{"id":35691,"date":"2025-05-09T09:04:25","date_gmt":"2025-05-09T08:04:25","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=award-winner&p=35691"},"modified":"2025-05-14T03:48:21","modified_gmt":"2025-05-14T02:48:21","slug":"most-innovative-use-of-ai-client-side-carrie-rath-lowes-food","status":"publish","type":"award-winner","link":"https:\/\/www.dataiq.global\/devstage\/award-winner\/most-innovative-use-of-ai-client-side-carrie-rath-lowes-food\/","title":{"rendered":"Most Innovative Use of AI (Client Side) \u2013 Carrie Rath, Lowes Food"},"content":{"rendered":"

Carrie Rath\u2019s work at Lowes Foods exemplifies what innovation in AI looks like when rooted in frontline expertise and business-critical impact. She applied Zebra\u2019s Workcloud Actionable Intelligence software and transformed how it is used, building a new standard for loss prevention that is proactive, precise, and profit-protecting.<\/span>\u00a0<\/span><\/p>\n

Loss prevention is often reactive, investigating only after the damage is done. By combining deep domain knowledge with AI-driven analytics, Carrie created a living system that detects and anticipates theft. Her ability to surface unique behavioral patterns (350 of the 400 identified so far) is a sign of how she has systematized insight discovery, turning Zebra\u2019s toolset into a dynamic loss prevention engine.<\/span>\u00a0<\/span><\/p>\n

What sets her work apart from others is the nuance. Carrie collaborates with Zebra analysts to build views that consider operational context, human behavior, and long-term risk patterns. Whether identifying subtle POS manipulation or decoding layered pricing errors that were actively losing the business money, Carrie is using AI to find problems and explain them.<\/span>\u00a0<\/span><\/p>\n

Carrie has automated monitoring workflows that expose inconsistencies in markdown timings, such as a case involving fraudulent crab leg discounts after hours, and uses external data feeds to cross-reference with internal POS activity. This innovation is hands-on, iterative, and integrated with store operations.<\/span>\u00a0<\/span><\/p>\n

Regarding pricing loss detection, by revealing how stacked temporary price reductions were inadvertently over-discounting items, Carrie found a costly blind spot and helped engineer safeguards to stop it from recurring.\u00a0<\/span>\u00a0<\/span><\/p>\n

Importantly, Carrie\u2019s use of AI does not treat automation as a replacement for human judgement but actually amplifies it. Her approach reflects an inquisitive mindset and a refusal to let \u201cgood enough\u201d stand when \u201cbetter\u201d is possible.<\/span>\u00a0<\/span><\/p>\n

In a landscape saturated with AI hype, Carrie Rath stands out for creating measurable, ongoing impact. Her work solves real business problems with sophistication and rigor detecting theft, monitoring pricing, and safeguarding revenue. Carrie has shown how AI, when led by human insight, can elevate operational integrity across the board.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Carrie Rath\u2019s work at Lowes Foods exemplifies what innovation in AI looks like when rooted in frontline expertise and business-critical impact. She applied Zebra\u2019s Workcloud Actionable Intelligence software and transformed how it is used, building a new standard for loss prevention that is proactive, precise, and profit-protecting.\u00a0 Loss prevention is often reactive, investigating only after […]<\/p>\n","protected":false},"featured_media":36255,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"tags":[1385],"class_list":["post-35691","award-winner","type-award-winner","status-publish","format-standard","has-post-thumbnail","hentry","tag-awards-north-america-2025"],"acf":{"introduction":"","winner":"Carrie Rath, Lowes Food ","award_programme":"DataIQ Awards North America 2025","year":"2025","region":"USA","award_category":"Most Innovative Use of AI (Client Side)","thumbnail":36255,"additional_image":"","affinoid":""},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/award-winner\/35691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/award-winner"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/award-winner"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/award-winner\/35691\/revisions"}],"predecessor-version":[{"id":36257,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/award-winner\/35691\/revisions\/36257"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/36255"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=35691"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=35691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}