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在企业背景下创建AI价值
如何建立公司大脑可以支持更准确，成本效益高的AI和自主agents.
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在整个企业中，任何数量的人工智能平台、系统和工具都可能同时运行，例如人工智能驱动的客户平台、对话助理以及工作流程和流程自动化工具。 每一个都精密，先进，反应迅速。 但是，在开始工作时，所产生的结果和行动可能是错误的、令人困惑的或缺乏对企业的适当理解。 许多组织发现自己处于这种境地，这可能是它们努力争取AI投资收益的主要原因之一。
麻省理工学院技术评论(MIT Technology Review Inservers 民调发现)认为，虽然被调查的每个组织今天都在一定程度上使用AI，但不到五分之一(16%)的AI正在从其部署中提取变革价值。 最大比例(40%)的受访者说，他们在具体任务或团队中取得了中度成绩，而21%的受访者报告取得了有意义的成绩，而23%的受访者看到回报很少，甚至根本没有回报。
公司大多仍在尝试AI使用案例(根据38%的被调查者)或用它来帮助员工的工作，如副驾驶或聊天助理(35%)等工具。 只有12%的企业在生产中拥有AI agents,3%的企业说它们正在使用AI来完成关键流程的大部分工作。
Atlan的共同创始人称 Prukalpa Sankar指出，“模型的能力与模型在组织内部的实际作用之间有很大差距。 “对于每一个现实的用意和目的，人工一般情报已经在这里。 今天，这在大多数公司并不真正有用。
缺少的地层，Sankar 称，是上下文，或者AI如何理解一个企业。 一个背景层是位于一个组织的业务系统(例如记录和数据系统及其AI平台)之间的建筑基础设施。 它将企业智能转化为AI agents或模型的受管，机器可使用上下文，并帮助知识复合。 “没有人工智能的背景，几乎就像你雇用世界上最聪明的实习生
这些内容是由麻省理工学院技术评论的自定义内容分支Insights(Insights)制作的，而不是其编辑人员。 它由人类研究并撰写，拥有任何AI工具，可能仅限于人类监督下的生产过程。
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监视塔日益由大赦国际提供动力，它承诺对过境点作出更迅速的反应。 我们调查了塔楼附近的死亡事件发现塔楼未能交付
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本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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Across a business, any number of AI platforms, systems, and tools may be at work simultaneously—for example, AI-powered customer platforms, conversational assistants, and workflow and process automation tools. Each one is sophisticated, advanced, and quick to respond. But, when set to work, the outcomes and actions that emerge may be wrong, confused, or lacking a proper understanding of the business. Many organizations find themselves in this situation—and it could be one of key reasons they struggle to extract returns on their AI investments.
An MIT Technology Review Insights poll finds that while every organization surveyed is using AI today to some degree, fewer than one in five (16%) are extracting transformative value from their deployments. The largest percentage of respondents (40%) say they are seeing moderate gains in specific tasks or teams, while 21% report meaningful gains, leaving 23% that are seeing minimal returns or none at all.
Companies are mostly still piloting AI use cases (according to 38% of respondents) or using it to assist employees’ work with tools like co-pilots or chat assistants (35%). Just 12% of enterprises have AI agents in production and 3% say they are using AI to do most of the work in key processes.
“There’s a big gap between what the models are capable of and how the models are actually being useful inside organizations,” says Prukalpa Sankar, co-founder of Atlan. “For every real-life intent and purpose, artificial general intelligence is already here. It’s just not really useful in most companies today.”
The missing layer, Sankar says, is context, or how well AI understands a business. A context layer is the architectural infrastructure that sits between an organization’s business systems (for example, systems of record and data, and its AI platforms). It translates enterprise intelligence into governed, machine-usable context for AI agents or models and helps knowledge compound. “Without a context layer for AI, it’s almost like you’re hiring the smartest intern in the world, blindfolding them, and throwing them into your organization,” Sankar says.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
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