<p><img src="https://imageproxy.pbkrs.com/https://images.unsplash.com/photo-1454165804606-c3d57bc86b40/query-YXV0bz1mb3JtYXQmZml0PWNyb3AmaXhpZD1leUpoY0hCZmFXUWlPakV5TURkOSZpeGxpYj1yYi0xLjIuMSZxPTgwJnc9MTAwMA?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" width="1000" height="667" original-src="https://imageproxy.pbkrs.com/https://images.unsplash.com/photo-1454165804606-c3d57bc86b40/query-YXV0bz1mb3JtYXQmZml0PWNyb3AmaXhpZD1leUpoY0hCZmFXUWlPakV5TURkOSZpeGxpYj1yYi0xLjIuMSZxPTgwJnc9MTAwMA"/></p>
<p>图片来源：Unsplash</p>
<p>“没有解决方案，只有权衡，” 托马斯·索维尔写道。AI 系统的实施需要反映许多决策中固有的困难权衡，并在最终决策中体现人类价值观。</p>
<p>假设一个批发商的仓库是在 50 年前布局的，然后随着空间需求的变化而不断调整：新产品被引入，旧产品被淘汰，单个商品的销售量增长或减少。仓库一片混乱，但 AI 可以解决这个问题。一个 AI 驱动的工具将获取所有待存储产品的数据，包括尺寸、重量以及销售量。AI 可能还会获得一个货架和物料处理选项的目录，例如叉车和传送带。安全标准也会提供给系统。</p>
<p>这听起来是一个很好的方法，但考虑到目标和权衡时就不那么简单了。</p>
<h3><strong>AI 决策中的资本 - 劳动权衡</strong></h3>
<p>人类必须决定 AI 应该被要求做什么。仓库重新设计的一个明显目标可能是最小化人类搬运物品所花费的时间。但等等——AI 正在查看哪个仓库设备目录？可能有一个低预算的目录或一个 Neiman-Marcus 的方案。资本成本和劳动成本之间总是存在权衡。关于资本支出的决策需要了解资本投资的使用寿命以及未来劳动成本的变化。选择还会受到未来几年物料处理技术可能改进的影响，例如机器人。在一个静态的世界中，决策可能复杂但确定：输入唯一决定输出。但在一个变化的世界中，必须对技术的未来和劳动成本的前景做出一些判断。这些判断可能需要相当细致，注意到机器人叉车和滚筒轨道之间价格轨迹的差异。</p>
<p>资本和劳动支出之间的这种权衡发生在许多领域：快餐店的自助点餐机、健康诊所的在线预约管理、代替警察的红灯摄像头。如果利率较低，更多的自动化是合理的——但仍然依赖于对未来劳动成本的预测。我们习惯于工资水平上升，但 AI 会否减少对劳动的需求，以至于未来人类工人实际上会更便宜？我们可以要求 AI 系统预测技术发展和未来劳动成本，但不应期待完美。关键的商业判断仍然需要人类的判断。</p>
<h3><strong>AI 决策中的当前需求与灵活性权衡</strong></h3>
<p>许多商业系统和几乎任何物理结构的设计都有另一个权衡：满足当前需求的效率与变更的灵活性。那个仓库需要适应新产品、新的销售量以及可能不同的交付和发货模式。一个为今天优化的系统可能在改变时代价高昂。</p>
<p>AI 可以通过回顾过去来帮助开发未来变化分析的信息。新产品添加和旧产品淘汰的频率是多少？是否有向更重的箱子发展的趋势？但这些都是 AI 无法完美预测的。一个 AI 模型可能在预测方面比人类更出色，但人类在做出重大商业决策时应保持谨慎。</p>
<p>同样，这种权衡适用于许多商业流程决策。如果我们将企业客户的营销与小企业的营销分开，我们是否希望永远维持这个决定？如果不，那么改变会很困难吗？如果我们停止从州立大学招聘工程师，明年再回到他们的招聘会上会很困难吗？</p>
<p>AI 系统可以优化许多功能，但适应变化的难易程度必须是任何决策的一部分。这需要对未来想要变更的可能性和实施变更的成本进行判断。</p>
<h3><strong>AI 决策中的安全权衡</strong></h3>
<p>仓库示例建议为 AI 系统提供安全要求。在许多操作中，安全标准是由政府监管机构或行业协会制定的，或者至少是行业内的普遍做法。超越通常标准是否有意义？</p>
<p>在特定情况下，工作场所的事故可能通过更高成本的设计来减少。公司愿意为减少伤害支付多少？这可能基于医疗费用和缺勤时间的美元计算。美国公司在工人赔偿制度下可以从其保险公司获得事故成本的概念。但数字计算无法涵盖受伤者的痛苦，也无法涵盖目睹工作场所伤害后其他员工的恐惧。公司的价值观和文化也会发挥作用。</p>
<p>工作场所安全很快就会浮现在脑海中，但消费者产品安全也值得关注。数据安全超越了美元计算，还包括客户满意度——在他们的数据被黑客攻击后，客户满意度会下降。</p>
<h3><strong>权衡与价值观</strong></h3>
<p>许多重要决策的中心都是人类价值观的权衡。喜剧视频系列《谋杀机器人》中，一个机器人对人类因群体忠诚而冒个人风险感到困惑。但这正是我们人类所做的。我们关心他人：他们的福祉、他们的安全、他们的感受。当人类价值观发挥作用时，人类必须主导选择。AI 可以帮助我们理解权衡，但最终人们必须做出关键决策。</p>
<hr/>
<p><em>更多来自此作者的文章：</em></p>
<p>2026 年经济预测：增长将抵御放缓担忧</p>
<p>知识胜过权力：领导者需要的创新秘密</p>
<p>AI 泡沫还是商业革命？重要的商业洞察</p>

<p>这篇文章讨论了人工智能与人类判断在战略商业决策中的结合，特别是在仓库管理方面。它强调了资本与劳动力成本之间的权衡、当前需求与未来灵活性之间的关系，以及安全考虑。虽然人工智能可以优化运营，但人类的监督对于做出反映公司价值观并适应变化环境的细致决策至关重要。文章强调了在预测技术和劳动力成本时需要谨慎判断的重要性，以及在效率与适应未来变化能力之间保持平衡的重要性</p>

<p><img src="https://imageproxy.pbkrs.com/https://images.unsplash.com/photo-1454165804606-c3d57bc86b40/query-YXV0bz1mb3JtYXQmZml0PWNyb3AmaXhpZD1leUpoY0hCZmFXUWlPakV5TURkOSZpeGxpYj1yYi0xLjIuMSZxPTgwJnc9MTAwMA" alt="" original-src="https://images.unsplash.com/photo-1454165804606-c3d57bc86b40?ixlib=rb-1.2.1&amp;ixid=eyJhcHBfaWQiOjEyMDd9&amp;auto=format&amp;fit=crop&amp;w=1000&amp;q=80"/></p>
<p>Image Source: Unsplash</p>
<div class="lb-trans"><p>图片来源：Unsplash</p>
</div><p>“There are no solutions, only tradeoffs,” Thomas Sowell wrote. Implementation of AI systems needs to reflect the difficult tradeoffs inherent in many decisions, with human values reflected in final decisions.</p>
<div class="lb-trans"><p>“没有解决方案，只有权衡，” 托马斯·索维尔写道。AI 系统的实施需要反映许多决策中固有的困难权衡，并在最终决策中体现人类价值观。</p>
</div><p>Let’s say a wholesaler’s warehouse was laid out 50 years ago, then adjusted now and then as space needs changed: new products were introduced, old ones discontinued and sales volumes for individual items grew or shrank. The warehouse is a mess, but AI can fix that. An AI-powered tool will be fed data on all of the products to be stored, including sizes and weights as well as sales volumes. The AI will probably also be given a catalog of shelving and material handling options, such as fork lifts and conveyor belts. Safety standards would also be provided to the system.</p>
<div class="lb-trans"><p>假设一个批发商的仓库是在 50 年前布局的，然后随着空间需求的变化而不断调整：新产品被引入，旧产品被淘汰，单个商品的销售量增长或减少。仓库一片混乱，但 AI 可以解决这个问题。一个 AI 驱动的工具将获取所有待存储产品的数据，包括尺寸、重量以及销售量。AI 可能还会获得一个货架和物料处理选项的目录，例如叉车和传送带。安全标准也会提供给系统。</p>
</div><p>This sounds like a great approach until one considers goals and tradeoffs.</p>
<div class="lb-trans"><p>这听起来是一个很好的方法，但考虑到目标和权衡时就不那么简单了。</p>
</div><h3><strong>Capital-Labor Tradeoffs In AI Decisions</strong></h3>
<div class="lb-trans"><h3><strong>AI 决策中的资本 - 劳动权衡</strong></h3>
</div><p>Humans have to decide what the AI should be asked to do. One obvious goal for the warehouse redesign might be to minimize human time spent moving things. But hold on—what warehouse fixtures catalog is the AI looking at? There might be a low-budget catalogue or a Neiman-Marcus approach. There’s always a tradeoff between capital costs and labor costs. The decision on capital expenditures needs information on how long the capital investment will last and how labor costs will change in the future. The choice will also be influenced by likely improvements in material handling technology in the coming years, such as robots. In a static world, the decision may be complex but deterministic: the inputs uniquely determine the output. But in a changing world, some judgment must be made about the future of technology and the outlook for labor costs. And the judgments may have be fairly granular, noting the difference in price trajectory between robotic forklifts and roller tracks.</p>
<div class="lb-trans"><p>人类必须决定 AI 应该被要求做什么。仓库重新设计的一个明显目标可能是最小化人类搬运物品所花费的时间。但等等——AI 正在查看哪个仓库设备目录？可能有一个低预算的目录或一个 Neiman-Marcus 的方案。资本成本和劳动成本之间总是存在权衡。关于资本支出的决策需要了解资本投资的使用寿命以及未来劳动成本的变化。选择还会受到未来几年物料处理技术可能改进的影响，例如机器人。在一个静态的世界中，决策可能复杂但确定：输入唯一决定输出。但在一个变化的世界中，必须对技术的未来和劳动成本的前景做出一些判断。这些判断可能需要相当细致，注意到机器人叉车和滚筒轨道之间价格轨迹的差异。</p>
</div><p>This tradeoff between capital and labor spending occurs in many areas: order kiosks in a fast food restaurant, online appointment management at a health clinic, red light cameras in lieu of police officers. If interest rates are low, more automation is justified—but still dependent on a forecast of future labor costs. We are used to wage rates rising, but will AI reduce demand for labor so much that human workers will actually be cheaper in the future? We can ask an AI system to forecast technological development and future labor costs, but we should not expect perfection. Human judgment will be needed for critical business judgments.</p>
<div class="lb-trans"><p>资本和劳动支出之间的这种权衡发生在许多领域：快餐店的自助点餐机、健康诊所的在线预约管理、代替警察的红灯摄像头。如果利率较低，更多的自动化是合理的——但仍然依赖于对未来劳动成本的预测。我们习惯于工资水平上升，但 AI 会否减少对劳动的需求，以至于未来人类工人实际上会更便宜？我们可以要求 AI 系统预测技术发展和未来劳动成本，但不应期待完美。关键的商业判断仍然需要人类的判断。</p>
</div><h3><strong>Current Needs Versus Flexibility Tradeoffs In AI Decisions</strong></h3>
<div class="lb-trans"><h3><strong>AI 决策中的当前需求与灵活性权衡</strong></h3>
</div><p>Designs of many business systems and almost any physical structure have another tradeoff: efficiency in meeting current needs versus flexibility for change. That warehouse will need to accommodate new products, new sales volumes and possibly different patterns of deliveries and shipments. A system optimized for today may be costly to change.</p>
<div class="lb-trans"><p>许多商业系统和几乎任何物理结构的设计都有另一个权衡：满足当前需求的效率与变更的灵活性。那个仓库需要适应新产品、新的销售量以及可能不同的交付和发货模式。一个为今天优化的系统可能在改变时代价高昂。</p>
</div><p>AI can help develop information for analysis of future change by looking at the past. How frequently are new products added and old ones discontinued? Is there a trend toward heavier boxes? But these are predictions that AI will not make perfectly. An AI model may come to be better at predictions than humans, but humans should be cautious about turning major business decisions over to the AI.</p>
<div class="lb-trans"><p>AI 可以通过回顾过去来帮助开发未来变化分析的信息。新产品添加和旧产品淘汰的频率是多少？是否有向更重的箱子发展的趋势？但这些都是 AI 无法完美预测的。一个 AI 模型可能在预测方面比人类更出色，但人类在做出重大商业决策时应保持谨慎。</p>
</div><p>Again, this tradeoff applies to many business process decisions. If we separate marketing to enterprise clients from marketing to small businesses, will we want to live with that decision forever? If not, will it be hard to change? If we stop recruiting engineers from State U, will it be hard to get back into their job fair next year?</p>
<div class="lb-trans"><p>同样，这种权衡适用于许多商业流程决策。如果我们将企业客户的营销与小企业的营销分开，我们是否希望永远维持这个决定？如果不，那么改变会很困难吗？如果我们停止从州立大学招聘工程师，明年再回到他们的招聘会上会很困难吗？</p>
</div><p>AI systems can optimize many functions, but how easy to adapt to changing circumstances must be part of any decision. And that requires a judgement about both the likelihood of wanting a change in the future and the cost of implementing a change.</p>
<div class="lb-trans"><p>AI 系统可以优化许多功能，但适应变化的难易程度必须是任何决策的一部分。这需要对未来想要变更的可能性和实施变更的成本进行判断。</p>
</div><h3><strong>Safety Tradeoffs In AI Decisions</strong></h3>
<div class="lb-trans"><h3><strong>AI 决策中的安全权衡</strong></h3>
</div><p>The warehouse example suggested providing the AI system with safety requirements. In many operations, safety standards are created by government regulators or trade associations, or at least by common practice within an industry. Would it ever make sense to go beyond the usual standards?</p>
<div class="lb-trans"><p>仓库示例建议为 AI 系统提供安全要求。在许多操作中，安全标准是由政府监管机构或行业协会制定的，或者至少是行业内的普遍做法。超越通常标准是否有意义？</p>
</div><p>On-the-job accidents in a particular situation might be reduced by a higher-cost design. How much is the company willing to pay to reduce injuries? There could be a dollars-and-cents calculation based on medical costs and out-of-work time. American companies under the workers’ compensation system can get an idea of the cost of accidents from their insurer. But a numerical computation won’t cover the pain to the injured person nor the fear that other employees may have after witnessing a workplace injury. The company’s values and culture come into play.</p>
<div class="lb-trans"><p>在特定情况下，工作场所的事故可能通过更高成本的设计来减少。公司愿意为减少伤害支付多少？这可能基于医疗费用和缺勤时间的美元计算。美国公司在工人赔偿制度下可以从其保险公司获得事故成本的概念。但数字计算无法涵盖受伤者的痛苦，也无法涵盖目睹工作场所伤害后其他员工的恐惧。公司的价值观和文化也会发挥作用。</p>
</div><p>On-the-job safety comes to mind quickly, but consumer product safety merits attention. Data security goes beyond the dollars-and-cents calculus to include customer satisfaction—which will drop after their data are hacked.</p>
<div class="lb-trans"><p>工作场所安全很快就会浮现在脑海中，但消费者产品安全也值得关注。数据安全超越了美元计算，还包括客户满意度——在他们的数据被黑客攻击后，客户满意度会下降。</p>
</div><h3><strong>Tradeoffs And Values</strong></h3>
<div class="lb-trans"><h3><strong>权衡与价值观</strong></h3>
</div><p>Many important decisions have human values at the center of the tradeoff. The comedy video series Murderbot has a robot puzzled by humans taking personal risks out of group loyalty. But that’s something that we humans do. We care about others: their welfare, their safety, their feelings. When human values come into play, humans must drive the choices. AI can help us understand the tradeoffs, but ultimately people must make the critical decisions.</p>
<div class="lb-trans"><p>许多重要决策的中心都是人类价值观的权衡。喜剧视频系列《谋杀机器人》中，一个机器人对人类因群体忠诚而冒个人风险感到困惑。但这正是我们人类所做的。我们关心他人：他们的福祉、他们的安全、他们的感受。当人类价值观发挥作用时，人类必须主导选择。AI 可以帮助我们理解权衡，但最终人们必须做出关键决策。</p>
</div><hr/>
<div class="lb-trans"><hr/>
</div><p><em>More By This Author:</em></p>
<div class="lb-trans"><p><em>更多来自此作者的文章：</em></p>
</div><p>Economic Forecast 2026: Growth Will Defy Slowdown Fears</p>
<div class="lb-trans"><p>2026 年经济预测：增长将抵御放缓担忧</p>
</div><p>Knowledge Trumps Power: The Innovation Secret Leaders Need</p>
<div class="lb-trans"><p>知识胜过权力：领导者需要的创新秘密</p>
</div><p>AI Bubble Or Business Revolution? Essential Business Insights</p>
<div class="lb-trans"><p>AI 泡沫还是商业革命？重要的商业洞察</p>
</div>

将人工智能与人类判断相结合以进行战略商业决策