{"id":127420,"date":"2026-10-29T03:43:17","date_gmt":"2026-10-29T03:43:17","guid":{"rendered":"https:\/\/youzum.net\/creating-ai-value-with-enterprise-context\/"},"modified":"2026-10-29T03:43:17","modified_gmt":"2026-10-29T03:43:17","slug":"creating-ai-value-with-enterprise-context","status":"publish","type":"post","link":"https:\/\/youzum.net\/it\/creating-ai-value-with-enterprise-context\/","title":{"rendered":"Creating AI value with enterprise context"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Across a business, any number of AI platforms, systems, and tools may be at work simultaneously\u2014for 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\u2014and it could be one of key reasons they struggle to extract returns on their AI investments.<\/p>\n<figure class=\"wp-block-image size-large is-resized\"><a href=\"https:\/\/atlan.com\/resources\/mit-report-ai-business-value\/\" target=\"_blank\" rel=\" noopener\"><img fetchpriority=\"high\" decoding=\"async\" height=\"2000\" width=\"1555\" src=\"https:\/\/wp.technologyreview.com\/wp-content\/uploads\/2026\/10\/MIT_Atlan-Cover-28-26.png?w=840\" alt=\"\" class=\"wp-image-1147797\" \/><\/a><\/figure>\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-b192c3d7 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill is-style-fill--1\"><a class=\"wp-block-button__link has-mittr-white-color has-text-color has-background has-custom-font-size wp-element-button\" href=\"https:\/\/atlan.com\/resources\/mit-report-ai-business-value\/\" target=\"_blank\" rel=\"noopener\">DOWNLOAD THE REPORT<\/a><\/div>\n<\/div>\n<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>\n<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\" \/><\/a><\/figure>\n<p class=\"wp-block-paragraph\">Companies are mostly still piloting AI use cases (according to 38% of respondents) or using it to assist employees\u2019 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>\n<p class=\"wp-block-paragraph\">\u201cThere\u2019s a big gap between what the models are capable of and how the models are actually being useful inside organizations,\u201d says Prukalpa Sankar, co-founder of Atlan. \u201cFor every real-life intent and purpose, artificial general intelligence is already here. It\u2019s just not really useful in most companies today.\u201d<\/p>\n<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\" \/><\/figure>\n<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\u2019s 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. \u201cWithout a context layer for AI, it\u2019s almost like you\u2019re hiring the smartest intern in the world, blindfolding them, and throwing them into your organization,\u201d Sankar says.<\/p>\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/atlan.com\/resources\/mit-report-ai-business-value\/\"><strong>Download the report<\/strong><\/a><\/p>\n<p class=\"wp-block-paragraph\"><em>This content was produced by Insights, MIT Technology Review\u2019s 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>","protected":false},"excerpt":{"rendered":"<p>Across a business, any number of AI platforms, systems, and tools may be at work simultaneously\u2014for 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\u2014and it could be one of key reasons they struggle to extract returns on their AI investments. DOWNLOAD THE REPORT 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\u2019 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. \u201cThere\u2019s a big gap between what the models are capable of and how the models are actually being useful inside organizations,\u201d says Prukalpa Sankar, co-founder of Atlan. \u201cFor every real-life intent and purpose, artificial general intelligence is already here. It\u2019s just not really useful in most companies today.\u201d 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\u2019s 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. \u201cWithout a context layer for AI, it\u2019s almost like you\u2019re hiring the smartest intern in the world, blindfolding them, and throwing them into your organization,\u201d Sankar says. Download the report This content was produced by Insights, MIT Technology Review\u2019s 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.<\/p>","protected":false},"author":2,"featured_media":127421,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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