Who Reads Long Requirements Today? Your AI Teammate for sure.

 

For years, product teams have joked that “nobody reads long requirements documents.” But with the arrival of generative AI in every role — product, engineering, QA, design — the question “Who will read long requirements documents?” has changed completely.

Today, the most consistent, reliable, and detail-oriented reader on your team is no longer a person. It’s AI.

OpenRose, a free and open-source requirements management application / tool. For more information, visit 

https://github.com/openrose

AI Is Now Part of the Team — Your Team Isn’t Only Human Anymore

Don’t think only in terms of humans when you picture your product team. Today, AI is not just a tool — it’s an active team member that participates in almost every aspect of product development.

AI contributes by:

• Writing and interpreting requirements
• Summarizing complex documents
• Generating test cases
• Exploring design alternatives
• Documenting decisions
• Analyzing legacy systems
• Suggesting improvements and optimizations

Much like a human teammate, AI relies on context, clarity, constraints, and reasoning. Without full information AI cannot participate effectively and may make assumptions that lead to errors or hallucinations.

This shift changes how we think about documentation entirely. Requirements are no longer just for human team members; they are critical input for AI teammates, who now play an essential role in building, analyzing, and improving products. AI’s ability to help you depends directly on the quality, depth, and completeness of the requirements you feed it.

Requirements Are Not Just for Humans — They Train Your AI Teammate

Unlike humans, AI will:

• Reads everything you give it
• Remembers structure and details
• Connects ideas and identifies conflicts
• Checks past decisions
• Works tirelessly, without getting bored

But this only works if you provide enough context. Vague or incomplete requirements make AI unreliable. Detailed, clear, and structured requirements turn AI into your most powerful teammate.

AI Can Help — But It Needs Clear, Structured Input to Do It Well

There’s an irony in how teams work today: we use AI to write and refine requirements, yet AI can only do that effectively when it receives information in a structured, complete form. Requirements with well-defined properties — such as clear descriptions, acceptance criteria, constraints, decision history, and traceability links — give AI the context it needs to understand the bigger picture. When requirements are connected to related features, dependencies, decisions, and past discussions, AI can follow the reasoning just like a human would.

With this level of clarity and structure, AI becomes far more accurate and reliable. It doesn’t need to guess or fill in missing assumptions and rather it stays aligned with your product vision. In short, the more structured your requirements and traceability are, the more effectively AI can support, write, and improve them.

Onboarding AI Requires Clear Product Knowledge Up Front

When a new human joins the team, they learn gradually. They start with one module, ask questions, get guidance, and slowly build understanding with the help of others.

AI doesn’t work that way. When everyone on the team gets their own AI “buddy,” it’s like onboarding many new team members at once — and none of them can learn through trial, observation, or casual conversation. They need clear product knowledge immediately. The best way to make AI an effective teammate is to give it structured information about the product, the module you’re working on, and all the decisions, constraints, and guidelines around it.

AI Doesn’t Remember Everything — So Clear Inputs Matter Even More

AI won’t always remember past discussions, especially when you’re using freemium or evaluation versions. Starting a new session often means starting from zero, with no memory of previous context or corrections. Sometimes you even need to tell the AI to ignore earlier misunderstandings and begin fresh.

This makes detailed, long, and accurate requirements even more important. Every time you reset the conversation, well-written requirements become the foundation you can feed back into the AI so it understands the product, avoids confusion, and produces reliable output.

Requirements Are Becoming a Critical Asset in the AI Era

Traditionally, requirements existed to guide people — helping teams understand what needed to be built, delivered, or executed.

Today, requirements also serve a new purpose: they are structured context consumed by AI across all industries. Whether you’re building software, designing equipment, manufacturing products, developing medical devices, operating complex systems, or managing large-scale processes, AI uses requirements to support teams with tasks like:

• generating ideas and design options
• analyzing risks and constraints
• improving workflows
• producing documentation
• supporting planning and decision-making
• detecting inconsistencies or gaps

Requirements are no longer just “documentation.”

They have become operational data that powers AI-driven work across engineering, operations, design, manufacturing, compliance, and beyond.

The more structured, traceable, and complete your requirements are, the more effectively your AI teammate can support you.

AI Loves Long Inputs — and Uses Them to Re-Evaluate What’s Now Possible

Unlike humans, AI never gets tired of reading long requirements. In fact, it performs best when you give it more detail. With full historical context — including past decisions, rejected ideas, constraints, and shelved features — AI can combine that information with everything it has learned from the broader world: new technologies, changing markets, updated regulations, lower costs, and emerging trends.

This creates a powerful new capability for teams. AI can help you re-evaluate ideas that were impossible, too costly, or out of scope years ago. It can highlight what is now cheaper, faster, more feasible, or strategically important. By revisiting old requirements with fresh perspective, AI turns long documentation into a source of innovation — helping teams revive missed opportunities, uncover new value, and rethink what the product can become.

So, Who Reads Long Requirements Now?

Humans still read them selectively. But the real, consistent, tireless reader is:

AI — your new team member.

AI needs them. AI uses them. AI depends on them for accuracy.

And if you don’t write them:

• AI will hallucinate
• It will misunderstand your product
• It will make incorrect assumptions
• It will give misleading recommendations

The cost of poor documentation is no longer just team confusion. It’s AI confusion, and that can cascade across every role that relies on it.

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Conclusion

The joke that “no one reads requirements” was never truly accurate — but today, it’s obsolete.

You don’t write detailed requirements just for developers or designers or future hires.

You write them for:

• Yourself
• Your teammates
• Your future colleagues
• Your organization’s memory
• And now, for your AI assistants

Because an AI teammate with full context becomes a force multiplier. But an AI teammate without full context becomes a liability.

So write long requirements. Write detailed design specs. Document decisions, constraints, and exclusions.

Your AI will read it all — and your team will be stronger because of it.

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