This method is useful well beyond investing. Someone has to distinguish a document that merely sounds relevant from one that actually helps answer the question. Engineers can then use those judgments to measure whether a change improves the system. Business expertise becomes something the team can test against, rather than advice delivered after the application has shipped. It’s not a case of human intelligence or artificial intelligence. You need both.
Make the expertise usable
Of course, a skeptic could reasonably ask why a model can’t learn the business, too. It’s a fair critique, as AI can learn a great deal of it. After all, that’s the reason Bridgewater (and others) make a point of supplying the right documents, definitions, and examples. This ensures AI can take on more of the analysis, thereby reducing dependence on the few employees who currently carry essential knowledge in their heads.
But access to every document doesn’t settle which policy is authoritative when two departments disagree. Nor does it authorize a change to an approval process or establish who owns a mistake. A model can help expose contradictions and propose resolutions, but someone with responsibility for the business has to decide which resolution the company will adopt.
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