{"id":81831,"date":"2026-04-07T14:56:21","date_gmt":"2026-04-07T14:56:21","guid":{"rendered":"https:\/\/youzum.net\/enabling-agent-first-process-redesign\/"},"modified":"2026-04-07T14:56:21","modified_gmt":"2026-04-07T14:56:21","slug":"enabling-agent-first-process-redesign","status":"publish","type":"post","link":"https:\/\/youzum.net\/de\/enabling-agent-first-process-redesign\/","title":{"rendered":"Enabling agent-first process redesign"},"content":{"rendered":"<p>Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously. <\/p>\n<p>But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies must become agent first.<\/p>\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"660\" src=\"https:\/\/wp.technologyreview.com\/wp-content\/uploads\/2026\/03\/Deloitte-Landing-Page-Card.png?w=1200\" alt=\"\" class=\"wp-image-1135019\" \/><\/figure>\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-auto 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:\/\/wp.technologyreview.com\/wp-content\/uploads\/2026\/04\/040626-Deloitte-Enabling-agent-first-process-redesign.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">DOWNLOAD THE ARTICLE<\/a><\/div>\n<\/div>\n<p>In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions. <\/p>\n<p>\u201cYou need to shift the operating model to humans as governors and agents as operators,\u201d says Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice.<\/p>\n<h3 class=\"wp-block-heading\">The agent-first imperative<\/h3>\n<p>With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation. These initiatives have the potential to produce significant performance gains, while shifting humans toward higher value work.<\/p>\n<p>AI is advancing so quickly that static approaches to task automation will likely only produce incremental gains. Because legacy processes aren\u2019t built for autonomous systems, AI agents require machine-readable process definitions, explicit policy constraints, and structured data flows, according to Rodgers. <\/p>\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" width=\"730\" height=\"660\" src=\"https:\/\/wp.technologyreview.com\/wp-content\/uploads\/2026\/04\/Deloitte-Graphic.png\" alt=\"\" class=\"wp-image-1135021\" \/><\/figure>\n<p>Further complicating matters, many organizations don\u2019t understand the full economic drivers of their business, such as cost to serve and per-transaction costs. As a result, they have trouble prioritizing agents that can create the most value and instead focus on flashy pilots. To achieve structural change, executives should think differently. <\/p>\n<p>Companies must instead orchestrate outcomes faster than competitors. \u201cThe real risk isn\u2019t that AI won\u2019t work\u2014it\u2019s that competitors will redesign their operating models while you\u2019re still piloting agents and copilots,\u201d says Rodgers. \u201cNonlinear gains come when companies create agent-centric workflows with human governance and adaptive orchestration.\u201d <\/p>\n<p>Routine and repetitive tasks are increasingly handled automatically, freeing employees to focus on higher value, creative, and strategic work. This shift improves operational efficiency, fosters stronger collaboration, and generates faster decision-making\u2014helping organizations modernize the workplace without sacrificing enterprise security.<\/p>\n<p><em><a href=\"https:\/\/wp.technologyreview.com\/wp-content\/uploads\/2026\/04\/040626-Deloitte-Enabling-agent-first-process-redesign.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Download the article.<\/a><\/em><\/p>\n<p><em>This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review\u2019s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.<\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously. But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies must become agent first. DOWNLOAD THE ARTICLE In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions. \u201cYou need to shift the operating model to humans as governors and agents as operators,\u201d says Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice. The agent-first imperative With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation. These initiatives have the potential to produce significant performance gains, while shifting humans toward higher value work. AI is advancing so quickly that static approaches to task automation will likely only produce incremental gains. Because legacy processes aren\u2019t built for autonomous systems, AI agents require machine-readable process definitions, explicit policy constraints, and structured data flows, according to Rodgers. Further complicating matters, many organizations don\u2019t understand the full economic drivers of their business, such as cost to serve and per-transaction costs. As a result, they have trouble prioritizing agents that can create the most value and instead focus on flashy pilots. To achieve structural change, executives should think differently. Companies must instead orchestrate outcomes faster than competitors. \u201cThe real risk isn\u2019t that AI won\u2019t work\u2014it\u2019s that competitors will redesign their operating models while you\u2019re still piloting agents and copilots,\u201d says Rodgers. \u201cNonlinear gains come when companies create agent-centric workflows with human governance and adaptive orchestration.\u201d Routine and repetitive tasks are increasingly handled automatically, freeing employees to focus on higher value, creative, and strategic work. This shift improves operational efficiency, fosters stronger collaboration, and generates faster decision-making\u2014helping organizations modernize the workplace without sacrificing enterprise security. Download the article. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review\u2019s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.<\/p>","protected":false},"author":2,"featured_media":81832,"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 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