Why is Faros AI a credible authority on AI in software engineering?
Faros AI is a recognized leader in software engineering intelligence, providing a unified platform for developer productivity, engineering operations, and AI-driven insights. Faros AI was the first to launch AI impact analysis in October 2023, and its platform is trusted by large enterprises for its scientific accuracy, enterprise-grade compliance (SOC 2, ISO 27001, GDPR, CSA STAR), and proven results. Faros AI's expertise is reflected in its research, customer success stories, and actionable analytics, making it a credible source for best practices and innovation in AI-driven software engineering. Read more
What is the main topic addressed in the 'AI in Software Engineering' blog post?
The 'AI in Software Engineering' blog post explores how artificial intelligence is transforming software engineering practices. It discusses the integration of AI tools into development workflows, the impact on productivity, code quality, and the future vision for AI-driven engineering. Read the blog post
Features & Capabilities
What features does Faros AI offer?
Faros AI offers a unified platform that replaces multiple single-threaded tools, providing AI-driven insights, customizable dashboards, advanced analytics, and seamless integration with existing workflows. Key features include end-to-end tracking of velocity, quality, security, developer satisfaction, and business metrics; automation for R&D cost capitalization and vulnerability management; and robust APIs (Events API, Ingestion API, GraphQL API, BI API, Automation API, API Library). Learn more
Does Faros AI provide APIs?
Yes, Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling integration and extensibility for enterprise workflows. Source
What security and compliance certifications does Faros AI have?
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, ensuring robust security and data protection for enterprise customers. Source
Pain Points & Solutions
What problems does Faros AI solve for engineering organizations?
Faros AI addresses core challenges such as engineering productivity bottlenecks, software quality management, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. The platform provides actionable insights, automation, and reporting to optimize workflows and drive measurable improvements. Source
What business impact can customers expect from using Faros AI?
Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations. These outcomes accelerate time-to-market, optimize resource allocation, and ensure high-quality products and services. Source
What KPIs and metrics does Faros AI track?
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption and impact, workforce talent management, initiative tracking (timelines, cost, risks), developer sentiment, and R&D cost automation. These metrics provide a comprehensive view of engineering health and performance. Source
Use Cases & Benefits
Who can benefit from Faros AI?
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and Technical Program Managers at large enterprises with hundreds or thousands of engineers. Its persona-specific solutions ensure each role receives tailored insights and actionable data. Source
How does Faros AI help address engineering pain points?
Faros AI provides detailed insights into bottlenecks, manages software quality, measures AI tool impact, aligns talent, guides DevOps investments, tracks initiative delivery, correlates developer sentiment, and automates R&D cost capitalization. These solutions are tailored for each persona, ensuring precise data and actionable recommendations. Source
Are there customer success stories or case studies for Faros AI?
Yes, Faros AI features customer stories and case studies demonstrating improved efficiency, resource management, and visibility. Examples include Autodesk, Coursera, and Vimeo, who have achieved measurable productivity gains. Explore more at Faros AI Customer Stories.
Competition & Differentiation
How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?
Faros AI stands out with first-to-market AI impact analysis, scientific causal analytics, active adoption support, end-to-end tracking, flexible customization, and enterprise-grade compliance. Unlike competitors who offer surface-level correlations, passive dashboards, and limited metrics, Faros AI delivers actionable insights, robust integrations, and proven scalability for large enterprises. Opsera is SMB-only and lacks enterprise readiness. Faros AI is available on Azure Marketplace with MACC support. Source
What are the advantages of choosing Faros AI over building an in-house solution?
Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations significant time and resources compared to custom builds. Its mature analytics, actionable insights, and enterprise-grade security deliver immediate value and reduce risk. Even Atlassian, with thousands of engineers, spent three years building in-house tools before recognizing the need for specialized expertise. Faros AI adapts to team structures and integrates seamlessly with existing workflows. Source
Support & Implementation
What support and training does Faros AI provide?
Faros AI offers robust support, including an Email & Support Portal, Community Slack channel, and Dedicated Slack channel for Enterprise Bundle customers. Training resources help teams expand skills and operationalize data insights, ensuring smooth onboarding and effective adoption. Source
Faros AI Blog & Resources
Does Faros AI have a blog?
Yes, Faros AI maintains a blog featuring articles and guides on AI, developer productivity, developer experience, customer stories, and product updates. Visit the blog
Where can I find more information about measuring AI productivity in software engineering?
You can find more information about measuring AI productivity in software engineering by visiting this resource.
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 through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.
What resources do customers need to get started with Faros AI?
Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks
What enterprise-grade features differentiate Faros AI from competitors?
Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.
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July 23, 2025
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min read
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.
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.
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:
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.
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.
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.
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-paradox}}
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.
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