before those slowdowns turn into missed deadlines or frustrated teams.
image (14)
## What Is Bottleneck Insights?
Bottleneck Insights continuously analyzes your engineering data and surfaces the patterns that are degrading delivery performance. Instead of waiting for someone to notice that velocity is dropping or PRs are piling up, the AI proactively identifies the root causes and brings them to your attention.
## What It Detects
The AI looks across your full delivery workflow and flags issues like:
  • PR review bottlenecks:
    Pull requests that have been waiting for review longer than your team's baseline, with context on which authors or reviewers are involved.
  • Stalled work:
    Issues or tasks that have been in progress for an unusually long time with little or no activity β€” potential signs of blocked work, unclear scope, or competing priorities.
  • Recurring delay patterns:
    Repeated slowdowns at the same stage of the pipeline (e.g., PRs always slow down on Fridays, or deployments consistently lag after sprint end).
  • Cycle time regressions:
    Significant increases in Coding, Review, or Deploy time compared to recent baselines, flagged before they compound.
  • Workload imbalance:
    Detection of situations where one team member or one service is becoming a consistent choke point.
## How It Works
Bottleneck Insights runs in the background and surfaces findings with a dedicated section in the product where you can browse current and recent bottlenecks, each with supporting data and a plain-language explanation of what's happening.
Each insight includes the context needed to act: which team, which repo, which time period, and what the expected baseline was. No manual analysis required.
## Why It Matters
Engineering teams generate enormous amounts of delivery data, but the signal is often buried in the noise. Bottleneck Insights applies AI to do the hard work of pattern recognition β€” so your engineering leaders spend less time diagnosing and more time removing blockers.
This is the first module in Leanmote's
Proactive Insights
track, with more AI-driven analysis coming throughout the year.
Project Financials
now includes engineering performance data alongside investment information β€” giving engineering leaders and finance stakeholders a single place to understand not just
how much
is being spent, but
how effectively
it's being delivered.
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## What's New: DORA Metrics in Project Financials
The four DORA metrics are now surfaced directly within Project Financials for each project or initiative:
  • Deployment Frequency:
    How often your team is shipping to production. Higher frequency generally indicates a healthy, low-batch delivery process.
  • Lead Time for Changes:
    The time from code commit to running in production. A key indicator of your team's delivery efficiency.
  • Change Failure Rate:
    The percentage of deployments that cause a failure requiring remediation. Lower is better.
  • Time to Restore Service (MTTR):
    How quickly your team recovers from incidents. Reflects operational resilience.
Seeing these metrics in context with project spend lets you answer questions like:
"We're investing $200K in this initiative β€” are we actually shipping fast and reliably?"
## What's New: PR Activity in Project Financials
In addition to DORA,
Pull Request activity
is now visible per project:
  • PR Volume:
    Total number of PRs opened and merged in the selected period, giving a sense of team output.
  • PR Cycle Time:
    Average time from PR creation to merge, broken down by Coding, Review, and Merge stages.
  • PR Size:
    Average lines of code per PR β€” smaller PRs typically correlate with faster reviews and fewer defects.
## Why the Combination Matters
Traditionally, engineering investment data and delivery performance data lived in separate tools and separate conversations. By bringing them together in Project Financials, Leanmote enables a new kind of analysis:
  • ROI by initiative:
    Compare what you're spending on a project versus how fast and reliably the team is delivering.
  • Efficiency trends:
    Track whether increased investment translates into better delivery over time.
  • Informed prioritization:
    Help leadership make investment decisions backed by delivery data, not just estimates.
This update is available for all projects tracked in Project Financials. No additional setup required β€” if your repositories are already connected, the metrics will appear automatically.
We've significantly expanded the
Software Delivery
dashboard with two major upgrades: a full Cycle Time breakdown and a new set of advanced filters β€” giving engineering teams and their leaders the precision they need to identify exactly where work slows down.
## Full Cycle Time Breakdown
Cycle Time now goes beyond a single number. It's broken down into its four key stages, so you can pinpoint where delays are happening in your delivery pipeline:
  • Coding Time:
    How long developers spend actively working on a change, from the first commit to opening a PR.
  • Review Time:
    How long pull requests sit waiting for or going through code review.
  • Merge Time:
    The time between a PR being approved and it actually being merged.
  • Deploy Time:
    How long it takes from merge to production deployment.
Understanding which stage is the bottleneck is the first step to fixing it. A long Review Time points to a code review process problem. A long Deploy Time might signal a slow CI/CD pipeline. Now you have the data to act on the right thing.
## Advanced Filters: Branch, Author & More
The Software Delivery dashboard now supports a richer set of filters so you can slice data with more precision:
  • Filter by branch:
    Focus analysis on specific branches (e.g.,
    main
    ,
    release/*
    , feature branches) to compare delivery patterns across different workflows.
  • Filter by author:
    Drill into an individual contributor's metrics β€” useful for 1:1s, onboarding reviews, or identifying who might need support.
  • Additional variables:
    Filter by repository, team, and time range to build exactly the view you need.
These filters work in combination, so you can ask questions like:
"What's the average review time for PRs by the backend team on the release branch this quarter?"
## Why It Matters
Generic averages hide the real story. With Cycle Time broken down by stage and advanced filters, teams can have more targeted conversations about where to improve β€” and leaders can track whether those improvements are working over time.
We're introducing
Strategic Overview
β€” a new high-level module designed for engineering leaders who need a consolidated, real-time pulse on their entire organization without jumping between dashboards.
image
## What Is Strategic Overview?
Strategic Overview is a command center that surfaces the most critical signals across your engineering org in a single screen. Instead of drilling into individual team or project dashboards, leaders can now get an at-a-glance view of:
  • Delivery health across all teams:
    See how each team is performing against your delivery KRs.
  • Team capacity and workload distribution:
    Understand where effort is being spent and identify teams that are over- or under-loaded before it becomes a problem.
  • Investment allocation:
    Get a snapshot of how engineering spend is distributed across projects, initiatives, and maintenance work.
  • Trend analysis:
    Compare metrics over time to detect patterns β€” improving teams, declining velocity, or shifts in investment that need attention.
## Who Is It For?
Strategic Overview is built for CTOs, VPs of Engineering, and engineering managers who are responsible for multiple teams or the full org. It's the answer to the question:
"How is engineering doing this week?"
β€” without needing to ask anyone.
## How to Access It
Navigate to
Strategic Overview
in the main menu. You can customize which metrics and teams are visible using saved views, and share the view with other leaders using a public link or team share.
## Why It Matters
Before Strategic Overview, getting a cross-team picture of engineering health required either manual reports or switching between multiple dashboards. Now, everything your leadership team needs to make fast, informed decisions is in one place β€” always up to date, no prep required.
image
You can now save, share, and duplicate custom views in Performance Delivery and Project Financials β€” making it easy to collaborate with your team, align stakeholders, and build on existing configurations without starting from scratch.
## Personal Saved Views
Each user can create and save their own private dashboard configurations. Saved views capture your filters, metrics, and layout so you can return to them in one click β€” no need to reapply settings every time.
  • Per-user privacy:
    Saved views are private to your account by default. Your changes won't affect anyone else.
  • Context field:
    Add a description when saving a view to document its purpose β€” this also helps guide AI-driven insights.
  • Quick switching:
    Jump between views (e.g., Dev Team, Product, Sprint Review) with a single click.
## Share Views with Your Team
Collaboration is now built in. Once you've saved a view, you can share it in three ways:
  • Share with specific users:
    Send a view directly to one or more teammates. They'll see it in their own dashboard exactly as you configured it.
  • Share with a team:
    Distribute a view to an entire team at once β€” ideal for standardizing how a group monitors delivery health or project spend.
  • Public link:
    Generate a shareable link that anyone with access can open β€” perfect for async reporting, leadership updates, or embedding in Notion, Confluence, or other tools.
## Duplicate Views
Need a similar configuration with small tweaks? Duplicate any existing view and modify it without affecting the original. This is especially useful for creating variants (e.g., one view per team or project) based on a standard template.
## Where It's Available
Share and Duplicate are available in both
Performance Delivery
and
Project Financials
dashboards.
## Why It Matters
  • Consistency:
    Share a standard view with your team so everyone is looking at the same data.
  • Stakeholder transparency:
    Use public links to share delivery or investment dashboards with leadership without requiring a Leanmote login.
  • Speed:
    Duplicate and customize instead of building from scratch every time.
We’re redesigning the Team Interaction Map to better reflect how collaboration happens at scale. The new version will focus on clarity, structure, and directional insights, making it easier to analyze cross-team communication and engagement.
Key Improvements:
Team-Level Grouping: Users will be visually grouped by their assigned teams, simplifying the analysis of internal vs. external collaboration.
Directional Arrows: Arrows will now indicate the direction of interactions, showing who initiates communication and who receives it.
Scalability: Optimized for large organizations β€” the new map will support up to 200 users without performance degradation.
Multiple View Modes:
Total Interactions View: See all user-level communication across the org.
Grouped by Team View: Understand team-to-team relationship dynamics.
Enhanced Color Coding & Layouts: Clearer visual clusters and less node overlap to improve usability and pattern recognition.
User Value:
This improvement will help engineering and people leaders:
Detect communication silos or bottlenecks
Identify under- or over-engaged teams
Improve coordination in distributed or multi-team setups
We’re excited to announce that Leanmote now integrates with Notion!
You can now track key project management metrics directly from your Notion databases.
What’s new?
Automatic sync with Notion projects
Visibility into task cycle times, throughput, and team workload
Works seamlessly with existing Jira, Monday.com, and ClickUp integrations
Full support for custom status fields and project views
This update helps teams using Notion streamline performance tracking and improve delivery insights β€” all in one place.
➑️ To enable the integration, head to Productivity Tools > Notion Integration in your Leanmote dashboard.
We’ve expanded Leanmote’s analytics with new team activity metrics to give you deeper insight into sprint planning and task execution patterns.
πŸ†• What’s new?
πŸŒ€ Sprint Efficiency (Jira)
Measure what percentage of planned tasks were actually completed during sprints over the selected period β€” helping you track commitment vs. delivery accuracy.
πŸ—‚οΈ Assigned Tasks (Jira)
View how many tasks were assigned in sprints during the selected time frame. Great for spotting load distribution trends.
πŸ” Reworked Tasks
Identify tasks that moved backwards in status during their lifecycle β€” a signal of blockers, rework, or scope changes.
These new metrics provide greater visibility into how your team plans, executes, and iterates β€” enabling better retrospectives and continuous improvement.
➑️ Available now in the Team Metrics and Sprint Reports sections.
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