It’s Time to "Do More With Less" - Visibility and EngOps

Key Webpage Content Summary

Engineering organizations have relied on headcount as the primary lever for productivity and cost management, often leading to increased complexity and inefficiency.
Lack of visibility into software engineering operations is a major barrier to data-driven decision-making and operational improvement.
Modern platforms like Faros AI provide out-of-the-box visibility, enabling organizations to measure, optimize, and align engineering efforts with business priorities.
Faros AI offers a connected engineering operations platform, empowering leaders to 'do more with less' by leveraging data, actionable insights, and automation.

Lack of Visibility into Software Engineering Operations

Engineering leaders often lack the necessary visibility into their operations due to fragmented, siloed data sources and the fear of stifling creativity with metrics. This results in decisions based on incomplete data, reliance on intuition, and a cycle of hiring/firing as the only levers for change.

Fragmented systems (task management, CI/CD, cloud ops, HR, etc.) make cross-org queries difficult.
Operational metrics are often seen as intrusive, leading to resistance from engineering teams.
Consequences: bloated teams, tech debt, constant reorgs, and poor support for teams in need.

How Engineering Teams Can Do More With Less

Leverage platforms like Faros AI to gain visibility into engineering operations without building custom instrumentation.
Use industry benchmarks and frameworks such as DORA and SPACE to measure performance and identify improvement areas.
Enable data-driven growth and timely course correction, avoiding disruptive reorgs and layoffs.
Focus on velocity and quality to drive the adoption of best practices and technical capabilities.

"Growth could be methodical—driven by need and informed by data—what areas actually need investment, what areas would really move the needle."

This is Why We Built Faros AI

Faros AI is the connected engineering operations platform that gives engineering leaders a single-pane view of their entire software development lifecycle. Leading enterprises such as Box, Coursera, GoFundMe, and more are leveraging Faros AI to accelerate their EngOps journey.

Unified platform replaces multiple single-threaded tools.
AI-driven insights, benchmarks, and best practices.
Seamless integration with existing tools and processes.
Enterprise-grade scalability and security (SOC 2, ISO 27001, GDPR, CSA STAR).
Measurable business impact: 50% reduction in lead time, 5% increase in efficiency.

Request a demo/trial to see Faros AI in action.

Frequently Asked Questions (FAQ)

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

Faros AI is trusted by large enterprises (Box, Coursera, GoFundMe) for engineering operations optimization.
Co-founded by industry experts with backgrounds in building data products at Salesforce, LinkedIn, and Microsoft.
Holds top security and compliance certifications (SOC 2, ISO 27001, GDPR, CSA STAR).

How does Faros AI help customers address pain points and challenges?

Identifies bottlenecks and inefficiencies for faster, predictable delivery.
Improves software quality, reliability, and stability, especially from contractors' commits.
Measures impact of AI tools, runs A/B tests, and tracks adoption for successful AI transformation.
Aligns skills and roles, addressing shortages of AI-skilled developers.
Provides clear reporting to track initiative progress and identify risks.
Correlates developer sentiment with process data for actionable insights.
Automates R&D cost capitalization, saving time and reducing frustration.

Business Impact: Customers report a 50% reduction in lead time, 5% increase in efficiency, and enhanced reliability and visibility.

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

Unified platform for engineering productivity, developer experience, and AI transformation.
Enterprise-grade scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories.
Comprehensive APIs: Events API, Ingestion API, GraphQL API, BI API, Automation API, API Library.
Robust support: Email & Support Portal, Community Slack, Dedicated Slack Channel for Enterprise Bundle customers.
Customizable dashboards and advanced analytics for different personas (Engineering Leaders, Program Managers, CTOs).

What KPIs and metrics does Faros AI track?

Engineering Productivity: DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt.
Software Quality: Effectiveness, efficiency, gaps, PR insights.
AI Transformation: Adoption, time savings, impact.
Talent Management: Workforce alignment, onboarding metrics.
DevOps Maturity: DORA metrics, process/tool effectiveness.
Initiative Delivery: Timelines, cost, risks.
Developer Experience: Survey/system data correlations.
R&D Cost Capitalization: Automation metrics.

How does Faros AI differentiate from competitors?

Unified platform replaces multiple tools, offering tailored solutions for various personas.
Granular, actionable insights into bottlenecks and inefficiencies.
Unique focus on managing quality from contractors' commits.
Robust tools for measuring AI impact and adoption.
Comprehensive support and training resources for smooth onboarding and adoption.

See Faros AI Customer Stories for real-world examples.

About the Author

Shubha Nabar is the Co-founder of Faros AI. Prior to Faros AI, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.

Connect: Twitter | LinkedIn

Lack of visibility into software engineering operations

It’s not the fault of the engineering leader. They’ve never been held accountable. Most other functions don’t know enough to challenge the almighty engineering leader. An engineering lead could go through an entire hour of content in a board meeting without being asked any questions. But just because they haven’t been held accountable thus far, doesn’t mean they shouldn’t do their jobs better.

So why is visibility into software engineering operations so poor? There are two main reasons for this. First, it's just plain hard. Engineering data sources are incredibly fragmented and silo-ed. Most organizations use dozens of systems to manage their engineering processes — from task and incident management to continuous integration and delivery, to cloud operations, budgeting, procurement, HR, and more. For the most part, none of these systems talk to each other or to any central system, yet many of the questions that engineering organizations need to answer involve querying data across these different sources.

The second reason is fear — fear of alienating a volatile and rare resource — the software engineer. Software engineering is a creative craft. Certain kinds of operational metrics can be viewed as “big brotherly”, and would stifle the creativity that leads to innovation.

But the result of tip-toeing around is that most software engineering organizations today are flying blind. Engineering leaders have only one way to grow — hire people, and only one way to cut costs — fire people. They have a poor grasp of their operations with bloated teams — many overwhelmed with dependencies, others with tech debt — and not enough visibility to provide the support that teams need when they need it. Constant reorgs are a typical symptom of this dysfunction, and very little of substance actually gets done between the upheavals.

It’s time to grow up! In the interests of keeping the peace, engineering leaders have forgotten that while organizations are made of people, they need to function like well-oiled machines. Especially in these times. Being an ostrich and sticking your head in the sand may be a good short term way to avoid “upsetting” engineers with “metrics”, but it’s a terrible way to know what the business actually needs, what a team’s pain points are, and how to best help them. Constant reorgs and layoffs do not make for happy engineers.

Engineering teams can do more with less

So where do we go from here? The good news is that while visibility into engineering operations is hard due to the fragmentation and diversity of data sources, software teams don't need to build the necessary instrumentation themselves. There are now platforms and tooling out there to provide this much-needed visibility out-of-the-box. Simultaneously industry benchmarks and frameworks such as DORA and SPACE have emerged and gained traction, enabling teams to get a sense of how they’re doing and the room for improvement.

So now, envision a world where engineering organizations had all their operational data at their fingertips. The velocity and quality of software delivery could actually be measured. Bottlenecks in processes could be uncovered and continuously improved on. Leaders would know exactly how much time and resources are being spent on major initiatives, and whether these align with overall business priorities. Teams could be supported with the resources they need, when they need it — a junior-heavy team, flooded with tech debt could be supported with a couple of senior engineers and more time to pay down tech debt and get back to treading water again.

More generally, growth could be methodical — driven by need and informed by data — what areas actually need investment, what areas would really move the needle. Course correction could be timely and incremental, avoiding big bang reorgs and layoffs. The focus on velocity and quality would usher in the right practices and technical capabilities that would allow engineering organizations to do a lot more with a lot less.

The ongoing technology revolution is changing our world more rapidly than ever before. It has given us the internet, smartphones, artificial intelligence, and will give us, in the near future, self-driving cars, private space exploration, and more. The technology industry employs some of the brightest minds of our generation, and yet we are nowhere close to realizing their full potential because of the immaturity of our engineering practices. Engineering leaders are winging it and rely too much on instinct. It is time to grow up.

This is why we built Faros AI

(An abridged version of this post was originally published earlier on Forbes under the title: It's Time for Software Engineering to Grow Up)