By Sabrina Farmer, CTO at GitLab
CTOs I connect with often share a similar challenge: despite having hundreds or thousands of engineers and mature development processes, they’re overwhelmed by complexity. Every business unit has accumulated its own tools and processes over the years, and paradoxically, adding more engineers can result in slower delivery, not faster.
As AI continues to transform software development, engineering leaders need to be more strategic than ever before. Organizations must rethink their approach to growth and talent in this AI-driven landscape from the outset to unlock the full value of this transformation.
Strategic Growth: Three Scaling Obstacles
There are three nearly universal critical challenges when scaling that add to complexity:
- Inconsistencies across teams and tools
- No visibility, metrics, or risk profile across the engineering portfolio
- Exponential growth complexity compounds as organizations expand
To understand why scaling fails, we must examine how team dynamics evolve with size:
Small organizations: Teams manage the full stack for their domain. Trust is easier to build, and aligning on goals is straightforward through direct communication.
Medium organizations: Silos emerge as teams specialize. The “need to know everything” starts to break down, and cross-functional coordination shifts to quarterly cycles. Dependencies become challenging to manage.
Enterprise organizations: Teams work with complete autonomy, creating business unit silos. Trust becomes harder, competing priorities are common, and coordination happens annually. Platform retrofitting becomes nearly impossible.
At each stage, the temptation is to add more tools to solve immediate or localized problems. But this creates the core scaling trap: custom solutions that require ongoing maintenance, fragmented metrics that prevent organizational learning, and operational burdens that grow faster than teams.
Platform Strategy for Scale
The answer isn’t accepting inefficiency. It’s building technical work around platforms rather than products from day one. Here’s what you need to do:
- Evaluate your current tool stack. Map every tool your teams use and identify overlapping functions. Track the operational burden over time for each tool, and you’ll almost always hit a tipping point where maintenance costs outweigh benefits. Rather than asking “what’s the best tool for X?” ask “what choices best serve our company’s mission while maintaining agility to scale?”
- Adopt platform leadership thinking. Begin asking, “How do we solve this once for the entire organization?” instead of “How do we solve this for our team?” Move from optimizing individual team productivity to optimizing organizational efficiency. Map your development lifecycle end-to-end to identify redundancies and gaps, then choose platforms that grow with you rather than accumulating point solutions.
- Ready your teams for AI-driven workflow coordination. While 99% of C-Suite executives find the human element valuable in software development, the current reality shows that humans still handle three-quarters of the work, while AI contributes just one-quarter. This means AI is shifting engineering workloads from individual contributors to leaders who orchestrate complex human-AI collaboration systems. Look for platforms that support this transition by providing unified tooling for workflow coordination.
- Track what matters. Focus on DORA metrics (deployment frequency, lead time, change failure rate, recovery time) rather than individual productivity metrics.
- Implement a data strategy. Data is a critical resource that requires a clear strategy, and it’s the biggest hurdle to scaling up and down.
- Streamline system management. Develop operational capabilities that sustain themselves without requiring constant human intervention.
The Ongoing Evolution
Scaling never reaches completion. It requires continuous reassessment and adaptation. Platform-based approaches provide the foundation to minimize redundant work and silos while maintaining the agility needed to evolve as your organization grows.
The companies that thrive at scale aren’t those that accumulated the most sophisticated tools along the way. They’re the ones that made deliberate choices about how to structure both their technology and their teams from the beginning, understanding that sustainable growth requires thinking systematically about the human challenges of complexity, not just the technical ones.
As engineers become orchestrators of complex human-AI systems, the platform approach becomes even more critical. Rather than each engineer managing their own fragmented toolkit, platforms enable them to focus on what they do best: coordinating intricate workflows and ensuring quality across increasingly complex systems. Begin with platform thinking today, and your future engineering organization will thank you.
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ABOUT THE AUTHOR
Sabrina Farmer is the Chief Technology Officer at GitLab, where she leads software engineering, operations, and customer support teams to execute the company’s technical vision and strategy and oversee the development and delivery of GitLab’s products and services. Prior to GitLab, Sabrina spent nearly two decades at Google, where she most recently served as vice president of engineering, core infrastructure. During her tenure with Google, she was directly responsible for the reliability, performance, and efficiency of all of Google’s billion-user products and infrastructure. A long-time advocate for women in technology, Farmer earned a B.S. in Computer Science at the University of New Orleans, where she established two scholarships to help level the playing field for inclusion and empowerment in technology.
