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January 5, 2026

Why "What percentage of our code is AI-generated?" is the wrong question

Every few weeks, another headline lands: Google reports over 30% of new code is AI-generated, up from 25% just six months ago. Microsoft claims 20–30%. Meta's CEO predicts half of their development will be AI-driven within a year. And suddenly, every executive wants to know the same thing: "What percentage of our code is AI-generated?"

It's the wrong question.

Lines of code generated by AI is not just a vanity metric. It's a misleading vanity metric that creates a false sense of progress while obscuring what actually matters. The irony is hard to miss: lines of code was already widely dismissed as a flawed measure of developer productivity long before AI entered the picture. Why would it suddenly become the right metric for AI productivity?

There is one scenario where tracking AI-generated code volume makes sense: as a governance metric for repository risk and maintainability. But that's fundamentally different from using it as an outcome metric to prove ROI. The most valuable metrics for quantifying AI impact are outcome-based measures that directly tie to business value: cycle times, quality improvements, and delivery velocity.

Engineering leaders facing pressure to demonstrate AI ROI have options beyond what the headlines suggest. The path forward isn't counting lines of code because that's what Google reports. It's measuring outcomes that actually prove business value. Here's why the lines-of-code approach is failing organizations and what works better.

The fixation on AI-generated lines of code

The pressure is real. When Alphabet's earnings call reveals that AI code generation jumped from 25% to over 30% in six months, boards and CFOs start asking questions. When Microsoft's CEO discusses AI-generated code percentages at industry conferences, engineering leaders feel compelled to produce similar numbers.

But here's the fundamental flaw: an engineer might accept an AI suggestion, then delete it, refactor it, or rewrite it entirely before the code ever reaches a merge. The number that shows up in your dashboard has almost no relationship to the code that ships to production.

A social media platform we spoke with faced exactly this pressure. Leadership mandated measurement of "AI-generated lines of code" despite internal team skepticism about the metric's reliability. Similarly, a global professional services firm invested $150,000 annually in GitHub Copilot was wondering if AI lines of code was the best way to demonstrate ROI to executives. Both organizations are sophisticated engineering teams struggling with the same problem: proving AI is worth the investment using metrics that can't actually prove it.

Why lines of code metrics fail in practice

Lines of code was already a discredited productivity metric

Before AI coding assistants existed, the software industry had largely abandoned lines of code as a meaningful productivity measure. The problems were well documented: it incentivizes verbosity over elegance, penalizes developers who delete unnecessary code, varies wildly across programming languages, and tells you nothing about whether the code actually works or delivers value.

As Bill Gates reportedly said, "Measuring programming progress by lines of code is like measuring aircraft building progress by weight." The best developers often ship features by removing code, not adding it. A clever refactoring that eliminates 500 lines while improving performance is more valuable than adding 1,000 lines of redundant logic.

Yet somehow, when AI entered the picture, lines of code became the headline metric again. The same measurement that failed to capture human developer productivity is now being used to justify AI investments. It doesn't make sense.

Technical limitations make accurate measurement nearly impossible

The vendor ecosystem alone creates chaos. GitHub Copilot, Claude Code, Cursor, Windsurf, Augment, and other AI tools provide different data formats for this information with no standardization. Furthermore, developers increasingly use multiple AI tools simultaneously. One tool generates code, another refactors it, and a third helps debug it. Attributing specific lines to specific tools becomes an exercise in inference, not measurement.

Even within a single tool, the data is unreliable given the engineers’ tendency to "accept everything then modify" rather than selectively accepting suggestions. This creates false positives that inflate acceptance rates while telling you nothing about production impact. Comparing accepted lines to merged lines provides inference, not deterministic truth. You're making educated guesses based on indirect indicators rather than direct measurement.

Survey alternatives fail too. Asking engineers "what percentage of that PR was written by AI?" produces unreliable, non-deterministic results. Different work patterns compound the problem. Infrastructure engineers and application engineers use AI completely differently, making organization-wide comparisons meaningless.

The data tells a different story about AI code quality

The research on AI-generated code paints a concerning picture that lines-of-code metrics conveniently ignore.

GitClear's analysis of 211 million changed lines of code across 2020-2024 found multiple signatures of declining code quality. They tracked an 8-fold increase in code blocks with five or more duplicated lines, showing duplication ten times higher than two years prior. Code churn, the percentage of code reverted or updated within two weeks, is projected to double.

The Harness State of Software Delivery 2025 report found that developers now spend more time debugging AI-generated code and more time resolving security vulnerabilities than before AI adoption.

Faros AI's own research shows that AI adoption is consistently associated with a 154% increase in average PR size. More code per pull request means more to review, more to test, and more potential for defects to slip through. This isn't a productivity gain. It's a quality burden.

None of these quality signals show up when you're counting lines of code. You could report impressive AI code generation numbers while your delivery stability craters and your technical debt compounds.

What should you measure instead?

If lines of code is a misleading vanity metric, what actually tells you whether AI is delivering value? The answer is outcome-based metrics organized into three tiers based on their impact on business decisions.

Tier Metrics Use case Caution
Tier 1 PR cycle time, lead time, task cycle time, quality metrics, developer satisfaction Executive dashboards, ROI decisions, pricing adjustments Requires baseline data
Tier 2 AI tool usage frequency, adoption percentage Leading indicators, identifying adoption blockers Must analyze alongside outcomes carefully
Tier 3 Lines accepted/merged ratio, AI-generated code %, agent PRs Debugging patterns, risk management, repo maintainability Never use as primary KPI
Outcome metrics to measure instead of AI lines of code

Tier 1: Business value metrics

These metrics answer the fundamental question executives care about: "Are we delivering more value, faster?"

PR cycle time measures the duration from pull request creation to merge. It directly reflects delivery velocity and code review efficiency. When a global professional services firm asked whether they could reduce consulting service pricing because of Copilot, PR cycle time was the metric that could actually answer that question.

Lead time tracks the journey from first commit to production deployment. This end-to-end measure of software delivery performance directly correlates with feature velocity. DORA research consistently shows that lead time predicts organizational performance.

Task cycle time measures how long it takes to close Jira or ADO tickets or complete work items. This measures productivity at the unit of work level and is easier for stakeholders to understand than code-level metrics.

Quality metrics include bugs escaping to production, incident rates, change failure rates, and rework rates. These answer the critical question: "Is AI-generated code actually good?" Without quality metrics, you have no idea whether your AI-assisted velocity is creating technical debt that will slow you down later.

Developer satisfaction and experience provides essential qualitative input. Regular pulse surveys on AI tool satisfaction help identify friction points and inform tool selection decisions. Developer happiness predicts long-term adoption success. If engineers don't like a tool, they won't use it, regardless of what the acceptance rates say.

Tier 2: Adoption and engagement metrics

AI tool usage frequency measures how often the new AI coding assistants are used. This is a leading indicator of impact and identifies adoption hurdles. It's more reliable than acceptance rates because it shows actual engagement patterns.

Tracking frequency of use of AI tools over time, from infrequent, to moderate, frequent and ultimately “power usage.” Power users use the tool >20 days a month.

Percentage of the organization using AI tracks adoption trends over time. This metric becomes meaningful when you overlay it with outcome metrics to identify patterns. For organizations tracking hundreds or thousands of engineers across many projects, understanding adoption breadth is essential context for interpreting outcome changes. But be careful: seeing adoption rise alongside improved outcomes doesn't automatically prove AI caused the improvement. That requires more rigorous analysis, which we'll address later.

When adoption metrics stagnate or decline, the root cause often lies beyond the tool itself. Common blockers include inadequate training programs, lack of manager buy-in, unclear guidelines on when and how to use AI tools, and insufficient communication about the "why" behind AI adoption.

Tier 3: Supporting proxy metrics (use with caution)

Lines accepted from AI divided by lines in PRs can be useful for debugging specific usage patterns, but it's not suitable as a primary KPI. High variability based on engineering discipline makes comparisons problematic.

AI-generated versus handwritten lines is useful for identifying repos highly augmented by AI to manage risk and maintainability. This is the one context where tracking AI code volume matters, and we'll cover it in the next section.

Agent-generated pull requests applies only for autonomous agent tools like Claude Code. It's more deterministic than line-level tracking but still doesn't answer the quality question.

When tracking AI code volume actually matters

There's one scenario where knowing how much of your code is AI-generated becomes genuinely important: repository risk management.

If a significant portion of a codebase was generated by AI, that's information worth knowing for maintainability and quality planning. Repos highly augmented by AI may need extra review attention, more robust testing, and closer monitoring for the quality issues that research shows AI code tends to introduce.

Measuring lines of code written by AI maintains oversight over highly augmented repositories, which may require extra review attention, more robust testing, and closer monitoring for quality issues.

This is fundamentally different from using lines of code as a productivity metric. You're not asking "how productive are we?" You're asking "where might we have elevated risk?" The goal isn't to maximize AI code generation. It's to understand where AI-generated code exists so you can manage it appropriately.

A data protection company took this approach when evaluating AI coding assistants. Rather than tracking lines of code as a KPI, they measured adoption and usage patterns while correlating them with downstream impacts. They compared test groups using different tools and tracked actual productivity outcomes. The result was data-validated confidence in their chosen AI coding assistant, with 2x higher adoption, 3 additional hours saved per week per developer, and 40% higher ROI, all without misleading lines-of-code metrics.

How do you prove AI ROI without counting lines?

The right question isn't "How many lines of AI code did we generate?" It's "Are engineers delivering value faster and with higher quality when they use AI?"

Start with the problem, not the tool

Organizations fall into three buckets when adopting AI:

The "me too" bucket adopts AI to follow industry trends without clear objectives. Measurement here is nearly impossible because there's no definition of success.

The "top-down mandate" bucket sees executives mandate AI adoption without attaching it to an underlying goal or defining success criteria. These organizations struggle to prove ROI because they never specified what ROI would look like.

The "problem-first" bucket identifies a clear goal or challenge and evaluates AI as one lever for the solution. This is where measurement succeeds because success criteria exist before implementation begins.

Establish baselines before rollout

You cannot measure improvement without knowing where you started. Ingest historical data to show pre-AI performance across your key metrics. Note that the AI tools themselves often limit data to 30-100 days of usage history. An engineering productivity platform can remove this barrier to create a longer view.

Define success criteria tied to business outcomes

What does your CTO or CEO actually care about? It's rarely "lines of code generated." Common answers sound like "Engineers should do more in less time, and it should be better." That translates to higher throughput of PRs, faster PR completion, and improved quality metrics.

Set specific targets. "If 100% of developers use AI, we expect PR cycle time to drop by 50%" gives you something concrete to measure against.

Track correlated outcomes

Overlay AI adoption trends with outcome metrics. What percentage of engineers are using tools? How is that correlating with changes in your key engineering productivity metrics?

Instead of tracking AI lines of code, focus on how AI adoption is impacting outcomes. In this chart, AI usage correlates with a reduction in PR cycle time after reaching 50% adoption.

Those metrics might be DORA metrics like lead time, deployment frequency, change failure rate, and failed deployment recovery time. They might follow the SPACE framework covering satisfaction, performance, activity, communication, and efficiency. They might be something bespoke to your organization based on what your leadership actually cares about.

The point is measuring outcomes that matter to your business, not inputs that are easy to count.

Address the correlation-to-causation challenge

Here's the complication: most organizations have "tons of other things" happening beyond AI adoption. Quality initiatives, process changes, team reorganizations, new tooling, all of these confound your ability to attribute outcome changes to AI specifically.

When metrics move week-over-week, inference-based measurements make it "really challenging" to explain why. Engineering teams easily dismiss proxy metrics with "that won't work for us."

Good causal analysis requires access to comprehensive engineering data, the ability to control for confounding variables, and statistical rigor beyond dashboard visualizations. Charts showing "adoption went up and code smells went down" are compelling, but without proper statistical controls, you can't confidently claim AI caused the improvement.

Causal analysis knows how to attribute metric change to AI adoption directly. In this chart, Team A’s 16% reduction in code smells cannot be attributed to AI adoption, while Team B’s 5% reduction can.

This is where Faros AI differentiates. While most platforms stop at correlation dashboards, Faros AI provides causal analysis of the impact of AI on key quality metrics. That means isolating AI's effect from the noise of other initiatives happening simultaneously, giving you defensible ROI claims rather than speculative correlations.

Why individual productivity gains don't translate to organizational impact

Perhaps the most important insight from recent research is what Faros AI calls the AI productivity paradox: individual developers report significant productivity gains, but organizations see no measurable improvement in delivery outcomes.

The data is striking. 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.

This pattern reflects Amdahl's Law: a system moves only as fast as its slowest link. AI accelerates code generation, but if your code review process, testing infrastructure, and release pipelines can't match the new velocity, the gains evaporate.

The METR research nonprofit found that experienced developers took 19% longer to complete tasks when using AI coding assistants, despite believing they were 20% faster. The 39-percentage-point gap between perceived and actual productivity represents what researchers call the "perception tax."

Asana's research identified the same phenomenon among knowledge workers broadly. Super productive employees report saving 20+ hours per week with AI, but 90% say AI creates more coordination work between team members. Individual gains are consumed by coordination costs, quality taxes, and rework loops before they reach the bottom line.

Without lifecycle-wide modernization, AI's benefits are quickly neutralized. You can't just measure code generation speed and declare victory.

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.

The metrics hierarchy for AI impact

What leadership actually cares about became clear when the sales team of the professional services firm asked: "Can we now say that it's 25% cheaper to deliver our software development services because we use GitHub Copilot?"

That's the right question. The answer requires outcome metrics, not vanity metrics.

  • ‍For executive dashboards: Lead time from commit to production, feature velocity measured by stories or tickets completed per sprint, a quality index combining incidents, bugs, and rework rate, and AI adoption rate showing the percentage of team actively using tools.
  • ‍For engineering managers: PR cycle time, time to first review, task cycle time, and bottleneck analysis showing where delays occur.
  • ‍For individual contributors: Personal productivity trends in private dashboards, AI tool engagement frequency, and code review turnaround times.
  • ‍For data teams and DevOps who want to understand AI's contribution: Lines of code ratios for debugging patterns, agent-generated PR or review volume, and custom causal analysis on underlying data.

‍What to avoid: Leading with "X% of code generated by AI" claims, vendor-provided acceptance rates as KPIs, survey-based self-reporting, and any metric that can't be explained when it fluctuates.

The pragmatic path forward

Accept that measurement will be imperfect. But focus on metrics that meet four criteria:

  1. ‍Drive decisions. Cn you adjust commitments, pricing, staffing, or investments based on this metric? If knowing you have 30% AI-generated code doesn't change any decisions, it's not worth tracking as a KPI.
  2. ‍Build trust. Can you explain week-over-week changes to engineering teams? If your metrics create "magic" that becomes unexplainable, trust erodes quickly.
  3. ‍Scale reliably. Does the metric work across 10 engineers? 1,000? 10,000? Metrics that break at scale aren't useful for enterprise organizations.
  4. ‍Correlate with outcomes. Does improving this metric actually deliver business value? Lines of code can go up while delivery speed, quality, and developer satisfaction all decline.

The industry hasn't solved deterministic AI attribution, automated causal inference at scale, or cross-tool normalization. These remain hard problems. But that doesn't mean you're stuck with misleading vanity metrics.

Outcome-based measurement works. It requires more thought than counting lines of code, but it tells you something that actually matters: whether AI is helping your organization deliver better software faster. And that's the only question worth answering.

Ready to measure what matters? Explore the AI Productivity Paradox research to understand why individual gains don't translate to organizational impact, then see how Faros AI's AI transformation measurement helps engineering leaders prove real ROI.

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).

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Frequently Asked Questions

Faros AI Authority & Credibility

Why is Faros AI considered a credible authority on measuring AI impact and developer productivity?

Faros AI is recognized as a leader in software engineering intelligence and developer productivity measurement. It was the first to market with AI impact analysis in October 2023 and published landmark research on the AI Productivity Paradox, analyzing data from 10,000 developers across 1,200 teams. Faros AI's platform is trusted by large enterprises and was an early GitHub Copilot design partner, demonstrating deep expertise in engineering metrics and outcome-based measurement. Read the research.

What makes Faros AI's approach to measuring AI impact different from other platforms?

Faros AI uses machine learning and causal analysis to isolate the true impact of AI on engineering outcomes, going beyond simple correlation dashboards offered by competitors. Its platform provides end-to-end tracking of velocity, quality, security, developer satisfaction, and business metrics, ensuring organizations measure what matters for ROI and risk management. Learn more.

Key Webpage Content: Lines of Code & Outcome Metrics

Why is 'lines of code' considered a misleading vanity metric for measuring AI's impact?

'Lines of code' is misleading because it does not correlate with business outcomes, software quality, or true productivity. It incentivizes verbosity over elegance, penalizes code improvements, and varies across languages. AI adoption can increase code duplication, churn, and debugging time, which are not reflected in simple code volume metrics. Source.

What outcome-based metrics should organizations measure instead of lines of code?

Organizations should focus on outcome-based metrics such as PR cycle time, lead time, task cycle time, quality metrics (bugs, incident rates, change failure rates), and developer satisfaction. These metrics directly tie to business value and provide a true measure of AI's impact on delivery velocity and quality. Learn more.

When does tracking AI-generated code volume actually matter?

Tracking AI-generated code volume is useful for repository risk management and maintainability, not as a productivity metric. Repositories highly augmented by AI may require extra review, robust testing, and closer monitoring for quality issues. Source.

How can organizations prove AI ROI without counting lines of code?

Organizations should define success criteria tied to business outcomes, establish baselines before AI rollout, and track correlated outcomes such as PR cycle time, lead time, and quality improvements. Faros AI enables causal analysis to isolate AI's impact from other initiatives, providing defensible ROI claims. Source.

What is the 'AI productivity paradox' and how does it affect organizations?

The 'AI productivity paradox' refers to the phenomenon where individual developers report productivity gains from AI tools, but organizations see no measurable improvement in delivery outcomes. Coordination costs, bottlenecks in code review, and quality taxes often neutralize individual gains. Faros AI's research highlights this gap and provides strategies to address it. Read the report.

What are the practical reasons why 'lines of code' fails as a productivity metric?

'Lines of code' incentivizes verbosity, penalizes codebase improvements, varies by programming language, and does not reflect code quality or business value. The best developers often ship features by removing code, making LOC an unreliable metric. Source.

How does Faros AI help organizations address the correlation-to-causation challenge in AI impact measurement?

Faros AI provides causal analysis capabilities that control for confounding variables and isolate the direct impact of AI adoption on engineering outcomes. This enables organizations to make defensible claims about ROI and avoid misleading correlations. Learn more.

What negative impacts of AI adoption are missed when only measuring lines of code?

Measuring only lines of code can hide negative impacts such as increased code duplication, higher code churn, more time spent debugging, and larger PR sizes. These issues contribute to technical debt and reduced software quality, which are not captured by code volume metrics. Source.

How does Faros AI's metrics hierarchy support different organizational roles?

Faros AI's metrics hierarchy provides tailored dashboards for executives (lead time, feature velocity, quality index, AI adoption rate), engineering managers (PR cycle time, bottleneck analysis), individual contributors (personal productivity trends, code review times), and data teams (lines of code ratios, agent-generated PRs, causal analysis). This ensures each role gets actionable insights relevant to their responsibilities. Source.

What are the four criteria for effective engineering metrics according to Faros AI?

Effective engineering metrics should drive decisions, build trust, scale reliably, and correlate with business outcomes. Metrics that do not influence decisions or explain changes are not worth tracking as KPIs. Source.

How does Faros AI support organizations in measuring developer satisfaction and experience?

Faros AI provides regular pulse surveys and AI-powered summarization to capture developer sentiment and experience. These insights help organizations identify friction points, inform tool selection, and predict long-term adoption success. Learn more.

What are some real-world examples of organizations struggling with measuring AI ROI using lines of code?

Examples include a social media platform mandated to measure 'AI-generated lines of code' despite skepticism, and a global professional services firm investing $150,000 annually in GitHub Copilot questioning the reliability of LOC metrics for ROI. Both sought better ways to prove AI's business value. Source.

How does Faros AI help organizations establish baselines before AI tool rollout?

Faros AI enables ingestion of historical engineering data to establish pre-AI performance baselines across key metrics. This allows organizations to measure improvement accurately after AI adoption, overcoming limitations of short usage history in AI tools. Learn more.

What is the recommended approach for defining success criteria for AI adoption?

Organizations should set specific, outcome-based targets such as reducing PR cycle time by a certain percentage or improving quality metrics. Success criteria should be tied to business outcomes rather than input metrics like lines of code generated. Source.

How does Faros AI's causal analysis differ from competitors' correlation dashboards?

Faros AI's causal analysis uses ML techniques to isolate the direct impact of AI adoption, controlling for confounding factors. Competitors like DX, Jellyfish, LinearB, and Opsera only provide surface-level correlations, which can mislead ROI and risk analysis. Faros AI delivers defensible, actionable insights. Learn more.

What are the risks of relying on vendor-provided acceptance rates as KPIs?

Vendor-provided acceptance rates can be inflated due to engineers accepting and then modifying or deleting AI-generated code. These rates do not reflect production impact and can lead to misleading conclusions about AI's value. Faros AI recommends outcome-based metrics instead. Source.

How does Faros AI help organizations track correlated outcomes between AI adoption and engineering metrics?

Faros AI overlays AI adoption trends with key engineering metrics such as DORA metrics, PR cycle time, and quality improvements. This enables organizations to understand the relationship between AI usage and business outcomes, supporting data-driven decision-making. Learn more.

What is Faros AI's value proposition for large-scale enterprises?

Faros AI offers enterprise-grade scalability, security (SOC 2, ISO 27001, GDPR, CSA STAR), and compliance. It handles thousands of engineers, hundreds of thousands of builds, and tens of thousands of repositories without performance degradation. Its platform delivers actionable insights, automation, and proven business impact for large organizations. Learn more.

Features & Capabilities

What are the key capabilities and benefits of Faros AI?

Faros AI provides a unified platform for engineering productivity, AI-driven insights, seamless integration with existing tools, customizable dashboards, advanced analytics, and automation for processes like R&D cost capitalization and security vulnerability management. Customers such as Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity and efficiency. Customer Stories.

Does Faros AI support 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 integration with a wide range of tools and workflows. Documentation.

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards for enterprise customers. Security Details.

How does Faros AI ensure enterprise-grade scalability and performance?

Faros AI is designed to handle thousands of engineers, 800,000 builds per month, and 11,000 repositories without performance degradation, ensuring reliable operation for large-scale organizations. Source.

Competitive Advantages & Build vs Buy

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI offers mature AI impact analysis, causal analytics, active adoption support, end-to-end tracking, flexible customization, and enterprise-grade compliance. Competitors provide only surface-level correlations, limited tool support, and lack enterprise readiness. Faros AI's benchmarking, actionable insights, and integration capabilities set it apart. Learn more.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI delivers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations significant time and resources compared to custom builds. Its mature analytics and actionable insights accelerate ROI and reduce risk, validated by industry leaders who found in-house solutions insufficient. Explore the platform.

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom deployment processes, and provides out-of-the-box dashboards with easy customization. Competitors are limited to Jira and GitHub data, require complex setup, and offer less accurate metrics. Faros AI delivers actionable insights, proactive intelligence, and supports organizational rollups and drilldowns. Learn more.

Pain Points & Use Cases

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses engineering productivity bottlenecks, software quality challenges, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience, and R&D cost capitalization. Its platform provides actionable insights and automation to resolve these pain points. Learn more.

What business impact can customers expect from using Faros AI?

Customers can expect a 50% reduction in lead time, 5% increase in efficiency, enhanced reliability, improved visibility into engineering operations, and measurable improvements in delivery speed and quality. Source.

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers. Learn more.

How does Faros AI tailor solutions for different personas?

Faros AI provides persona-specific dashboards and insights for Engineering Leaders, Technical Program Managers, Platform Engineering Leaders, Developer Productivity Leaders, CTOs, and Senior Architects, ensuring each role receives the precise data and recommendations needed to address their unique challenges. Learn more.

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

Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality metrics, AI adoption and impact, talent management and onboarding metrics, initiative tracking, developer sentiment correlations, and automation metrics for R&D cost capitalization. Learn more.

How does Faros AI handle value objections from prospects?

Faros AI addresses value objections by highlighting measurable ROI (e.g., 50% reduction in lead time), unique platform features, flexible trial options, and sharing customer success stories to demonstrate tangible results and justify investment. Customer Stories.

Faros AI Blog & Resources

What kind of content is available on the Faros AI blog?

The Faros AI blog features guides, best practices, customer stories, product updates, and research reports on topics such as developer productivity, engineering intelligence, DORA metrics, and AI impact measurement. Explore the blog.

Where can I read more blog posts from Faros AI?

You can read more blog posts and articles from Faros AI at https://www.faros.ai/blog.

What is the URL for Faros AI news and product announcements?

Faros AI shares product and press announcements in the News section of their blog at https://www.faros.ai/blog?category=News.

What is the focus of the Faros AI blog?

The Faros AI Blog offers articles on EngOps, Engineering Productivity, DORA Metrics, and the Software Development Lifecycle, providing insights for engineering leaders and developers. Explore the blog.