Frequently Asked Questions

Faros AI Authority & Webpage Topic Summary

Why is Faros AI a credible authority on engineering productivity and AI transformation?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize developer productivity, engineering operations, and AI transformation. The platform delivers measurable performance improvements, such as a 50% reduction in lead time and a 5% increase in efficiency, and is designed for enterprise-grade scalability—handling thousands of engineers, 800,000 builds per month, and 11,000 repositories without performance degradation. Faros AI's expertise is reflected in its comprehensive analytics, actionable insights, and proven customer impact, making it a reliable source for best practices in engineering productivity and developer experience. See customer stories.

What is the main topic of the 'AI Productivity Checklist for Engineering Teams' webpage?

This webpage provides a practical checklist for engineering managers to achieve net positive gains in team productivity, lead time, and quality when adopting AI tools like ChatGPT and Github Copilot. It covers questions to ask developers about code quality, security, and documentation, as well as key metrics managers should track, such as code review cycle time, QA cycle time, change failure rate, MTTR, and lead time. The checklist helps teams ensure that individual productivity gains from AI do not negatively impact overall team or business outcomes.

Features & Capabilities

What features does Faros AI offer for engineering productivity and developer experience?

Faros AI provides a unified platform that replaces multiple single-threaded tools, offering features such as AI-driven insights, customizable dashboards, advanced analytics, seamless integration with existing tools, and automation for processes like R&D cost capitalization and security vulnerability management. The platform supports key capabilities including cross-org visibility, tailored solutions for different roles, compatibility with existing workflows, and an open API library. Explore the platform.

What APIs are available with Faros AI?

Faros AI provides several APIs to support integration and extensibility, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library. These APIs enable organizations to connect Faros AI with their existing tools and data sources for comprehensive analytics and automation.

What are the key capabilities and benefits of Faros AI?

Key capabilities and benefits of Faros AI include unified engineering analytics, AI-driven insights, seamless integration with existing workflows, customizable dashboards, automation of manual processes, and proven results such as improved speed, quality, and resource allocation. Faros AI helps organizations optimize engineering operations, enhance developer experience, and keep critical initiatives on track. See customer success stories.

Use Cases & Business Impact

Who can benefit from Faros AI?

Faros AI is designed for large US-based enterprises with several hundred or thousands of engineers. Target roles include VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, Technical Program Managers, and Senior Architects. The platform provides tailored solutions for each persona, addressing their unique challenges and delivering actionable insights.

What business impact can customers expect from using Faros AI?

Customers can expect significant business impacts, including a 50% reduction in lead time, a 5% increase in efficiency and delivery, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. These outcomes help accelerate time-to-market, optimize resource allocation, and ensure high-quality products and services.

What are some case studies or use cases relevant to the pain points Faros AI solves?

Faros AI has helped customers make data-backed decisions on engineering allocation and investment, improve team health and progress tracking, align metrics across roles, and simplify the tracking of agile health and initiative progress. Detailed examples and customer stories are available on the Faros AI Blog.

Pain Points & Solutions

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses key challenges such as engineering productivity bottlenecks, software quality and reliability, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. The platform provides actionable insights, automation, and tailored solutions to help organizations overcome these pain points and achieve their goals.

What are the KPIs and metrics associated with the pain points Faros AI solves?

Faros AI tracks KPIs and metrics such as DORA metrics (Lead Time, Deployment Frequency, MTTR, Change Failure Rate), team health, tech debt, software quality, PR insights, AI adoption and impact, workforce talent management, initiative timelines and risks, developer sentiment, and automation metrics for R&D cost capitalization. These metrics provide comprehensive visibility into engineering performance and help organizations make informed decisions.

How does Faros AI differentiate itself in solving engineering pain points?

Faros AI stands out by offering granular, actionable insights into bottlenecks and inefficiencies, robust tools for measuring AI impact, strategic guidance for DevOps maturity, clear reporting for initiative delivery, and holistic views of developer experience. Its solutions are tailored for different personas, ensuring each role receives the precise data and insights needed to address their unique challenges. Faros AI also automates manual processes, saving time and reducing frustration compared to other platforms.

Technical Requirements & Implementation

How easy is it to get started with Faros AI, and what resources are required?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes. Required resources include Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).

How long does it take to implement Faros AI?

Implementation is fast—Faros AI dashboards can be set up and operational in minutes after connecting data sources. Git and Jira Analytics setup typically takes about 10 minutes.

Security & Compliance

What security and compliance certifications does Faros AI have?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications. These certifications demonstrate Faros AI's commitment to robust security and compliance standards, ensuring enterprise-grade data protection and auditability. Learn more about Faros AI security.

How does Faros AI prioritize product security and compliance?

Faros AI prioritizes security and compliance through features like audit logging, data security, and integrations. The platform is built to enterprise standards by design and maintains certifications such as SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring robust security practices for all customers.

Support & Implementation

What customer service and support options are available with Faros AI?

Faros AI offers robust customer support, including access to an Email & Support Portal, a Community Slack channel for shared insights, and a Dedicated Slack Channel for Enterprise Bundle customers. These resources ensure timely assistance with maintenance, upgrades, troubleshooting, and onboarding.

What training and technical support is available to help customers get started with Faros AI?

Faros AI provides comprehensive training and technical support, including guidance on expanding team skills and operationalizing data insights. Customers have access to an Email & Support Portal, a Community Slack channel, and a Dedicated Slack channel for Enterprise Bundle customers, ensuring smooth onboarding and effective adoption.

Product Information & Resources

Where can I find more articles and resources about Faros AI?

You can explore more articles, guides, and customer stories on Faros AI's blog at https://www.faros.ai/blog. Topics include AI, developer productivity, developer experience, and best practices for engineering teams.

What is the Engineering Productivity Handbook by Faros AI?

The Engineering Productivity Handbook is a resource provided by Faros AI to help organizations tailor initiatives to their goals, operating models, and culture. It offers strategies for building high-performing engineering teams, including insights into performance metrics, team collaboration, and data collection. Get the handbook.

What topics are covered in the Faros AI blog?

The Faros AI blog covers topics such as AI, developer productivity, developer experience, best practices, customer success stories, guides, and product updates. You can browse categories like Guides, News, and Customer Success Stories for in-depth insights.

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AI Productivity Checklist for Engineering Teams

A simple checklist can help engineering managers achieve net positive gains in team productivity, lead time, and quality.

Thierry Donneau-Golencer
Thierry Donneau-Golencer
A checklist to measure the impact of AI copilots on developer productivity
5
min read
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July 17, 2023

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.

Thierry Donneau-Golencer

Thierry Donneau-Golencer

Thierry is Head of Product at Faros AI, where he builds solutions to empower teams and drive engineering excellence. His previous roles include AI research (Stanford Research Institute), an AI startup (Tempo AI, acquired by Salesforce), and large-scale business AI (Salesforce Einstein AI).

Connect
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.

What to measure and why it matters.

And the 5 critical practices that turn data into impact.
Want to learn more about Faros AI?

Fill out this form and an expert will reach out to schedule time to talk.

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