Frequently Asked Questions

About Faros AI & Authority on AI Productivity

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

Faros AI is a leading software engineering intelligence platform trusted by enterprises to provide data-driven insights into engineering productivity, developer experience, and DevOps analytics. The company leverages telemetry from over 10,000 developers across 1,255 teams, as demonstrated in its landmark AI Productivity Paradox Report 2025. Faros AI integrates data from source control, project management, CI/CD, incident tracking, and HR systems, enabling comprehensive, research-backed analysis of engineering operations and AI adoption at scale.

What is the main topic of the AI Productivity Paradox Report 2025?

The report investigates why AI coding assistants increase individual developer output but do not always translate into measurable company-level productivity gains. It explores the systemic barriers, adoption patterns, and strategies required to realize a measurable return on AI investment in software engineering.

How does Faros AI support its research findings?

Faros AI's research is based on rigorous analysis of telemetry data from multiple engineering systems, using standardized metrics and statistical methods (e.g., Spearman rank correlation) to ensure credible, actionable insights. The methodology is transparently documented in the report.

Features & Capabilities

What is Faros AI and what does it offer?

Faros AI is a Software Engineering Intelligence Platform that provides a single-pane view across velocity, quality, goals, and more. It offers unified analytics, AI-driven insights, customizable dashboards, and seamless integration with existing tools and workflows. Key capabilities include:

  • Engineering Efficiency: Measure and improve the complete developer experience.
  • AI Transformation: Operationalize AI across the software development lifecycle.
  • Delivery Excellence: Plan and deliver cross-team initiatives with advanced capacity and risk insights.
  • Enterprise-Grade Data Infrastructure: Built for complex, global teams.
  • Interoperability: Connect to any tool—cloud, on-prem, or custom-built.
  • Analytics: Trustworthy, actionable insights with no data cleanup needed.
  • Customization: Tailor metrics and workflows to organizational goals.
  • Catalogs: One source of truth for teams, systems, and ownership.
  • Robust APIs: Including Events, Ingestion, GraphQL, BI, Automation, and an API Library.

What are the key benefits of using Faros AI?

  • 50% reduction in lead time, accelerating time-to-market.
  • 5% increase in efficiency and delivery.
  • Enhanced reliability, availability, and product quality.
  • Improved visibility into engineering operations and bottlenecks.
  • Enterprise-grade scalability, handling thousands of engineers and large-scale codebases.
  • Proven results for customers such as Autodesk, Coursera, and Vimeo.

Does Faros AI provide APIs for integration?

Yes, Faros AI offers several APIs, including Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling seamless integration with your existing tools and workflows.

How does Faros AI ensure security and compliance?

Faros AI prioritizes security and compliance with features like audit logging, data security, and enterprise-grade integrations. It holds certifications such as SOC 2, ISO 27001, GDPR, and CSA STAR, demonstrating its commitment to robust security practices.

What technical requirements are needed to get started with Faros AI?

To get started, you need Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).

Pain Points & Business Impact

What problems does Faros AI solve for engineering organizations?

  • Identifies bottlenecks and inefficiencies to enable faster, more predictable delivery.
  • Ensures consistent software quality, reliability, and stability.
  • Measures the impact of AI tools, runs A/B tests, and tracks adoption for successful AI integration.
  • Aligns skills and roles, addressing shortages of AI-skilled developers.
  • Guides investments in platforms, processes, and tools to improve DevOps maturity.
  • Provides clear reporting to track progress and identify risks in critical projects.
  • Correlates developer sentiment with process data for actionable insights.
  • Automates and streamlines R&D cost capitalization.

What tangible business impacts have customers achieved with Faros AI?

  • 50% reduction in lead time, accelerating product and initiative delivery.
  • 5% increase in efficiency and resource allocation.
  • Enhanced reliability and availability of products and services.
  • Improved visibility and actionable insights into engineering operations.

What are the main pain points Faros AI addresses?

  • Engineering productivity bottlenecks and inefficiencies.
  • Software quality and stability issues, especially from contractors' commits.
  • Challenges in measuring and scaling AI transformation.
  • Talent management and skill alignment.
  • Uncertainty in DevOps investment impact.
  • Lack of clear reporting for initiative delivery.
  • Incomplete developer experience data.
  • Manual, time-consuming R&D cost capitalization.

What KPIs and metrics does Faros AI track to address these pain points?

  • Engineering Productivity: DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt.
  • Software Quality: Effectiveness, efficiency, gaps, PR insights (capacity, constraints, progress).
  • AI Transformation: Adoption, time savings, impact metrics.
  • Talent Management: Workforce talent management and onboarding metrics.
  • DevOps Maturity: DORA metrics and process/tool effectiveness indicators.
  • Initiative Delivery: Timelines, cost, risk tracking.
  • Developer Experience: Correlations between survey and system data.
  • R&D Cost Capitalization: Automation and accuracy metrics.

How does Faros AI differentiate itself from other developer productivity and DevOps analytics platforms?

Faros AI offers a unified platform that replaces multiple single-threaded tools, providing tailored solutions for different personas (Engineering Leaders, Technical Program Managers, Platform Engineering Leaders, CTOs). It delivers AI-driven insights, customizable dashboards, advanced analytics, and robust support, making it versatile for large-scale enterprises. Its focus on actionable, granular data and proven business impact sets it apart from competitors.

Are there real-world examples or case studies of Faros AI helping customers?

Yes. Customers have used Faros AI to make data-backed decisions on engineering allocation, improve team health, align metrics across roles, and simplify tracking of agile health and initiative progress. Explore detailed examples and customer stories at Faros AI Customer Stories.

Use Cases & Target Audience

Who is Faros AI designed for?

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.

How does Faros AI tailor its solutions to different personas?

  • Engineering Leaders: Detailed insights into bottlenecks and workflow optimization.
  • Technical Program Managers: Clear reporting tools for initiative tracking and risk identification.
  • Platform Engineering Leaders: Strategic guidance on platform/process/tool investments.
  • Developer Productivity Leaders: Actionable insights correlating sentiment and activity data.
  • CTOs/Senior Architects: Tools to measure AI coding assistant impact and adoption.

What are some common use cases for Faros AI?

  • Engineering productivity optimization
  • AI transformation and adoption measurement
  • DevOps maturity improvement
  • Initiative and project tracking
  • Developer experience enhancement
  • R&D cost capitalization automation

Implementation, Support & Training

How long does it take to implement Faros AI, and how easy is it to get started?

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, making it easy to start.

What training and technical support does Faros AI provide?

Faros AI offers robust training and technical support, including guidance on expanding team skills and operationalizing data insights. Support options include an Email & Support Portal, Community Slack channel, and a Dedicated Slack channel for Enterprise Bundle customers.

What customer service is available after purchasing Faros AI?

Customers have access to an Email & Support Portal, Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers, ensuring timely assistance with maintenance, upgrades, and troubleshooting.

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, demonstrating its commitment to robust security and compliance standards.

How does Faros AI ensure data security?

Faros AI implements audit logging, data security features, and secure integrations to protect customer data and meet enterprise security requirements.

Faros AI Blog & Resources

Where can I find more information and articles from Faros AI?

What topics are covered in the Faros AI blog?

  • AI in software engineering
  • Developer productivity and experience
  • Best practices, customer stories, and product updates

Where can I read the full AI Productivity Paradox Report?

The full report is available at https://www.faros.ai/ai-productivity-paradox.

Summary of Key Webpage Content

The AI Productivity Paradox Report 2025 by Faros AI reveals that while AI coding assistants boost individual developer output (e.g., 21% more tasks completed, 98% more pull requests merged), organizational productivity does not always improve due to bottlenecks in review, uneven adoption, and systemic barriers. Faros AI provides actionable strategies and enablers—workflow design, governance, infrastructure, training, and cross-functional alignment—to help engineering leaders unlock measurable ROI from AI investments.

The AI Productivity Paradox Report 2025

Key findings from the AI Productivity Paradox Report 2025. Research reveals AI coding assistants increase developer output, but not company productivity. Uncover strategies and enablers for a measurable return on investment.

Neely Dunlap
Neely Dunlap
A report cover on a blue background. The cover reads:The AI Productivity Paradox: AI Coding Assistants Increase Developer Output, But Not Company Productivity. What Data from 10,000 Developers Reveals About Impact, Barriers, and the Path Forward
July 23, 2025

AI coding assistants increase developer output, but not company productivity

Generative AI is rewriting the rules of software development—but not always in the way leaders expect. While over 75% of developers are now using AI coding assistants, many organizations report a disconnect: developers say they’re working faster, but companies are not seeing measurable improvement in delivery velocity or business outcomes.

Drawing on telemetry from over 10,000 developers across 1,255 teams, Faros AI’s recent landmark research report confirms: 

  • Developers using AI are writing more code and completing more tasks
  • Developers using AI are parallelizing more workstreams
  • AI-augmented code is getting bigger and buggier, and shifting the bottleneck to review
  • Any correlation between AI adoption and key performance metrics evaporates at the  company level

This phenomenon, which we term the “AI productivity paradox,” raises important questions and concerns about why widespread individual adoption is not translating into significant business outcomes and how AI-transformation leaders should chart the road ahead. 

For engineering leaders looking to unlock AI’s full potential, the data points to both promising leverage and persistent friction. 

Our key findings continue below. 

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.

#1 Individual throughput soars, review queues balloon

Developers on teams with high AI adoption complete 21% more tasks and merge 98% more pull requests, but PR review time increases 91%, revealing a critical bottleneck: human approval. 

AI‑driven coding gains evaporate when review bottlenecks, brittle testing, and slow release pipelines can’t match the new velocity—a reality captured by Amdahl’s Law: a system moves only as fast as its slowest link. Without lifecycle-wide modernization, AI’s benefits are quickly neutralized.

#2 Engineers juggle more workstreams per day

Developers on teams with high AI adoption touch 9% more tasks and 47% more pull requests per day. 

Historically, context switching has been viewed as a negative indicator, correlated with cognitive overload and reduced focus. 

AI is shifting that benchmark, signaling the emergence of a new operating model: in the AI-augmented environment, developers are not just writing code—they are initiating, unblocking, and validating AI-generated contributions across multiple workstreams. 

As the developer’s role evolves to include more orchestration and oversight, higher context switching is expected.

#3 Code structure improves, but quality worsens 

While we observe a modest correlation between AI usage and positive quality indicators (fewer code smells and higher test coverage from limited time series data), AI adoption is consistently associated with a 9% increase in bugs per developer and a 154% increase in average PR size.

AI may support better structure or test coverage in some cases, but it also amplifies volume and complexity, placing greater pressure on review and testing systems downstream. 

#4 No measurable organizational impact from AI

Despite these team-level changes, we observed no significant correlation between AI adoption and improvements at the company level. 

Across overall throughput, DORA metrics, and quality KPIs, the gains observed in team behavior do not scale when aggregated. 

This suggests that downstream bottlenecks are absorbing the value created by AI tools, and that inconsistent AI adoption patterns throughout the organization—where teams often rely on each other—are erasing team-level gains.

Four AI adoption patterns help explain the plateau

Even with rising usage, we identified four adoption patterns that help explain why team-level AI gains often fail to scale, namely: 

  1. AI adoption only recently reached critical mass. In most companies, widespread usage (>60% weekly active users) only began in the last two to three quarters, suggesting that adoption maturity and supporting systems are still developing. 
  2. Usage remains uneven across teams, even where overall adoption appears strong. And because software delivery is inherently cross-functional, accelerating one team in isolation rarely translates to meaningful gains at the organizational level.
  3. Adoption skews toward less tenured engineers. Usage is highest among engineers who are newer to the company (not to be confused with junior engineers who are new to the profession). This likely reflects how newer hires lean on AI tools to navigate unfamiliar codebases and accelerate early contributions. In contrast, lower adoption among senior engineers may signal skepticism about AI’s ability to support more complex tasks that depend on deep system knowledge and organizational context.
  4. AI usage remains surface-level. Across the dataset, most developers use only autocomplete features. Advanced capabilities like chat, context-aware review, or agentic task execution remain largely untapped. 

What should engineering leaders do next?

In most organizations, AI usage is still driven by bottom-up experimentation with no structure, training, overarching strategy, instrumentation, or best practice sharing. 

The rare companies that are seeing performance gains employ specific strategies that the whole industry will need to adopt for AI coding co-pilots to provide a measurable return on investment at scale.

Explore the full report to uncover these strategies plus the five enablers—workflow design, governance, infrastructure, training, and cross‑functional alignment—that prime your organization for agentic development.

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.

Methodology Note

Background
This study analyzes the impact of AI coding assistants on software engineering teams, based on telemetry from task management systems, IDEs, static code analysis tools, CI/CD pipelines, version control systems, incident management systems, and metadata from HR systems, from 1,255 teams and over 10,000 developers across multiple companies. The analysis focuses on development teams and covers up to two years of history, aggregated by quarter, as teams increased AI adoption.

Definitions
We define AI adoption in this report as the usage of developer-facing AI coding assistants—tools including GitHub Copilot, Cursor, Claude Code, Windsurf, and similar. These are generative AI development assistants that integrate directly into the software development workflow—typically through IDEs or chat interfaces—to help developers write, refactor, and understand code faster. Increasingly, these tools are expanding beyond autocomplete to offer agentic modes, where they can autonomously draft pull requests, run tests, fix bugs, and perform multi-step tasks with minimal human intervention.

Approach
To isolate the relationship between AI adoption and engineering outcomes, we:

  • Standardized all metrics per company to remove inter-org variance
  • Used Spearman rank correlation (ρ) to assess relationships of metrics to AI usage 
  • Reported only those metrics with data from ≥6 companies and statistically significant correlations (p-value < 0.05)
  • For each team, we calculated the percent change in metric values between the two quarters with the lowest AI adoption and the two quarters with the highest
  • Excluded outlier data and metrics with insufficient historical coverage

This approach enables comparisons within each company over time and avoids misleading aggregate assumptions across different org structures.

Versioning note: This version of the report reflects analysis as of June 2025. Future editions may expand coverage as AI usage matures across more organizations and product features evolve.

About Faros AI

Faros AI improves engineering efficiency and the developer experience. By integrating data across source control, project management, CI/CD, incident tracking, and HR systems, Faros gives engineering leaders the visibility and insight they need to drive velocity, quality, and efficiency at scale. Enterprises use Faros AI to transform how software is delivered—backed by data, not guesswork.

Learn more at www.faros.ai

Neely Dunlap

Neely Dunlap

Neely Dunlap is a content strategist at Faros AI who writes about AI and software engineering.

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