Why Does "AI Cost Control" Keep Failing?

 

Why Does "AI Cost Control" Keep Failing?

You've tried everything. Better prompts. Usage limits. Budget caps.
Every best practice in the "book". And yet, your AI costs keep climbing.

What if the tools you're using were never designed for the problem you're trying to solve?

Here's the uncomfortable truth: Traditional cost control assumes you understand what you're controlling. But with genAI, most organizations are flying blind.

They measure activity. They track spending. They monitor usage.
But they can't answer the one question that matters:
Which of these costs actually are relevant and really deliver value?

How much of your AI spending is truly necessary?

In my experience auditing different AI setups, the answer is shocking. Less than half of AI spending delivers measurable value. The rest? It's waste that traditional tools can't even detect.

It's not waste because of poor engineering. It's waste because no one knows to question it. It happens because of the "that's how we've always done it." mentality. Because of "the metrics look good." excuse and because of "never change a running system." philosophy.

If the harder you try to control costs, the more they grow.
What does that tell you?

It tells you that your effort isn't the problem. Direction is. You're solving the wrong problem with the wrong tools, getting predictably wrong results.

The companies that have their AI budgets under control? They're not working harder. They're working with a fundamentally different understanding of what the problem actually is.

"Plans fail for lack of counsel, but with many advisers they succeed."

– Proverbs 15:22 (NIV)