Not every problem needs AI. Not every AI problem needs generative AI.
The builder's job is to see the shape of the problem and match it to the right approach. Not the most impressive approach. Not the most exciting approach. The most appropriate approach.
This requires literacy across the full spectrum — from simple data structures to complex hybrid systems. A builder who can only think in one paradigm will misfit solutions to problems.
Sometimes the answer is not AI at all. It's structured data, well-organized, with good search and filtering.
When this is the right choice:
Example: A manufacturing company needs to track which tools are available, where they're located, which machines they fit, and when they need maintenance.
The knowledge is structured and relational. The user needs to query, filter, retrieve. The right answer is always a lookup, not a judgment.
The solution: A well-designed relational database with clear entities — Tool, Machine, Location, MaintenanceSchedule — with proper relationships and good indexing. The interface is search and filter. No AI required.
The failure mode if you picked Gen AI: Natural language querying that sounds nice but returns inconsistent results. The model hallucinates tool IDs. The user asks "show me all available drills for machine X" and gets 4 out of 6 because the model missed two. Unacceptable for operational use.
Evaluation: Data completeness, query accuracy, retrieval speed. Deterministic — same query, same results, every time.