Me, Myself, and AI: A New Podcast on Artificial Intelligence in Business
为什么只有 10% 的公司在人工智能方面取得成功？ 在 MIT SMR 和 BCG 推出的系列节目中，我们采访了那些在人工智能领域取得巨大成就的企业领袖，了解他们如何取得成功。倾听来自沃尔玛、DHL 等公司的专家们如何充满热情地进行工作，并了解他们眼中成功的关键。
敬请收听
规模化人工智能可以创造巨大的竞争优势，但仅仅投资尖端技术和算法是不够的。为了获取价值，需要重新调整决策和运营——并且要投资于员工才能使获取价值更加长久。在 BCG，我们称之为 10,20,70 规则。
作为大规模人工智能的先驱，那些已经将人工智能扩展到整个企业并从投资中获得有意义价值的公司通常会将 10% 的人工智能投资用于算法，20% 用于技术，70% 用于将人工智能纳入业务流程和新的工作方式中。换言之，这些组织在人员和流程上的投资是技术投资的两倍。
当公司在人力和流程方面投资不足时，他们很快就会失去发展人工智能方面的动力。这是因为，推出一系列成功的人工智能试点项目看似简单。如果没有正确的方法以及对变革管理战略的重视，几乎不可能将人工智能规模扩展至整个业务。
为什么只有 10% 的公司在人工智能方面取得成功？ 在 MIT SMR 和 BCG 推出的系列节目中，我们采访了那些在人工智能领域取得巨大成就的企业领袖，了解他们如何取得成功。倾听来自沃尔玛、DHL 等公司的专家们如何充满热情地进行工作，并了解他们眼中成功的关键。
敬请收听A recent BCG-MIT Sloan Management Review study suggests that in order to achieve significant financial benefits from their machines, companies will have to look beyond automation—and focus instead on learning and organizational transformation. A symbiotic relationship is necessary, where companies don't just teach machines what humans already know; they deploy whatever human-machine interaction the situation calls for, adapting as needed to changing context, circumstances, and scenarios.
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我们的人工智能顾问采用混合的、跨职能的团队形式，确保公司拥有全方位的专业知识，推动整个公司的 数字化转型。以下只是我们为大规模人工智能项目带来的一些功能：
Artificial intelligence can help doctors identify patients at risk at an early stage – through a tool developed prior to COVID-19 by One O One, Professor Jean-Daniel Chiche, and BCG.
Using AI-driven digital twin, a mining conglomerate saved $200 million and achieved a 10x reduction in demurrage costs.
BCG's patented Spend AI tool uses natural language processing, neural networks, and machine learning to help companies free up funds, rev up profits, and kick-start growth.
BCG's real-time personalization engine for a global food and beverage chain increased revenue per customer by a factor of three, resulting in more than $150 million in annual impact.
Are companies using artificial intelligence to make critical decisions inadvertently hindering efficiency? Sylvain Duranton advocates for implementing AI systems alongside human judgement.
Companies are encountering the “AI paradox”: it is deceptively easy to launch projects with AI but fiendishly hard to reach scale.
