Metrics

A24Z tracks comprehensive metrics to help you understand and optimize AI tool usage. These metrics are based on industry best practices for measuring AI coding assistant performance and ROI.

Core Metric Categories

1. Performance Metrics

Tool Success Rate

What it measures: Percentage of tool executions that complete successfully without errors.Why it matters: The most critical indicator of AI tool effectiveness. Low success rates mean developers spend time fixing errors instead of being productive.Benchmarks:

🟢 Excellent: >90%
🟡 Good: 85-90%
🟠 Needs improvement: 75-85%
🔴 Critical: <75%

What to do:

Rising: Great! Document what’s working
Stable: Keep monitoring for consistency
Declining: Review recent failures, refine prompts, check for tool issues

Average Execution Time

What it measures: How long tools take to execute on average.Why it matters: Slow execution times break developer flow and reduce productivity.Benchmarks:

🟢 Excellent: <3 seconds
🟡 Good: 3-5 seconds
🟠 Acceptable: 5-10 seconds
🔴 Slow: >10 seconds

Factors affecting speed:

Context size (more context = slower)
Tool complexity (file operations vs simple queries)
API response times
Network latency

Error Rate & Types

What it measures: Percentage and categorization of failed executions.Common error types:

Syntax errors: AI generated invalid code
Permission errors: File system or access issues
Timeout errors: Operation took too long
API errors: Service unavailable or rate limited

Target: <15% overall error rateAction items:

Track error patterns over time
Group by error type to identify root causes
Share common errors with team for learning

First-Time Success Rate

What it measures: Percentage of tasks completed successfully on the first attempt.Why it matters: Shows prompt quality and tool understanding. Higher first-time success = better prompts and tool usage.Target: >70%Improvement strategies:

Refine prompts to be more specific
Provide better context up front
Use examples in prompts
Learn from successful patterns

2. Productivity Metrics

Time to First Commit

What it measures: Time from starting work to first commit with AI assistance.Why it matters: Indicates how quickly developers become productive. AI tools should reduce this time significantly.Benchmark comparison:

Traditional: 30-60 minutes
With AI tools: 10-20 minutes
Target improvement: 50% reduction

Track by:

Developer experience level
Task complexity
Time of day

Cycle Time

What it measures: Time from task start to completion.Components:

Coding time
Testing time
Review iterations
Bug fixing

AI Impact:

Expected reduction: 20-30%
Tracks actual productivity gains
Justifies AI tool investment

AI-Assisted Commits

What it measures: Number and percentage of commits made with AI assistance.Adoption indicator:

<30%: Low adoption
30-60%: Moderate adoption
60-80%: High adoption
>80%: Excellent adoption

Track trends:

Growing percentage shows increasing reliance
Stable high percentage shows sustained adoption

Context Efficiency

What it measures: Ratio of output quality to input tokens used.Formula: Quality Score / Total Input TokensWhy it matters: Shows how efficiently developers use AI - getting better results with less context.Optimization tips:

Remove unnecessary context
Use precise, focused prompts
Reference files instead of copying
Clear session history regularly

3. Usage Metrics

Token Consumption

Input Tokens:

Prompt text and context
File contents
Conversation history

Output Tokens:

AI-generated responses
Code suggestions
Explanations

Optimization strategies:

Monitor trends over time
Identify token-heavy sessions
Compare to team averages
Set per-developer budgets

Session Patterns

Daily Active Users (DAU):

Percentage using AI tools each day
Target: >85% for adopted teams

Session Frequency:

Sessions per developer per day
Typical range: 3-8 sessions/day

Session Duration:

Average: 15-30 minutes
>1 hour may indicate context issues

Peak Usage Times:

Identify when team is most active
Plan maintenance windows accordingly

Tool Distribution

Most Used Tools:

Identifies workflow patterns
Shows tool preferences
Reveals missing capabilities

Tool Success by Type:

Tool Type	Typical Success Rate
File read/write	95%+
Code generation	85-90%
Debugging	80-85%
Complex refactoring	70-80%

Use insights to:

Train on underutilized tools
Improve prompts for low-success tools
Request new tool integrations

4. Cost Metrics

Cost per Developer

Typical ranges:

Light usage: $50-100/month
Average usage: $100-200/month
Heavy usage: $200-500/month

What affects cost:

Session frequency
Token consumption per session
Model selection (GPT-4 vs GPT-3.5)
Context window size

ROI calculation:

If developer saves 4 hours/week:
Value = 4 hours × $75/hour × 4 weeks = $1,200/month
Cost = $150/month
ROI = 8x

Cost per Feature

Track costs by:

Feature type (new vs enhancement)
Complexity level
Team or project

Compare:

AI-assisted vs traditional development
Different approaches to same task
Team vs individual costs

Use for:

Project budgeting
ROI analysis
Resource allocation

Budget Variance

Monitor:

Actual vs budgeted spend
Weekly and monthly trends
Cost per team comparison

Alert thresholds:

🟡 Warning: >10% over budget
🟠 Concern: >25% over budget
🔴 Critical: >50% over budget

Cost optimization:

Reduce redundant tool calls
Optimize prompt efficiency
Use appropriate models
Implement token budgets

5. Quality Metrics

Code Quality Impact

Defect Density:

Bugs per 1000 lines of code
Compare AI-assisted vs traditional
Target: 20-30% reduction

Code Review Iterations:

Number of review rounds needed
Time spent in review
Types of feedback received

Test Coverage:

Percentage of code covered by tests
AI tools should help increase this

Post-Release Bugs

Track:

Bugs in AI-assisted code
Bugs in traditional code
Bug severity distribution

Expected impact:

Similar or lower bug rates
Faster bug detection
More consistent code patterns

Red flags:

Higher bug rates in AI code
Specific tool causing issues
Need for better review process

6. Business Impact Metrics

Return on Investment (ROI)

Components:1. Productivity Gains

Velocity Increase × Team Size × Avg Salary
Example: 25% × 50 devs × $150K = $1.875M/year

2. Time Savings

Hours Saved/Week × Hourly Rate × 52 weeks
Example: 8 hours × $75 × 52 = $31,200/year/dev

3. Quality Improvements

Reduced Bugs × Cost per Bug
Example: 200 bugs × $2,000 = $400K/year

Total Investment:

Tool costs: $12-18K/year
Training: $10-20K/year
Setup/integration: $5-10K (one-time)

Typical ROI: 15-25x in year one

Developer Satisfaction

Survey metrics:

Tool usefulness rating (1-10)
Frequency of use
Likelihood to recommend
Impact on daily workflow

Qualitative feedback:

What works well?
What’s frustrating?
Feature requests
Workflow improvements

Collection frequency:

Monthly pulse surveys
Quarterly deep dives
Annual comprehensive review

Time to Market

Measure:

Feature delivery time
Sprint velocity trends
Project completion rates

AI impact:

Expected: 20-30% faster delivery
Tracks business value directly
Justifies continued investment

Dashboard Views

Executive Dashboard

For: CTOs, VPs of Engineering Key widgets:

Organization-wide success rate
Total monthly costs vs budget
ROI calculation
Adoption rate (% active users)
Velocity impact
Cost per developer

Update frequency: Weekly

Manager Dashboard

For: Engineering Managers, Team Leads Key widgets:

Team success rate trend
Individual performance comparison
Tool usage heatmap
Cost by team member
Blockers and issues
Weekly progress

Update frequency: Daily

Individual Dashboard

For: Engineers Key widgets:

Personal success rate
Today’s sessions
Token usage
Most used tools
Session duration
Personal trends

Update frequency: Real-time

Metric Interpretations

🟢 Healthy Signals

Success rate increasing:

Developers improving their AI usage
Better prompts and workflows
Tool proficiency growing

Action: Document and share what’s working Consistent performance:

Stable success rates >85%
Predictable costs
Regular usage patterns

Action: Maintain current practices Positive ROI trends:

Increasing productivity gains
Stable or decreasing costs
Growing adoption

Action: Plan to scale and expand

🟡 Warning Signs

Success rate plateau:

No improvement after initial gains
Stuck at 75-80%
Wide variance between team members

Action: Provide advanced training, share best practices Cost creep:

Gradual increase in cost/developer
No corresponding productivity gain
Token usage growing

Action: Review usage patterns, implement optimization Adoption stagnation:

Some team members not using tools
Declining daily active users
Low engagement

Action: Individual outreach, address barriers, show value

🔴 Critical Issues

Success rate declining:

Dropping >10% month-over-month
Increasing error rates
Growing frustration

Action: Immediate investigation, pause rollout if needed, address root causes Budget overruns:

>25% over budget
Unpredictable costs
No ROI justification

Action: Implement strict budgets, optimize usage, review necessity No adoption:

<50% team usage
Tools not delivering value
Technical barriers

Action: Reassess approach, gather feedback, may need different tools

Benchmarking

Industry Benchmarks

Based on research from leading engineering organizations:

Metric	Typical Range	Top Performers
Success Rate	80-85%	>90%
Cost/Developer	$100-200/mo	$80-120/mo
Velocity Increase	15-25%	>30%
Adoption Rate	70-85%	>95%
Time Savings	6-10 hrs/week	>12 hrs/week
ROI	10-20x	>25x

Team Comparisons

Use comparisons to:

Identify best practices from top performers
Find coaching opportunities
Set realistic targets
Motivate improvement

Avoid:

Punitive measures based on metrics
Unfair comparisons (different work types)
Ignoring context (junior vs senior)

Next Steps

Sessions

Deep dive into session metrics

Tools

Understanding tool-specific metrics

Engineers Playbook

How to use metrics to improve

Managers Playbook

Team performance tracking

Getting Started

Core Concepts

Integrations

Security & Compliance

Support

Metrics

Metrics

Core Metric Categories

1. Performance Metrics

2. Productivity Metrics

3. Usage Metrics

4. Cost Metrics

5. Quality Metrics

6. Business Impact Metrics

Dashboard Views

Executive Dashboard

Manager Dashboard

Individual Dashboard

Metric Interpretations

🟢 Healthy Signals

🟡 Warning Signs

🔴 Critical Issues

Benchmarking

Industry Benchmarks

Team Comparisons

Next Steps

Sessions

Tools

Engineers Playbook

Managers Playbook

Getting Started

Core Concepts

Integrations

Security & Compliance

Support

​Metrics

​Core Metric Categories

​1. Performance Metrics

​2. Productivity Metrics

​3. Usage Metrics

​4. Cost Metrics

​5. Quality Metrics

​6. Business Impact Metrics

​Dashboard Views

​Executive Dashboard

​Manager Dashboard

​Individual Dashboard

​Metric Interpretations

​🟢 Healthy Signals

​🟡 Warning Signs

​🔴 Critical Issues

​Benchmarking

​Industry Benchmarks

​Team Comparisons

​Next Steps

Sessions

Tools

Engineers Playbook

Managers Playbook

Metrics

Core Metric Categories

1. Performance Metrics

2. Productivity Metrics

3. Usage Metrics

4. Cost Metrics

5. Quality Metrics

6. Business Impact Metrics

Dashboard Views

Executive Dashboard

Manager Dashboard

Individual Dashboard

Metric Interpretations

🟢 Healthy Signals

🟡 Warning Signs

🔴 Critical Issues

Benchmarking

Industry Benchmarks

Team Comparisons

Next Steps