Tianze Wang王天泽
中文/EN

Ten essays, one question: once AI makes judgment cheap, how should a company make decisions?

They come in three groups. New here? Start with this one: Performing AI, or AI-Native?

A

After judgment gets cheap 2 posts

Once AI makes judgment cheap, decisions nobody had time for become affordable, and the middle layers of a company may slowly change shape.

  1. AI’s Value Is in the Decisions Nobody Was Making

    Measured in hours saved, AI’s value tends to look small. A bigger share lies in decisions nobody used to have time for.

  2. The I-Beam Society

    The two limits that created the middle of the corporate pyramid are loosening. What’s left may slowly come to look like an I-beam.

B

Handing decisions over 4 posts

How to pick an enterprise AI project, how to build it, and how a system earns decision rights one rung at a time.

  1. Performing AI, or AI-Native?

    Once an AI project is live, has any decision actually been handed to the system? It’s the simplest way to tell a performance from AI-native work.

  2. Models Are Bought. Harnesses Are Built.

    Whether AI works reliably inside a company depends largely on the layer around the model, often called the harness. Anyone can buy the model; the harness has to be written bit by bit.

  3. Many AI Projects Die Before Anyone Picks a Model

    Whether an AI use case can work is largely settled before a model is chosen. Two things matter most: how much judgment it asks of the AI, and whether the company’s data can support that.

  4. AI Has to Earn Its Decision Rights

    Big companies say they want AI involved in decisions, then stall, because nobody can answer one question: who is responsible when it’s wrong?

C

Inside pharma commercial 4 posts

The same ideas in the industry I know best: annual planning, frontline adoption, and a map of ten kinds of AI apps.

  1. The Market Changes Weekly. The Company Decides Once a Year.

    One way to find where an AI decision system will pay off is to compare two frequencies: how often a decision’s inputs change, and how often the decision itself is made. The wider the gap, the stronger the case.

  2. Annual Planning Is a Compromise

    I used that planning method for years before I understood that every step of it was a compromise forced by expensive judgment.

  3. Why the Front Line Ignores AI’s Advice

    When front-line staff ignore AI’s advice, the usual problem is that the advice isn’t tied to their quotas and bonuses. Model accuracy comes second.

  4. Ten Kinds of AI Apps. Eight Still Stop at Answering and Suggesting.

    Most AI applications I’ve seen in pharma stop at answering questions and making suggestions. The kind where the system proposes the default decision doesn’t exist yet at most companies.