Creating AI value with enterprise context
Summary
MIT Technology Review Insights 在 Atlan 赞助的报告中指出，多数企业虽已使用 AI，但仅 16% 获得变革性价值，关键缺失在于让 AI 理解业务的『上下文层』架构。
<p class="wp-block-paragraph">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.</p>
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<p class="wp-block-paragraph">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.</p>
<figure class="wp-block-image size-full"><a href="https://atlan.com/resources/mit-report-ai-business-value/" target="_blank" rel=" noopener"><img decoding="async" width="1200" height="675" src="https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_FigureSocials3.png" alt="" class="wp-image-1147799" srcset="https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_FigureSocials3.png 1200w, https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_FigureSocials3.png?resize=300,169 300w, https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_FigureSocials3.png?resize=768,432 768w" sizes="(max-width: 1200px) 100vw, 1200px" /></a></figure>
<p class="wp-block-paragraph">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.</p>
<p class="wp-block-paragraph">“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.”</p>
<figure class="wp-block-image size-full"><img decoding="async" width="1200" height="675" src="https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_Quotes_Rev3-1.png" alt="" class="wp-image-1147809" srcset="https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_Quotes_Rev3-1.png 1200w, https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_Quotes_Rev3-1.png?resize=300,169 300w, https://wp.technologyreview.com/wp-content/uploads/2026/10/MITTR2026_Quotes_Rev3-1.png?resize=768,432 768w" sizes="(max-width: 1200px) 100vw, 1200px" /></figure>
<p class="wp-block-paragraph">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.</p>
<p class="wp-block-paragraph"><a href="https://atlan.com/resources/mit-report-ai-business-value/"><strong>Download the report</strong></a></p>
<p class="wp-block-paragraph"><em>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.</em></p>
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# Creating AI value with enterprise context
Source: [https://www.technologyreview.com/2026/10/28/1147709/creating-ai-value-with-enterprise-context](https://www.technologyreview.com/2026/10/28/1147709/creating-ai-value-with-enterprise-context)
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\.
[](https://atlan.com/resources/mit-report-ai-business-value/)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\.
[](https://atlan.com/resources/mit-report-ai-business-value/)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**](https://atlan.com/resources/mit-report-ai-business-value/)
*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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