This image captures the "Iceberg of AI Reality" It’s the classic disconnect between leadership’s vision and the engineering team’s reality.. The 'íceberg' is divided in different layers. The promise at the top: The people in the boat at the top are looking at **Return on Investment (ROI)** and **Market Transformation**. Below that, under water: Legacy Systems: AI thrives on modern, accessible data architectures. Trying to hook a cutting-edge LLM into a 20-year-old COBOL mainframe or a siloed on-prem database is like trying to plug a Tesla into a lemon—it’s just not built for that kind of energy transfer. Data Pipelines: "Garbage in, garbage out" has never been more true. If your pipelines are leaky, inconsistent, or lack proper metadata, your AI will generate confidently wrong insights. Cleaning this is 80% of the work. Integration Debt: This is the cumulative cost of all those "quick fixes" and "we'll fix it later" patches. When you try to integrate a centralized AI, you realize those patches have created a web of dependencies that break the moment you touch them. Undocumented Code: This is the "Submarine Level." It’s where the tribal knowledge lives—or dies. If no one knows why a certain script runs at 3 AM or what a specific variable actually represents, you can't reliably automate or replace that process with AI.

I just saw this picture presenting why the larger your organization is, the more difficult it is to add AI to your data processing development.

Many of us must experience the same despair when we are told to provide users with AI.

#AI #DataProcessing #Despair #NoQuickSolutions