借助企业上下文创造 AI 商业价值

目前，大多数企业仍处于 AI 用例试点阶段（占受访企业的 38%），或者仅将其用于辅助员工工作，例如使用 Copilot 或对话助手等工具（占 35%）。仅有 12% 的企业在生产环境中正式部署了 AI Agent，而表示在核心业务流程中使用 AI 完成绝大部分工作的企业仅占 3%。

“模型自身具备的能力与模型在企业内部实际创造的效用之间，存在着巨大鸿沟，”Atlan 联合创始人 Prukalpa Sankar 指出，“从一切现实应用与目的来看，通用人工智能（AGI）实际上已经到来，只不过它在当今大多数企业中还未真正发挥作用。”

Sankar 认为，缺失的关键层在于“上下文”（Context），也就是 AI 对具体业务的理解深度。上下文层是横跨在企业业务系统（例如核心记录系统与数据平台）与其 AI 平台之间的架构基础设施。它能够将企业智能转化为针对 AI Agent 或底层模型受治理、机器可用的上下文，并促进组织知识的复利沉淀。“如果缺少针对 AI 的上下文层，这就好比你聘请了全世界最聪明的实习生，却把他们的双眼蒙上，然后直接扔进你的企业里去干活。”Sankar 说道。

下载完整报告

本内容由《麻省理工科技评论》定制内容部门 Insights 制作，非编辑部采写。内容由人类研究并撰写，过程中所使用的任何 AI 工具均严格限定在人类监督下的制作流程之内。

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.

Download the report

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.