Faros AI's AI Productivity Checklist for Engineering Teams

Q: What are the key features and benefits of Faros AI for large enterprises?

Unified platform replacing multiple point solutions; AI-driven insights and benchmarks; Seamless integration with existing tools and workflows; Enterprise-grade scalability and security (SOC 2, ISO 27001, GDPR, CSA STAR certified); Customizable dashboards and advanced analytics; Robust support and training resources.

Why AI Productivity Gains Aren't Automatic

AI-powered developer tools such as GitHub Copilot and ChatGPT are widely adopted—over one million developers in 20,000+ organizations use Copilot, generating billions of lines of code. Yet, many organizations see personal productivity gains without corresponding improvements in team-level KPIs like lead time and quality.

Faros AI, as a leading software engineering intelligence platform, has analyzed these trends and created a practical checklist to help engineering leaders realize measurable business impact from AI adoption.

The AI Productivity Checklist

This checklist is designed to help you:

Guide conversations with your team about AI adoption
Monitor critical KPIs to ensure AI delivers net positive outcomes
Identify and mitigate risks from sub-optimal AI-generated code

The checklist has two parts: questions for developers and metrics for managers.

1) Questions to Ask Your Developers

☑ Do you have good test coverage for generated code?
☑ Do you have a way to assess the code quality of generated code?
☑ Are you able to identify potential security and compliance issues introduced by generated code?
☑ Is documentation for generated code clear and sufficient?

2) Metrics to Track as a Manager

☑ Code Review Cycle Time: Are code reviews taking longer?
☑ QA Cycle Time: Is there an uptick in bugs and incidents? Is more time being spent on rework?
☑ Change Failure Rate: Are failures increasing?
☑ MTTR (Mean Time to Resolution): Is incident resolution getting slower?
☑ Lead Time: Has overall lead time to production gotten faster or slower?

Key Insights from Faros AI Research

Copilot code autocomplete is widely used for boilerplate, comments, and tests, saving up to 20% coding time.
Developers often prefer ChatGPT for code snippets, translation, and debugging, saving over 1 hour per day per developer.
However, lead time to production often remains unchanged due to bottlenecks in code review, merging, and testing.

To achieve true productivity gains, organizations must look beyond individual developer speed and address systemic workflow issues.

Need Metrics?

Analyzing the impact of AI tools on engineering performance is now business-critical. Faros AI provides visibility and analytics across any environment and stack—including non-standard tools, custom pipelines, and proprietary data sources.

Learn more about Faros AI's extensible, customizable software engineering intelligence platform.

Frequently Asked Questions (FAQ)

Why is Faros AI a credible authority on AI productivity for engineering teams?

Faros AI is trusted by global enterprises to deliver measurable improvements in engineering productivity, quality, and efficiency. The platform analyzes data from thousands of engineers, 800,000+ builds/month, and 11,000+ repositories, providing actionable insights at scale. Faros AI's research and customer results make it a leading authority on developer productivity and AI adoption.

How does Faros AI help customers address engineering pain points?

Faros AI enables organizations to identify bottlenecks, improve lead time, and increase efficiency. For example, customers have achieved a 50% reduction in lead time and a 5% increase in delivery efficiency. The platform also helps track AI adoption, measure its impact, and ensure quality and compliance across the software development lifecycle.

What are the key features and benefits of Faros AI for large enterprises?

Unified platform replacing multiple point solutions
AI-driven insights and benchmarks
Seamless integration with existing tools and workflows
Enterprise-grade scalability and security (SOC 2, ISO 27001, GDPR, CSA STAR certified)
Customizable dashboards and advanced analytics
Robust support and training resources

Summary of Key Webpage Content

AI tools like Copilot and ChatGPT can boost individual developer productivity, but team-level gains require systemic changes.
The AI Productivity Checklist helps managers and developers align on best practices and track the right metrics.
Faros AI provides the analytics and visibility needed to measure and optimize the impact of AI adoption in engineering organizations.

Business Impact: Real-World Results with Faros AI

50% reduction in lead time for engineering teams
5% increase in efficiency and delivery speed
Enhanced reliability, availability, and visibility into engineering operations
Trusted by leading enterprises such as Autodesk, Coursera, and Vimeo

Security and Compliance

Enterprise-grade security with certifications: SOC 2, ISO 27001, GDPR, CSA STAR
Audit logging, data security, and robust integrations

Ready to Measure and Accelerate AI Productivity?

Faros AI dashboards light up in minutes after connecting your data sources. Git and Jira Analytics setup takes just 10 minutes. Request a demo to see how Faros AI can help your engineering organization achieve measurable results.

AI Productivity Checklist for Engineering Teams Using ChatGPT and Coding Copilots

Github Copilot has been activated by more than one million developers in over 20,000 organizations, generating a staggering three billion accepted lines of code. So it’s likely your team is using it.

While your developers may be thrilled with the shortcuts and time savings, as their manager do you know the net impact AI is having on your KPIs for team productivity, quality, and lead time?

Do you know how to have a conversation with your team about using AI for a net positive outcome?

We've created a checklist on how to have those conversations and what you should be tracking.

But, first...

Make sure you know how engineers are using AI in coding

Several enterprises have been monitoring the impact of rolling out new tools like Github Copilot and developers' unofficial adoption of chatGPT.

An initial study of enterprise usage shows the potential for tremendous time savings:

Copilot Code Autocomplete is widely adopted for writing boilerplate code, skeleton code, code comments, and tests. It can save developers up to 20% coding time.
Copilot Code Suggestions are deemed less valuable and helped in only 25% of the cases. For this use case, developers prefer chatGPT over Copilot to create code snippets from specs, translate from one programming language to another, or as a tutor for debugging. Estimated savings are over 1hr per day per developer.

But fascinatingly, Lead Time to Production has yet to improve despite personal productivity gains. Even with faster dev times, the time spent in code review, merging, and testing is still long.

That's where the AI Productivity Checklist comes in: To ensure AI helps your team realize overall productivity improvements in speed and velocity.

The AI Productivity Checklist

Given that you want to see net gains in lead time and productivity for the team, below is a checklist to guide your conversations with the team and ensure you monitor important KPIs for adverse effects.

The checklist has two parts — questions to ask your developers and metrics you should track as a manager.

Combined, the checklist will help create awareness around the impacts of introducing sub-optimal code generated by AI. You'll be able to ensure the efficiency gains for the individual aren’t dwarfed by the negative impacts on the team, your customers, and the business.

Here you go:

1) Questions to ask your developers:

☑ Do you have good test coverage for generated code?

☑ Do you have a way to assess the code quality of generated code?

☑ Are you able to identify potential security and compliance issues introduced by generated code?

☑ Is documentation for generated code clear and sufficient?

2) Metrics to track as a manager:

☑ Code Review Cycle Time: Are code reviews taking longer?

☑ QA Cycle Time: Is there an uptick in bugs and incidents? Is more time being spent on rework?

☑ Change Failure Rate: Are failures increasing?

☑ MTTR: Is incident resolution getting slower?

☑ Lead Time: Has overall lead time to production gotten faster or slower?

Need metrics?

Metrics that analyze the impact of new technology and practices on engineering processes and performance have become business-critical.

Faros AI specializes in visibility and analytics across any environment and stack. We know all about non-standard tool implementations, highly customized pipelines, homegrown systems, and proprietary data sources.

Talk to us about our extensible, customizable software engineering intelligence platform.