{"id":24237,"date":"2026-09-21T13:36:44","date_gmt":"2026-09-21T13:36:44","guid":{"rendered":"https:\/\/intuceo.com\/?p=17997"},"modified":"2026-09-25T06:42:30","modified_gmt":"2026-09-25T06:42:30","slug":"computer-vision-inspection-manufacturing-decision-framework","status":"publish","type":"post","link":"https:\/\/puffertech.com\/clients\/intuceo\/blog\/computer-vision-inspection-manufacturing-decision-framework\/","title":{"rendered":"A Decision Framework for Revisiting Shelved Computer Vision Inspection Projects in Manufacturing (2026)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"24237\" class=\"elementor elementor-24237\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4480a55 e-flex e-con-boxed e-con e-parent\" data-id=\"4480a55\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-079cc1e elementor-widget elementor-widget-text-editor\" data-id=\"079cc1e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tMany plants carry at least one computer vision inspection initiative that cleared a pilot and then went quiet. The reasons were usually sound. The defect was too rare to build a training set. Parts changed faster than the model could be retrained. The labeling bill outgrew the savings, or integration with the line took longer than anyone had budgeted.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3604bee elementor-widget elementor-widget-text-editor\" data-id=\"3604bee\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Vision models have moved on since then, and some of the constraints that stopped a computer vision inspection program in manufacturing have loosened-while others have not. For operations and quality leaders, the task is sorting <\/span><b>shelved computer vision projects<\/b><span style=\"font-weight: 400;\"> into three groups: the ones newer AI makes worth a revisit, the ones that need a scoped test first, and the ones that should stay exactly where they are. This framework is built for that sort.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d79eeab elementor-widget elementor-widget-text-editor\" data-id=\"d79eeab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<strong>KEY TAKEAWAYS<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6a558e elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"f6a558e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">      The original reason a project stopped is the best predictor of whether newer models can help.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">   Data scarcity and frequent part changes are the constraints newer computer vision inspection models address most directly in manufacturing environments.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">  Line speed, lighting, integration, and ownership gaps have not been solved by better models.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"> A short reassessment on archived images costs far less than restarting a full program on assumptions.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ccd0f5 elementor-widget elementor-widget-heading\" data-id=\"2ccd0f5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why Computer Vision Inspection Projects in Manufacturing Get Shelved\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fadbea elementor-widget elementor-widget-text-editor\" data-id=\"3fadbea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Before scoring anything, name the real reason each project stopped. Most <\/span><b>paused AI projects in manufacturing<\/b><span style=\"font-weight: 400;\"> trace back to one of six causes:<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb7a450 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"bb7a450\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Not enough defect examples. <\/b>The failure mode was important but occurred too rarely to collect a training set.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Too much variation.<\/b> Part variants, colors, or suppliers changed often enough that retraining never caught up.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Labeling cost outweighed value. <\/b>Expert annotation hours made the business case collapse.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Deployment friction.<\/b> Hardware, network, or line integration proved harder than the model itself.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Business context changed. <\/b>The sponsor moved on, the line was retired, or priorities shifted.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b4ec24 elementor-widget elementor-widget-text-editor\" data-id=\"6b4ec24\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">The first three are data problems. The next two are engineering and operating problems. The last has nothing to do with technology at all. That distinction decides which projects newer models can realistically help.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0aa4517 elementor-widget elementor-widget-heading\" data-id=\"0aa4517\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Has Changed in Manufacturing Computer Vision-and What Hasn\u2019t\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b09f873 elementor-widget elementor-widget-text-editor\" data-id=\"b09f873\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Foundation models pretrained on broad image collections have reduced the labeled data required by certain inspection use cases. Zero-shot and open-vocabulary approaches let teams describe what to look for rather than training a separate model for every defect category from scratch.\u00a0<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de2ff64 elementor-widget elementor-widget-text-editor\" data-id=\"de2ff64\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tModern AI defect detection in manufacturing now includes these foundation models, which reduce the labeled data required for industrial inspection across a wider range of part types and defect categories.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ebeb2d elementor-widget elementor-widget-text-editor\" data-id=\"4ebeb2d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Industrial anomaly detection also has a standard reference point. The MVTec Anomaly Detection benchmark, built specifically for industrial inspection, provides defect-free training images for each category and a test set containing both defective and defect-free images, with pixel-precise annotations of the anomalies. It holds more than 5,000 high-resolution images across fifteen object and texture categories. [<\/span><a href=\"https:\/\/www.mvtec.com\/company\/research\/datasets\/mvtec-ad\"><span style=\"font-weight: 400;\">1<\/span><\/a><span style=\"font-weight: 400;\">]\u00a0<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08359fc elementor-widget elementor-widget-text-editor\" data-id=\"08359fc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">That setup mirrors a common plant reality: plenty of good parts, very few bad ones. For manufacturing plants where visual inspection AI programs stalled because defects were rare, anomaly detection methods built around defect-free training images are worth a fresh look.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ae5934 elementor-widget elementor-widget-heading\" data-id=\"2ae5934\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What has not changed\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9dbf56e elementor-widget elementor-widget-text-editor\" data-id=\"9dbf56e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tBetter models do not fix optics. Poor lighting, glare, vibration, and inconsistent part positioning still break inspection. Large models can still be too slow for decisions inside a single machine cycle. Integration with manufacturing execution systems (MES) and programmable logic controllers (PLCs) still takes engineering effort. And a project without a clear owner on the plant floor will stall again, whatever model sits underneath.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64c0fef elementor-widget elementor-widget-heading\" data-id=\"64c0fef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Six Criteria for Evaluating a Paused Computer Vision Pilot\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa60ac5 elementor-widget elementor-widget-text-editor\" data-id=\"aa60ac5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tUse the table below to score each shelved project. It is designed to be completed in a working session with quality, operations, and data teams in the same room.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc545e0 elementor-widget elementor-widget-text-editor\" data-id=\"bc545e0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table style=\"width: 100%; border-collapse: collapse; font-family: sans-serif; table-layout: fixed;\">\n<thead>\n<tr style=\"background-color: #0b1a37; color: white; text-align: left;\">\n<th style=\"padding: 12px; border: 1px solid #ddd; width: 22%;\">Criterion<\/th>\n<th style=\"padding: 12px; border: 1px solid #ddd; width: 26%;\">Question to ask<\/th>\n<th style=\"padding: 12px; border: 1px solid #ddd; width: 26%;\">Signal to revisit<\/th>\n<th style=\"padding: 12px; border: 1px solid #ddd; width: 26%;\">Signal to leave shelved<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Original blocker<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Why did the project stop?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Data scarcity, variation or labeling cost<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Business context changed or no owner<\/td>\n<\/tr>\n\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Value today<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Is the machine vision quality control gap still costing money in escapes, scrap or inspector hours?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Escapes, scrap or inspector hours persist<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Line retired or defect designed out<\/td>\n<\/tr>\n\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Data on hand<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Do archived images still exist?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Images and some confirmed defects available<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Nothing retained from the pilot<\/td>\n<\/tr>\n\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Speed requirement<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">How fast must a decision happen?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Seconds or sampled inspection is acceptable<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Hard real-time at very high line speed<\/td>\n<\/tr>\n\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Integration path<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Can results reach the line?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Clear route to MES, PLC, or operator screen<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">No path, and no budget to build one<\/td>\n<\/tr>\n\n<tr>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Ownership<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Who will run it in production?<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Named quality or operations owner<\/td>\n<td style=\"padding: 12px; border: 1px solid #ddd;\">Only a data team champion<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7a2ddb elementor-widget elementor-widget-text-editor\" data-id=\"b7a2ddb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tScore each row as a clear yes, partial, or no. Projects with strong answers on original blocker, value, and ownership are the best candidates, even if the other rows need work.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dedd2e3 elementor-widget elementor-widget-heading\" data-id=\"dedd2e3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Sorting projects into three groups\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be80305 elementor-widget elementor-widget-heading\" data-id=\"be80305\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Reopen now\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7f1438 elementor-widget elementor-widget-text-editor\" data-id=\"d7f1438\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tThese projects stopped because of data scarcity, part variation, or labeling cost, but still address a live problem and have a named owner. Archived images exist. Speed requirements are manageable. For these, zero-shot inspection or anomaly detection trained on good parts can often be tested within a short, contained assessment.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d964d4c elementor-widget elementor-widget-heading\" data-id=\"d964d4c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Reassess with a scoped test\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ad71f3 elementor-widget elementor-widget-text-editor\" data-id=\"8ad71f3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tHere the value is real, but one or two rows are uncertain. The line may run fast, or integration may have been the original sticking point. Run a focused test that answers the uncertain question directly, such as whether the model can meet cycle time on the current hardware, before committing to a restart.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ef2c96 elementor-widget elementor-widget-heading\" data-id=\"1ef2c96\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Leave on the shelf\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b6b40a elementor-widget elementor-widget-text-editor\" data-id=\"6b6b40a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tSome projects should stay paused. If the line was retired, the defect was designed out, or nobody in operations will own the result, better models change nothing. Closing these formally clears space for the candidates that matter.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51fb052 elementor-widget elementor-widget-heading\" data-id=\"51fb052\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">A practical reassessment sequence\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc716c6 elementor-widget elementor-widget-text-editor\" data-id=\"fc716c6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Deciding <\/span><b>when to restart vision AI<\/b><span style=\"font-weight: 400;\"> work is easier with a fixed sequence. The steps below keep the reassessment small and evidence-based.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-69969c8 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"69969c8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b> Recover the original documentation.<\/b> Acceptance criteria, pilot results, defect definitions and the reason for stopping.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>    Rebuild a small evaluation set.<\/b> Pull archived images and confirm a modest set of labeled good and defective examples.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>   Test newer approaches on that set. <\/b>Compare a zero-shot or anomaly detection approach against the original pilot baseline.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b> Cost the running version.<\/b> Include compute, review time, integration, and change management, not just the model.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>  Decide against the original acceptance bar.<\/b> If the new result clears it, plan a restart. If not, record why.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6588358 elementor-widget elementor-widget-heading\" data-id=\"6588358\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Common mistakes when reopening a shelved project\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58f9cc1 elementor-widget elementor-widget-text-editor\" data-id=\"58f9cc1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tTeams that restart vision work too quickly tend to repeat common errors. Watching for them saves a second shelving.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa7ebf9 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"fa7ebf9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Testing on the pilot's best images.<\/b> The original project rarely failed on clear, well-lit examples. It failed on the difficult ones, so those belong in the evaluation set.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b> Ignoring what changed on the line.<\/b> New fixtures, suppliers, or camera positions since the pilot can make archived images a poor guide to today's conditions.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>  Treating a demo as a validation.<\/b> A model that highlights a defect in a presentation has not yet shown it can do so consistently across shifts.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b> Skipping the operator view.<\/b> If line staff cannot act on a result quickly, even an accurate model adds work instead of removing it.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>Restarting without a stop rule.<\/b> Agree in advance what result would close the project again, so the reassessment produces a decision rather than an open-ended pilot.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15b1e20 elementor-widget elementor-widget-heading\" data-id=\"15b1e20\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How to Rebuild the AI Visual Inspection Business Case for 2026\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e2e4c0 elementor-widget elementor-widget-text-editor\" data-id=\"0e2e4c0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Old business cases carry old assumptions. Rebuilding <\/span><b>AI inspection ROI<\/b><span style=\"font-weight: 400;\"> for 2026 means checking which inputs have actually moved since the original approval:<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b68519 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"5b68519\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>    Labeling hours. <\/b>Often lower for use cases suited to foundation models. Verify on your own images before assuming it.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>      Retraining cadence.<\/b> Potentially less frequent where models generalize across variants.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>      Compute cost.<\/b> Possibly higher per image if larger models run at the line.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><b>        Value of a catch. <\/b>Largely unchanged. The cost of an escape or a false reject is set by your process, not by the model.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89d5fc6 elementor-widget elementor-widget-text-editor\" data-id=\"89d5fc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">One caution: a shelved project that looks attractive only because the new model was tested on easy images has been re-pitched rather than reassessed. Insist on the same difficult cases that stopped it the first time. <\/span><b>Manufacturing defect detection AI<\/b><span style=\"font-weight: 400;\"> that clears those, at production line speed and under production lighting, has earned a restart budget. Anything less has earned another test.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ad94f5b elementor-widget elementor-widget-heading\" data-id=\"ad94f5b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How Intuceo Approaches Computer Vision Inspection Reassessments\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-24a4cfc elementor-widget elementor-widget-text-editor\" data-id=\"24a4cfc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Reassessments run on the same structure Intuceo uses to judge any AI initiative before it reaches production. The<\/span><a href=\"https:\/\/intuceo.com\/accelerators\/modular-ai\/\"> <b>DARWIN AI governance framework<\/b><\/a><span style=\"font-weight: 400;\"> separates the question into dimensions that can each be answered independently:<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3929f11 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"3929f11\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Data, whether archived images and labels support a fair test<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Architecture, how a revived pilot would stage toward a working deployment<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"> Workflow, whether results are explainable and usable to the people on the line<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"86\" height=\"95.001\" viewBox=\"0 0 86 95.001\"><g id=\"bullet-point\" transform=\"translate(-7.01 -2.499)\"><path id=\"Path_931\" data-name=\"Path 931\" d=\"M86,37.87,28.1,4.45C15.84-2.62,2,10.88,8.81,23.3l10.92,20a14.06,14.06,0,0,1,0,13.44L8.81,76.7C2,89.12,15.84,102.62,28.1,95.55L86,62.13a14,14,0,0,0,0-24.26Z\" fill=\"#212529\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"> Infrastructure and Security, whether the compute and deployment model fit the plant.<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14d1b4b elementor-widget elementor-widget-text-editor\" data-id=\"14d1b4b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tA project can pass on model accuracy and still fail on three of those four, which is why the judgment is never made on a model alone.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed833b9 elementor-widget elementor-widget-text-editor\" data-id=\"ed833b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">For vision work, two capabilities carry most of the delivery. High-fidelity visual inspection applies image segmentation and sub-pixel anomaly detection to sub-millimeter defects, including precision medical optics, where surface anomalies, edge irregularities, and contaminants have to be caught at production speed. Intuceo\u2019s multi-modal vision intelligence capability brings video, image, and metadata streams together so an inspection result lands alongside line system signals instead of a separate console-see how this is applied across our<\/span><a href=\"https:\/\/intuceo.com\/solutions\/advanced-manufacturing\/\"> <span style=\"font-weight: 400;\">advanced manufacturing AI solutions<\/span><\/a><span style=\"font-weight: 400;\">.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-033824e elementor-widget elementor-widget-text-editor\" data-id=\"033824e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Deployment is half of a reassessment that has to survive audit-a challenge Intuceo addressed directly in its<\/span><a href=\"https:\/\/intuceo.com\/case-studies\/advanced-ml-inspection-pipeline\/\"> <span style=\"font-weight: 400;\">advanced ML inspection pipeline case study<\/span><\/a><span style=\"font-weight: 400;\">. Intuceo delivers in on-premise, private cloud, and air-gapped environments, and client data is not used to train public models. For a plant that cannot accept a hosted model changing behavior between validation runs, that removes the variable rather than managing around it.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4565ec elementor-widget elementor-widget-text-editor\" data-id=\"e4565ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">PhD-led engineering teams with prior deployments in regulated and high-precision environments deliver solutions to fit your unique needs. See how that applies across our<\/span><a href=\"https:\/\/intuceo.com\/solutions\/advanced-manufacturing\/\"> <span style=\"font-weight: 400;\">advanced manufacturing AI solutions<\/span><\/a><span style=\"font-weight: 400;\"> and to our<\/span><a href=\"https:\/\/intuceo.com\/capabilities\/artificial-intelligence\/\"> <span style=\"font-weight: 400;\">computer vision and AI capabilities<\/span><\/a><span style=\"font-weight: 400;\"> built for it.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3e1394f e-con-full fadeup-animate e-flex e-con e-child\" data-id=\"3e1394f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-befb4cb e-con-full e-flex e-con e-child\" data-id=\"befb4cb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b646574 elementor-widget elementor-widget-heading\" data-id=\"b646574\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Have a vision project sitting on the shelf?\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9f8ab2c elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"9f8ab2c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">AI Dream Session, Vol. 2: Computer Vision Reimagined is a 45-minute live session with Intuceo on Thursday, September 24, 2026, at 11:00 AM ET. It covers which previously rejected use cases may now be viable, where limitations still hold in production, and the questions worth asking before any project is reopened.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6eabe45 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"6eabe45\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tGet the reassessment questions before your next project review \t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c949ab2 e-con-full e-flex e-con e-child\" data-id=\"c949ab2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-176e920 elementor-align-center white-cta elementor-widget elementor-widget-button\" data-id=\"176e920\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/intuceo.com\/book-a-demo\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Schedule a consultation<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-75c4a89 e-flex e-con-boxed e-con e-parent\" data-id=\"75c4a89\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a32b051 elementor-widget elementor-widget-heading\" data-id=\"a32b051\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Frequently Asked Questions<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0513ec faq-list elementor-widget elementor-widget-n-accordion\" data-id=\"b0513ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1840\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1840\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> <span class=\"faq\" style=\"display:flex;gap: 8px\">   <span>1.<\/span>How do I know if a shelved vision project is viable with new AI tools?<\/span> <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"2\" viewBox=\"0 0 16 2\" fill=\"none\"><line x1=\"1\" y1=\"1\" x2=\"15\" y2=\"1\" stroke=\"black\" stroke-width=\"2\" stroke-linecap=\"round\"><\/line><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"17\" height=\"17\" viewBox=\"0 0 17 17\" fill=\"none\"><path d=\"M16.3333 7.87504V8.45831C16.3333 8.78048 16.0722 9.04167 15.75 9.04167H9.04166V15.75C9.04166 16.0721 8.78048 16.3333 8.4583 16.3333H7.87503C7.55285 16.3333 7.29166 16.0721 7.29166 15.75V9.04167H0.583337C0.26117 9.04167 0 8.78048 0 8.45831V7.87504C0 7.55286 0.26117 7.29167 0.583337 7.29167H7.29166V0.583328C7.29166 0.261162 7.55285 0 7.87503 0H8.4583C8.78048 0 9.04166 0.261162 9.04166 0.583328V7.29167H15.75C16.0722 7.29167 16.3333 7.55286 16.3333 7.87504Z\" fill=\"#313B51\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1840\" class=\"elementor-element elementor-element-2d5f2e0 e-con-full e-flex e-con e-child\" data-id=\"2d5f2e0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-966b1ed elementor-widget elementor-widget-text-editor\" data-id=\"966b1ed\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>A shelved vision project is worth revisiting if it originally failed due to data scarcity, part variation, or labeling cost-not due to line speed limits, integration barriers, or lack of ownership. Start with the reason it stopped. If the blocker was too few defect examples, frequent part variation, or labeling cost, newer vision AI models may help. Then test on archived images from the original pilot, measured against the original acceptance criteria. If the blocker was line speed, integration, or lack of ownership, better models alone are unlikely to change the outcome.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1841\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1841\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> <span class=\"faq\" style=\"display:flex;gap: 8px\">   <span>2.<\/span> What criteria should I use to evaluate paused computer vision investments?<\/span> <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"2\" viewBox=\"0 0 16 2\" fill=\"none\"><line x1=\"1\" y1=\"1\" x2=\"15\" y2=\"1\" stroke=\"black\" stroke-width=\"2\" stroke-linecap=\"round\"><\/line><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"17\" height=\"17\" viewBox=\"0 0 17 17\" fill=\"none\"><path d=\"M16.3333 7.87504V8.45831C16.3333 8.78048 16.0722 9.04167 15.75 9.04167H9.04166V15.75C9.04166 16.0721 8.78048 16.3333 8.4583 16.3333H7.87503C7.55285 16.3333 7.29166 16.0721 7.29166 15.75V9.04167H0.583337C0.26117 9.04167 0 8.78048 0 8.45831V7.87504C0 7.55286 0.26117 7.29167 0.583337 7.29167H7.29166V0.583328C7.29166 0.261162 7.55285 0 7.87503 0H8.4583C8.78048 0 9.04166 0.261162 9.04166 0.583328V7.29167H15.75C16.0722 7.29167 16.3333 7.55286 16.3333 7.87504Z\" fill=\"#313B51\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1841\" class=\"elementor-element elementor-element-6f787cc e-con-full e-flex e-con e-child\" data-id=\"6f787cc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4b2ca1b elementor-widget elementor-widget-text-editor\" data-id=\"4b2ca1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Six criteria determine whether a paused computer vision project is worth restarting in manufacturing:<br \/>(1) original blocker,<br \/>(2) current cost of the problem, (3) availability of archived images,<br \/>(4) line speed requirements,<br \/>(5) integration path to MES or PLC, and<br \/>(6) named operational ownership. Projects that score well on blocker, value, and ownership are usually the strongest candidates.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1842\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1842\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> <span class=\"faq\" style=\"display:flex;gap: 8px\">   <span>3.<\/span>Which inputs in an old inspection business case are worth re-checking?<\/span> <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"2\" viewBox=\"0 0 16 2\" fill=\"none\"><line x1=\"1\" y1=\"1\" x2=\"15\" y2=\"1\" stroke=\"black\" stroke-width=\"2\" stroke-linecap=\"round\"><\/line><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"17\" height=\"17\" viewBox=\"0 0 17 17\" fill=\"none\"><path d=\"M16.3333 7.87504V8.45831C16.3333 8.78048 16.0722 9.04167 15.75 9.04167H9.04166V15.75C9.04166 16.0721 8.78048 16.3333 8.4583 16.3333H7.87503C7.55285 16.3333 7.29166 16.0721 7.29166 15.75V9.04167H0.583337C0.26117 9.04167 0 8.78048 0 8.45831V7.87504C0 7.55286 0.26117 7.29167 0.583337 7.29167H7.29166V0.583328C7.29166 0.261162 7.55285 0 7.87503 0H8.4583C8.78048 0 9.04166 0.261162 9.04166 0.583328V7.29167H15.75C16.0722 7.29167 16.3333 7.55286 16.3333 7.87504Z\" fill=\"#313B51\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1842\" class=\"elementor-element elementor-element-5eea93d e-con-full e-flex e-con e-child\" data-id=\"5eea93d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f90f21a elementor-widget elementor-widget-text-editor\" data-id=\"f90f21a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Four inputs in an old computer vision inspection business case are worth re-checking in 2026: labeling effort (often lower for foundation model use cases), retraining frequency (potentially less frequent where models generalize across part variants), compute cost per image (potentially higher with larger models at the line), and the value of catching a defect (unchanged-set by your process, not by the model). Re-check the first three against your own parts before any approval, and leave the fourth where the original case had it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1843\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1843\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> <span class=\"faq\" style=\"display:flex;gap: 8px\">   <span>4.<\/span>Should I revisit computer vision projects I canceled in 2023 or 2024?<\/span> <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"2\" viewBox=\"0 0 16 2\" fill=\"none\"><line x1=\"1\" y1=\"1\" x2=\"15\" y2=\"1\" stroke=\"black\" stroke-width=\"2\" stroke-linecap=\"round\"><\/line><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"17\" height=\"17\" viewBox=\"0 0 17 17\" fill=\"none\"><path d=\"M16.3333 7.87504V8.45831C16.3333 8.78048 16.0722 9.04167 15.75 9.04167H9.04166V15.75C9.04166 16.0721 8.78048 16.3333 8.4583 16.3333H7.87503C7.55285 16.3333 7.29166 16.0721 7.29166 15.75V9.04167H0.583337C0.26117 9.04167 0 8.78048 0 8.45831V7.87504C0 7.55286 0.26117 7.29167 0.583337 7.29167H7.29166V0.583328C7.29166 0.261162 7.55285 0 7.87503 0H8.4583C8.78048 0 9.04166 0.261162 9.04166 0.583328V7.29167H15.75C16.0722 7.29167 16.3333 7.55286 16.3333 7.87504Z\" fill=\"#313B51\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1843\" class=\"elementor-element elementor-element-a91205b e-con-full e-flex e-con e-child\" data-id=\"a91205b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7fe0d24 elementor-widget elementor-widget-text-editor\" data-id=\"7fe0d24\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Revisit the ones canceled for data or variation reasons, where the underlying problem still exists, and a plant owner is ready to run the result. Leave closed the projects whose lines were retired, whose defects were designed out, or that lacked operational ownership. A short, documented reassessment is the fastest way to tell the difference.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"1.How do I know if a shelved vision project is viable with new AI tools?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A shelved vision project is worth revisiting if it originally failed due to data scarcity, part variation, or labeling cost-not due to line speed limits, integration barriers, or lack of ownership. Start with the reason it stopped. If the blocker was too few defect examples, frequent part variation, or labeling cost, newer vision AI models may help. 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Parts changed faster than the model could be retrained. The labeling bill outgrew the savings, or integration with the line took longer [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":24238,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[13],"tags":[153,232,295,253,296],"class_list":["post-24237","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-advanced-manufacturing","tag-ai-consulting","tag-computer-vision","tag-manufacturing-ai","tag-quality-control"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Computer Vision Inspection Manufacturing: Revisiting Shelved Projects<\/title>\n<meta name=\"description\" content=\"Computer vision inspection manufacturing projects often fail for fixable reasons; use this six-criteria framework to identify which paused projects newer AI can revive.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Computer Vision Inspection Manufacturing: Revisiting Shelved Projects\" \/>\n<meta property=\"og:description\" content=\"Computer vision inspection manufacturing projects often fail for fixable reasons; 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