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TZID:America/Denver
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DTSTART:20070101T000000
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DTSTART;TZID=America/Denver:20261021T120000
DTEND;TZID=America/Denver:20261021T133000
X-MICROSOFT-CDO-ALLDAYEVENT:FALSE
SUMMARY:CAMA Event: Prepare Your Data for AI
DESCRIPTION:Most AI pilots at mid-market manufacturers stall for the same reason.  The model is fine.  The data underneath it is not ready.This session covers what has to be true before AI gives you an answer you would actually act on. We start with a real failure: two fields\, ship date and shipped date\, that meant different things in two systems and turned into a missed customer commitment with a dollar figure attached.From there we walk the sequence. Define the process before you automate it.   Connect and standardize data across your operational systems. Build a semantic model so a term means the same thing in every plant and every report. Then let dashboards answer the common questions and put AI on the long tail where it earns its keep.WHAT YOU WILL LEARNWhy definitional drift\, not model quality\, is what kills most manufacturing AI pilotsHow to define a process well enough that automating it is safe with well defined data definitions What connecting and standardizing across operational systems actually requiresHow a semantic model keeps terms consistent across systems\, plants\, and reportsWhere dashboards belong and where AI belongs\, and why the split matters for costWHO SHOULD ATTENDCOOs\, VP Operations\, plant managers\, IT and operations leadership\, and finance partners at discrete manufacturers. Useful for anyone who owns a number that gets argued about in a monthly review. No technical background required.
X-ALT-DESC;FMTTYPE=text/html:<!DOCTYPE html><html><head><title></title></head><body aria-disabled="false"><p><a fr-original-style="" href="https://co-cama.org/event-6843523" style="user-select: auto\;"><img src="https://res.cloudinary.com/micronetonline/image/upload/q_auto\,f_auto\,c_limit\,w_1536\,h_796/v1790721653/tenants/1aa13b26-fb96-4611-8e7f-3084f7667e86/57521040f3ac46b08a1b2fe65cd7368d/cama-applied-newsletter-banner-template-copy.jpg" width="791" height="410" fr-original-style="" fr-original-class="fr-draggable" style="position: relative\; max-width: 100%\; cursor: pointer\; padding: 0px 1px\;" /></a><br /></p><p><span style="color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;">Most AI pilots at mid-market manufacturers stall for the same reason. &nbsp\;The model is fine. &nbsp\;The data underneath it is not ready.</span></p><p><span style="font-size: 14px\;"><span style="font-family: Arial\,Helvetica\,sans-serif\;"><span style="color: rgb(0\, 0\, 0)\;">This session covers what has to be true before AI gives you an answer you would actually act on. We start with a real failure: two fields\, ship date and shipped date\, that meant different things in two systems and turned into a missed customer commitment with a dollar figure attached.</span></span></span></p><p><span style="font-size: 14px\;"><span style="font-family: Arial\,Helvetica\,sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\; color: rgb(0\, 0\, 0)\;">From there we walk the sequence. Define the process before you automate it. <span style="white-space: pre-wrap\;">&nbsp\;&nbsp\;</span>Connect and standardize data across your operational systems. Build a semantic model so a term means the same thing in every plant and every report. Then let dashboards answer the common questions and put AI on the long tail where it earns its keep.</span></span></span></p><p><span style="font-size: 14px\;"><span style="font-family: Arial\,Helvetica\,sans-serif\;"><strong fr-original-style="font-family: Aptos\, Arial\, Helvetica\, sans-serif\; font-size: 12pt\; background-color: rgb(255\, 255\, 255)\; font-weight: bold\;" style="font-family: Aptos\, Arial\, Helvetica\, sans-serif\; font-size: 12pt\; background-color: rgb(255\, 255\, 255)\; font-weight: bold\;"><span style="color: rgb(0\, 0\, 0)\;">WHAT YOU WILL LEARN</span></strong></span></span></p><ul fr-original-style="" style="list-style-position: inside\;"><li style="margin-left: 15px\; color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\;">Why definitional drift\, not model quality\, is what kills most manufacturing AI pilots</span></li><li style="margin-left: 15px\; color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\;">How to define a process well enough that automating it is safe with well defined data definitions&nbsp\;</span></li><li style="margin-left: 15px\; color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\;">What connecting and standardizing across operational systems actually requires</span></li><li style="margin-left: 15px\; color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\;">How a semantic model keeps terms consistent across systems\, plants\, and reports</span></li><li style="margin-left: 15px\; color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;"><span style="background-color: rgb(255\, 255\, 255)\;">Where dashboards belong and where AI belongs\, and why the split matters for cost</span></li></ul><div style="font-size: 12pt\; font-variant-ligatures: normal\; background-color: rgb(255\, 255\, 255)\; direction: ltr\; font-family: Aptos\, Arial\, Helvetica\, sans-serif\;"><span style="font-size: 14px\;"><span style="font-family: Arial\,Helvetica\,sans-serif\;"><span style="color: rgb(0\, 0\, 0)\;"><strong fr-original-style="font-weight: bold\;" style="font-weight: bold\;">WHO SHOULD ATTEND</strong></span></span></span></div><div style="font-size: 12pt\; font-variant-ligatures: normal\; background-color: rgb(255\, 255\, 255)\; font-family: Aptos\, Arial\, Helvetica\, sans-serif\;"><p style="margin-top: 0px\;"><span style="color: rgb(0\, 0\, 0)\; font-size: 14px\; font-family: Arial\, Helvetica\, sans-serif\;">COOs\, VP Operations\, plant managers\, IT and operations leadership\, and finance partners at discrete manufacturers. Useful for anyone who owns a number that gets argued about in a monthly review. No technical background required.</span></p></div><p><br style="color: rgb(34\, 34\, 34)\; font-family: Arial\, Helvetica\, sans-serif\; font-size: small\; font-style: normal\; font-variant-ligatures: normal\; font-variant-caps: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text-align: start\; text-indent: 0px\; text-transform: none\; widows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; white-space: normal\; background-color: rgb(255\, 255\, 255)\; text-decoration-thickness: initial\; text-decoration-style: initial\; text-decoration-color: initial\;" /></p></body></html>
LOCATION:
UID:e.3612.1650550
SEQUENCE:3
DTSTAMP:20261002T005118Z
URL:https://members.wtcdenver.org/cama-event-calendar/Details/cama-event-prepare-your-data-for-ai-1942413?sourceTypeId=Hub
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