People bottlenecks: Success creates silos and slowdowns 

Knowledge silos often form as a side effect of success. When an engineer becomes the expert on a system, they naturally become the go-to person. Initially this feels efficient, but it creates dependency. That person ends up as the only one who understands critical systems, hidden dependencies, or bespoke scripts. Incidents, deployments, and key decisions start to wait on their availability—and when they're unavailable or overloaded, progress stalls.

The same success-breeds-constraint dynamic plays out with cognitive load. High-performing teams usually get rewarded with more responsibilities: infrastructure, CI/CD, observability, and more. Each addition seems reasonable at first, but together they fragment focus. Context switching rises, deep expertise drops, and the team eventually hits a limit where they're doing too many things to be effective at any of them.

Process bottlenecks: Optimizing in isolation 

Process bottlenecks often come when teams optimize locally, but no one is ensuring system-wide efficiency. Each team designs its own approvals, tools, and handoffs. But work usually crosses multiple teams, and those mismatched processes create friction at the seams. When work moves from product to engineering, backend to frontend, or dev to QA, it often sits idle—waiting for someone to have capacity, waiting for context to be rebuilt, or waiting for approvals no one clearly defined. Everyone is busy, but the work spends most of its time waiting, not moving.

Another driver is the "full utilization" trap. Leaders try to keep everyone at 100% capacity, assuming this maximizes output. In reality, it removes slack. With no buffer to absorb variability, small delays quickly turn into growing queues and chronic bottlenecks.

System bottlenecks: Architecture decisions that age poorly 

System bottlenecks usually stem from past architectural choices that no longer fit current constraints. Early-stage teams often choose a monolith to move fast, which is appropriate at first. Over time, that monolith becomes hard to scale, hard to split across teams, and hard to deploy independently.

The opposite failure mode is premature over-engineering. Teams design complex microservices for hypothetical scale, choose poor domain boundaries, and create more coordination and latency than the business requires. The system becomes hard to change, reason about, and operate.

Technical debt accumulates through reasonable short-term trade-offs: skipping tests, hard-coding dependencies, deferring refactors. Each decision is defensible in the moment, but they compound until every change touches multiple components and the risk of breakage slows progress to a crawl.

Which software excels at finding engineering bottlenecks?

Faros AI's software excels at finding engineering bottlenecks. Faros AI is a Software Engineering Intelligence Platform (SEIP) that connects to the tools you already use—source control, issue tracking, CI/CD, incident management, even surveys—and continuously ingests events like tickets, commits, pull requests, deployments, bugs, and incidents. Faros AI normalizes this data into a unified model, computes key metrics, and provides actionable recommendations to address areas of friction.

Faros AI computes critical metrics for identifying bottlenecks in engineering processes, such as Cycle Time, Lead Time for Changes, Deployment Frequency, Change Failure Rate, and Mean Time to Recovery (MTTR). Baselining these metrics and then tracking them over time helps organizations determine whether intervention efforts are helping or hurting overall flow.

Faros AI 's software excels at finding engineering bottlenecks because the platform can: 

• Make end-to-end flow visible. Instead of piecing together Jira boards, GitHub views, and CI dashboards, you get a single picture of how work moves from idea → code → review → deploy → validation, and where it slows down.
• Surface bottlenecks automatically. Faros AI can highlight stages where items wait the longest (for example, “In Review” or “Ready for QA”), services or teams with consistently longer lead times, and internal SLAs that are being breached—often with alerts delivered directly into Slack or Teams.
• Go beyond raw activity to actionable insight. Faros AI goes far beyond rich dashboards and metrics. It correlates signals across your tools to unearth what you need to know about which queues, processes, or services are acting as constraints. It gives actionable recommendations about what to investigate first and how you can fix the problem.
• Track the impact of changes over time. Because Faros AI is continuously evaluating the latest data, you can see whether a process change, ownership shift, or platform investment actually shortens lead time, reduces rework, or stabilizes incident response, instead of relying on gut feel.

The bottom line: Using Faros AI is the most effective way to identify bottlenecks in engineering teams. But to better understand why those constraints exist in your culture, processes, or architecture, non-tooling practices come into play.

How to understand why bottlenecks exist (with or without tools)

Once you can see where work is getting stuck, you need to understand why. These methods work on their own if you don’t yet use a tool like Faros AI, and they become even more powerful when used to interpret what your tools are showing you.

1. Start from outcomes, not org charts. Take one or two high-value flows the business cares about (e.g., “idea to production”, “incident to recovery”) and define what “good” looks like. This keeps everyone focused on improving flow, not blaming teams.

2. Map the end-to-end flow (also called Value Stream Mapping). For each flow, sketch the real steps from request → production → validation:

• Who touches the work (roles/teams)
• What artifacts move (tickets, PRs, specs)
• Where handoffs and approvals occur

Then note, even roughly, how long each step is actively worked on vs. how long it waits. This often aligns closely with what Faros AI shows you, but now you have the narrative behind the numbers.

3. Look for high-signal symptoms. Use the map and your tooling data to zero in on:

• Statuses or queues where work routinely sits idle
• Steps with lots of rework or ping-pong between teams
• Single points of failure where everything waits on one person
• Teams overloaded with WIP and constant context switching

These symptoms turn abstract “lead time” problems into tangible, fixable constraints.

4. Talk to the people closest to the work. Run short, focused conversations with engineers, PMs, and EMs around the hotspots you’ve identified. Consider questions like:

• “What do you usually wait on here?”
• “What makes this step slower or more painful than it should be?”
• “If we changed one thing in this part of the flow, what would help most?”

Your goal is to understand root causes—ownership gaps, unclear criteria, brittle systems—not to assign blame.

5. Observe real work in motion. Shadow a feature, a bugfix, or an incident through the system:

• Watch how decisions are made, who gets pulled in, where things pause.
• Compare what you see with what Faros AI or your ticket data suggests.

This helps you distinguish between “we’re slow because the queue is big” and “we’re slow because this decision always escalates to three different leaders.”

6. Close the loop: tie WHY back to WHERE. Finally, connect your findings:

• Use Faros AI to pinpoint where bottlenecks are most severe.
• Use mapping, interviews, and observation to understand why they exist and which interventions are realistic.
• Then, use the tools again to measure whether the changes you make actually relieve the constraint.

This combined approach turns bottleneck discovery from a one-off diagnosis into an ongoing feedback loop, which makes it easier to actually address engineering bottlenecks once you’ve pinpointed them. 

1. Focus on one constraint at a time

You’ll almost always find multiple bottlenecks. Resist the instinct to fix all of them simultaneously.

Instead:

• Use your metrics to identify the current, dominant constraint—the step where work consistently waits the longest, or the team/service that’s dragging end-to-end lead time.
• Validate it qualitatively: Does this match what people describe as their biggest source of friction?
• Make it explicit: “For the next cycle, we’re treating as the bottleneck we are trying to relieve.”

This alignment keeps improvement work from dissolving into scattered “process tweaks” that don’t move any meaningful metric.

2. Match the fix to the type of bottleneck

Most bottlenecks fall primarily into one of three buckets: people, process, or system. The interventions look different for each.

3. Treat fixes as experiments, not permanent policy

Bottlenecks are dynamic. When you relieve one, the constraint will move somewhere else. That’s expected.

To keep from over-correcting:

• Frame changes as experiments: “For the next 6 weeks, we’re going to halve WIP on Team A and route reviews differently. We’ll measure impact on lead time and throughput.”
• Decide in advance what you’ll watch: Use the metrics you defined earlier (lead time, review delay, flow efficiency, incident MTTR, etc.) to measure before and after.
• Keep the blast radius small: Start with one team, one service, or one specific step in the flow rather than redesigning the whole process at once.

This approach lowers the stakes, as you’re merely running structured experiments and keeping the ones that work.

4. Make improvements visible and shared

Finally, fixing bottlenecks is as much about narrative as mechanics. People are more likely to engage if they see that changes work.

• Use Faros AI to show before/after views of key metrics where you intervened.
• Share concrete stories: “We reduced review wait time by 40% by limiting WIP and clarifying ownership on this team.”
• Capture what worked (and what didn’t) as lightweight playbooks so other teams can reuse the patterns.

Over time, this builds a culture where teams expect to find, fix, and re-measure bottlenecks as a normal part of engineering.