// Part One · Chapter 2

The Experimental Trap

Why chasing models instead of solving problems leads to failure.

The Model Chaser's Dilemma

Every year, a new model is announced. Every year, organizations pivot: 2020, GPT-3; 2022, generative AI; 2024, agentic systems; 2025, the latest model. The technology keeps changing. The organizational chaos keeps repeating.

The specific model matters far less than you think. What matters is:

• Can it integrate with your systems?
• Can you satisfy regulatory requirements?
• Can you explain decisions to auditors?
• Can you maintain it over time?

The Gap Between Lab and Production

In controlled environments, an AI system handles 100 requests with 95% accuracy. In production, it faces:

• Data quality issues — real-world data is messy. Missing fields, inconsistent formats. Accuracy drops.
• Scale variations — 100 requests fine; 10,000 requests crash the system.
• User behavior — real users do unexpected things.
• Integration failures — each integration point is a potential failure mode.
• Regulatory requirements — the demo didn't need audit trails. Production does.

Breaking Free of the Trap

Step 1 — Define success first. What business outcome? How will you measure it?

Step 2 — Start simple. Begin with the simplest solution that could work.

Step 3 — Design for operations. How will you monitor, handle failures, and satisfy regulators?