Using AI Wisely Starts Before The First Prompt明智地使用 AI，始于第一个提示词之前

Every generation gets its own cautionary tale about shortcuts. For Gen Z, one of the latest is Obsession.每一代人都有关于“捷径”的警世故事。对于 Z 世代来说，最新的故事之一就是电影《Obsession》。

On the surface, it’s a psychological horror film about Bear Bailey, a music store employee, discovers the One Wish Willow-a magical shortcut that promises to make his long time crush, Nikki Freeman, fall in love with him. His wish comes true, but not in the way he imagined. What begins as a solution slowly turns into possession.表面上看，这是一部心理恐怖片，讲述了音乐商店员工贝尔·贝利（Bear Bailey）发现了一棵名为“One Wish Willow”的神奇柳树，它承诺能让贝尔暗恋已久的尼基·弗里曼（Nikki Freeman）爱上他。愿望成真了，但并非以他想象的方式。起初的解决方案，慢慢演变成了占有。

The Willow isn’t inherently evil. It simply grants Bear exactly what he asks for, without questioning whether that’s what he truly needs. AI adoption looks surprisingly similar.这棵柳树本身并无恶意，它只是精准地实现了贝尔的愿望，而不会去质疑这是否是他真正需要的。AI 的采用过程惊人地相似。

As a Gen Z, I love when a piece of pop culture becomes more than entertainment. Sometimes it unexpectedly explains the way we think about technology better than another whitepaper or conference talk ever could. It gives us a language for recognizing patterns we might otherwise miss.作为 Z 世代的一员，我喜欢流行文化超越娱乐本身的时候。有时，它能比任何白皮书或会议演讲都更直观地解释我们对技术的思考方式。它为我们提供了一种识别那些本可能被忽略的模式的语言。

That’s exactly what Obsession did for me. It wasn’t just a psychological horror film, which is funny because after watching it, I realized we might be doing the exact same thing with AI. Not because AI is dangerous. But because, much like Bear in the film, we sometimes become so focused on getting the outcome we want that we stop questioning the path we’re taking.这正是《Obsession》带给我的启示。它不仅仅是一部心理恐怖片，有趣的是，看完电影后我意识到，我们在 AI 领域可能正在做着同样的事情。这并非因为 AI 本身危险，而是因为像电影里的贝尔一样，我们有时过于专注于获得想要的结果，以至于不再去审视我们所采取的路径。

Every AI Initiative Starts with Good Intentions每一个 AI 项目都始于良好的初衷

Let’s automate documentation.”
“Let’s summarize meetings.”
“Let’s build a support agent.“让我们自动化文档吧。” “让我们总结会议内容吧。” “让我们构建一个支持代理吧。”

Nothing about these ideas is inherently wrong. In fact, many of them create real value. The challenge begins when AI quietly shifts from being a means to becoming the objective itself.这些想法本身并没有错。事实上，其中许多都能创造真正的价值。挑战在于，当 AI 悄然从一种“手段”转变为“目标”本身时。

At some point, the conversation changes. Instead of asking,在某个节点，对话发生了转变。不再问：

Does this create value?这能创造价值吗？

Teams begin asking,团队开始问：

Where else can we put AI?我们还能在哪里加入 AI？

That’s the same version of making another wish.这和许下另一个愿望如出一辙。

AI Maturity Isn’t About Building More AIAI 成熟度并非在于构建更多的 AI

Bear doesn’t lose himself because of one decision. He loses himself because every decision after the first becomes easier to justify. Teams experience the same drift.贝尔并不是因为某一个决定而迷失自我，而是因为在第一个决定之后，每一个后续决定都变得更容易合理化。团队也经历着同样的漂移。

Teams that misunderstand AI maturity decided to measure success by adoption.误解 AI 成熟度的团队决定以“采用率”来衡量成功。

• How many AI features are shipped?发布了多少 AI 功能？
• How many agents are running?运行了多少个 AI 代理？
• How many workflows use LLMs?有多少工作流使用了大语言模型（LLM）？

Mature teams measure something entirely different.成熟的团队衡量的是完全不同的东西。

• Which workflows actually improved outcomes?哪些工作流真正改善了成果？
• Which ones reduced operational cost?哪些降低了运营成本？
• Which one customer genuinely use?哪些是客户真正会使用的？
• Which ones should never have been built?哪些根本就不该被构建？

AI maturity is measured by how deliberately AI is introduced and how confidently teams choose not to use it when it doesn't add value.AI 成熟度的衡量标准在于引入 AI 的审慎程度，以及当 AI 无法创造价值时，团队是否有信心选择不使用它。

Governance Exists to Interrupt Momentum治理的存在是为了打断惯性

One of the quietest tragedies in Obsession isn't the wish itself. It's that Bear stops questioning whether the outcome he's chasing still has meaning. He becomes focused on preserving the wish rather than understanding its consequences.《Obsession》中最令人唏嘘的并非愿望本身，而是贝尔不再质疑他所追求的结果是否还有意义。他变得专注于维持愿望，而不是理解其后果。

AI governance exists for exactly this reason. Its job is to protect the quality of decision-making.AI 治理的存在正是为了这个原因。它的职责是保护决策的质量。

Governance creates space for teams to keep asking questions that momentum tends to erase.治理为团队创造了空间，让他们能够不断提出那些容易被惯性抹去的问题。

• Why are we using AI here?我们为什么要在这里使用 AI？
• Does this solve a customer problem?这能解决客户的问题吗？
• Is AI actually the best approach?AI 真的是最好的方法吗？
• Would a simpler workflow achieve the same result?更简单的工作流能达到同样的效果吗？
• What happens if we remove AI entirely?如果我们完全移除 AI 会怎样？

The Most Expensive Prompt Is the One That Never Needed to Exist最昂贵的提示词，是那些根本不需要存在的提示词

Engineering practices like token optimization, caching, workflow orchestration, and production-ready AI systems are incredibly valuable. But they all assume one thing: that the workflow itself deserves to exist.诸如提示词优化、缓存、工作流编排和生产级 AI 系统等工程实践非常有价值。但它们都基于一个假设：工作流本身值得存在。

The workflow deserves to exist if they’re valuable. Before optimizing prompts, teams should first optimize the decision that introduced the prompt in the first place.工作流只有在有价值时才值得存在。在优化提示词之前，团队首先应该优化最初引入该提示词的那个决策。

Using AI wisely isn’t about saying yes to every opportunity. Using AI requires knowing when saying no creates more value than another workflow ever could.明智地使用 AI 并非对每一个机会都说“是”。使用 AI 需要知道在何时说“不”，因为这比任何工作流所能创造的价值都要大。

The Difference Between Making Wishes and Making Decisions许愿与决策的区别

The tragedy in Obsession wasn’t that Bear made a wish. It was that, once the wish worked, he stopped questioning whether it was still leading him toward what he truly wanted.《Obsession》的悲剧不在于贝尔许了愿，而在于当愿望实现后，他不再质疑这是否依然引导他走向他真正想要的东西。

AI behaves much the same way. It can generate. Summarize. Classify. Automate.AI 的表现也是如此。它可以生成、总结、分类、自动化。

But it won’t ask whether any of those things create meaningful value. That responsibility still belongs to us.但它不会问这些事情是否创造了有意义的价值。这份责任依然属于我们。

AI is incredibly good at granting wishes. Governance exists to make sure we’re wishing for the right things.AI 非常擅长实现愿望。而治理的存在，是为了确保我们许下的是正确的愿望。