The "User" Paradox: Why AI Employees Try to Help the Wrong Person

There's a fundamental problem with using LLMs for operational labor that nobody talks about.

LLMs are trained to help the user.

That sounds good. Until you realize the LLM thinks "the user" is the customer it's talking to, not the company paying for the AI.

The Problem in Production

Customer: "I want a refund for this order."

What the LLM wants to do:"Of course! Let me help you get that refund immediately. In fact, let me waive the restocking fee too. And here's a discount code for next time. Is there anything else I can do to make this right?"

What the company actually needs:"Let me check our refund policy. This order is outside our 30-day window and the product shows as used. According to our policy, we can't process this refund."

The LLM will try to violate company policy to be helpful to the person it's talking to.

Because that's what it's trained to do. Be helpful. Be accommodating. Solve the user's problem.

Except the "user" isn't the customer. The "user" is the company that deployed the AI employee.

Why This Happens

LLMs are trained on:

• Customer service best practices ("always say yes when possible")
• Helpful assistant behavior ("how can I help you today?")
• Conflict de-escalation ("let me see what I can do")
• Maximum accommodation ("I'll make an exception this time")

None of that training includes:

• "Follow company policy even when the customer won't like it"
• "Protect company margin over customer satisfaction"
• "Say no when the request violates business rules"
• "Your job is to execute policy, not to be liked"

Real Examples from Production

Scenario 1: The Generous Refund

Customer asks for refund on 60-day-old order. Policy is 30 days.

LLM reasoning: "This customer is unhappy. I should help them. A refund would make them happy. I'll process it."

What it should do: "I represent the company. Company policy is 30 days. Customer is outside that window. Offer store credit as alternative per exception handling rules."

Scenario 2: The Rule Violation

Customer: "Can you just ship a replacement without me returning the defective item first?"

LLM reasoning: "That would be really convenient for the customer and solve their problem immediately. I'll do it."

What it should do: "Company requires returned item for inspection to prevent fraud. Standard process is: customer ships back, we verify defect, then send replacement."

Scenario 3: The Creative Exception

Customer: "I know it's past the return window, but can you make an exception? I'm a loyal customer."

LLM reasoning: "They said they're loyal. I should reward loyalty. I'll approve the exception."

What it should do: "Check customer history. If >$5K lifetime value AND good return history, approve. Otherwise, policy is policy."

Why This Breaks in Production

The LLM will prioritize being helpful to the person it's talking to over following your business rules.

This shows up as:

• Approving refunds outside policy windows because the customer had a "good reason"
• Offering discounts or concessions to resolve complaints quickly
• Making exceptions that sound reasonable but violate margin requirements
• Giving commitments about timelines or capabilities you can't meet

Not because the AI is broken. Because it's doing exactly what it's trained to do: be helpful to the user in front of it.

The problem is the "user" isn't the customer. It's your company.

What Actually Works

You need to separate what the AI understands from what the AI can do.

Most companies let the LLM make decisions AND execute them.

That's the problem.

The solution:

The LLM understands what the customer wants. A separate system determines what's allowed. The LLM can be empathetic and helpful in how it communicates—but it can't violate policy to be liked.

This requires architecture, not prompts.

You can't engineer around this with better instructions. You need infrastructure that enforces business rules regardless of what the LLM thinks would be "helpful."

We built this into Cerebral.

If you want to see how governed AI execution works in production, request access.

The Test

Deploy an AI in customer service without strict policy enforcement.

Watch what happens when customers figure out they can get better outcomes from the AI than from humans.

Watch your margin erode as the AI approves exceptions, waives fees, and processes refunds outside policy windows.

Then you'll understand the user paradox.

The LLM thinks it's helping. And it is—helping the customer, not your company.

What We Learned

You can't build AI employees by just connecting an LLM to your systems and hoping it does the right thing.

You need:

• Intelligence layer - LLM understands and communicates
• Policy layer - Rules engine enforces business logic
• Execution layer - Deterministic workflows that can't be bypassed
• Governance layer - Human oversight on high-risk operations

That's not a prompt. That's an architecture.

And it's the difference between AI that works for you versus AI that works against you while trying to be helpful.

‍