How to spot and unblock engineering bottlenecks

Estimated reading time: 8 minutes

We all want our engineering teams to run like our best systems: finely tuned, so that code flows from idea to production in a smooth, predictable pipeline.

But in practice, things get stuck, and when they do, it can be surprisingly hard to figure out where the blockage is.

What is an engineering bottleneck?

In the management realm, bottleneck has become a bit of jargon that’s often used imprecisely. For this article, we won’t be talking about bottlenecks in the sense of physical constraints like GPU processing speed. Rather, we’ll discuss bottlenecks in terms of process, be it issues with how a team works, or the resources they lack to perform at maximum capacity. 

“Sometimes [leaders] are just talking about waste – they really mean stuff that’s superfluous, that doesn’t need to be happening, or is happening too much, or just not in the way they’d hope,” Amy Carrillo Cotten, director of client transformation at Uplevel said. “Or it’s bottlenecks as friction, where it’s not really a true bottleneck. It’s just things that are impeding the smooth flow.”

A good definition of a bottleneck comes from the Theory of Constraints (TOC), an influential management paradigm laid out by Eliyahu M. Goldratt in the 1980s. In the TOC, a bottleneck or “system constraint” is the step in a process that limits the output of the system as a whole. Think of it as the weakest link in the chain. 

From the perspective of an engineering leader, the team members will be working at capacity on the tasks related to this step, while those team members dedicated to steps after it are idle waiting for it to be completed. 

Carrillo Cotten gives a simple example: “Imagine Faisal is the only guy who knows how to do this licensing technology within our whole organization.” Since only Faisal can complete licensing tasks, if those tasks are handed off to him at a rate that’s faster than he can complete them, they pile up, and a bottleneck is formed. Deeper still, if others need to do licensing things, they have to wait until Faisal is free and his priorities align with theirs.”

Common bottlenecks engineering teams face

Let’s look at some of the structural or organizational constraints that limit how work flows through the system. 

These issues aren’t slowing down delivery because teams are being lazy. Bottlenecks arise because the system around the employees makes it difficult to move work forward efficiently.

• Lack of documentation and knowledge silos: When developers struggle to contribute outside their area of focus due to poor documentation or setup, knowledge becomes localized. This creates single-threaded progress paths and makes the team brittle because only specific individuals can unblock work.
• External decision-making and dependencies: When critical decisions or inputs are made outside the team, engineers are blocked, sitting idle while waiting for approvals or input. This introduces hard gates into the system – dependencies that break flow and fragment ownership, causing uneven throughput and avoidable delays. This is the most common type of bottleneck, as reported by Uplevel’s 2025 Engineering Leader Survey.
• Misaligned feedback loops: If customer, product, and engineering teams aren’t tightly integrated, miscommunication can lead to rework or last-minute pivots. These cause friction and force work backward in the pipeline, slowing overall progress.
• Siloed teams: When teams operate in isolation, work stalls at organizational boundaries. Handoffs become bottlenecks, and organization-wide flow suffers due to poor coordination and duplicated or missed effort.
• Leadership inflexibility: Rigid planning can constrain an organization’s adaptability. Leaders need to adjust to real-time signals like shifting priorities or blocked teams; if they fail to do so, bottlenecks will persist, piling up wasted time and making it harder to recover momentum.

Costs of engineering bottlenecks

Bottlenecks don’t just slow down individual tasks; those slowdowns create ripple effects that degrade productivity, weigh on budgets, and lower team well-being. Left unaddressed, they quietly drain resources and momentum from even the most capable engineering teams in a number of ways.

Slower processes mean higher costs. When work slows down, costs go up, especially on large-scale initiatives. McKinsey found that 66% of big IT projects run over budget, and 70% deliver late. Bottlenecks drag out timelines, delay value delivery, and increase the financial burden of every sprint. 

Developers end up idle. When decisions stall or dependencies block progress, developers sit idle or spend time on low-value tasks. A study from Tidelift and The New Stack found that engineers spend only 32% of their time writing or improving code. The rest is lost to meetings, tooling issues, and coordination problems – many of which stem from systemic bottlenecks.

Bottlenecks are a form of tech debt. As Carillo Cotten puts it, “Bottlenecks are technical debt. They are organizational debt. They are process debt. They’re the result of things that made sense at the time, but now they’re just not serving the org anymore.” She adds that, like forms of tech debt, bottlenecks consume resources that should be spent improving systems or shipping features. “Sometimes all you have time for is just the simple fix, the band-aid,” she notes. 

Team morale suffers. Being stuck in a bottleneck is frustrating. Whether it’s waiting on decisions, navigating unclear priorities, or redoing work due to misaligned feedback, the lack of forward motion takes a toll. Over time, repeated friction erodes enthusiasm, engagement, and trust in leadership.

Does AI help or hurt bottlenecks?

The dizzying adoption of AI has come with promises of increased velocity, but can AI help teams deliver, or will it exacerbate bottlenecks? 

While there is emerging evidence that AI can help engineers code faster, if coding was not the bottleneck in the first place, then AI is unlikely to help orgs achieve exponential gains in speed. 

High performing teams will have the biggest gains from AI, and for teams with bottlenecks, it will make the bottlenecks more pronounced. AI may change the process, architecture, and other systemic issues, but those changes may or may not add up to improvement and may even introduce new bottlenecks. 

The most essential skills for engineering leaders grappling with the rise of AI will be sensing and responding to changes in their organization by using a structured, quantitative, and qualitative approach to engineering management.

How to identify bottlenecks holding your team back

Spotting bottlenecks isn’t always straightforward. While some show up as glaring failures, others lurk beneath the surface, only becoming visible when delivery slows or teams start to struggle, and separating bottlenecks from other types of failures further complicates bottleneck identification.

Carrillo Cotten says that effective leaders must use a combination of data, trusted insight, and intuition to uncover where flow is breaking down.

“Every leader we talk to has several layers of instrumentation going on,” she says. But the signs aren’t always in the numbers. “There’s usually trusted advisors, people who know what’s up,” and their field reports can be just as valuable as dashboards because many bottlenecks are context-dependent–context that doesn’t show on a deployment frequency chart.

Sometimes the clearest signal is a mismatch between what the data shows and what the team is experiencing. That’s when leadership instincts come into play. “It’s like their gut feeling that there’s something going on over here.”

The challenge is stitching these signals together into a coherent picture. The best leaders know how to blend hard data, frontline experience, and intuition to spot the constraints holding their teams back.

Metrics for finding and overcoming bottlenecks

Dashboards and metrics can shed some light on engineering bottlenecks, with quantitative analysis an essential first step.

One of the most widely used measurement frameworks is DORA (DevOps Research and Assessment), which focuses on delivery performance using four key metrics:

• Deployment frequency: How often code is released
• Lead time for changes: How long it takes for a commit to reach production
• Change failure rate: How often deployments cause issues
• Time to Recovery: How quickly teams recover from incidents

The DORA metrics don’t specify where the bottlenecks are, but they can give an initial signal that things are not flowing well from code to production. For example, achieving elite Time to Recovery (also called Mean Time To Recover, or MTTR) is only possible once key friction points and bottlenecks have been identified and eliminated. 

However, DORA metrics are lagging indicators of problems, whereas metric frameworks such as SPACE and WAVE can provide a more holistic view. Finding metric frameworks that focus on early warning signs, such as PR review times and WIP (Work in Progress), can help identify bottlenecks before they become problematic. 

Once you’ve identified where bottlenecks occur, the next step is deciding what to do about them. Determining if the solution is org-wide or localized is the first step, and designing context-dependent solutions ensures effective resolution. 

Avoid one-size-fits-all cures. If code reviews are lagging due to long first response times, you can adjust team responsibilities or introduce reviewer rotations. If large pull requests consistently slow down progress, you can coach teams on breaking work into smaller, more reviewable chunks.

Preventing future bottlenecks

Metrics also help distinguish between surface symptoms and deeper issues. And it’s those deeper issues that engineering leaders want to fix, because ultimately, the goal isn’t just to fix the immediate problem, but rather to notice when the same constraint appears again. 

“Engineering leaders are trained as engineers,” says Carrillo Cotten. “So they’re looking for patterns. They might say, well, that happened that one time – but if they see it again, they’ll say, ‘this is the thing we should really talk about.’”

Diagnosing recurring bottlenecks requires looking at systemic factors like team composition and team process, cultural factors, alignment, the product development process, or foundational technical issues that span teams and orgs. Often, the answer starts in the architecture. Was the system designed in a way that over-concentrated responsibility or technical complexity in one area? Could it be more resilient, decoupled, or modular to prevent this in the future?

But architecture is rarely the only issue. “Where there’s an architecture problem,” Carrillo Cotten says, “there’s usually some kind of process problem around it.” That might include siloed decision-making, lack of cross-team collaboration, or communication gaps that reinforce brittle structures. 

The leaders who go beyond firefighting and dig into these root causes are the ones who make meaningful, lasting improvements – and stop bottlenecks before they start.

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