用企业上下文释放 AI 价值：MIT Technology Review Insights 报告
Creating AI value with enterprise context
MIT Technology Review Insights 的调查显示，尽管所有受访企业都在一定程度上使用 AI，但仅 16% 从部署中获得了变革性价值。40% 的受访者表示在特定任务或团队中看到中等收益，21% 报告有实质性收益，23% 则收益甚微甚至没有。多数企业仍处于试点阶段（38%），或将 AI 用于辅助员工工作的 co-pilots、聊天助手等工具（35%）；仅 12% 的企业将 AI 智能体投入生产，3% 表示用 AI 完成关键流程的大部分工作。Atlan 联合创始人 Prukalpa Sankar 认为，模型能力与企业实际效用之间存在巨大鸿沟，缺失的是上下文层——位于业务系统与 AI 平台之间、将企业智能转化为受治理且机器可用的上下文的基础设施。
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.
来源：MIT Technology Review · AI · technologyreview.com