Lack of proper documentation stems from managerial neglect and developer disincentives
Code documentation in enterprise IT has become an afterthought. Everyone complains when it’s missing, especially for legacy systems, but very few leaders take accountability for why it’s not there in the first place. Managers rush to close projects, celebrate product delivery, and move on without pausing to ensure future teams will understand how the system works. Then, years later, the same teams complain about undocumented legacy code. The missing documentation is a direct result of decisions made during development, decisions typically driven by urgency over sustainability.
Developers play a role too. They don’t typically enjoy documenting code. The work doesn’t showcase their creativity or technical complexity. Once a feature or system works, most engineers want to move on. Managers reinforce this behavior by not requiring documentation in the first place. When delivery speed is the top KPI, documentation is optional. That’s the root of the problem: neither side has real incentives to change course.
This cycle continues until it becomes a critical issue, especially when those who originally wrote the code have moved on. At that point, reverse engineering large portions of code just to update or integrate systems becomes a resource sink, time-consuming, costly, and often risky.
For C-level executives, the message is clear: Documentation isn’t just an IT problem, it’s an operational risk multiplier. If you’re not prioritizing it from the top, then you’re complicit in creating future failure points. And if you’re expecting consistency across engineering teams without documentation standards, you’re setting your tech operations up for brittleness.
Developer resistance to documentation is driven by psychological factors and job security concerns
You have to understand how developers think. Most don’t see documentation as valuable to the craft. It’s not that they don’t understand its importance, it’s that they’ve learned to prioritize code that runs, not code that’s explained. If they write cleaner code, they expect that others should just be able to read it and grasp it quickly. That’s an idealistic assumption but one that’s deeply common across engineering teams.
Then there’s the self-preservation angle. Developers know that if code isn’t comprehensively documented, replacing them becomes harder. That knowledge boosts job security. And if they leave the company, lack of documentation often brings them back in lucrative consulting roles. This isn’t hypothetical, it’s happening across industries. People act in alignment with incentives and security. Until that alignment changes, don’t expect the behavior to change.
The other issue? The documentation process itself is seen as mind-numbing. It doesn’t feel like progress to developers. It feels like a slowdown. So unless there’s explicit value tied to it, like career progression, bonuses, or job recognition, it won’t get done with consistency or depth. It’s not a question of discipline. It’s a question of structure.
For executives, this means designing environments where documentation isn’t optional or untracked. If documentation doesn’t show up in performance reviews, roadmap reviews, or quality gates, then it will continue to fall through the cracks. Leaders must treat documentation the same way they treat technical debt: if you let it build up, it will cost you later.
The potential of generative AI to support documentation faces practical challenges
There’s plenty of interest right now around whether generative AI tools, like large language models, can solve the documentation problem. The technology is promising. In theory, it can help automate the tedious task of writing documentation, especially when developers resist doing it themselves. But the real-world application has limits, and we need to be honest about them.
Generative AI that’s applied only after the code is written won’t meet enterprise-grade standards for reliability. If the system doesn’t know the intention behind the logic, it has to guess. That introduces error risk and often results in documentation that’s technically incomplete, or worse, misleading. For AI-generated documentation to have value, the tool has to work alongside the developer during the actual coding process. It needs to track the decisions, logic paths, and assumptions in real time. That level of integration isn’t widely implemented yet, and without it, the promise of automated documentation stays mostly conceptual.
Another layer to consider: developers may actively resist AI-generated documentation, not because it’s inaccurate, but because it threatens perceived job security. If AI builds high-quality documentation consistently, it reduces the dependency on the developer. And if that developer sees their role being evaluated for automation, their motivation to cooperate with the tool drops. So even when the tech works, human behaviors will resist it unless there are clear expectations and incentives.
This is where C-level leadership comes in. Execs need to position these tools not as a replacement for developers, but as an operational support layer. Deploying AI in a way that integrates with developer workflows, without undermining their role, is the only viable path forward. Attempting to drive adoption through top-down tech mandates, without addressing trust and structure, won’t deliver results.
Documentation challenges differ based on the age of the code, requiring tailored strategies
Not all documentation problems are the same. They fall into three distinct categories, depending on when the code was written: legacy code, recent code, and ongoing development.
Legacy code presents the hardest problem. The original developer is often gone, and there’s rarely any clear trail explaining why the code base looks the way it does. No matter how experienced or capable your current engineering team is, trying to decode intentions from 10 or 15 years ago is slow, expensive, and error-prone. It’s also where most large enterprises suffer the most, modernizing outdated systems becomes a blocker because the structural understanding is missing.
Code written more recently still suffers from poor documentation, but the original engineer is usually still around. The issue here is not lack of access, it’s lack of process. Teams are racing to meet delivery deadlines, and documentation gets discarded as a nice-to-have. This is where small structural changes, like project-level documentation requirements or mandatory peer reviews, can have immediate impact.
For code being written today, the opportunity is clear: you can make documentation non-negotiable. But only if you design expectations into the day-to-day process. That includes assigning documentation ownership, defining which artifacts are required, and tracking documentation like any other deliverable in the sprint.
From a leadership standpoint, assuming that one-size-fits-all policies will address all of this is short-sighted. You’ll need three strategies across the codebase based on the lifecycle stage of the code. For older systems, budget for clean-up and knowledge extraction. For recent systems, tie in accountability while the author is still available. For new systems, bake documentation into the coding process as an active lifecycle component.
Realigning incentive structures for developers is essential to improve documentation practices
Let’s keep this simple. Developers work toward what they’re rewarded for. Right now, documentation isn’t one of those things. Engineers get promoted, receive bonuses, and earn recognition for shipping features, solving bugs, and closing tickets, not for clean, clear, well-documented codebases. As long as this is the case, documentation will sit on the bottom of the priority list.
This isn’t about motivational posters or encouraging developers to “care more.” It’s about aligning performance with expectations. If you want documentation, you need to make it part of the team’s KPIs. That could mean tying bonuses directly to documentation quality and completeness, or enforcing review gates that block a release if required docs aren’t included. You can’t expect engineering teams to focus on something they’re not measured on, especially when they’re under constant pressure to meet delivery deadlines.
There’s nothing controversial here. It’s just operations. You align incentives with outcomes. When you do that, behavior changes almost immediately. When you don’t, developers will continue doing what the system rewards, fast commits, quick turnarounds, and feature velocity.
This also applies to leadership culture. Managers need to call out good documentation in performance reviews. Senior engineers should mentor junior developers not just on writing efficient code but also on leaving behind a usable knowledge trail. That shift only works if leadership treats documentation as a product-critical responsibility.
Key highlights
- Managerial habits drive documentation debt: Poor documentation isn’t a developer oversight, it’s a leadership failure. Leaders should set and enforce documentation standards early or risk compounding long-term technical debt.
- Developer resistance is structurally reinforced: Developers deprioritize documentation because it’s unrewarded, seen as tedious, and sometimes viewed as a job security buffer. Executives must restructure incentives to promote maintainability alongside delivery.
- AI won’t fix the documentation gap alone: Generative AI has potential but requires real-time integration and developer cooperation. Leaders should avoid overrelying on automation and instead focus on aligning tools with team workflows.
- Old, recent, and current code need different solutions: Legacy systems need retroactive investment, recent code requires time-bound accountability, and new development must bake in documentation from the start. Tailor your strategy to code lifecycle stages.
- Incentives shape developer behavior: Developers document when it pays off. Tie documentation quality to performance reviews, bonuses, and career growth to drive lasting change.
