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Artificial intelligence人工智能

Achieving operational excellence with AI借助AI实现卓越运营

As AI reshapes how work gets done, organizations with strong process frameworks are best positioned to lead and maintain operational rigor at scale.随着人工智能重塑工作方式,拥有强大流程框架的组织最有能力引领并大规模保持严谨运营。

In association withTeleperformance与Teleperformance联合呈现

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to embed habits of measurement, analysis, and accountability into day-to-day company culture.像精益六西格玛和业务流程管理(BPM)这样的框架最初之所以受到推崇,是因为它们承诺在混乱中带来清晰——一种将杂乱无章、庞杂的运营变得有序的结构化方法。精益六西格玛强调统计严谨性和质量控制;BPM则创建了工作如何跨部门流动的端到端地图。两者都提供了一种可重复的方式,将衡量、分析和问责的习惯融入日常公司文化中。

But today, those time-tested playbooks are evolving as companies seek to embed AI into established process excellence methodologies. By some estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade. In one study, a full 88% of business leaders anticipated increasing investments into AI-infused process intelligence in the next 12 to 18 months.但如今,随着企业试图将AI融入已有的流程卓越方法论,这些经过时间考验的指南正在演变。据一些估计,AI驱动的流程优化市场预计在未来十年内将超过1130亿美元。在一项研究中,高达88%的企业领导者预计在未来12至18个月内增加对AI注入流程智能的投资。

Yet without the right foundations, many of those investments may not fully deliver on their potential. Companies that already operate with discipline have an edge. They can channel new tools into proven systems rather than bolting them onto shaky foundations. Organizations with mature process disciplines are also better positioned to translate AI ambition into real outcomes, as they are already accustomed to data-driven decision-making and process discipline—precisely the cultural foundation AI systems need to deliver value.然而,如果没有正确的基础,许多投资可能无法充分发挥其潜力。已经具备严谨运营的公司拥有优势。它们可以将新工具引入经过验证的系统,而不是将其附加在摇摇欲坠的基础上。具有成熟流程纪律的组织也更有能力将AI雄心转化为实际成果,因为它们已经习惯了数据驱动决策和流程纪律——这正是AI系统实现价值所需的文化基础。

Simply put: AI can accelerate process excellence, but existing process excellence is what makes AI truly impactful. Technology and process are no longer separate levers, and only organizations that pull them together stand to realize the full value of both.简而言之:AI可以加速流程卓越,但现有的流程卓越才能使AI真正产生影响力。技术和流程不再是独立的杠杆,只有将两者结合起来的组织才能实现两者的全部价值。

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.此内容由MIT Technology Review的定制内容部门Insights制作。并非由MIT Technology Review的编辑人员撰写。内容由人类作者、编辑、分析师和插画师研究、设计和撰写。这包括调查的撰写和调查数据的收集。可能使用的AI工具仅限于经过人工审核的辅助制作流程。

Deep Dive深度解读

Artificial intelligence

A startup claims it broke through a bottleneck that’s holding back LLMs一家初创公司声称突破了大语言模型发展的瓶颈

Subquadratic has now shared more details about its new model. But some are still skeptical.Subquadratic现已分享了其新模型的更多细节,但一些人仍持怀疑态度。

Anthropic found a hidden space where Claude puzzles over conceptsAnthropic发现了Claude思考概念时的一个隐藏空间

A new technique has let the company probe deeper than ever into the weird workings of an LLM.一项新技术让该公司比以往更深入地探究了大语言模型的奇异工作机制。

Claude Science is Anthropic’s newest flagship productClaude Science是Anthropic最新的旗舰产品

The company is doubling down on AI for science.该公司正在加大对AI用于科学领域的投入。

The $400 million machine powering the future of chipmaking这台价值4亿美元的机器驱动着芯片制造的未来

The AI era needs ever faster chips. ASML has a monopoly on the expensive contraptions needed to pattern them. Can anyone catch up?AI时代需要更快的芯片。ASML垄断了制造这些芯片所需的昂贵设备。有谁能迎头赶上吗?

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