Recognizing The Symptoms
In Part I of this eight-part series, I demonstrated that many performance problems within an engineering organization were not solely due to workforce size but are influenced by the decision-making structures governing operations. In this part, I delve into the diagnostic process, focusing on the observed behaviors.
To begin, I pose that staffing appears to be the problem because overworked employees look the same as blocked employees.
Common Organizational Symptoms
- The “More People, Less Progress” Paradox – Operational chaos is evident in daily operations. Increased resources paradoxically slow progress, as more engineers and managers spend time in coordination meetings. When the primary issues coordination, decision latency, or unclear ownership, additional resources add complexity without an equivalent level of output.
- Organizational Self-Preservation – Systems prioritize self-preservation, leading to employee adaptation and a focus on positive metrics. Organizational incentives and adherence to processes hinder problem-solving and innovation as it is better to fail by the book instead of acting outside the system.
- The “Surprise” Crisis – Executives, disconnected from engineering reality due to filtered access gates, experience shock when bad news is finally delivered. Issues often simmer until they warrant executive attention, which usually requires operating in crisis mode. The executive question is less “Why did this fail?” and more “Why are we just now hearing about this?”
- The Analysis Paralysis Bottleneck – Flawed decision architecture makes minor choices agonizing. Managers need multiple signatures for basic requests. The organization creates endless task forces, steering committees, and working groups to study problems as a low-friction response to difficult decisions. Engineering team disagreements escalate to committee discussions, cluttering calendars, and producing remedial efforts.
- Strategic Decoupling – The company’s high-level strategy disconnects from its engineering capabilities. Senior leaders promise features and timelines unaware of the crumbling technical platform. Vanity metrics appear good but ignore systemic health metrics. Teams may prioritize visible deliverables over addressing foundational issues when organizational incentives reward progress signals more strongly than overall health.
- Accelerated Attrition – A broken architecture exhausts engineers working on problems. Top performers, concluding things are unlikely to change, quietly leave. Those remaining adopt cynicism and “just do what they’re told.” Promotions reward navigating the organization over improving the system.
- Systemic “Shadow” Architectures – When the official organizational system fails to accomplish work, employees create an underground system to survive. They stop using official channels and adopt the “easier to ask for forgiveness than ask for permission” approach. Reported work is replaced by rework and other tasks to get things done. Teams intentionally hoard data to protect their turf. Surprisingly, these informal pathways can become more effective than the formal path.
An organization with slowing delivery, despite hiring, green status reports, and leaders shocked by technical failures, lacks a staffing shortage. It suffers from a flawed decision architecture that distorts reality, corrupting speed, strategy, talent, and trust.
Reading Between the Lines
Project trackers, status updates, and key metrics display perfect health until a catastrophic failure occurs. Despite repeated missed milestones, progress is presented narratives spun to soften the picture. Negative information is penalized in decision architecture, and no one addresses it. Middle managers manually refine data, concealing technical risks and delaying revelation until the system fails. The organization is not necessarily hiding information intentionally. The reporting system may simply reward optimistic interpretations.
Significant issues that should’ve been addressed earlier are highlighted. The story “this has recently grown to the point where we need executive attention” acknowledges the problem while requesting help. Rather than escalating problems early, managers are implicitly expected to contain them until they become impossible to hide. It undermines individual decision-making and ownership.
Frontline teams lack clear authority or psychological safety, so issues must be escalated to executives or committees. However, bottlenecks at higher levels often lead to poor decisions due to unclear ownership of choices. Consequently, managers avoid blame by passing the buck upward.
When local accountability and authority are lacking, employees perceive the system favoring conformity and penalizing decisiveness or concerns. Shadow networks and siloed knowledge emerge, rendering official decision-making ineffective. An unofficial network circumvents processes, creating its own hierarchy and tribal knowledge, leading to increased employee stress and frustration.
When employees become stressed and frustrated with the system, they often leave. If a crucial engineering team member departs, the organization suffers. The backfill person faces immense pressure to live up to the departing employee’s reputation, leading to stress and burnout.
False Diagnoses
Organizations often misdiagnose broken decision architecture by treating systemic failures as isolated human or resource problems. These false diagnoses are appealing because they are visible, measurable, and can be addressed without redesigning reporting relationships, incentives, or decision authority.
- The “Lack of Accountability” False Diagnosis: Failure are attributed as a lack of care, discipline, or accountability. Stricter reviews are implemented, rigid software is installed, or public firings are carried out. Yet employees are rarely indifferent; they’re trapped by misaligned incentives. When a system punishes truth-telling, employees adapt their behavior to the incentives and consequences around them. Punishing individuals only deepens technical problems and teaches employees to hide things.
- The “Culture Problem” False Diagnosis – Pervasive issues within the company, such as a lack of collaboration, trust, and a growth mindset, persist despite attempts to address them through workshops, motivational speakers, and marketing campaigns aimed at changing the company culture. However, culture is a trailing indicator, not a root cause. Silos and territorial infighting stem from a broken architecture that compels teams to compete for resources. A pep talk won’t rectify a structural communication barrier.
- The “Velocity/Laziness” False Diagnosis – When engineering delivery slows, leaders assume teams are slow, wasteful, or lack urgency. While individual performance issues exist, the same problems occurring across teams indicate the system should be examined before assuming widespread individual failure. Minor action require executive approval, work is left in bureaucratic limbo while engineers wait for permissions.
- The “Outdated Tooling” False Diagnosis – When outdated software, manual workflows, or technology platforms cause engineering or operational failures, companies migrate to new enterprise software, AI tracking tools, or cloud infrastructure. These tools only automate existing processes. Overlaying sophisticated tracking tools on fragmented decision-making and communication silos creates automated chaos, resulting in the same “watermelon” dashboards–green on the outside, red underneath.
- The “Skill/Training Gap” False Diagnosis – Technical failures are assumed to stem from staff lacking modern skills, training, or intelligence to handle complex systems. Mandatory retraining, certifications, or compliance classes ensue, but broken reporting channels override expert judgment. More training doesn’t address the root cause: prioritizing deadlines over risk.
Every single one of these false diagnoses shares a common trait: they treat the symptoms as the disease. They allow an organization to look busy and decisive without ever having to audit their own reporting lines, change their incentive structures, or reconsider establishing reporting relationships and decision authority.
Why Staffing Appears to be the Problem
Staffing appears to be the problem because overworked employees look the same as blocked employees. Leaders see teams that are stressed, missed deadlines, and growing backlogs. The most logical, common-sense conclusion is that there is too much work and not enough people.
- Employees in fragmented systems are exhausted from fighting the system, not building the product. This creates a false conclusion that more engineers are needed, when the real issue is bureaucratic friction.
- When frontline workers lack decision-making authority, work accumulates, creating a backlog. Leaders mistakenly attribute this to an overwhelmed team and respond by adding more staff, rather than addressing the root cause of the issue.
- Adding more people to a project can worsen bottlenecks due to increased communication and coordination challenges, leading to a vicious cycle of perceived understaffing and further hiring.
- Staffing shortages are a socially acceptable excuse for managers, executives, and recruiters, protecting egos and shifting blame away from leadership failures.
Staffing appears to be the problem because it’s the most visible asset. You can see an empty chair, but not a broken information flow, a toxic incentive, or a blurry boundary line on a spreadsheet. Organizations default to staffing because headcount is visible. Decision architecture is not.
In Closing
No single symptom proves an organization has a decision architecture problem. However, when these patterns repeatedly appear across teams and programs, they indicate that the organization’s operating system, not the individual effort, is likely constraining performance.
In Part III, I dig into where these issues generally lie within an organization.