利用人工智能技术打造面向未来的企业能力
内容总结：
人工智能技术正从实验阶段加速迈向企业级规模化应用。在克劳德拉公司曼娜西·瓦尔塔克、弗雷斯特研究公司迈克·瓜尔蒂耶里与亚马逊云科技埃迪·金等行业专家看来，这场变革已突破试点局限，正在能源、零售、法律等关键领域重塑工作流程——过去需要数小时的处理流程被压缩至分钟级，员工得以专注于更高价值的工作。
生成式AI与智能体技术为业务流程自动化注入全新动能。智能体系统通过秒级处理合同审查、物流咨询等任务，显著提升响应效率。同时，AI工具可用性的提升正推动技术民主化，让非技术人员也能自主应用智能工具。
然而规模化应用仍面临三大挑战：大语言模型的准确性、数据隐私安全、可持续运营成本。随着企业探索自主智能体与领域大模型等前沿方向，建立可信赖的治理体系已成为核心议题。亚马逊云科技专家埃迪·金强调：“企业领导者必须制定兼顾机遇与风险的AI战略，并为员工提供掌握新技能的培训路径。”
实践案例印证了转型成效：某全球能源企业将威胁检测时间从1小时缩短至7分钟；财富100强法务团队通过合同自动审阅节省数百万美元；人道主义组织运用AI提升灾变响应速度。这些成功范例表明，当数据、基础设施与专业能力形成合力，AI将催生颠覆性变革。
未来企业AI竞争的关键，在于如何统筹创新突破与规模化部署、安全保障及战略规划间的平衡。这场关乎未来的竞赛，正在看不见的赛道悄然进行。
（本文由MIT科技评论定制内容团队原创制作，人工智能工具仅辅助经过严格人工审核的次要生产环节）
中文翻译：
赞助内容
用人工智能技术构筑面向未来的企业能力
克劳德拉的玛娜西·瓦塔克、弗雷斯特的迈克·瓜尔蒂耶里与亚马逊云科技的埃迪·金共同指出：AI智能体正从试点应用迈向企业级部署，而信任机制与治理体系始终是成功的关键所在。
本文由克劳德拉与亚马逊云科技协同创作
人工智能始终被寄予提升速度、优化效率及开创解题新径的厚望。但过去几年间，最显著的变化是这些承诺正以前所未有的速度照进现实。从油气开采到零售消费，从物流配送到法律咨询，AI已突破试点项目或实验性研究的藩篱，深度融入核心工作流程——将动辄数小时的处理时长压缩至分秒之间，让员工得以聚焦更高价值的创造性工作。
克劳德拉首席AI架构师玛娜西·瓦塔克阐释道："业务流程自动化并非新概念，而生成式AI与智能体的真正价值，在于为这项技术注入了颠覆性潜能。"
这股变革浪潮主要由两股力量推动：AI智能体的崛起与AI工具的快速普及。无论是专注于自动化还是辅助决策，AI智能体在加速响应速度、优化复杂流程方面展现惊人效能。过去需要人工解读索赔单据、审阅合同条款或处理配送司机咨询的环节，如今AI智能体可在秒级时间内完成大规模处理。
与此同时，技术易用性的提升使非技术岗位员工也能驾驭AI工具，让跨职能团队根据实际需求灵活应用成为可能。
前行之路仍存挑战。数据隐私、系统安全及大语言模型准确性仍是亟待解决的问题。企业还需应对成本管控、数据质量保障及构建可持续AI体系等现实课题。当业界开始探索自主智能体、垂直领域模型乃至通用人工智能的下一阶段时，关于技术可信度、治理框架与伦理部署的讨论愈发关键。
亚马逊云科技AI与现代数据战略首席顾问埃迪·金强调："企业领导层必须制定兼顾机遇与风险的AI战略，同时为员工提供掌握新技能的通道，使其能娴熟运用各类AI工具。"
成功案例已颇具说服力：某全球能源巨头将威胁检测时间从逾一小时缩短至七分钟；财富100强法务团队通过合同审阅自动化节省数百万美元；人道主义救援组织借助AI加速应急响应。这些突破性成果表明，当数据、基础设施与AI专业能力形成合力，所产生的变革力量远超渐进式改良。
企业AI的未来图景，将取决于组织如何精准平衡创新突破与规模化落地、安全合规及战略布局——这正是当下竞赛的真正焦点。
本文由《麻省理工科技评论》定制内容团队Insights创作完成，所有内容均经过人类撰稿人、编辑、分析师及插画师的深度研发与精心制作。若在次要生产环节使用AI工具，均需通过严格的人工审核。
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Future-proofing business capabilities with AI technologies
AI agents are moving from pilots to enterprise scale, but trust and governance remain the linchpins of success, says Cloudera’s Manasi Vartak, Forrester’s Mike Gualtieri, and AWS’ Eddie Kim.
In collaboration withCloudera and AWS
Artificial intelligence has always promised speed, efficiency, and new ways of solving problems. But what’s changed in the past few years is how quickly those promises are becoming reality. From oil and gas to retail, logistics to law, AI is no longer confined to pilot projects or speculative labs. It is being deployed in critical workflows, reducing processes that once took hours to just minutes, and freeing up employees to focus on higher-value work.
“Business process automation has been around a long while. What GenAI and AI agents are allowing us to do is really give superpowers, so to speak, to business process automation.” says chief AI architect at Cloudera, Manasi Vartak.
Much of the momentum is being driven by two related forces: the rise of AI agents and the rapid democratization of AI tools. AI agents, whether designed for automation or assistance, are proving especially powerful at speeding up response times and removing friction from complex workflows. Instead of waiting on humans to interpret a claim form, read a contract, or process a delivery driver’s query, AI agents can now do it in seconds, and at scale.
At the same time, advances in usability are putting AI into the hands of nontechnical staff, making it easier for employees across various functions to experiment, adopt and adapt these tools for their own needs.
That doesn’t mean the road is without obstacles. Concerns about privacy, security, and the accuracy of LLMs remain pressing. Enterprises are also grappling with the realities of cost management, data quality, and how to build AI systems that are sustainable over the long term. And as companies explore what comes next—including autonomous agents, domain-specific models, and even steps toward artificial general intelligence—questions about trust, governance, and responsible deployment loom large.
“Your leadership is especially critical in making sure that your business has an AI strategy that addresses both the opportunity and the risk while giving the workforce some ability to upskill such that there's a path to become fluent with these AI tools,” says principal advisor of AI and modern data strategy at Amazon Web Services, Eddie Kim.
Still, the case studies are compelling. A global energy company cutting threat detection times from over an hour to just seven minutes. A Fortune 100 legal team saving millions by automating contract reviews. A humanitarian aid group harnessing AI to respond faster to crises. Long gone are the days of incremental steps forward. These examples illustrate that when data, infrastructure, and AI expertise come together, the impact is transformative.
The future of enterprise AI will be defined by how effectively organizations can marry innovation with scale, security, and strategy. That’s where the real race is happening.
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. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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