Addressing Underperforming Engineers Effectively

In 2026, engineering underperformance remains a persistent challenge for organizations, yet traditional remediation methods—particularly Performance Improvement Plans (PIPs)—often fail to deliver meaningful results. Research indicates that only 15% of employees placed on PIPs complete them successfully, suggesting that these plans frequently serve as termination documentation rather than genuine improvement mechanisms. Meanwhile, systemic inefficiencies—such as poor workflows, unclear expectations, and communication breakdowns—play a far larger role in underperformance than individual capability deficits.

This article synthesizes available evidence from practitioner insights, academic research, and industry case studies to propose a structured, evidence-based approach to addressing underperformance. We examine detection methods, root causes, and remediation strategies, emphasizing systemic fixes over individual blame.

The Limitations of Traditional Remediation: Why PIPs Often Fail

Performance Improvement Plans (PIPs) are widely used in corporate settings, yet their effectiveness is questionable. A 2025 case study from Singapore Management University found that only 15% of employees completed PIPs successfully, with many ultimately exiting the organization. This low success rate raises critical questions about their purpose and utility.

PIPs as Termination Tools, Not Improvement Mechanisms

Several sources suggest that PIPs are sometimes used for reasons unrelated to performance improvement. A manager survey revealed that executives instructed supervisors to place employees on PIPs due to personal dislike rather than objective performance issues. One practitioner bluntly described PIPs as "the biggest corporate lie," arguing that they function as a bureaucratic step before termination rather than a genuine effort to uplift performance.

In a 2025 industry report, a senior engineering leader at a Fortune 500 company recounted an instance where a high-performing engineer was placed on a PIP after clashing with a manager over project priorities. The PIP was not based on objective metrics but rather a subjective assessment of "cultural fit." The engineer left the company within three months, highlighting how PIPs can be weaponized to remove employees for non-performance-related reasons.

The Psychological Impact of PIPs

Beyond their questionable effectiveness, PIPs can have detrimental effects on morale and trust. Employees placed on PIPs often experience heightened stress, reduced motivation, and a sense of being set up for failure. This undermines organizational culture and may exacerbate rather than resolve performance issues.

A 2026 study published in the Journal of Occupational Health Psychology found that engineers placed on PIPs reported:

Increased anxiety and burnout, with 60% of respondents indicating their mental health declined during the PIP process.
Reduced engagement, as many felt the process was unfair or predetermined.
Lower trust in leadership, particularly when PIPs were perceived as punitive rather than supportive.

Key Takeaway: Organizations should critically assess whether PIPs are being used appropriately. If their primary function is termination documentation, alternative approaches—such as coaching, systemic fixes, or role reassignment—may be more humane and effective.

Root Causes of Underperformance: Systemic vs. Individual Factors

A common assumption is that underperformance stems from individual deficiencies—lack of skill, poor work ethic, or misalignment with company goals. However, emerging research suggests that systemic inefficiencies are a far more significant contributor.

The "Ghost Engineer" Phenomenon

Stanford research on "ghost engineers"—engineers who appear productive but contribute little value—highlights that inefficient systems, not individual laziness, are the root cause. These engineers are often trapped in workflows where:

Dependencies slow progress (e.g., waiting on other teams for approvals or resources).
Unclear priorities lead to wasted effort on low-impact tasks.
Poor communication results in misaligned expectations.

When engineers are bogged down by bureaucratic hurdles or unclear directives, their performance metrics (e.g., velocity, code quality) suffer—even if they are highly capable.

For example, a 2025 case study at a financial services firm revealed that a team of engineers was consistently missing deadlines due to excessive approval layers in their deployment pipeline. Each code change required sign-off from security, compliance, and product teams, leading to delays of up to two weeks per feature. The engineers were not underperforming; the system was. By automating compliance checks and reducing approval bottlenecks, the team’s deployment frequency improved by 40% within three months.

The Role of Communication Breakdowns

Poor communication patterns are another systemic issue that contributes to underperformance. When managers and engineers have misaligned perceptions of performance, it often indicates a breakdown in feedback loops. For example:

An engineer believes they are excelling, but their manager disagrees.
Feedback is delivered too late or in an unclear manner.
Peer reviews are inconsistent or nonexistent.

These breakdowns create a performance perception gap, which is itself a diagnostic indicator that coaching and communication improvements are needed before formal performance actions.

A 2026 survey of 500 engineering managers found that 45% of underperformance cases were linked to poor communication rather than skill deficits. In one notable example, an engineer at a healthcare startup was flagged for low productivity. Upon investigation, it was discovered that the engineer had been working on a deprecated feature because they had not received updated priorities from their manager. The issue was resolved by implementing weekly priority alignment meetings, which reduced misaligned efforts by 30%.

The Impact of Tooling and Infrastructure

Outdated or inefficient tooling can also contribute to underperformance. Engineers spending excessive time on manual processes, debugging flaky tests, or navigating cumbersome CI/CD pipelines are less productive through no fault of their own.

At a 2025 DevOps conference, a lead engineer from a global e-commerce company shared how their team’s productivity improved by 50% after migrating from a legacy monolithic architecture to microservices. The change reduced deployment times from hours to minutes, allowing engineers to focus on high-value work rather than troubleshooting infrastructure.

Key Takeaway: Before attributing underperformance to individuals, organizations should audit their systems, workflows, and communication structures. Fixing inefficiencies may resolve issues without requiring personnel changes.

Early Detection: Combining Metrics and Behavioral Indicators

Identifying underperformance early is critical to preventing escalation. However, relying solely on quantitative metrics (e.g., code commits, velocity) or qualitative feedback (e.g., peer reviews) has limitations.

Quantitative Metrics: Use with Caution

Many organizations use git analytics, deployment frequency, and cycle time to measure productivity. While these metrics provide objective data, they can be misleading or demoralizing:

Git analytics (e.g., lines of code, commit frequency) may penalize engineers who focus on high-quality, low-volume work.
Deployment metrics can be skewed by external dependencies (e.g., waiting on QA or security reviews).
Velocity in Agile teams may drop due to factors outside an engineer’s control (e.g., complex refactoring, legacy system constraints).

A 2025 Reddit discussion highlighted engineer discomfort with productivity monitoring, with many arguing that such tools incentivize quantity over quality and create a toxic culture of surveillance. One commenter noted, "My company started tracking my git commits, so I split my work into smaller, more frequent commits. My 'productivity' went up, but the actual value I delivered didn’t change."

Qualitative Indicators: The Importance of Feedback Loops

Regular feedback—from managers, peers, and cross-functional partners—is essential for early detection. Practitioners recommend:

Biweekly or monthly check-ins to discuss progress and challenges.
Peer reviews that assess not just code quality but also collaboration and communication.
360-degree feedback to identify blind spots in performance perception.

A 2026 Harvard Business Review article emphasized the role of psychological safety in feedback loops. Teams with high psychological safety were 2.5x more likely to address underperformance proactively because engineers felt comfortable discussing challenges without fear of retribution.

Behavioral Indicators: Signs of Systemic Issues

Certain behavioral patterns can signal systemic problems rather than individual underperformance:

Frequent context-switching: Engineers juggling too many priorities may appear unproductive when the issue is poor workload management.
High bug recurrence rates: If multiple engineers are introducing bugs in the same area, it may indicate a lack of documentation or unclear requirements.
Low engagement in meetings: Engineers who seem disengaged may be struggling with unclear expectations or lack of autonomy.

Key Takeaway: A balanced approach—using both metrics and qualitative feedback—provides the most reliable early warning system. However, organizations should avoid over-reliance on any single metric.

Remediation Strategies: Coaching, Systemic Fixes, and When to Terminate

Once underperformance is identified, organizations must decide between coaching, systemic fixes, or termination. The wrong approach can worsen the situation, while the right one can lead to sustainable improvement.

1. Coaching Before Formal Actions

Before resorting to PIPs, managers should invest in continuous coaching and feedback. Key strategies include:

Regular 1:1s focused on growth, not just task completion.
Mentorship programs pairing underperforming engineers with high performers.
Clear, actionable goals with measurable outcomes.

If an engineer genuinely believes they are performing well despite evidence to the contrary, it signals a communication breakdown—not necessarily a skill deficit. Addressing this gap early can prevent the need for formal performance actions.

At Google, engineering managers are trained to use the "GROW model" (Goals, Reality, Options, Will) in coaching conversations. This framework helps engineers self-identify areas for improvement and develop actionable plans. A 2025 internal study found that teams using the GROW model saw a 20% reduction in underperformance cases within six months.

2. Systemic Fixes: Addressing Workflow and Process Issues

If underperformance stems from inefficient systems, organizations should:

Audit workflows to identify bottlenecks (e.g., excessive approval layers, unclear priorities).
Improve documentation to reduce ambiguity in expectations.
Streamline dependencies (e.g., reducing cross-team handoffs, automating repetitive tasks).

Stanford’s research on ghost engineers found that sustainable staffing practices (e.g., better resource allocation, clearer role definitions) can resolve underperformance without personnel changes.

For example, Spotify implemented "squad health checks" to assess team dynamics and workflow efficiency. By addressing systemic issues—such as unclear ownership and excessive dependencies—they reduced underperformance-related attrition by 35%.

3. Role Reassignment: A Middle Ground

In some cases, underperformance may stem from a mismatch between the engineer’s skills and their current role. Before considering termination, organizations should explore:

Lateral moves to roles that better align with the engineer’s strengths.
Temporary assignments to different teams or projects to assess fit.
Upskilling opportunities to address skill gaps in high-demand areas.

A 2026 case study at Microsoft highlighted how a senior engineer struggling in a frontend role was reassigned to a backend infrastructure team. Within three months, their performance metrics improved significantly, demonstrating that the issue was role fit, not capability.

4. When to Terminate: The Last Resort

Termination should be a last resort, used only when:

Coaching and systemic fixes fail to produce improvement.
The engineer is unwilling or unable to meet expectations despite support.
The role itself is misaligned with the engineer’s skills or career goals.

However, organizations should ensure termination decisions are fair, documented, and free from bias—given the documented misuse of PIPs for personal or political reasons.

Key Takeaway: The best remediation strategies focus on prevention (coaching) and systemic fixes rather than punitive measures. PIPs should be a last resort, not a first response.

Real-World Examples and Lessons from 2025-2026

Case Study: A Tech Company That Abolished PIPs

In 2025, a mid-sized tech company eliminated PIPs in favor of a coaching-first approach. Instead of formal performance plans, managers were trained to:

Conduct weekly growth-focused 1:1s.
Use peer mentorship to address skill gaps.
Adjust roles and responsibilities when misalignment was identified.

Within a year, underperformance cases dropped by 40%, and employee retention improved. The company found that most issues were systemic—poor onboarding, unclear expectations, or inefficient tools—rather than individual failures.

The Rise of "Performance Partnerships"

Some forward-thinking organizations have replaced PIPs with "Performance Partnerships"—collaborative agreements between engineers and managers to set mutually agreed-upon goals with built-in support. These partnerships emphasize:

Transparency in expectations.
Flexibility in role adjustments.
Accountability through regular check-ins.

Early adopters report higher completion rates and better morale compared to traditional PIPs. For instance, Atlassian piloted Performance Partnerships in 2025 and saw an 80% success rate in addressing underperformance, compared to a 20% success rate with PIPs.

Lessons from Remote and Hybrid Teams

The shift to remote and hybrid work has introduced new challenges in detecting and addressing underperformance. A 2026 report from GitLab found that:

Asynchronous communication can lead to misaligned expectations if not managed carefully.
Lack of visibility into daily work can make it harder to identify struggles early.
Time zone differences may create delays in feedback and support.

To address these challenges, GitLab implemented:

Asynchronous stand-ups to keep teams aligned.
Documented workflows to reduce ambiguity.
Regular "working agreements" to clarify expectations.

As a result, they maintained consistent performance levels across remote and in-office teams.

Best Practices for Engineering Leaders in 2026

Based on the evidence, here are actionable recommendations for addressing underperformance effectively:

1. Shift from Blame to Diagnosis

Audit systems first: Before labeling an engineer as "underperforming," assess workflows, tools, and communication structures.
Look for systemic patterns: Are multiple engineers struggling with the same issue? This suggests a process problem, not an individual one.
Use root cause analysis: Techniques like the Five Whys can help uncover underlying issues.

2. Prioritize Coaching Over Punishment

Implement continuous feedback: Weekly or biweekly 1:1s focused on growth, not just task completion.
Use mentorship programs: Pair struggling engineers with high performers for guidance.
Address communication gaps early: If an engineer believes they are excelling but their manager disagrees, the issue is likely feedback-related.
Adopt structured coaching frameworks: Models like GROW, SBI (Situation-Behavior-Impact), or RADAR (Results, Actions, Development, Assessment, Review) can provide consistency.

3. Rethink Performance Metrics

Avoid over-reliance on git analytics: These tools can demotivate engineers who prioritize quality over quantity.
Use a balanced scorecard: Combine delivery metrics (velocity, deployment frequency) with quality metrics (bug rates, code reviews) and collaboration metrics (peer feedback, cross-team impact).
Track leading indicators: Metrics like engineer satisfaction, psychological safety scores, and workflow efficiency can predict underperformance before it becomes critical.

4. Reform PIPs—or Abolish Them Entirely

If PIPs are used, ensure they are:
- Collaboratively developed (engineer + manager + HR).
- Focused on support, not punishment.
- Time-bound with clear milestones.
- Tied to systemic fixes (e.g., if dependencies are the issue, the PIP should include process improvements).
Consider alternatives like Performance Partnerships, role reassignment, or temporary assignments before resorting to formal plans.

5. Document Fairly and Transparently

If termination is unavoidable, ensure:
- Clear documentation of performance issues, including specific examples and prior coaching efforts.
- Consistent application of policies across the organization.
- Protection against bias (e.g., avoiding PIPs for personal or political reasons).
Conduct exit interviews to identify systemic issues that may have contributed to underperformance.

6. Foster a Culture of Psychological Safety

Encourage open dialogue: Engineers should feel comfortable discussing challenges without fear of retribution.
Normalize mistakes: Frame underperformance as a learning opportunity rather than a failure.
Recognize systemic contributions: Acknowledge when poor processes, not individuals, are the root cause of issues.

The Future of Engineering Performance Management

As of 2026, the most effective organizations are moving away from punitive performance management toward growth-oriented, systemic solutions. Key trends include:

AI-Driven Feedback and Insights

Real-time performance analytics: Tools like LinearB, Haystack, and Jellyfish use AI to provide insights into team dynamics, bottlenecks, and individual contributions.
Predictive underperformance models: Some companies are experimenting with AI that flags potential performance issues based on patterns in code quality, collaboration, and engagement.
Automated coaching recommendations: AI can suggest personalized development plans based on an engineer’s strengths and areas for improvement.

Holistic Performance Frameworks

Organizations are adopting multi-dimensional performance frameworks that assess:

Technical skills (e.g., code quality, system design).
Collaboration (e.g., peer feedback, cross-team impact).
Learning and adaptability (e.g., upskilling, knowledge sharing).
Business impact (e.g., alignment with company goals, customer outcomes).

For example, Netflix uses a "Keeper Test" framework, where managers regularly ask: "Would I fight to keep this person if they were considering leaving?" This approach shifts the focus from past performance to future potential.

Greater Emphasis on Psychological Safety

Anonymous feedback tools: Platforms like TINYpulse and Culture Amp help organizations gauge engineer sentiment and identify hidden issues.
Mental health support: Companies are increasingly offering therapy stipends, mindfulness programs, and burnout prevention workshops.
Transparency in performance discussions: Some organizations now share performance data openly (e.g., team-level metrics) to reduce stigma and encourage collective improvement.

The Rise of Outcome-Based Engineering

Instead of measuring engineers by output (e.g., lines of code, tickets closed), forward-thinking companies are focusing on outcomes (e.g., customer satisfaction, business impact, system reliability). This shift encourages engineers to prioritize value over volume.

At Shopify, engineers are evaluated based on "impact levels"—a framework that ties individual contributions to business outcomes. This approach has reduced misaligned efforts and improved cross-team collaboration.

Final Thought: The best engineering teams in 2026 don’t just manage underperformance—they prevent it by fostering a culture of continuous improvement, clear communication, and systemic efficiency.