The AI-Generated Change Request: A New Low in Tech Support

A developer sits down to review a new change request. The document is impeccably formatted, with clear paragraphs and proper terminology. The causal links are logical, and the terms are used correctly. At first glance, it seems like a standard, well-written technical ticket.

However, a nagging sense of déjà vu begins to surface. The text feels both familiar and strangely hollow, like reading an instruction manual for a microwave rather than a description of a real-world problem. The developer reads it again, struggling to grasp the core issue through the polished but impersonal language.

It was like reading an instruction manual for a microwave, not a description of a real problem.

The realization hits like a blow: the request is not the work of a human colleague. It is the product of an AI, tasked with translating a client's query into technical instructions without any genuine understanding of the system it describes.

The initial experience was one of professional confusion. The document appeared to be a model of clarity. Every element was in its right place, from the subject line to the detailed steps. The developer found themselves nodding along, accepting the surface-level logic without sensing the underlying emptiness.

This is the core of the problem. The AI-generated text successfully mimics the form of a technical report but completely misses its substance. It provides instructions on what to add and how to fix it, yet it possesses zero knowledge of the actual codebase or the nuanced context of the issue.

• Grammatically perfect structure and formatting
• Correct use of technical terminology
• Logically sound causal relationships
• Complete absence of system-specific knowledge

The developer was faced with a document that was technically flawless yet functionally useless. It was a simulation of understanding, crafted by an algorithm that could parse language but could not comprehend the problem.

The moment of clarity was not gentle. The developer realized that the support and product management teams had found what they considered an "ideal" way to save time on technical discussions. The process was simple: a client asks a question, and the query is run through an AI.

The AI then performs a deep, diligent analysis of the request. It generates a comprehensive text, explaining in detail what needs to be done and how to do it. The output is polished, professional, and ready to be sent to a developer. The problem is that the AI is operating in a vacuum, with no access to or understanding of the proprietary codebase it aims to modify.

My dear geniuses in tech support and product management found the 'ideal' way to save on discussing the technical side of the problem with me.

This discovery prompted a significant emotional response. The realization that complex technical issues were being reduced to AI-generated prompts, bypassing essential human collaboration, was infuriating. It represented a fundamental breakdown in communication and a devaluation of technical expertise.

This incident points to a deeper trend in the technology industry: the growing reliance on AI as a substitute for genuine expertise and collaboration. While AI tools can be powerful assistants, their misuse in complex, context-dependent fields like software development creates a dangerous illusion of efficiency.

The core issue is not that the AI generated text, but that the human teams chose to use it as a final product. By outsourcing the communication of technical requirements, they severed the vital link between the problem and the solution. The developer is left to decipher a message from a machine that has never seen the code, while the client's original, nuanced request is lost in translation.

• AI cannot understand proprietary codebases or business logic.
• It creates a false sense of completeness and accuracy.
• It discourages necessary human-to-human technical dialogue.
• It places an unfair burden on developers to interpret AI-generated prompts.

The result is a new, subtle form of technical debt—not in the code itself, but in the quality and clarity of the communication that guides its development.

The developer's frustration was not just about one poorly conceived ticket. It was about hitting a "new low" in professional interaction. The incident represents a shift from collaborative problem-solving to automated, impersonal instruction-giving.

This trend undermines the very foundation of effective technical work, which relies on shared context, mutual respect, and clear, two-way communication. When a perfectly formatted but meaningless document replaces a conversation, teams lose the ability to ask clarifying questions, challenge assumptions, and build a shared understanding of the problem.

And here I really blew up. And not quietly, but very, very loudly.

The loud reaction from the developer was a defense of this principle. It was a rejection of the idea that efficiency can be achieved by removing human understanding from the equation. This event serves as a stark warning about the potential pitfalls of uncritical AI adoption in the workplace.

The incident of the AI-generated change request is a microcosm of a larger debate about the role of AI in professional environments. It forces a critical question: are we using these tools to augment our capabilities, or are we allowing them to replace essential human functions?

The path forward requires a balanced approach. AI can be a valuable assistant for drafting initial notes or summarizing information, but it cannot replace the critical thinking and contextual knowledge of a human expert. The key is to use AI as a starting point for discussion, not as the final word.

Ultimately, the most complex problems are solved not by algorithms, but by people working together. Preserving the human element in technical communication is not just a matter of preference; it is a prerequisite for building robust, reliable systems and maintaining a healthy, collaborative work culture.