{"id":55887,"date":"2025-12-06T08:25:39","date_gmt":"2025-12-06T08:25:39","guid":{"rendered":"https:\/\/youzum.net\/harnessing-human-ai-collaboration-for-an-ai-roadmap-that-moves-beyond-pilots\/"},"modified":"2025-12-06T08:25:39","modified_gmt":"2025-12-06T08:25:39","slug":"harnessing-human-ai-collaboration-for-an-ai-roadmap-that-moves-beyond-pilots","status":"publish","type":"post","link":"https:\/\/youzum.net\/fr\/harnessing-human-ai-collaboration-for-an-ai-roadmap-that-moves-beyond-pilots\/","title":{"rendered":"Harnessing human-AI collaboration for an AI roadmap that moves beyond pilots"},"content":{"rendered":"

The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters<\/a> of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.<\/p>\n

\"\"<\/a><\/figure>\n
\n
WATCH THE WEBCAST<\/a><\/div>\n<\/div>\n

\u201cMost organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,\u201d says Shirley Hung, partner at Everest Group. \u201cThey have rigid, fragmented workflows that don\u2019t adapt well to change, technology systems that don\u2019t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.\u201d<\/p>\n

The central challenge, then, lies in rethinking how people, processes, and technology work together.<\/p>\n

Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures\u2014centralized decision-making, fragmented workflows, data spread across incompatible systems\u2014are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.<\/p>\n

\u201cIt is very important that humans continue to verify the content. And that is where you\u2019re going to see more energy being put into,\u201d Ryan Peterson, EVP and chief product officer at Concentrix.<\/p>\n

Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a \u201cvirtual worker,\u201d this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.<\/p>\n

\u201cMy advice would be to expect some delays because you need to make sure you secure the data,\u201d says Heidi Hough, VP for North America aftermarket at Valmont. \u201cAs you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.\u201d<\/p>\n

Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.<\/p>\n

\u201cOptimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,\u201d says Hung.<\/p>\n

Watch the webcast.<\/a><\/em><\/p>\n

This webcast is produced in partnership with Concentrix.<\/em><\/p>\n

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":"

The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains. WATCH THE WEBCAST \u201cMost organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,\u201d says Shirley Hung, partner at Everest Group. \u201cThey have rigid, fragmented workflows that don\u2019t adapt well to change, technology systems that don\u2019t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.\u201d The central challenge, then, lies in rethinking how people, processes, and technology work together. Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures\u2014centralized decision-making, fragmented workflows, data spread across incompatible systems\u2014are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute. \u201cIt is very important that humans continue to verify the content. And that is where you\u2019re going to see more energy being put into,\u201d Ryan Peterson, EVP and chief product officer at Concentrix. Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a \u201cvirtual worker,\u201d this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy. \u201cMy advice would be to expect some delays because you need to make sure you secure the data,\u201d says Heidi Hough, VP for North America aftermarket at Valmont. \u201cAs you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.\u201d Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate. \u201cOptimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,\u201d says Hung. Watch the webcast. This webcast is produced in partnership with Concentrix. 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":55888,"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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NU","author_link":"https:\/\/youzum.net\/fr\/members\/adminnu\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/youzum.net\/fr\/category\/ai-club\/\" rel=\"category tag\">AI<\/a> <a href=\"https:\/\/youzum.net\/fr\/category\/committee\/\" rel=\"category tag\">Committee<\/a> <a href=\"https:\/\/youzum.net\/fr\/category\/news\/\" rel=\"category tag\">News<\/a> <a href=\"https:\/\/youzum.net\/fr\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","rttpg_excerpt":"The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to\u2026","_links":{"self":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts\/55887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/comments?post=55887"}],"version-history":[{"count":0,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts\/55887\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/media\/55888"}],"wp:attachment":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/media?parent=55887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/categories?post=55887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/tags?post=55887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}