Engineers often overlook the human and organizational dimensions of software migrations. They typically perceive these projects as technical undertakings, but institutional knowledge, communication gaps, team morale and resistance to change can significantly influence timelines and outcomes. Examining common pitfalls, effective strategies and emerging trends reveals a comprehensive view of the migration process from a human-centered perspective.  

Recent research continues to validate that delays in software migration are the rule rather than the exception. In a 2023 McKinsey & Company report, over 70% of large-scale digital transformations fell short of timeline or return on investment (ROI) expectations, with a lack of cross-functional alignment and insufficient change management cited as leading causes. Similarly, a 2024 Boston Consulting Group survey revealed that nearly half of global C-suite executives reported that over 30% of their organization’s technology development projects suffered delays or budget overruns. 

Understanding Software Migrations as a Human-Centered Process 

Software migration involves transitioning systems, applications, or infrastructure from one environment to another. These transitions may include moving from on-premises deployments to cloud platforms, retiring legacy systems in favor of modern stacks, or switching technology between vendors. Despite their technical appearance, such migrations cause ripple effects throughout organizational workflows and operations. 

These migrations often require retraining end users, maintaining legacy systems during the transition and navigating competing priorities. The disruption can stall development cycles and introduce operational risk. Overlooked in most planning stages is how much these projects rely on people’s undocumented knowledge, assumptions and behavioral responses. 

Human and Organizational Barriers That Derail Migration Efforts 

Scope creep or technical hurdles are frequently blamed as the cause of migration delays, yet deeper analysis reveals that resistance to change and knowledge gaps within teams are often more significant obstacles. End users familiar with a particular interface or workflow may be reluctant to adopt a new one. Internally, this resistance is not always voiced directly but manifests as disengagement or low adoption rates. Business stakeholders, too, may not fully grasp the strategic rationale behind a migration effort. Executive alignment weakens when the justification isn’t well articulated and cross-departmental cooperation becomes harder to sustain. 

Undocumented institutional knowledge is another persistent challenge. Many decisions about data structures, hard-coded values, or interface dependencies exist only in the minds of long-tenured employees. When these employees move on or get reassigned mid-project, teams can lose the rationale behind legacy implementations. This knowledge vacuum causes surprises during implementation, especially when hidden dependencies emerge. 

Managing Mid-Migration Surprises and Mitigating Risk 

No matter how well-scoped a project is, assumptions made by different teams often go unspoken. Teams often operate in silos, assuming shared understanding when none exists. This misalignment can result in components built on conflicting premises, only discovered during testing or go-live. Mid-migration surprises include unanticipated manual workarounds, deprecated features that remain mission-critical and inconsistent definitions of business logic across systems. Manual processes known only to a few employees become barriers when they cannot be replicated in the new environment and uncovering these issues late in the process leads to delays and rework. 

 Incorporating discovery sprints before execution has proven effective in managing these risks. These sprints create space to unearth undocumented logic, validate inter-team assumptions and highlight edge cases early. Including a buffer—typically 10–20% of the timeline—also reduces the risk of missed deadlines. 

Strategies to Reduce Rework and Support Successful Transitions 

While tooling plays a supporting role, successful migrations hinge on clear communication and iterative execution. Automated migration tools can minimize manual tasks and streamline repetitive processes, such as data copying or schema translation. Relying solely on automation overlooks the need for continuous stakeholder alignment, however. 

Here are some ways to reduce rework:  

• Cross-team workshops. Use workshops to identify assumptions early and establish shared understanding.  

• Regular check-ins. Catch evolving issues before they escalate by employing check-ins throughout migration.  

• Phased rollouts. Transitioning rollouts over time rather than all-at-once helps organizations validate changes incrementally and apply insights from one phase to the next. 

Communication breakdowns lead to duplicate efforts, reactive firefighting and coordination overhead. Maintaining old and new systems requires continuous attention and introduces operational drag. Factoring in learning curves and user adaptation time into project schedules can help set more realistic expectations. Offering flexibility during migration is equally vital for maintaining morale and sustaining trust. 

Future Trends and Measuring Human Impact 

Recent innovations are changing the migration approach. AI-powered tools now enable faster and more accurate transformations. In one notable case, Airbnb completed a migration, estimated initially to take two years, in just six weeks using AI tooling. These platforms automate repetitive code translations and configuration tasks, reducing reliance on large teams while preserving accuracy. 

Low-code and no-code migration platforms simplify transitions by offering visual interfaces and guided workflows. These innovations allow a broader set of employees to participate in the migration process, increasing organizational resilience. 

While AI accelerates execution, it does not eliminate the need for human oversight. Engineers now spend less time on rote tasks and more on designing, monitoring and validating migration paths. Prompt engineering, anomaly detection and performance monitoring are emerging as core responsibilities in this new paradigm. 

Tools like GitHub Copilot and Amazon CodeWhisperer are increasingly integrated into migration workflows to assist with repetitive coding tasks, generate test cases and validate syntax conversions. These tools reduce the cognitive load on developers and help teams move faster through lower-level tasks, freeing up human resources to focus on architecture decisions, validation and user-centric improvements. 

Metrics That Reveal Human Impact 

Quantifying the human side of migration is complex but not impossible. Several indicators can provide insight into where people are struggling and may need additional support: 

• On-call ticket volume. A spike in support requests during the migration window often reflects friction points, such as unclear interfaces, poor documentation, or insufficient training. 

• Employee satisfaction scores. More extensive morale trends can reveal team emotional fatigue or frustration. Measuring satisfaction at different stages of migration provides valuable feedback loops. 

• Attrition rates. Higher turnover among technical staff or support teams may signal disengagement, burnout, or disillusionment with the process. 

• User feedback on new systems. Capturing qualitative input from end users via surveys, interviews, or feature usage can help determine whether the migrated tools effectively serve their needs. 

• Project timeline adherence. Deviations from planned schedules often point to organizational misalignment, unanticipated rework, or breakdowns in communication. 

These metrics collectively paint a more nuanced picture than completion percentages or delivery milestones. They help teams identify where to adjust code, expectations, workflows and morale. 

Redefining Successful Software Migration 

Technical upgrades may motivate software migrations, but the real complexity lies in navigating organizational behavior, knowledge gaps and human responses to change. Recognizing this dimension transforms software migration planning and execution. It’s not enough to have the right tools—teams benefit by fostering alignment, surfacing assumptions early and communicating continuously. 

The future of software migration lies not just in AI and automation, but in embracing the human elements that make these transitions sustainable. As platforms evolve, the ability to manage systems and people will be the defining skill of successful engineering leaders. 

Disclaimer: The opinions expressed in this article are those of the author. They do not purport to reflect the views of his employer.