✨ AI Impact: Measure How AI Tools Are Affecting Your Engineering Delivery

Leanmote now includes

AI Impact

— 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?"

Captura de pantalla 2026-04-02 a la(s) 10

## 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 — 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

— lines produced via tab-style completions (e.g., Copilot suggestions accepted by the developer)

Delegated

— 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 — 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
— all in one place
LOC Suggested
— 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 — 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 —
Positive
,
Mixed
, or
Negative
.
Spend breakdown:
Total AI spend for the period, monthly cost across all seats, and — importantly — 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 —
Power
,
Casual
,
Idle
,
New
, and
Unlicensed
— 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 — 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 — with real delivery and financial data — whether that investment is paying off.