Charlie Munger famously said: “All I want to know is where I’m going to die, so I’ll never go there.”
When it comes to implementing AI, most leaders are desperately trying to figure out how to be brilliant. They want to disrupt the market, revolutionize their workflows, and implement the most bleeding-edge models available.
But Mungerโs philosophy of inversion suggests a much more practical starting point. Instead of asking how to win, ask: How do we guarantee this AI project will completely fail?
If your goal is to kill an AI initiative, here is the exact playbook:
- Start with the tech, not the problem: Buy an expensive enterprise LLM license first, then wander around the company looking for a vague use case to justify the cost.
- Ignore your data foundation: Assume the AI will magically untangle years of undocumented, siloed, and messy legacy data. Garbage in, garbage out – at scale.
- Remove the human immediately: Automate a high-stakes, customer-facing workflow end-to-end on day one without a “human-in-the-loop” to catch the inevitable edge cases or hallucinations.
- Skip change management: Drop a powerful new AI tool on your employees’ desks without training them on how to use it, or adjusting their KPIs to reflect their new workflows.
In the rush to adopt AI, the tech world is obsessed with seeking brilliance. But in complex systems, avoiding stupidity is often the faster path to ROI.
Don’t ask how AI is going to make your company a genius. Figure out what will cause the implementation to die – and then just don’t go there.
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