No, AI Won't Make Your Processes Faster — But Here's What Will

"I don't think AI will make your processes go faster."

That's the title of a new piece climbing the Hacker News front page — and the author makes a genuinely good point. The problem is, many people will read the title and use it as an excuse to dismiss AI entirely.

Let's dig into what the author actually said, where they're right, and where the real opportunity lies.

The Author's Argument

Drawing on classics like The Toyota Way and The Goal, the author explains that most process optimization efforts miss the mark. They focus on the visual bottleneck — the part of the Gantt chart that looks longest — without understanding why it's slow in the first place.

The example: software development looks like the bottleneck on a project timeline. So companies throw people or AI at it, assuming faster coding = faster delivery.

But the real bottleneck? Understanding the problem.

"What does 'send mail to user once sale is completed' mean? Ok, we can send a mail, but what should be in the mail? What if there was an issue in the sales process, do we still send an error mail? When is a sale completed?"

Every developer knows this pain. The slow part isn't writing code — it's figuring out what to write. And AI that generates code faster doesn't help if you're generating the wrong thing faster.

Where the Author Is Right

The core insight is solid: speed of execution is not the same as speed of delivery. If your bottleneck is upstream (requirements, understanding, alignment), optimizing downstream (coding, writing, generating) won't help.

This is Theory of Constraints 101. You can't fix a system by speeding up a non-bottleneck step.

Where the Author Misses the Mark

Here's the thing: the author assumes AI is only useful for the execution step. That's a narrow view.

AI can help with the bottleneck itself — understanding the problem:

1. Clarifying vague requirements

Tools like ChatGPT and Claude excel at asking clarifying questions. Instead of blindly generating code from a vague ticket, use AI to:

• "What are the edge cases for this feature?"
• "What questions would you ask before implementing this?"
• "Generate a checklist of acceptance criteria from this one-line description"

2. Mapping process flows

Use Claude or Perplexity to analyze your current process documentation and identify:

• Where are the decision points?
• What information is needed at each step?
• Where do handoffs introduce delays?

3. Documentation that actually gets written

One of the author's examples shows legal and documentation taking 15 days. AI can compress this:

• Notion AI can draft documentation from meeting notes
• Cursor and GitHub Copilot can generate code comments and docs inline
• AI can summarize decisions and keep everyone aligned

4. Finding the real bottleneck

The author's own Gantt chart example shows the mistake: looking at which step takes the longest and assuming that's the bottleneck. AI can analyze process data to find the actual constraint — which is often a step that's not even on the chart (approvals, waiting, rework loops).

The Correct Take

The author is right that blindly throwing AI at your process won't fix it. That's the "AI psychosis" problem MitchellH warned about — applying a solution without understanding the problem.

But the correct take isn't "AI won't help." It's "use AI to understand the problem first, then use it to execute the solution."

The companies that win with AI won't be the ones generating code fastest. They'll be the ones using AI to:

1. Understand what needs to be built
2. Clarify vague requirements
3. Identify actual bottlenecks
4. Then execute faster

Speed without understanding is just faster chaos. But understanding without execution is just theory. AI helps with both — if you use it right.

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