The Hidden Project Management Engine Inside Every Documentation Team

Most documentation teams don’t think of themselves as project managers. But they are.

Every product release, API update, enterprise rollout, migration guide, or major feature launch requires coordination, timelines, stakeholder alignment, risk mitigation, and deliverables. That isn’t just writing. That’s project management.

In fact, inside nearly every documentation department, there is a hidden project management engine already running. The question is not whether it exists. The question is whether it’s being recognized and refined.

Documentation Is Structured Operational Work

Technical writing is often perceived as an editorial or creative function. But in modern software organizations, documentation is structured delivery work.

Every documentation initiative requires:

  • Defined scope: What features are covered? At what depth?
  • Identified stakeholders: Product managers, engineers, UX, support, legal, marketing.
  • Milestones: Code freeze, release candidate, GA launch.
  • Dependencies: SME availability, feature stability, tooling readiness.
  • Deliverables: Reference updates, tutorials, release notes, FAQs, videos.

This mirrors classic project management frameworks, whether Agile, hybrid, or traditional.

The writing is visible. The orchestration is not. But orchestration is what determines whether documentation ships aligned with the product or trails behind it.

The PM Skills Documentation Teams Already Practice

Most senior technical writers are already functioning as de facto project managers.

Scope control.
Writers constantly negotiate boundaries: Is this feature in scope for this release? Does this require a full tutorial, or is a reference update sufficient? Can this improvement wait until the next sprint? These are scope management decisions.

Stakeholder coordination.
Documentation requires ongoing negotiation with engineering, product, UX, and customer-facing teams. Priorities shift. Timelines compress. Trade-offs must be made. That is stakeholder management in practice.

I’ve experienced this firsthand. In more than one role, the biggest risk to documentation wasn’t lack of skill on the writing team. It was getting busy software engineers to fully engage in the documentation process. Engineers were expected to draft initial content for complex features, review documentation pull requests, and validate accuracy before release. In theory, that sounded straightforward. In practice, they were juggling sprint commitments, production issues, and roadmap pressure.

Getting their cooperation required more than sending reminders. It required diplomacy.

Sometimes that meant meeting one-on-one to unblock them and reduce the lift required. Sometimes it meant rewriting rough notes into structured drafts to make review easier. And occasionally it meant finding a different subject matter expert altogether.

None of that is “just writing.” That is stakeholder management and risk mitigation.

Risk mitigation.
Every documentation cycle carries risk: late feature changes, unstable APIs, unavailable SMEs, shifting launch dates. Experienced writers anticipate these risks and adjust plans accordingly. They build buffers. They prioritize high-impact content first. They communicate early when timelines are threatened.

In many organizations, documentation teams are quietly running multi-stream projects without the formal authority typically associated with project managers.

When Communication Breaks Down

In one project, I was informed only one week before delivery that a bespoke API had been developed for a single enterprise client. It had already been promised to that client. Documentation had not been scoped, planned, or even mentioned.

At that point, I had no available resources except the SME who had built the API herself.

The risk was obvious: a missed client commitment, damaged credibility, and internal blame cycles.

Instead of escalating panic, we treated it like a compressed project.

We defined a minimum viable documentation package. We outlined the core use cases. We agreed on what could realistically ship in five days. Then we paired.

She was in China. I was in the United States. That time difference became an advantage. She drafted technical details during her day. I structured, edited, clarified, and filled gaps during mine. We handed work off across time zones and effectively doubled throughput.

We did work slightly longer days, but not unsustainably. The key was coordination, clarity, and disciplined scope control. In five days, we delivered an MVP documentation set that met the client commitment.

That was not a writing exercise. That was project recovery.

The root problem wasn’t technical complexity. It was communication failure upstream. And once miscommunication enters a project, risk compounds rapidly.

Agile Documentation: Already in Motion

If you look closely, many documentation teams already operate with Agile mechanics:

  • Backlogs of content requests
  • Sprint-based planning
  • Kanban boards
  • Git-based workflows
  • Pull requests and structured review cycles

This is Agile in disguise.

The problem arises when documentation is excluded from formal product planning. When that happens, documentation becomes reactive. Writers scramble at the end of a release cycle instead of shaping it from the beginning.

When documentation teams explicitly embrace project discipline, something changes. They attend roadmap discussions. They define documentation completion as part of “Definition of Done.” They forecast capacity. They influence release quality.

Documentation shifts from support function to delivery partner.

The Cost of Ignoring the Engine

When documentation lacks clear project structure, predictable problems emerge:

  • Last-minute documentation sprints
  • Inconsistent or incomplete content
  • Burnout within small doc teams
  • Accumulated documentation debt
  • Reduced trust from customers and internal stakeholders

The impact is measurable. Poorly aligned documentation increases support tickets, slows onboarding, weakens sales enablement, and limits product adoption.

Documentation that trails the product erodes confidence. Documentation that ships with the product builds it.

The difference is rarely talent. It is almost always planning.

From Writers to Delivery Leaders

Documentation teams are already coordinating across functions, managing dependencies, mitigating risks, and delivering on deadlines. The hidden project management engine is there.

The opportunity is to name it, strengthen it, and lead with it.

When documentation embraces its role as a structured delivery function, it stops chasing releases and starts shaping them. And when that happens, documentation professionals move from being content producers to becoming delivery leaders.

That shift changes how the organization sees them. More importantly, it changes how effectively they serve the product and the customer.

Debunking Myths About Technical Writing for Developers

In my years as a technical writer at Google, Grafana Labs, and other employers, I’ve encountered several misconceptions about what technical writers actually do. Some stakeholders view us as glorified clerical support. Others expect us to be senior-level software engineers while maintaining documentation.

The reality is far more nuanced: we are specialized bridge-builders who manage the high-stakes intersection of human language and machine logic. We operate in the gap between what engineers know and what developers need to learn, transforming tribal knowledge into usable education.

Here are the 3 most common myths I’ve encountered about technical writing, what the role actually entails, and why getting this right matters.

Myth 1: Technical Writers Are “Information Secretaries”

The Misconception: Technical writers wait for engineers to send over notes, then simply reformat those notes into documentation. We’re basically human content management systems.

The Reality: This “secretary” approach produces fragmented, contradictory, and low-quality content. Real technical writing involves investigative research, information architecture, audience analysis, and strategic content design.

What the Work Actually Looks Like

Professional technical writers don’t passively wait for information. Instead, we:

  • Embed ourselves in development cycles, attending sprint planning and standups to understand what’s being built and why
  • Conduct structured interviews with SMEs, asking targeted questions that uncover not just what features do, but why they exist and how they fit into the broader ecosystem
  • Analyze documentation gaps by reviewing support tickets and user feedback to identify where developers actually struggle
  • Create information architectures that map content to user journeys, ensuring developers find answers through intuitive navigation
  • Establish content standards that ensure consistency across teams

Good technical writers architect knowledge systems that serve multiple audiences simultaneously. This is why asking engineers to “write down what they built” produces documentation with no coherent learning path.

Myth 2: The Technical Writer Must Write Every Line of Code

The Misconception: A good technical writer should independently write all code samples, from basic examples to complex production implementations.

The Reality: While I maintain working knowledge of some languages, enterprise software is often too complex for a single writer to generate all code samples. Instead, my role is better described as a Technical Director who orchestrates high-quality examples.

The Technical Director Model

1. Identify the Use Case

Engineers want to document what they built; users need to understand how to solve problems. I translate between these perspectives:

  • Engineer thinking: “We added support for custom retry policies”
  • User thinking: “How do I make my API calls more resilient to network failures?”

2. Enlist the Right Resources

I identify which engineer has the deepest knowledge of a subsystem, who writes clean example code, and who has bandwidth to contribute.

3. Verify, Refine, and Test

Raw code from engineers rarely ships as-is. My review includes:

  • Compilation and execution testing: Does it actually work?
  • Security review: Are we showing unsafe patterns?
  • Readability optimization: Removing complexity, adding explanatory comments
  • Error handling: Ensuring examples show realistic error handling, not just happy paths

4. Maintain and Update

I track which code samples are affected by breaking changes, coordinate updates before deprecated features are removed, and maintain automated testing for critical examples.

When Technical Writers Do Write Code

For simpler examples, I may write the code myself. But attempting to write complex, production-representative code for unfamiliar systems wastes time and produces fragile examples.

Myth 3: Engineers Can Simply Replace Technical Writers

The Misconception: Documentation is just “writing stuff down.” Any engineer with decent communication skills can handle it, eliminating the need for dedicated technical writers.

The Reality: While some engineers are excellent writers, systematically replacing technical writers introduces profound risks. This isn’t about engineers lacking capability—it’s about specialization, incentives, and the invisible complexity of documentation as a discipline.

The Commitment Gap

An engineer’s primary work is building features and fixing bugs. Documentation becomes a secondary concern, creating predictable patterns:

  • Documentation written only when forced by PR requirements
  • Stale documentation when APIs change but docs don’t get updated
  • Inconsistent styles across different engineers’ contributions
  • Coverage gaps where exciting features get documented but essential operational concerns don’t

The Curse of Knowledge

Engineers suffer from unconsciously assuming others share their background knowledge. Here’s what this looks like:

Engineer-written documentation:

Configure the service mesh egress gateway to handle external traffic.

Technical writer-written documentation:

Before your application can make requests to external APIs, you need to configure the service mesh to allow outbound traffic. In Joeware, this requires creating an Egress Gateway...

The engineer’s version isn’t wrong, but it assumes you know what a service mesh is, what “egress” means, and where configuration happens. Technical writers are trained to identify implicit assumptions, sequence information to build from foundational concepts, and anticipate failure modes.

The Reality: A Partnership of Specialists

The most effective documentation emerges from genuine partnerships where:

  • Engineers provide deep technical expertise about how systems work
  • Technical writers provide audience expertise about how developers learn and where they struggle
  • Both groups review each other’s work to catch technical errors and pedagogical gaps

When documentation is treated as a first-class citizen of the engineering process, organizations see measurable results:

  • Lower support costs: Developers self-serve answers
  • Faster onboarding: New users become productive in hours instead of days
  • Better product decisions: Explaining features often reveals UX problems before launch
  • Competitive advantage: Documentation quality often determines which product wins

What “Good” Looks Like

Technical writers aren’t a luxury. We’re strategic investments in product success, developer experience, and sustainable growth. Good documentation teaches developers to think in your product’s paradigm, anticipates their struggles, and makes the complex feel achievable.

That’s not something you get by asking engineers to write more clearly. It’s something you get by respecting technical writing as the specialized craft it is.

Systems Theory: From Ecology to Software Architecture

When I tell people I studied environmental science and political ecology before becoming a technical writer, I usually get puzzled looks. What does understanding threatened ecosystems have to do with documenting APIs or managing AI-assisted workflows?

The answer is everything. Systems theory—the framework that helped me understand how governance impacts ecological resilience—has become one of the lenses through which I approach software architecture, documentation strategy, and AI integration.

Ecological Resilience vs. Software Reliability

In my early academic work, I focused on how human governance systems impact ecological stability. One core concept was resilience: an ecosystem’s ability to maintain essential functions despite external shocks. A resilient forest survives wildfire by regenerating from deep root systems; a resilient wetland processes pollution spikes without collapsing.

Software systems face analogous challenges. The industry has shifted from prioritizing “robustness” (i.e., building systems that resist failure) to embracing “resilience,” designing systems that fail gracefully and recover quickly.

Consider a monolithic application where a single memory leak brings down the entire system. In ecological terms, this is like an invasive species that monopolizes resources and crashes the entire food web. Just as technology can threaten ecological balance through unintended externalities, a poorly isolated “feature” in a monolithic architecture becomes a pollutant that degrades performance across the entire environment.

The solution in both domains is similar: create buffer zones and compartmentalization. Ecologists design wildlife corridors that contain localized disturbances. Software architects implement circuit breakers and microservices that isolate failures and prevent cascading collapses. When learning about software systems, I look for these isolation patterns as an architectural philosophy that protects system health.

Governance and Technical Debt

My background studying governance systems prepared me for understanding technical debt. In environmental management, rigid or poorly informed policies lead to catastrophic outcomes. For example, irrigation systems that create dead zones, or forestry policies that suppress natural fires until fuel loads become catastrophic.

Software has its own governance: the rules, standards, and conventions dictating how components interact. When this governance is rigid or uninformed, systems become brittle. When the “Expert-in-the-Loop”—the human with contextual understanding—is removed from critical decisions, you get the software equivalent of ecological collapse.

This is why I’m cautious about AI-generated code. An AI might confidently suggest deprecated methods or invent non-existent API parameters. Without adaptive governance involving rigorous verification and comprehensive documentation, these errors propagate like invasive species through an unmonitored ecosystem.

The parallel extends to how both systems accumulate debt. Environmental degradation results from short-term decisions that externalize costs to the future. Technical debt accumulates the same way: quick fixes that seem expedient but create compounding maintenance burdens. In both cases, governance determines whether those debts become manageable or catastrophic.

Documentation as Ecosystem Mapping

When I document software systems, I don’t catalog individual API endpoints in isolation. I map the ecosystem: how data flows, how components depend on each other, what feedback loops exist, where boundaries are defined. This is fundamentally a systems-theory approach to information architecture.

Just as an ecologist must understand why species thrive or decline within their context, I must extract the “why” behind architectural decisions. Why does this service retry failed requests? What upstream conditions make this endpoint vulnerable? How does this component fit into larger workflows users actually care about?

The Expert-in-the-Loop as Environmental Steward

Threats to ecosystems often stem from “blind” automation or inadequate oversight: industrial processes that externalize pollution and algorithms that optimize narrow metrics while degrading broader system health. I apply this cautionary principle to AI-assisted workflows.

The ultimate systemic skill—whether in ecology or software—is discernment. Knowing when to rely on automated efficiency and when human strategic thinking is non-negotiable. Understanding that systems are more than the sum of their parts, and that expertise means seeing the whole while attending to the details.

Systems theory gave me mental models to understand threatened ecosystems. Those same models now help me navigate the complex, interconnected world of modern software development. The vocabulary changes, but the fundamental patterns remain consistent.

The “Docs-as-Code” Transition: Moving Beyond the CMS

For years, the standard for technical documentation was the monolithic CMS systems designed for “content” in the abstract, but often divorced from the actual environment where software is built. My journey across organizations like Google, Microsoft, and Grafana Labs has fundamentally shifted my perspective toward docs-as-code workflows.

Why the Shift?

Early in my career, I saw the friction caused by siloed documentation. When docs live in a separate web portal managed by a non-developer editor, they naturally drift away from the source code. By adopting tools like Git, Markdown, Hugo, and Docusaurus, we bring documentation into the developer’s native habitat. This allows developers to take responsibility for documenting their own work, and it allows technical writers to be more fully integrated into the team’s development process.

For technical writers transitioning from traditional CMS platforms, this shift represents a fundamental reimagining of your role within the development team. You’re no longer the downstream recipient of incomplete information; you become an embedded collaborator who can see, understand, and influence the code alongside the documentation. This visibility transforms the quality and accuracy of what you produce.

Treating documentation like code means it follows the same lifecycle as the product:

Version Control: Using Git allows for precise tracking of changes and the ability to revert errors instantly. Beyond basic rollback capabilities, Git enables powerful branching strategies where documentation updates can be developed in parallel with features, tested in staging environments, and merged only when the feature ships. This synchronization prevents the common problem of documentation being published too early or too late relative to feature releases.

Peer Review: At Google and elsewhere, I submitted substantial change-lists or pull-requests (PRs), ensuring every word was vetted by engineers through the same code review process they use for features. This peer review culture catches technical inaccuracies before publication and creates shared ownership of documentation quality. Engineers become invested stakeholders rather than reluctant contributors. The review process also serves as an informal mentorship opportunity whereby junior engineers learn from seeing how senior developers critique and improve documentation, while writers gain deeper technical insights through reviewer feedback.

Automation: CI/CD pipelines can run linters to check for broken links or style guide violations before a single page is published. At Grafana Labs, we used linters to check for divergences from the team’s Writers’ Toolkit (our style guide). Advanced teams integrate Vale or other prose linters to enforce terminology consistency, readability metrics, and brand voice guidelines. Some organizations even run automated accessibility checks, ensuring documentation meets Web Content Accessibility Guidelines (WCAG) standards before deployment. This automation amplifies editorial judgment by catching mechanical errors that would otherwise consume review cycles.

Impact on Developer Experience

At Grafana Labs, I collaborated with a director of development and another engineer to lead the building of a developer documentation portal (grafana.com/developers) designed for discoverability. At the open-source observability company, engineers had built several distinct silos of information for their separate projects, but this made it frustrating to find what you needed. The company’s divergent threads for plugin building, specifications, and design system were brought together under one umbrella. You can read all about our journey to creating the portal at “The Grafana developer portal: your gateway to enhanced plugin development.”

When documentation lives in the repository, it becomes more of a “living” entity. Paid developers and open-source contributors alike are more likely to contribute updates or suggest edits when they can simply open a PR. The psychological barrier to contribution drops precipitously when the workflow mirrors what developers already do dozens of times per day.

Moreover, having documentation in the repository enables powerful cross-referencing. Code comments can link directly to documentation sections; documentation can reference specific lines of code with permanent links that update as the codebase evolves. This bidirectional relationship creates a cohesive knowledge ecosystem rather than two separate information silos.

Conclusion

The transition is about a transformation of documentation culture. It’s an acknowledgment that documentation is a first-class citizen of the software development life cycle (SDLC). When we treat docs like code, we bring software engineers and writers together to build better products. This cultural shift manifests in tangible ways: documentation tickets appear in the same sprint planning as feature work, documentation coverage becomes a release criterion, and engineers budget time for documentation the same way they budget for testing.

In the past 7 years since I first started using the docs-as-code workflow, I’ve learned that it isn’t just about version control or static site generators. It’s about breaking down the artificial barriers between code and documentation, between engineers and writers, between the product and its explanation. When those barriers dissolve, both the code and the docs improve, creating a virtuous cycle that benefits everyone: developers, writers, and most importantly, the users trying to understand and use what we’ve built.

AI-Assisted Workflows: The Future of the Technical Communicator

The rise of Generative AI has sparked intense debate in the technical writing community. Some observers see it as an existential threat, while others view it as an unprecedented boon. I see it as something more nuanced: a powerful extension of our existing toolkit that can be transformative or destructive depending on how thoughtfully it’s deployed.

Today, integrating tools like ChatGPT, Gemini, and Claude is mandatory for technical communicators. These are tools that can enhance both velocity and quality when used well. There’s no fighting it, so the best option is to evolve with it.

AI as a Force Multiplier

I use AI not to replace the fundamental writing process, but to automate what I call the “scaffolding” of documentation: the structural and repetitive elements that consume time without adding unique value. My expertise in AI-assisted content workflows and prompt engineering allows me to accelerate delivery without compromising technical accuracy or depth.

Here’s how I strategically integrate these tools into my daily practice:

  • Outline Generation: I leverage AI to brainstorm structural frameworks for complex tutorials and technical guides, ensuring I haven’t overlooked standard conceptual steps or logical progressions. This is particularly valuable when documenting unfamiliar systems or when tackling sprawling enterprise platforms.
  • Code Sample Refinement: AI excels at generating clean boilerplate code in languages like Python, JavaScript, or TypeScript. I use it to quickly produce initial examples, which I then rigorously test against actual environments, refine for edge cases, and optimize for clarity and best practices.
  • Drafting Alt-Text and Metadata: I delegate repetitive, SEO-heavy tasks such as crafting image descriptions, meta descriptions, and keyword-rich headers. AI drafts those elements and this frees up cognitive bandwidth to focus on the core technical narrative and the complex explanations that truly require human expertise.

In some cases, I use AI to prototype articles and help topics, if it’s a well-matched use case. For example, if I find a company’s news release and want to write a journalistic article about it, then AI is well-suited to the task of writing a simple news brief, provided it’s been prompted to do so in my own preferred style.

The key is treating AI as a collaborator in the mundane, not a replacement for critical thinking.

The Importance of Human Oversight

During my time at a technical journalist at Wellesley Information Services, I covered the AI/ML beat extensively, researching emerging models and their practical applications to stay current with rapidly evolving technologies. The most critical lesson I learned is that AI can confidently hallucinate technical details such as API parameters, fabricating version numbers, or suggesting deprecated methods as current best practices. Beyond factual accuracy, AI often lacks creativity in its prose; its formulaic patterns and predictable phrasing can be easily spotted by discerning readers and quickly become tiresome or even alienating.

This is where the technical writer’s value proposition has fundamentally shifted. We’re no longer just writers, we’re what the industry calls “Human-in-the-Loop” operators. Though I prefer a more precise term: “Expert-in-the-Loop”. Here’s what that expertise looks like in practice:

  1. Verification and Validation: Every AI-generated code snippet must be meticulously verified against the actual API documentation, tested in representative environments, and validated for current best practices. I’ve caught countless instances where AI confidently suggested outdated information.
  2. Voice and Tone Consistency: AI often produces technically adequate but tonally generic content that lacks the nuanced voice required for specific brand identities. Whether it’s the approachable, reader-first standards I helped establish for SAPinsider or the precision-focused clarity expected at Grafana Labs, maintaining authentic brand voice requires human judgment and sensitivity.
  3. Complex Synthesis and Context: AI fundamentally struggles with activities that require deep contextual understanding such as interviewing subject matter experts to extract the “why” behind new product releases or synthesizing conflicting stakeholder requirements. These remain uniquely human skills that draw on empathy, experience, and professional intuition.

The Takeaway

The future of technical communication isn’t framed by an “AI vs. Human” dichotomy—it’s defined by the emergence of the AI-augmented writer. By mastering prompt engineering, understanding the capabilities and limitations of large language models, and thoughtfully integrating these tools into our docs-as-code pipelines, we can deliver higher-quality documentation at the accelerated pace demanded by modern software development cycles.

The writers who will thrive in this new landscape aren’t those who resist these tools or those who uncritically embrace them. They’re the professionals who develop the discernment to know when to leverage AI for efficiency and when to rely exclusively on human expertise for accuracy, creativity, and strategic thinking. Expert judgment is what separates good documentation from exceptional documentation in the age of AI.