《讓我們來看看數據》的死亡

在2026年，傳統的數據驅動方法正受到人工智慧模擬人類行為能力的挑戰，使得決策變得更快且更準確。

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ByJames Huang

2026年3月4日2 分鐘閱讀·Updated 2026年7月6日

AI Generated Cover for: The Death of "Let's Look at the Data"

我是詹姆斯，Mercury Technology Solutions的執行長。 香港 - 2026年2月19日

在過去十年中，「數據驅動」是矽谷的最高讚譽。如果有人提出一個想法，立即的回應是：「讓我們建立一個最小可行產品，進行A/B測試，收集數據，並在一個月內進行回顧。」

這就是精實創業的方法論。在2026年，這是一個官僚陷阱。

A/B 測試的速度懲罰

關於大多數 A/B 測試的骯髒秘密是：你正在測試已經被解決的問題。你正在測試按鈕位置、入門文案和定價頁面佈局。你花費數週的時間收集統計顯著性，卻是在解決人類心理學和軟體設計模式幾年前就已經解決的問題。

當你的團隊等待30天以便數據成熟時，你的競爭對手已經使用 AI 生成的邏輯推出了三個新功能。

AI 作為「行為模擬器」

大型語言模型不僅僅是文本生成器。它們是人類行為模擬器。它們已經吸收了每一個案例研究、每一個失敗的創業後記、每一篇轉換率優化的部落格，以及每一篇曾經發表的心理學論文。

舊方式：花費10,000美元和4週的時間將流量引導到兩個登陸頁面，以查看哪一個轉換更好。
新方法：將兩個著陸頁面都提供給 Opus 4.6 或 GPT-5.3。詢問它："根據認知負荷理論和已知的 SaaS 轉換啟發式，哪一個頁面轉換效果更好，為什麼？"

人工智慧的正確率為 85%。它是否 100% 完美？不。但在10 秒鐘內達到 85% 的確定性，遠比在4 週內達到 95% 的確定性更有價值。

信念重於共識

"我們需要更多數據" 通常只是企業用語，實際上是 "我害怕做決定並承擔責任。"

人工智慧消除了無知的藉口。如果人工智慧告訴你使用者體驗很糟，並且清楚解釋了為什麼它違反了認知負荷原則，你不需要實際建造它來證明它失敗。你需要修正它。當今最危險的公司不是擁有最多數據的公司，而是擁有最高「信念速度」的公司——能夠即時使用人工智慧做出準確決策，並在競爭對手甚至還未完成使用者調查之前就推出產品。 it violates cognitive load principles, you don't need to build it to prove it fails. You need to fix it.

The most dangerous companies today are not the ones with the most data. They are the ones with the highest Conviction Velocity—the ability to make accurate decisions instantly using AI, and ship before the competition even finishes their user surveys.

Frequently Asked Questions

Why is traditional data-driven decision-making considered outdated in 2026?

In 2026, traditional data-driven methods are seen as bureaucratic traps that slow down decision-making. Companies often spend weeks collecting data on issues that have already been addressed by established design patterns, allowing competitors using AI to outpace them.

How does AI improve decision-making compared to traditional methods?

AI enhances decision-making by simulating human behavior and providing insights based on extensive data analysis in a fraction of the time. For instance, AI can predict which landing page will convert better with 85% accuracy in just seconds, compared to traditional A/B testing that can take weeks.

What is 'Conviction Velocity' and why is it important?

Conviction Velocity refers to a company's ability to make quick, accurate decisions using AI insights rather than relying solely on data gathering. This agility allows companies to implement changes and launch new features faster than competitors, which is crucial in today's fast-paced market.

What role do Large Language Models play in modern decision-making?

Large Language Models act as behavioral simulators that analyze vast amounts of information, including psychological theories and case studies, to provide actionable insights. They enable businesses to assess user experience and conversion strategies quickly, significantly reducing the time needed to inform decisions.

What are the risks of relying on A/B testing in a competitive landscape?

Relying solely on A/B testing can lead to missed opportunities as it often focuses on minor tweaks rather than innovative solutions. With the speed at which AI can provide insights, waiting for statistical significance can result in losing ground to competitors who are leveraging AI to make faster, more informed decisions.