The God Quotient Problem with AI
The God Quotient Problem with AI
For the last few months, across every forum and industry conversation, I keep seeing one consistent emotion from peers: disappointment.
Not with AI. With what they expected AI to be.
People walked into AI implementation expecting something with a God Quotient. An entity with complete context of the universe and a ready solution to every problem. They expected to plug it in, press a button and watch miracles happen.
That entity does not exist. Not in silicon and not elsewhere.
The Data Problem Nobody Mentions
Before celebrating AI as your next big transformation, ask one uncomfortable question. What is the quality of data you are feeding it?
If you put junk in, AI produces more junk. Faster. At scale. That is not an AI problem. That is your problem. AI is just honest enough to reflect your mess back at you with full confidence and zero apology.
Without clean data, your AI initiative is an expensive way of automating existing chaos.
The Unglamorous Heroes Nobody Wants to Talk About
Your IT team has been quietly running automation scripts and workflow tools for years without anyone throwing a conference around it. Because it works. It is boring, predictable and does exactly what you tell it to do.
But boring does not get budget approved. So organisations skip straight to AI and wonder why outcomes are inconsistent and costs are climbing.
A well designed workflow engine follows rules without improvising. It does not hallucinate a new step in your loan disbursement process. Robotic Process Automation and rules based workflow engines have been solving high volume structured problems for years at a fraction of what you are being quoted for AI today.
The smartest architectures being built right now are hybrid. AI handles ambiguity and unstructured inputs. Workflow and automation handle the structured repetitive layers underneath. Fix the foundation first. Automate what can be automated cleanly. Then bring AI in. In that order.
The Token Bill and the Annual Report Paragraph
The more you use AI, the more tokens you burn and the more costs climb toward the ceiling. Several problems being thrown at Large Language Models could be solved more stably and cheaply through strong ML models and structured automation.
And yes, we all know that one carefully drafted AI paragraph in the annual report. Worded just right to signal you are not behind the curve. Investors read it, analysts note it and market sentiment holds for a quarter or two.
But when AI starts bleeding your balance sheet because the architecture was wrong and the data was dirty, shareholder patience disappears quickly. The market does not reward theatre for long.
One Last Thing
AI hallucination is not a memory problem. It is a mathematics problem sitting inside the transformer model itself. Better infrastructure will not solve it.
Quantum computing might. But not before 2028 to 2030 commercially. And not cheaply.
Every organisation needs to decide what business they are actually in. Focus on the core. Build right. Build stable. Let the researchers do frontier work till the dust settles.
Your job is to run a business and not a lab.
More on transformer models and hallucination in the next post.
