When “One Hour for Requirements” Tells You Everything You Need to Know

I was invited to a one-hour meeting.

The invitation said we were going to “go over requirements” for a new system. We were asked to come prepared with examples of what we do today: reports we rely on, reports we don’t, pain points, and ideas for new functionality that might make our jobs easier.

I read it twice. One hour.

I remember thinking: Wow. Seriously? You’re going to turn on meeting notes, listen carefully, and by the end of the hour we’ll have requirements?

Is this the new speed of AI at play?

Read a book at 5× speed and still comprehend it anyway? Or better yet: summarize everything you do into an hour-long conversation and then program that for me?

That’s how this felt. Compact, concise, efficiency to produce a clean list; a simplified flow; a handful of “key steps;” and a confidence that we could “handle the edge cases later.”

The sequel is predictable. The system gets built quickly. The demo looks great. And somewhere downstream, a very important capability quietly disappears — not because anyone was careless, but because it was simplified away early in the name of speed.

That meeting invitation crystallized something I’d been feeling for a while. AI hasn’t just made development faster. It’s changed how we think about when understanding is “good enough.”

And in some domains, that shift can be dangerous.

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The Mechanic and the Model Builder

From the user’s side — the mechanic — the request felt almost absurd.

You don’t explain a complex operational process in an hour. Not the real one. Not the version that survives pressure, audit, volume spikes, bad data, and human behavior.

You don’t even know where to start. Half of what matters no longer feels like “steps.” It’s instinct. It’s experience. It’s the things you don’t do anymore because you learned not to.

From the developer’s side, the request made perfect sense.

Come in. Tell us what you do. We’ll abstract it. We’ll get something running. We’ll iterate. The system is smart. We can refine later.

That confidence is new. And it’s seductive.

AI technologists move fast. They build impressive things quickly. The models are clever, the demos are smooth, and the system runs almost immediately.

And increasingly, that speed can be a problem — not because developers are careless, but because the tools now make it feel safe to simplify first and reason later.

From the user’s side, the frustration hasn’t changed. You explain the process. You give them the full flow. Not a sketch. Not a simple path. Numerous steps meticulously laid out, learned the hard way.

The developer listens, nods, and comes back with something elegant: a simplified version. A reduced model. Five or six steps. A minimum viable product.

“Don’t worry,” they say. “The edge cases can come later.”

You sense immediately something is wrong. Because those “edge cases” are the reason the process exists.

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Where AI Makes This Worse

In the past, developers simplified because they had to. Systems were rigid. Change was expensive. You negotiated scope because everything hurt to modify.

Today, the belief is the opposite. Models feel elastic. Refactoring feels cheap. Iteration feels infinite. There’s a quiet confidence that intelligence can compensate for missing structure.

“Let’s just get the vehicle running.”

That philosophy works — sometimes.

In a contact center, for example, experimenting with different approaches to connecting with customers is not just acceptable, it’s smart. You try things. You learn. You adapt. The downside is limited. The system is designed to explore behavior.

But in finance systems, that mindset can be fatal.

Finance systems exist to:

• preserve definition
• enforce sequence
• constrain judgment
• survive audit, pressure, and scrutiny

They are not learning systems. They are defensive systems.

When you simplify a finance flow, you are not removing noise. You are removing protection.

Here’s the uncomfortable truth for users: even when you provide a complete flow map, developers will still simplify — not because they didn’t understand it, but because they believe the flow is negotiable.

In the name of efficiency and the sprint, they assume steps can be deferred; definitions can evolve; constraints can be layered on later.

In contrast, you assume steps are load-bearing; definitions are historical; constraints are the system.

You are not disagreeing about the flow. You are disagreeing about which parts are optional. And AI has made this worse.

AI systems optimize for what happens most often. Operational finance systems exist to manage what happens least often — and hurts the most when it does.

Fraud doesn’t look like the median. Regulatory failure doesn’t look like the intended path. Quarter-end behavior doesn’t look like training data.

By pushing “rare” conditions into future work, teams quietly lock in early data definitions, early abstractions, and early assumptions about authority and sequence.

Once the system is live, those assumptions harden.

The vehicle is running — but the frame is set.

This is the new arrogance in system design. Not arrogance of ego. Arrogance of adaptability: the belief that because systems are smart, structure can be postponed.

That belief is true in some domains.

It is lethal in others.

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The Failure Conversation Never Changes

The tragedy is that when these systems fail, the blame conversation is always the same.

The user says: “I told you about that.” The developer says: “You never mentioned that — and if you mentioned it, you said we can handle it later.”

Both are right. Sort of.

But by the time “later” arrives, the cost of changing the core is no longer technical. It’s organizational.

This isn’t an argument against speed. It’s an argument for discriminating speed.

Some systems are meant to learn. Others are meant to hold the line. Confusing the two is how elegant models turn into expensive rewrites. And that’s not a tooling problem. It’s a design choice.

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Back to the Meeting Request

And by the way, on that meeting request?

We didn’t sit there and try to summarize a month of work in sixty minutes. We sent pages of system flow requirements in advance — each step annotated with its purpose, each pain point explained. We asked that it be pre-read.

By the time the meeting started, the flow was clear. The edge cases were visible. And the reality of what we actually do — not what “felt obvious” — was now impossible to gloss over.

It wasn’t elegant. It wasn’t fast. But it worked.

And it reminded me why, even in the age of AI speed and model-first thinking, real understanding still takes time, care, and respect for the invisible steps.