I learned this lesson when watching talented professionals struggle with productivity and quality issues while refusing to use tools that could help them succeed.
During a major system documentation project, I was working with a team of experienced technical writers who needed to create comprehensive API documentation, user guides, and technical specifications under tight deadlines. The scope was enormous: dozens of endpoints, complex integration scenarios, and multiple user personas requiring different levels of technical detail.
Several team members were spending weeks crafting documentation from scratch, wrestling with formatting, struggling to maintain consistency across different sections, and burning out from the repetitive nature of creating similar content structures. The quality was inconsistent, deadlines were slipping, and everyone was frustrated.
AI assistance
I suggested we explore AI assistance for initial drafts, content structuring, and consistency checking. The response was immediate and negative. “That’s not real writing,” one senior writer said. “AI content is inferior, synthesized garbage. Our work should be authentic, not machine-generated.”
But I pushed back: “Using a hammer doesn’t make you a master carpenter, and knowing how to paint doesn’t make you Michelangelo. The tool doesn’t determine the quality: your expertise, judgment, and craftsmanship do.”
I started using AI tools to generate initial content structures, then applied my technical knowledge to refine accuracy, add context, and ensure the content met our users’ actual needs. I used AI for formatting consistency, cross-reference checking, and identifying gaps in coverage. But every piece of content bore my signature, my understanding of the systems, my knowledge of user workflows, my judgment about what information mattered.
The difference was dramatic.
My documentation was delivered on time, maintained consistent quality, and received positive feedback from both technical teams and end users. Meanwhile, my colleagues continued struggling with manual processes, missing deadlines, and producing inconsistent results.
Six months later, those same writers were quietly asking how I’d achieved such efficiency and quality. When I explained my approach, they realized they’d been confusing tool usage with professional skill. The AI didn’t make me a better technical writer: my expertise did that. The AI just freed me from repetitive tasks so I could focus on the strategic thinking, user empathy, and technical accuracy that actually determined quality.
That experience taught me: use the tools that you have available. Professional effectiveness isn’t about the purity of your process, it’s about the quality of your outcomes.
Further Reading
- #26 Real Leaders Own Outcomes, Not Just Effort – Taking responsibility for results regardless of process or tools used
- #21 Trying Is Not the Same as Delivering – Focus on outcome quality rather than process purity
- #47 Always Be Open to New Things – Maintaining adaptability and openness to new tools and approaches
- #40 You Don’t Have to Go Fast, But You Do Have to Move – Making progress using available resources rather than waiting for perfect conditions
- #35 Make Sure You Add Value – Understanding how tool integration enhances rather than diminishes professional contribution
- #3 Not Everything Matters – Strategic focus on what actually creates value rather than process traditions
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