The Expensive Digital Yes-Men

"You're absolutely correct." There's a phenomenon happening in enterprise AI deployments. Organizations rush to implement LLM-powered chat interfaces, celebrate the initial "wow" moments, then quietly watch as these tools become expensive digital yes-men.

We've forgotten a fundamental truth: conversational interfaces do not rank very high amongst the interfaces that move work forward. They have a place, but they shouldn't be everywhere.

Enter the Directon Problem:

Every LLM interaction begins with a prompt. That prompt sets the trajectory for everything that follows. Get it right, and the LLM becomes a powerful reasoning engine. Get it wrong, and you've just launched an meaningless conversation into the wrong orbit.

LLMs are designed to be helpful. Pathologically helpful. Ask a question poorly, and the model won't push back; it'll just give you the best answer it can to the question you actually asked, not the one you should have asked.

"How can I automate this approval process?"

The LLM will happily enumerate automation strategies. What it won't tell you: maybe the approval process shouldn't exist at all. Maybe you're asking the wrong question entirely.

Direction is set in the first exchange. Everything that follows is downstream of that initial framing. Now imagine you're three turns deep into a conversation. The LLM has been elaborating on automation strategies. You suddenly realize: wait, we're solving the wrong problem.

Try pivoting.

Go ahead. Try to steer a conversational LLM that's already committed to a direction back to first principles. Watch as it politely acknowledges your new direction while still carrying the baggage of everything discussed before. The context window remembers. The reasoning path remembers. The model's helpful nature means it will try to reconcile your new direction with the old one, creating a Frankensteined response that serves neither purpose well. That's also a problem applied to vibe coding. We know, we've been there. Trying to create code without the correct framing, without the requiremets detailed early in the covnersation, just creates a spagetti mess, very often resulting in more work, not less. Conversations compound context. Each exchange adds weight. The further you go in the wrong direction, the harder it becomes to course-correct. It's like realizing you're on the wrong highway twenty miles in, yes, you can turn around, but you've already paid the cost in time, frustration and distance.

This is why unstructured chat interfaces for business processes are fundamentally broken. They optimize for natural conversation, not for correctness.

"But our employees love the chat interface! It feels natural!"

Of course it does. Conversation is how humans communicate. But feeling natural and being efficient are not the same thing.

Consider what happens in an uncontrolled prompt environment:

• Every user asks the question differently

• Every conversation takes a unique path

• Every output requires interpretation

• Every result needs validation

You've replaced a structured process with thousands of bespoke conversations, each one a snowflake of inefficiency. The LLM is working hard. Your employees feel heard. And absolutely nothing is standardized, measurable, or improvable.

The illusion of ease masks the reality of chaos.

The most sophisticated AI implementations we've seen don't look like chat interfaces. They look like structured workflows with AI embedded at specific decision points.

The prompt isn't freeform, it's templated with variable injection. The direction isn't negotiated through conversation, it's predetermined by process design. The evaluation isn't optional, it's built into the workflow before results move forward.

Structure is not the enemy of AI power. Structure is what makes AI power usable.

When you need an LLM to classify a document, you don't give users a chat box and ask them to describe the document. You build a classification endpoint, feed it structured inputs, evaluate outputs against known patterns, and route results deterministically.

When you need business context translation, you don't let every stakeholder prompt however they want. You design the transformation once, test it systematically, and apply it consistently.

The LLM does the cognitive work. The structure ensures the cognitive work actually matters.