Scaling Up: How to Maintain Speed and Quality as You Grow

Small technology teams thrive on agility, passion, and very few rules, but as they grow the lack of methodology can slow things down. Veteran technology leader Christoph Hesterbrink offers advice on introducing rigor while maintaining innovation as companies grow.

Early-stage technology startups thrive on speed and passion. Small teams working together in a room can make rapid decisions, pushing innovation ahead. What propels these organizations forward — agility, drive, informal collaboration — works well for a time. But as they grow, a silent killer emerges: friction caused by scale.

For technology leaders, understanding why releases begin to slow, bugs start to multiply, and customer satisfaction commences to slip is critical. This is not failure, but rather the result of the inevitable complexity that emerges as the organization brings on more people, teams become distributed, and expectations increase.

As companies succeed and therefore grow rapidly, their original playbooks no longer suffice. Technology leaders must keep an eye out for warning signs and adopt new mindsets and practices to scale effectively.

Four Signs Your Growth Model Needs an Upgrade

Successful growth involves more than just onboarding more employees or winning additional customers. It requires an organizational evolution. Informal processes work for five to ten people but falter once there are 50 to 100 distributed engineers.

When starting up, methodology is not typically top of mind. Even once these organizations grow and they see that agile approaches may be helpful, adoption is often inconsistent or met with great resistance. Meanwhile, dependencies proliferate, communication gaps widen, and velocity stalls.

Scaling often feels slower than startup chaos because the illusion of simplicity fades. Growth multiplies complexity, and complexity breeds friction. Too often, however, leaders don’t recognize this “growth trap” until it’s too late. There are four signs that indicate a change in approach is warranted:

1. Workflows slow. Whereas code was once shipped weekly, releases stretch to months. Dependencies, communication issues, and redundant work increase, clogging pipelines. These issues are exacerbated when team members are working across time zones.
2. Quality suffers. Testing becomes less thorough as teams struggle to meet deadlines, leading to more post-release bugs and the erosion of customer trust. Disconnected team members and poor documentation result in knowledge silos and increased risk.
3. Customer satisfaction slips. Unreliable delivery schedules signal deeper organizational dysfunction rather than bad luck, damaging customer trust.
4. Unmanaged complexity breeds chaos. Each additional team or feature increases complexity. Ignoring rules — skipping test cycles, regression testing, or demos to product owners during development — erodes order; overcomplicating them stifles innovation.

Successful Scaling Starts With People

Growth demands new mindsets and skills so technology leaders must first turn their attention to the developers and engineers in their organizations. Some of the changes required can significantly impact the way that they are used to working. These include:

• Embracing documentation and knowledge sharing to preserve institutional memory.
• Adopting agile methods to manage complexity without stifling innovation.
• Prioritizing communication and cross-team collaboration, adopting a standard set of tools (such as messaging platforms, videoconferencing tools) suited for distributed teams and using them consistently.
• Fostering accountability aligned with scalable practices (for example, testing automation, well- documented code, data quality standards,) not heroics (such as pulling all-nighters to fix problems which should have been done right in the first place).

Not everyone who thrived in startup mode will be ready or capable of changing their thinking and ways of working. Leaders must keep an eye out: yesterday’s top performers may resist these changes, viewing them as unnecessary bureaucracy. These former stars can block progress and create bottlenecks, so it’s essential to help them see the value in evolving or leaving the organization.

Key Metrics to Guide Interventions

To manage a startup’s growth, leaders can baseline and track key metrics reflecting throughput, quality, employee engagement, and customer trust. When these measures start to inch up or down, they can point to emerging issues and suggest what interventions may be required. They are technical in nature, but can help to expose people and process issues. These measures include:

• Release Cycle Time: Average time from completing development to production release. Increasing cycle times signal workflow bottlenecks.
• Bug Count and Severity: Number and criticality of post-release bugs. Increasing counts or severity indicate a decline in quality.
• Test Coverage and Pass Rates: The percentage of code covered by automated tests and test success rates. Drops in these KPIs warn of mounting technical debt.
• Deployment Frequency: How often releases occur. Declines here may reveal slower development or increased risk aversion.
• On-Time Delivery Rate: Percentage of features and releases delivered as promised. Dips in this metric tell of issues in the development process, from poor specifications to insufficient testing.
• Employee Engagement and Feedback: Surveys capture morale and communication issues. Decreasing engagement or increased negative feedback — the people side of throughput — can point to what needs attention (often workloads or misaligned expectations). 

Measuring and monitoring these quantitative and qualitative metrics helps leaders identify the sources of friction— process, technology, or people. With this more nuanced insight, they can develop targeted root-cause fixes. For example, a decline in test pass rates may have its root cause in an expectation gap between the development team and the product owner.

How to Scale Effectively: Do’s and Don’ts

I’ve seen rapidly expanding businesses fall into the growth trap at least half a dozen times due to immature processes, insufficient tools, and lack of coordination across growing teams. An electronics firm that grew to $1 billion in revenue and expanded its IT team internationally but struggled to integrate different methodologies and systems leading to communication breakdowns and an inability to properly estimate, prioritize, and manage business and customer demand. A services company whose loss of key talent and associated knowledge after the pandemic exposed gaps in documentation and processes, resulting in system failures with direct customer impact and the need to hire costly external experts to address the issues. A small pharmaceutical company that, after acquiring another company, where cultural and process incompatibilities —exacerbated by the absence of a shared playbook or compatible methodologies — derailed the merger’s success.

Addressing the challenges of scaling is hard. But growth without adaptation leads to stagnation or collapse.

The good news is that there are some proven best practices companies can adopt (and common mistakes they can avoid) to scale more effectively. 

Do…

• Invest early in process maturity aligned to the growth phase. In a small team, one person often wears many hats and there is no need for documented processes. That won’t work when team sizes double or triple.
• Form cross-functional teams owning end-to-end delivery of applications. This promotes, greater accountability, communication, and efficiency throughout the project lifecycle.
• Adopt tools and processes to support asynchronous communication and transparency. Using platforms like Slack for messaging, Jira for task tracking, and Confluence for shared documentation allows team members to stay updated and contribute on their own schedules.
• Encourage continuous learning and adaptation. Technology leaders can give their team members and resources time to stay current on certifications and publicly recognize outstanding contributions.
• Prioritize root cause analysis over band-aid fixes. This may take longer than fixing the issues of the moment but can prevent recurrence and improve overall reliability.
• Baseline and track key metrics regularly. This provides visibility into performance trends and progress, enabling data-driven decision-making and timely course corrections.

Don’t…

• Rely solely on informal communication or “tribal knowledge.” It leads both to overload of and dependency on select individuals while slowing down onboarding of new team members.
• Assume bigger teams mean faster delivery. Larger, more distributed teams do not guarantee speed. In fact, they can create unexpected overhead. Leaders should carefully assign roles and responsibilities, limit overlap, and establish clear accountability.
• Punish resistance to change without support. This breeds fear and resentment, leading to disengagement and reduced morale. Instead, leaders should get to the root causes of resistance, provide training and resources, and foster an environment that encourages open dialogue and gradual adaptation.
• Enact bureaucracy that kills agility. Growth demands rules (for example, coding standards, sprint ceremonies, documentation), but when these become rigid checklists, they stifle creativity and progress. The goal is to put in place enough process to manage complexity without suffocating innovation, regularly revisiting and adjusting rules as the organization matures.
• Ignore warning signs hoping issues resolve themselves. They won’t. Leaders should take off the blinders and act.

Accepting the Challenge: Evolution or Extinction

Many companies fail not due to a lack of talent or vision, but from refusing to evolve their operating models. Early success blinds them to coordination costs, culture shifts, and the realities of distributed work. With the adoption of a common set of tools and a methodology with a common language and process (typically agile), the chances of maintaining the momentum of the smaller organization are magnitudes higher.

For tech executives, growth is a leadership crucible. They can either evolve with their organizations, embracing complexity and empowering their teams, or cling to their old ways of thinking, missing signs  of dysfunction until it’s too late.

The risks are real—but so is the opportunity.

Success demands bold leadership capable of tackling root causes, not resorting to quick fixes. By focusing on people and mindset, recognizing warning signs, monitoring critical metrics, and balancing process with agility, technology leaders can transform growth-induced friction into competitive advantage. Understanding and embracing this evolution is the only way to keep passion and speed alive in a larger, distributed, and more complex organization.