MIT Technology Review Insights发布的一项针对企业人工智能应用现状的调查显示，尽管所有受访组织均已采用AI技术，但仅有16%的组织从中提取出了变革性价值 1。数据进一步揭示，大多数企业仍处于AI应用的初级阶段：38%的公司主要在试点AI用例，35%的企业使用副驾驶或聊天助手等辅助工具 1。在更高级的应用形态上，仅12%的企业将AI代理投入生产环境，只有3%的公司利用AI处理关键流程的大部分工作 1。对于整体收益情况，40%的受访者表示在特定任务或团队中看到了适度收益，而23%的受访者报告称收益极少甚至没有 1。
阻碍AI在企业内部发挥最大效用的核心原因在于缺乏连接业务系统与AI平台的“上下文层” 1。这一缺失的关键层级位于两者之间，其主要职责是将企业内部智能转化为机器可用的受控上下文 1。Atlan联合创始人Prukalpa Sankar指出：“模型的能力与它们在组织内部的实际有用性之间存在巨大差距。” 1
A recent survey conducted by MIT Technology Review Insights reveals a stark disconnect between artificial intelligence adoption and its realized business impact. While every organization surveyed is currently using AI, only 16% have managed to extract transformative value from these deployments 1. The data indicates that the majority of enterprises remain in early or supportive stages of integration, with 38% primarily testing AI use cases in pilot programs and 35% relying on auxiliary tools such as copilots and chat assistants 1. In contrast, just 12% of companies have deployed AI agents into production environments, and merely 3% are leveraging AI to handle the bulk of work in critical processes 1.
The survey highlights that moderate gains are common but limited; 40% of respondents reported seeing some benefits in specific tasks or teams, while 23% experienced little to no return on their AI investments 1. Prukalpa Sankar, co-founder of Atlan, identified the root cause of this disparity, stating, “There is a massive gap between the capability of models and how useful they actually are inside an organization” 1. According to the analysis, the missing component is the “context layer,” which sits between business systems and AI platforms 1. This layer is essential for translating enterprise intelligence into controlled context that machines can effectively utilize, serving as the key barrier preventing broader AI utility 1.
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