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在企业背景下创建AI值
如何建立公司大脑可以支持更准确，成本效益更高的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程序自动化工具。 每个都精密，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、令人困惑的或对企业缺乏正确理解的。 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 Inspects 民调发现)认为，虽然被调查的每个组织今天都在某种程度上使用AI，但不到五分之一(16%)的组织从其部署中提取了变革价值。 最大的百分比(40%)的受访者说，他们看到特定任务或团队取得了中度收益，而21%的受访者报告取得了有意义的收益，而23%的受访者发现回报很少，甚至根本没有回报。
公司大多仍在试用AI(根据38%的受访者)，或用它来协助员工使用副驾驶或聊天助理等工具(35%). 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来完成关键工艺的大部分工作。
Atlan的共同创始人称 Prukalpa Sankar指出，“这些模型的能力与这些模型在组织内部的实际作用之间存在着很大的差距。 “对于每一个现实的用意和目的来说，人工一般情报已经在这里。 这在今天大多数公司并不真正有用。
缺少的一层，Sankar 称，是上下文，或者AI如何理解一个企业。 一个背景层是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或模型的受管制的，机器可用的上下文，并帮助知识复合。 「没有人工智能的背景
这些内容是由麻省理工学院技术评论的自定义内容臂膀Insights制作的，而不是其编辑人员。 它由人类研究和撰写，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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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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