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aem-Grid--default--12",":items":{"flexible_column_cont":{"id":"flexible-column-container-335f8296e5","propertiesId":"hub-hero-breadcrumbs","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-99311c04ea",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"breadcrumb":{"id":"breadcrumb-5d1cfcfb18","items":[{"id":"breadcrumb-5d1cfcfb18-item-086a1ac238","link":{"valid":true,"url":"/de/"},"active":false,"current":false,"title":"Deutsch",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-5d1cfcfb18-item-b4dc78da23","link":{"valid":true,"url":"/de/artificial-intelligence/"},"active":false,"current":false,"title":"Künstliche Intelligenz",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-5d1cfcfb18-item-9f0e0cfae9","link":{"valid":true,"url":"/de/artificial-intelligence/industries/"},"active":true,"current":true,"title":"KI in verschiedenen Branchen",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-16e7b5b19c","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-db30120142",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_759888059":{"id":"text-e6588e0e62","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGrundlagen\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-71dc298a4c","additionalClasses":"hub-hero__headline","type":"heading1","lines":["KI in Aktion: Ein Verzeichnis von KI-Anwendungsfällen in verschiedenen Branchen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-22f809071e","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EKI wird immer spezialisierter, aber die Ergebnisse hängen von einer wiederholbaren Grundlage ab. Dieser Leitfaden untersucht, wie branchenspezifische KI-Anwendungen Gestalt annehmen – und warum kontrollierte Daten, semantischer Kontext und eine speziell entwickelte Infrastruktur darüber entscheiden, ob sie zuverlässige Ergebnisse liefern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-beeb553ba4",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-782e9b1309","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie lesen"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?preferwebp=true&quality=85","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-d2dcf0f37f","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/gulnaz-sharipova/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie lesen"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f7c921fe-d10f-4d90-ac38-c79aff7cf132/gulnaz-photo.jpg?preferwebp=true&quality=85","alt":"Gulnaz Sharipova","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Gulnaz Sharipova","Lokale Redakteurin, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_759888059","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-23a828250d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-060fd49e40","youtubeVideoId":"F34xlRoQ3eQ","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-a8a03f80b5","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-448b1fa665",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-9a311e90cb","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThemen zu KI nach Branche:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-5ecd3d32b6","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-advertising/\"\u003EKI in der Werbebranche\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EKI in der Cybersicherheit\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EKI im Finanzsektor\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/industries/ai-in-healthcare/\"\u003EKI im Gesundheitswesen\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EKI in der Fertigung\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EKI im Handel\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-7e84581cb2","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_intro_0":{"id":"text-816a7566c8","text":"\u003Cp\u003EDie Ausgaben für branchenspezifische Anwendungen für generative KI erreichten im Jahr 2025 schätzungsweise 3,5 Milliarden USD, fast das Dreifache des Vorjahreswertes, so \u003Ca href=\"https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/\" target=\"_blank\"\u003EMenlo Ventures\u003C/a\u003E. Dieses Wachstum spiegelt einen wachsenden Markt für KI wider, die auf die Workflows, Daten und Betriebsanforderungen bestimmter Branchen zugeschnitten ist.\u003C/p\u003E\n\u003Cp\u003EUmgebungsübergreifend nutzen Unternehmen prädiktives \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/\"\u003Egenerative KI\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EAgentic AI\u003C/a\u003E – oft unter Einbeziehung von \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/computer-vision/\"\u003EComputer Vision\u003C/a\u003E und anderen multimodalen Funktionen –, um Geschäftsprozesse zu unterstützen, die von Erkennung und Prognosen bis hin zu Forschung, Entscheidungsunterstützung und operativer Automatisierung reichen.\u003C/p\u003E\n\u003Cp\u003EDieses Verzeichnis untersucht die KI-Anwendungsfälle, die 13 Branchen prägen, wie sie sich in spezifische Geschäftsprozesse einfügen und was Unternehmen benötigen, um diese Funktionen in zuverlässige Ergebnisse umzuwandeln. Außerdem werden grundlegende Überlegungen aufgezeigt, die erfolgreiche KI-Initiativen unterstützen können.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-shapes-ai-outcomes-across-industries":{"id":"title-v2-ce100de038","additionalClasses":"anchor-title anchor-title--what-shapes-ai-outcomes-across-industries","type":"heading2","lines":["Was KI-Ergebnisse branchenübergreifend prägt"],":type":"snowflake-site/components/title-v2"},"text_what-shapes-ai-outcomes-across-industries_0":{"id":"text-c15533a26d","text":"\u003Cp\u003EEin KI-Modell kann leistungsstarke Analyse- und Schlussfolgerungsfunktionen bieten, aber das umgebende System hat starken Einfluss darauf, ob die Ausgabe auf relevanten Daten basiert, im richtigen Geschäftskontext interpretiert und im Rahmen der geltenden Richtlinien und Verfahren bereitgestellt wird. Diese Grundlage bestimmt, wie zuverlässig die Funktionen des Modells im Unternehmen angewendet werden können.\u003C/p\u003E\n\u003Ch3\u003EKontrollierte, vertrauenswürdige Daten\u003C/h3\u003E\n\u003Cp\u003EEin KI-System beginnt mit den ihm zur Verfügung stehenden Daten. Wenn Datensätze unvollständig, veraltet oder inkonsistent definiert sind, überträgt sich diese Schwäche auf das Ergebnis. Eine Vorhersage von Geräteausfällen hängt beispielsweise von genauen Sensormesswerten, der Wartungshistorie und der Anlagenidentität ab. Fehlende Serviceaufzeichnungen oder Signale, die der falschen Komponente zugeordnet sind, können die Vorhersage untergraben, bevor das Modell mit seiner Analyse beginnt.\u003C/p\u003E\n\u003Cp\u003EVertrauen hängt auch davon ab, wie auf diese Informationen zugegriffen wird und wie sie genutzt werden. Sensible Daten müssen geschützt bleiben, wenn sie in Modell-, Abruf- und Agenten-Workflows einfließen. Teams benötigen ausreichend Nachvollziehbarkeit, um festzustellen, welche Datensätze, Modelle und Anweisungen zu einer Ausgabe beigetragen haben, insbesondere wenn das System Informationen abrufen, ein Tool aufrufen oder eine Aktion initiieren kann.\u003C/p\u003E\n\u003Cp\u003EGovernance garantiert nicht, dass ein Modell ein korrektes Ergebnis liefert, aber sie schafft die notwendigen Bedingungen, damit Teams das Ergebnis überprüfen, seine Quelle identifizieren und einschränken können, wer oder was die zugrunde liegenden Informationen nutzen darf.\u003C/p\u003E\n\u003Ch3\u003ESemantischer Kontext\u003C/h3\u003E\n\u003Cp\u003EUnternehmensdaten haben fast immer eine Bedeutung, die nicht offensichtlich aus dem Labelnamen hervorgeht. Beispielsweise könnte sich eine Spalte mit der Bezeichnung „Umsatz“ auf Buchungen, Abrechnungen oder realisierte Umsätze beziehen, was jeweils zu einer anderen Antwort auf dieselbe Frage führen würde.\u003C/p\u003E\n\u003Cp\u003EMitarbeiter:innen im Unternehmen verstehen diese Unterschiede durch Erfahrung, Richtlinien und etablierte Geschäftsregeln. Ein KI-System benötigt diese Dinge in expliziter Form. Metrikdefinitionen, Entitätsbeziehungen, Terminologie, Lineage, Metadaten und genehmigte Berechnungslogik verknüpfen eine Anfrage mit den richtigen Daten und helfen dabei, die vom Unternehmen beabsichtigte Bedeutung zu bewahren.\u003C/p\u003E\n\u003Ch3\u003EKI-Infrastruktur in der Nähe von kontrollierten Daten\u003C/h3\u003E\n\u003Cp\u003EKI-Anwendungen für Unternehmen benötigen oft strukturierte Datensätze, Dokumente, Bilder, Event-Streams und extern bezogene Daten innerhalb desselben Workflows. Das Verschieben dieser Assets in eine separate KI-Umgebung kann zusätzliche Kopien, fragmentierte Kontrollen und Pipelines erzeugen, die Berechtigungen beibehalten müssen, wenn Daten Systemgrenzen überschreiten.\u003C/p\u003E\n\u003Cp\u003EWenn Modellinferenz, Abruf, Feature Engineering und Agenten-Workflows in der Nähe von kontrollierten Daten gehalten werden, kann dies unnötige Datenverschiebungen reduzieren. Abhängig von der Architektur und Konfiguration können Teams Zugriffskontrollen anwenden, kürzlich aktualisierte Quellinformationen nutzen und die Datennutzung über unterstützte Teile des KI-Workflows hinweg überwachen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-use-cases-built-on-sensitive-regulated-data":{"id":"title-v2-ce8f32568a","additionalClasses":"anchor-title anchor-title--ai-use-cases-built-on-sensitive-regulated-data","type":"heading2","lines":["KI-Anwendungsfälle basierend auf sensiblen, regulierten Daten"],":type":"snowflake-site/components/title-v2"},"text_ai-use-cases-built-on-sensitive-regulated-data_0":{"id":"text-7a91705146","text":"\u003Cp\u003EUnternehmen in den Bereichen Gesundheitswesen, Life Sciences und Finanzdienstleistungen arbeiten mit Informationen, die kommerziell wertvoll, persönlich sensibel und streng reguliert sind. KI-Anwendungen in diesen Branchen erfordern feingranulare Zugriffskontrollen, Nachvollziehbarkeit bis zu den Quelldatensätzen, definierte Schwellenwerte für die menschliche Überprüfung und Überwachung über Patientenpopulationen, Kundensegmente, Produkte und Regionen hinweg.\u003C/p\u003E\n\u003Cp\u003EDie Zusammenarbeit mit Anbietern, Forschungspartnern, Gegenparteien und Aufsichtsbehörden bringt eine weitere Anforderung mit sich: Sensible Daten müssen kontrolliert bleiben, wenn die Analyse Unternehmensgrenzen überschreitet.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle für Gesundheitswesen und Life Sciences\u003C/h3\u003E\n\u003Cp\u003EDiese Anwendungen sind darauf ausgelegt, das Urteilsvermögen qualifizierter klinischer oder regulatorischer Fachkräfte zu unterstützen – nicht zu ersetzen. Sie sollten auch für den spezifischen Anwendungsfall, die Patientenpopulation und die Betriebsumgebung validiert werden.\u003C/p\u003E\n\u003Ch4\u003EFrühere Krankheits- und Risikoerkennung\u003C/h4\u003E\n\u003Cp\u003EPrädiktive Modelle können Laborergebnisse, Diagnosen, Medikamentenhistorie, klinische Beobachtungen und frühere Inanspruchnahme kombinieren, um Patient:innen zu identifizieren, deren Risiko steigt, bevor ein einzelnes Ergebnis Anlass zur Sorge geben würde. Um einen operativen Mehrwert zu erzielen, muss der Score mit einer definierten Reaktion verknüpft sein – wie z. B. einer ärztlichen Überprüfung, zusätzlichen Tests oder Care Management – und anhand von Kennzahlen wie vermeidbaren Einweisungen, Zeit bis zur Diagnose und Komplikationsraten bewertet werden.\u003C/p\u003E\n\u003Ch4\u003EKlinische Entscheidungsunterstützung\u003C/h4\u003E\n\u003Cp\u003EKI kann relevante Nachweise abrufen, die jüngste Vorgeschichte von Patient:innen zusammenfassen und Behandlungsoptionen mit Kontraindikationen oder Risikofaktoren vergleichen. Da unvollständige Zusammenfassungen ebenso folgenschwer sein können wie falsche, benötigen Ärzt:innen Zugriff auf die zugrunde liegende Akte, klare Hinweise auf Unsicherheiten und die Befugnis über die endgültige Entscheidung.\u003C/p\u003E\n\u003Ch4\u003EMedizinische Bildgebung und Diagnoseunterstützung\u003C/h4\u003E\n\u003Cp\u003EComputer Vision kann dabei helfen, Befunde in radiologischen Bildern, pathologischen Schnitten, Netzhautscans und anderen diagnostischen Medien zu identifizieren, während multimodale Systeme diese Bilder mit Berichten, Laborergebnissen und der Patientenanamnese kombinieren. Die Validierung muss Scannertyp, Bildqualität, Aufnahmeprotokoll und Patientenpopulation berücksichtigen, wobei die Ergebnisse anhand von Lesezeit, diagnostischer Durchlaufzeit, Screening-Durchsatz oder Erkennungsraten gemessen werden.\u003C/p\u003E\n\u003Ch4\u003EPatientenfluss und Kapazitätsplanung\u003C/h4\u003E\n\u003Cp\u003EPrognosemodelle können die Ankünfte in der Notfallabteilung, den stationären Bedarf, den Entlassungszeitpunkt und das Verfahrensvolumen schätzen, während Optimierungsmethoden Betten, Operationssäle, Infusionsstühle und Personal zuweisen. Das Modell muss geplante Eingriffe, die Akuität der Patient:innen, personelle Einschränkungen und nachgelagerte Kapazitäten abbilden; nützliche Kennzahlen umfassen Wartezeit, abgesagte Eingriffe, Überstunden und Aufenthaltsdauer.\u003C/p\u003E\n\u003Ch4\u003ERevenue Cycle und Schadensbearbeitung\u003C/h4\u003E\n\u003Cp\u003EKI kann Informationen aus der klinischen Dokumentation extrahieren, Codierungen vorschlagen, fehlende Datensätze identifizieren und Ablehnungen vorhersagen. Agenten können unterstützende Dokumente sammeln, die Anforderungen der Kostenträger prüfen und Widersprüche für die fachliche Überprüfung vorbereiten, vorausgesetzt, der Workflow bewahrt die Verknüpfungen zwischen klinischen Ereignissen, Genehmigungen, Verträgen, Codes und dem Status der Forderung. Ablehnungsraten, Tage in den Forderungen aus Lieferungen und Leistungen und manuelle Eingriffe pro Forderung zeigen, ob die Anwendung Nacharbeiten reduziert.\u003C/p\u003E\n\u003Ch4\u003EPatientenbindung und Care Navigation\u003C/h4\u003E\n\u003Cp\u003EKI-gestützte Navigation kann Fragen zur Vorbereitung oder Abdeckung beantworten, Patient:innen an geeignete Ressourcen verweisen und Erinnerungen an einen Pflegeplan anpassen. Prädiktive Modelle können auch Patient:innen identifizieren, bei denen das Risiko besteht, dass sie Termine verpassen oder die Behandlung abbrechen, wobei die Personalisierung durch Einwilligung, Kanalpräferenz, Sprache und Gesundheitskompetenz geprägt wird. Terminwahrnehmung, Schließung von Versorgungslücken und Contact-Center-Volumen bieten praktische Kennzahlen.\u003C/p\u003E\n\u003Ch4\u003EArzneimittelforschung und klinische Entwicklung\u003C/h4\u003E\n\u003Cp\u003EMaschinelles Lernen kann dabei helfen, therapeutische Ziele zu priorisieren, molekulare Eigenschaften vorherzusagen und Kandidatenverbindungen vor der Laborvalidierung einzugrenzen. Während der klinischen Entwicklung kann KI das Protokolldesign, die Standortauswahl, das Patienten-Matching, die Überprüfung von Sicherheitssignalen und die Studienprognose unter Verwendung wissenschaftlicher Literatur, Assay-Daten, molekularer Strukturen, Studienaufzeichnungen und Real-World Evidence unterstützen. Da diese Ergebnisse in experimentelle und regulatorische Beurteilungen einfließen, bleiben Reproduzierbarkeit und Versionskontrolle unerlässlich.\u003C/p\u003E\n\u003Ch4\u003EPharmakovigilanz und Regulatory Intelligence\u003C/h4\u003E\n\u003Cp\u003EDocument Intelligence kann unerwünschte Ereignisse aus Fallberichten, medizinischer Literatur und Gesprächsnotizen extrahieren, während Klassifizierungsmodelle Fälle nach Schweregrad, Erwartbarkeit und Meldefrist weiterleiten. Generative KI kann Fälle zusammenfassen oder neue Richtlinien mit bestehenden Verpflichtungen vergleichen, vorausgesetzt, das System bewahrt eine zuverlässige Entitätsauflösung und verknüpft jede Schlussfolgerung mit den Quellnachweisen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-healthcare/\"\u003EErfahren Sie mehr über KI im Gesundheitswesen \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_ai-use-cases-built-on-sensitive-regulated-data_0":{"id":"card-v2-6ca57a470e","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer Story: Sydney Local Health District"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--95b4f982-4831-4b60-bad4-f3cfc9bc8cb7/sydney-local-health-district%25403x.png?preferwebp=true&quality=85","alt":"Sydney Local Health District","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EDer Sydney Local Health District nutzt die Snowflake AI Data Cloud, um seinem Women and Babies Service Einblicke in Mutterschaftsdaten in nahezu Echtzeit zu geben, was Ärzt:innen hilft, bei Geburten mit medizinischen Eingriffen, Traumata, Mortalität und Morbidität schneller zu handeln. Mit Snowflake reduzierte das Team die Datenabfragezeiten von mehr als einer Stunde auf 55 Sekunden, erweiterte den Abruf von Mutterschaftsdaten von drei Monaten auf mehrere Jahre und kann neue Berichtsanfragen in Stunden statt in Monaten bereitstellen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"text_ai-use-cases-built-on-sensitive-regulated-data_1":{"id":"text-a3719274e5","text":"\u003Ch3\u003EKI-Anwendungsfälle für Finanzdienstleistungen\u003C/h3\u003E\n\u003Ch4\u003EBetrugserkennung und Transaktionsüberwachung\u003C/h4\u003E\n\u003Cp\u003EBetrugsmodelle bewerten Gerät, Standort, Händler, Kontohistorie, Zahlungsmuster und Netzwerkbeziehungen, um verdächtige Aktivitäten zu identifizieren. Graphentechniken können koordiniertes Verhalten über Konten hinweg aufdecken, die einzeln betrachtet legitim erscheinen, während Erkennungsrate, falsch-positive Ergebnisse, Untersuchungszeit und Störungen für legitime Kund:innen die operative Performance anzeigen.\u003C/p\u003E\n\u003Ch4\u003EUntersuchungen zur Bekämpfung von Geldwäsche\u003C/h4\u003E\n\u003Cp\u003EKI kann Warnmeldungen priorisieren, verbundene Entitäten verknüpfen und Beweise aus Know-Your-Customer-Datensätzen, Transaktionshistorien, früheren Fällen und externen Risikoquellen zusammenfassen. Ermittler:innen müssen nachvollziehen können, wie das System die einzelnen Beziehungen identifiziert hat, insbesondere wenn die Ergebnisse in eine behördliche Meldung einfließen.\u003C/p\u003E\n\u003Ch4\u003EKreditrisiko und Underwriting\u003C/h4\u003E\n\u003Cp\u003EPrädiktive Modelle können Ausfallrisiko, Verlusthöhe und Erschwinglichkeit anhand von Applikationsdaten, Kontoverhalten und Cashflow-Informationen abschätzen, während Document Intelligence finanzielle Details aus Kontoauszügen, Steuerunterlagen und Verträgen extrahiert. Institutionen benötigen klare Begründungen für Entscheidungen, Pfade für manuelle Überprüfungen und ein Monitoring über Kreditnehmersegmente, Produkte und wirtschaftliche Bedingungen hinweg.\u003C/p\u003E\n\u003Cp\u003EDer Einsatz bei Kreditentscheidungen muss zudem den geltenden Anforderungen an faire Kreditvergabe, Ablehnungsbescheide, Erklärbarkeit und Verbraucherschutz entsprechen.\u003C/p\u003E\n\u003Ch4\u003EPersonalisierte Finanzerlebnisse\u003C/h4\u003E\n\u003Cp\u003EEine 360-Grad-Kundensicht kann Kontoaktivitäten, Produktbestände, Transaktionen und Interaktionshistorien verknüpfen, sodass Institutionen eine relevante nächste Aktion empfehlen können. Berechtigung, Eignung und Zustimmung müssen das Ergebnis neben dem prognostizierten Interesse prägen, wobei Produktakzeptanz, Kundenbindung und Servicenachfrage einen umfassenderen Maßstab bieten als reines Engagement.\u003C/p\u003E\n\u003Ch4\u003EMarkt-, Liquiditäts- und Portfoliorisiko\u003C/h4\u003E\n\u003Cp\u003EIm Investmentbanking kann maschinelles Lernen dabei helfen, sich ändernde Risikopositionen zu überwachen, ungewöhnliche Korrelationen zu markieren und die Szenarioanalyse über Positionen und Gegenparteien hinweg zu verbessern. Natürlichsprachliche Schnittstellen helfen Analyst:innen bei der Untersuchung von Risikotreibern, während generative KI Änderungen über Portfolios oder Berichtszeiträume hinweg zusammenfasst. Diese Anwendungen sind auf konsistente Identifikatoren, Preisdaten, Positionshistorien und genehmigte Berechnungen angewiesen.\u003C/p\u003E\n\u003Ch4\u003EEinhaltung gesetzlicher Vorschriften und Berichterstattung\u003C/h4\u003E\n\u003Cp\u003EDocument Intelligence kann neue Verordnungstexte mit bestehenden Richtlinien, Kontrollen und Meldepflichten vergleichen, während Agenten Beweise sammeln und Teile wiederkehrender Einreichungen vorbereiten. Die Wissensdatenbank muss Zuständigkeiten, das Datum des Inkrafttretens und Ausnahmen bewahren, wobei die rechtliche oder Compliance-Prüfung für neuartige Interpretationen beibehalten wird.\u003C/p\u003E\n\u003Ch4\u003EAutomatisierung von Abläufen\u003C/h4\u003E\n\u003Cp\u003EKI kann Anfragen klassifizieren, Informationen aus Formularen extrahieren, Datensätze abgleichen und Ausnahmen weiterleiten. Agenten können Dokumente für eine Kontoänderung sammeln, sie anhand von Richtlinien validieren und die Aktualisierung zur Genehmigung vorbereiten. Dunkelverarbeitung, Nacharbeit und der Rückstand bei Ausnahmen zeigen, ob die Automatisierung den Prozess verbessert, anstatt seine Komplexität nur zu verbergen.\u003C/p\u003E\n\u003Ch4\u003EVersicherungs-Pricing, Schadensfälle und Verlustprävention\u003C/h4\u003E\n\u003Cp\u003EVersicherer können KI für Risikoeinschätzung, Schadens-Triage, Schadensbewertung und Betrugserkennung einsetzen. Computer Vision wertet Bilder von beschädigtem Eigentum oder Fahrzeugen aus, während generative KI die Notizen von Schadensregulierer:innen und den Text von Versicherungspolicen zusammenfasst. Präzise Beziehungen zwischen Versicherungsnehmer:innen, versicherten Vermögenswerten, Vorfällen und früheren Schadensfällen unterstützen Kennzahlen wie die Durchlaufzeit von Schadensfällen, Leakage, Betrugsverluste und Underwriting-Konsistenz.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EErfahren Sie mehr über KI in Finanzdienstleistungen\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-use-cases-built-on-machine-and-operational-data":{"id":"title-v2-48914a7588","additionalClasses":"anchor-title anchor-title--ai-use-cases-built-on-machine-and-operational-data","type":"heading2","lines":["KI-Anwendungsfälle basierend auf Maschinen- und Betriebsdaten"],":type":"snowflake-site/components/title-v2"},"text_ai-use-cases-built-on-machine-and-operational-data_0":{"id":"text-40d7dc3581","text":"\u003Cp\u003EUnternehmen aus den Bereichen Fertigung, Automobil, Logistik, Energie und Bauwesen generieren kontinuierliche Aufzeichnungen über das Verhalten physischer Anlagen. KI-Anwendungen in diesen Umgebungen sind auf eine zuverlässige Telemetrie-Erfassung, synchronisierte Zeitstempel, Anlagenhierarchien sowie Wartungs- oder Ausfallhistorien angewiesen, die jedem Signal eine Bedeutung verleihen. Einige Entscheidungen erfordern zudem Edge-Processing, auch wenn Inspektionsergebnisse und Betriebsereignisse für eine umfassendere Analyse weiterhin in eine kontrollierte Umgebung zurückgeführt werden müssen.\u003C/p\u003E\n\u003Cp\u003EBei sicherheitskritischen oder folgenschweren Anwendungen erfordern KI-Ergebnisse eine anwendungsfallspezifische Validierung, definierte Betriebsgrenzen, eine angemessene menschliche Aufsicht und die Einhaltung der geltenden Anforderungen der Branche.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in der Fertigung\u003C/h3\u003E\n\u003Ch4\u003EVorausschauende Wartung\u003C/h4\u003E\n\u003Cp\u003EModelle für vorausschauende Wartung kombinieren Vibration, Temperatur, Druck, akustische Signale und Servicehistorie, um das Ausfallrisiko oder die verbleibende Nutzungsdauer abzuschätzen. Warnmeldungen müssen früh genug eintreffen, um die Personal-, Teile- und Produktionsplanung zu beeinflussen, ohne übermäßige Fehlalarme auszulösen. Daher werten Hersteller in der Regel Ausfallzeiten, Notfallarbeitsaufträge, Wartungskosten und die Anlagenverfügbarkeit aus.\u003C/p\u003E\n\u003Ch4\u003EVisuelle Qualitätsprüfung\u003C/h4\u003E\n\u003Cp\u003EComputer Vision kann Kratzer, Verunreinigungen, Maßabweichungen, fehlende Komponenten und Montagefehler in Produktionsgeschwindigkeit erkennen. Eine zuverlässige Prüfung hängt von konsistenter Beleuchtung, Kameraplatzierung und Bilderfassung ab, gepaart mit menschlicher Überprüfung in unsicheren Fällen und einem Prozess zum Hinzufügen neu entdeckter Fehlertypen.\u003C/p\u003E\n\u003Ch4\u003EProzessoptimierung\u003C/h4\u003E\n\u003Cp\u003EKI kann die Beziehung zwischen Maschineneinstellungen, Materialeigenschaften, Umgebungsbedingungen und der Qualität des Endprodukts modellieren und anschließend Anpassungen empfehlen, die Ausschuss, Zykluszeit oder Energieverbrauch reduzieren. Viele Hersteller beginnen mit der Entscheidungsunterstützung und automatisieren nur Anpassungen, die innerhalb definierter Betriebsbereiche zuverlässig bleiben.\u003C/p\u003E\n\u003Ch4\u003EProduktionsplanung und Bedarfsabstimmung\u003C/h4\u003E\n\u003Cp\u003EBedarfsprognosen können in Produktionspläne einfließen, die Lagerbestände, Vorlaufzeiten von Lieferanten, Umrüstkosten und verfügbare Kapazitäten berücksichtigen. Produkthierarchien, Substitutionsregeln und gemeinsam genutzte Komponenten bestimmen, ob der Plan machbar ist, wobei Engpässe, Überbestände und Terminunterbrechungen nützliche Ergebniskennzahlen liefern.\u003C/p\u003E\n\u003Ch4\u003EDigitale Zwillinge und Simulation\u003C/h4\u003E\n\u003Cp\u003EEin digitaler Zwilling – ein virtuelles Modell einer physischen Anlage – kombiniert Designinformationen, Anlagenkonfiguration, Sensormesswerte und Betriebshistorie in einer kontinuierlich aktualisierten Darstellung. Teams können Prozessänderungen, Wartungsstrategien oder Produktionspläne simulieren, ohne den laufenden Betrieb zu stören, vorausgesetzt, der Zwilling bleibt mit den Geräteänderungen und den aktuellen Betriebsbedingungen synchronisiert.\u003C/p\u003E\n\u003Ch4\u003ESupply-Chain-Risiko und Bestandsoptimierung\u003C/h4\u003E\n\u003Cp\u003EKI kann Lieferantenverzögerungen abschätzen, den Komponentenbedarf prognostizieren und Abhängigkeiten identifizieren, die ein Produktionsrisiko darstellen. Agenten können Bestellungen, Lagerbestände, Lieferantenkommunikation und externe Störungen überwachen und den Planer:innen anschließend betroffene Produkte und Reaktionsmöglichkeiten präsentieren. Eine breitere Transparenz über Lieferantenebenen hinweg verbessert die Analyse, sofern Kooperationsvereinbarungen dies zulassen.\u003C/p\u003E\n\u003Ch4\u003EWissensabruf für Engineering und Wartung\u003C/h4\u003E\n\u003Cp\u003EEin generativer KI-Assistent kann Handbücher, technische Zeichnungen, Standardarbeitsanweisungen und Wartungsprotokolle durchsuchen und anschließend das relevante Verfahren, kürzlich durchgeführte Reparaturen und kompatible Teile anzeigen. Eine genaue Anlagenidentität und Dokumentenversionierung sind unerlässlich, insbesondere wenn Sicherheits- oder Garantieanforderungen gelten.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EErfahren Sie mehr über KI in der Fertigung\u003C/a\u003E\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in der Automobilindustrie\u003C/h3\u003E\n\u003Ch4\u003EFortschrittliche Fahrerassistenzsysteme\u003C/h4\u003E\n\u003Cp\u003EFortschrittliche Fahrerassistenzsysteme kombinieren Computer Vision, Radar, Lidar und Fahrzeugtelemetrie, um Spurhaltung, Objekterkennung und Notbremsungen zu unterstützen. Die Entwicklung erfordert vielfältige Fahrdaten und eine Validierung über Straßendesign, Wetter, Geografie, Beleuchtung, Fahrzeugkonfiguration und seltene Szenarien hinweg, mit Rückverfolgbarkeit von jeder Modellversion zu ihren Testdaten.\u003C/p\u003E\n\u003Ch4\u003EDiagnose für vernetzte Fahrzeuge\u003C/h4\u003E\n\u003Cp\u003EKI kann Fahrzeugtelemetrie analysieren, um sich entwickelnde Fehler zu erkennen und wiederkehrende Probleme in einer Flotte zu identifizieren, wenn sie mit Reparatur- und Garantieaufzeichnungen verknüpft wird. Da derselbe Messwert je nach Modelljahr, Komponentenlieferant oder Softwareversion unterschiedliche Bedeutungen haben kann, hängt eine zuverlässige Diagnose von einer präzisen Fahrzeugkonfiguration und Servicehistorie ab.\u003C/p\u003E\n\u003Ch4\u003EBatteriezustand und Reichweitenprognose\u003C/h4\u003E\n\u003Cp\u003EBei Elektrofahrzeugen kann maschinelles Lernen den Gesundheitszustand der Batterie und die verbleibende Reichweite anhand von Ladezyklen, Temperatur, Fahrverhalten und Zellzustand abschätzen. Modelle müssen auch bei extremen Temperaturen, Schnellladungen und atypischen Fahrmustern zuverlässig bleiben, da ungenaue Schätzungen hier die größten betrieblichen Kosten verursachen.\u003C/p\u003E\n\u003Ch4\u003EFertigungsqualität und Garantie-Analytics\u003C/h4\u003E\n\u003Cp\u003EKI kann Werksmessungen, Lieferantenchargen, Fahrzeugkonfigurationen und Garantieansprüche verknüpfen, um wiederkehrende Defekte zu identifizieren. Sprachmodelle extrahieren Muster aus den Berichten von Techniker:innen, während Computer Vision die Produktionsprüfung unterstützt. Detaillierte Stücklisten und Softwarehistorien helfen dabei, Korrekturmaßnahmen auf die betroffenen Fahrzeuge einzugrenzen.\u003C/p\u003E\n\u003Ch4\u003ESoftwaredefinierte Fahrzeugentwicklung\u003C/h4\u003E\n\u003Cp\u003EKI kann Testergebnisse, Protokolle und Fehlerberichte über viele Hardware- und Softwarekombinationen hinweg analysieren. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003ECoding-Agenten\u003C/a\u003E unterstützen bei abgegrenzten Entwicklungsaufgaben, während die Anomalieerkennung Regressionen in Testflotten aufdeckt. Eine strenge Konfigurationskontrolle ist erforderlich, da ein Fehler möglicherweise nur in einer bestimmten Hardware-Revision, einem bestimmten Modelljahr oder einem bestimmten Software-Build auftritt.\u003C/p\u003E\n\u003Ch4\u003EPersonalisierte Erlebnisse im Fahrzeug\u003C/h4\u003E\n\u003Cp\u003EEmpfehlungssysteme können Navigations-, Unterhaltungs-, Klima- und Ladevorschläge auf die aktuellen Fahrer:innen und den Kontext zuschneiden, während natürlichsprachliche Schnittstellen den Zugriff auf Fahrzeugfunktionen vereinfachen. Datenschutzkontrollen müssen Daten auf Fahrzeugebene von individuellen Fahrerdaten unterscheiden, und Empfehlungen müssen Ablenkung und Sicherheit berücksichtigen.\u003C/p\u003E\n\u003Ch4\u003EHändler-, Service- und Teilebetrieb\u003C/h4\u003E\n\u003Cp\u003EBedarfsprognosen können Händlern helfen, das Servicevolumen und den Teilebedarf vorherzusehen, während Agenten die Terminplanung, Reparaturfreigaben und Kundenaktualisierungen koordinieren. Aktuelle Lagerbestände, Technikerkapazitäten und Fahrzeughistorien unterstützen Kennzahlen wie die Erstlösungsquote, die Vorlaufzeit für Termine und die Teileverfügbarkeit.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in der Logistik\u003C/h3\u003E\n\u003Ch4\u003ERouten- und Netzwerkoptimierung\u003C/h4\u003E\n\u003Cp\u003EModelle zur Routenoptimierung balancieren Lieferfenster, Fahrzeugkapazität, Verkehr, Kraftstoffkosten, Fahrerpläne und Depotbeschränkungen in einem Netzwerk aus. Pläne können bei sich ändernden Bedingungen aktualisiert werden, wobei pünktliche Lieferung, Leerkilometer, Emissionen und Kosten pro Stopp praktische Kennzahlen liefern.\u003C/p\u003E\n\u003Ch4\u003ELager- und Fulfillment-Optimierung\u003C/h4\u003E\n\u003Cp\u003EKI kann den Arbeitskräftebedarf prognostizieren, Lagerorte auswählen, Kommissionierwege optimieren und Bestandsabweichungen erkennen. In hochautomatisierten Anlagen können Modelle die Ausrüstung koordinieren oder Engpässe aufdecken. Jeder Standort benötigt einen lokalen Betriebskontext, da sich Lagertopologie, Serviceanforderungen und Bestellprofile unterscheiden.\u003C/p\u003E\n\u003Ch4\u003EFlottenwartung und -auslastung\u003C/h4\u003E\n\u003Cp\u003ETelemetrie, Inspektionen und Reparaturhistorien können die vorausschauende Wartung unterstützen und ungenutzte oder schlecht positionierte Anlagen bei Lkw, Anhängern, Flugzeugen, Schiffen und Flurförderzeugen aufdecken. Eine zuverlässige Anlagenidentität ist entscheidend, wenn sich Zugmaschinen, Anhänger und Komponenten unabhängig voneinander bewegen.\u003C/p\u003E\n\u003Ch4\u003EVersandrisiko und Ausnahmemanagement\u003C/h4\u003E\n\u003Cp\u003EAgenten können Wetter, Standortereignisse, Zollstatus und Spediteurkommunikation überwachen, um gefährdete Sendungen zu identifizieren, und anschließend alternative Routen, Bestandsoptionen und Kundenzusagen zur Überprüfung zusammenstellen. Genehmigungsgrenzen sollten die Konsequenzen jeder Aktion widerspiegeln, von der Empfehlung einer Route bis hin zur Änderung eines Lieferversprechens oder Spediteurs.\u003C/p\u003E\n\u003Ch4\u003EDokumenten- und Zollautomatisierung\u003C/h4\u003E\n\u003Cp\u003EDocument Intelligence kann Felder in Frachtbriefen, Packlisten, Rechnungen und Zollformularen extrahieren und vergleichen und anschließend fehlende oder inkonsistente Informationen an Spezialist:innen weiterleiten. Agenten können auch Dokumentationspakete zusammenstellen und deren Fertigstellung verfolgen, wobei Abfertigungszeit, Lagergebühren und manuelle Eingriffe zeigen, ob sich der Workflow verbessert.\u003C/p\u003E\n\u003Ch4\u003EBedarfs- und Kapazitätsprognose\u003C/h4\u003E\n\u003Cp\u003EPrognosemodelle können das Versandvolumen nach Strecke, Kund:innen und Servicelevel abschätzen und Spediteuren dabei helfen, Ausrüstung zu positionieren und Kapazitäten auszuhandeln. Da eine höhere Nachfrage auf einer Strecke andernorts zu Engpässen führen kann, muss das Modell eine netzwerkweite Planung unterstützen und nicht nur eine lokale Optimierung.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle im Bauwesen\u003C/h3\u003E\n\u003Ch4\u003EProjektzeitplan und Verzögerungsvorhersage\u003C/h4\u003E\n\u003Cp\u003EKI kann Projektpläne, Tagesberichte, Beschaffungsstatus, Belegschaftsdaten und historische Performance kombinieren, um Aktivitäten zu identifizieren, die sich wahrscheinlich auf den kritischen Pfad auswirken. Eine konsistente Projekthierarchie über Zeitpläne, Verträge und Außendienstsysteme hinweg unterstützt Kennzahlen wie Zeitplanabweichungen, Erholungszeiten und Risiken, die vor dem Erreichen von Meilensteinen identifiziert werden.\u003C/p\u003E\n\u003Ch4\u003EKostenprognosen und Änderungsauftragsrisiken\u003C/h4\u003E\n\u003Cp\u003EModelle können die Kosten bei Abschluss prognostizieren, ungewöhnliche Ausgaben identifizieren und Umfangsänderungen aufdecken, die wahrscheinlich zu Überschreitungen oder Streitigkeiten führen. Document Intelligence verbindet Änderungsanfragen mit Vertragsbedingungen, Zeichnungen, Genehmigungen und Korrespondenz, während die Versionskontrolle sicherstellt, dass das System die aktuell für die Arbeit geltende Sprache verwendet.\u003C/p\u003E\n\u003Ch4\u003EÜberwachung der Baustellensicherheit\u003C/h4\u003E\n\u003Cp\u003EComputer Vision kann fehlende Schutzausrüstung, unsichere Nähe zu Maschinen und das Betreten von Sperrbereichen erkennen, während prädiktive Modelle Vorfallhistorie, Wetter, Zeitdruck und Arbeitsmuster kombinieren, um ein erhöhtes Risiko zu identifizieren. Kalibrierung, Schweregrad der Warnungen und klare Verantwortlichkeiten bestimmen, ob das System die Reaktion verbessert.\u003C/p\u003E\n\u003Ch4\u003EFortschrittsüberprüfung\u003C/h4\u003E\n\u003Cp\u003EBilder von Kameras, Drohnen und mobilen Geräten können mit Building Information Models und Projektzeitplänen verglichen werden, um installierte Mengen oder den Baufortschritt abzuschätzen. Konsistente Erfassungsprotokolle helfen dem Modell, abgeschlossene Arbeiten von bereitgestellten Materialien oder teilweise installierten Komponenten zu unterscheiden.\u003C/p\u003E\n\u003Ch4\u003EAusrüstungs- und Flottenoptimierung\u003C/h4\u003E\n\u003Cp\u003ETelematik kann die Wartung, Nutzungsanalyse und Zuweisung von Kränen, Baggern und anderen Geräten über verschiedene Standorte hinweg unterstützen. Leerlaufzeiten, Mietkosten, ungeplante Ausfallzeiten und der Transport zwischen Standorten zeigen an, ob die Anwendung die Anlagennutzung verbessert.\u003C/p\u003E\n\u003Ch4\u003EVertrags- und Dokumentenintelligenz\u003C/h4\u003E\n\u003Cp\u003EGenerative KI kann Verträge, Spezifikationen, Informationsanfragen, Einreichungen und Besprechungsprotokolle durchsuchen, um Projektfragen zu beantworten oder widersprüchliche Anforderungen zu identifizieren. Das System muss die aktuell genehmigte Version abrufen und gleichzeitig ersetzte Datensätze für Audits und Streitbeilegungen aufbewahren.\u003C/p\u003E\n\u003Ch4\u003EAngebots- und Ressourcenplanung\u003C/h4\u003E\n\u003Cp\u003EKI kann Kosten, Arbeitskräftebedarf und Materialanforderungen anhand von Konstruktionsdokumenten, Standort, historischen Angeboten und Marktbedingungen schätzen. Da Kostencodes, Liefermethoden und regionale Annahmen variieren, müssen historische Daten normalisiert werden, bevor sie zuverlässige Vergleiche unterstützen können.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_ai-use-cases-built-on-machine-and-operational-data_0":{"id":"card-v2-cd137ae1a5","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer Story: Siemens Energy"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/de/customers/all-customers/video/siemens-energy/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story ansehen"},"image":{"id":"image","height":"234","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9d8b3fc8-6209-4a5e-a714-287c2c1b1f76/siemens-energy.png?preferwebp=true&quality=85","alt":"Siemens Energy Logo","lazyEnabled":true,"isLcpImage":false,"width":"416",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ESiemens Energy nutzt Snowflake und Cortex AI, um papierbasierte technische Informationen in durchsuchbare, verwertbare Daten für Mitarbeitende im gesamten Unternehmen umzuwandeln. Mit Snowflakes AI Data Cloud, Cortex AI und einer von Streamlit unterstützten Benutzeroberfläche machte Siemens Energy mehr als 800.000 Seiten über KI-Chatbots zugänglich. Dies hilft Teams, relevante Informationen für Designoptimierung, Kostenänderungen, Schulungen und zukünftige Anwendungsfälle wie Verkaufsdokumente und Fabrikverfahren zu finden – während die Daten in Snowflake sicher bleiben.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_ai-use-cases-built-on-documents-and-language":{"id":"title-v2-ef76273a80","additionalClasses":"anchor-title anchor-title--ai-use-cases-built-on-documents-and-language","type":"heading2","lines":["KI-Anwendungsfälle basierend auf Dokumenten und Sprache"],":type":"snowflake-site/components/title-v2"},"text_ai-use-cases-built-on-documents-and-language_0":{"id":"text-b6581f1332","text":"\u003Cp\u003ERechtsdienstleistungen und das Bildungswesen speichern einen Großteil ihres operativen Wissens in Verträgen, Richtlinien, Forschungsergebnissen, Lehrmaterialien und anderen unstrukturierten Daten. KI-Anwendungen in diesen Umgebungen benötigen zuverlässiges Parsing über Scans, PDFs, Tabellen und Formulare hinweg, berechtigungsgesteuerten Abruf, Dokumentenversionierung, Zitate und eine klare Trennung zwischen quellbasierter Ausgabe und Modellinferenz.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle im Rechtswesen\u003C/h3\u003E\n\u003Ch4\u003EVertragsanalyse und -prüfung\u003C/h4\u003E\n\u003Cp\u003EDocument Intelligence kann Parteien, Daten, Verpflichtungen, Verlängerungsbedingungen und Haftungsbestimmungen extrahieren, während generative KI die Sprache mit einem genehmigten Playbook vergleicht und Abweichungen zusammenfasst. Das System muss die Bedeutung über verschiedene Formulierungen hinweg erkennen, wobei Zykluszeit, Prüfungskonsistenz und wesentliche Probleme, die vor der Unterzeichnung aufgedeckt werden, nützliche Kennzahlen liefern.\u003C/p\u003E\n\u003Ch4\u003EVertragsportfolio-Intelligenz\u003C/h4\u003E\n\u003Cp\u003EKI kann Vereinbarungen identifizieren, die von einer regulatorischen Änderung, einem Preisereignis, einer Akquisition oder einer Lieferantenunterbrechung in einem bestehenden Portfolio betroffen sind. Genaue Entitätsauflösung, Änderungsketten und Gültigkeitsdaten bestimmen, welche Gegenpartei und Verpflichtung aktuell gelten.\u003C/p\u003E\n\u003Ch4\u003EJuristische Recherche und Abruf von Präzedenzfällen\u003C/h4\u003E\n\u003Cp\u003EGenerative KI kann Fälle, Gesetze, Vorschriften, interne Memoranden und frühere Arbeitsprodukte abrufen und dann die mit einer Frage verbundenen Autoritäten organisieren. Die professionelle Nutzung erfordert überprüfbare Zitate, die Erkennung von ersetztem Material und die Unterscheidung zwischen bindender und überzeugender Autorität.\u003C/p\u003E\n\u003Ch4\u003EUnterstützung bei Rechtsstreitigkeiten und Untersuchungen\u003C/h4\u003E\n\u003Cp\u003EKI kann Dokumente klassifizieren, Entitäten identifizieren, Zeitpläne rekonstruieren und konzeptionell verwandte Kommunikationen über große Prüfbestände hinweg aufdecken. Rechtsteams benötigen Reproduzierbarkeit, eine lückenlose Beweiskette und aufbewahrte Aufzeichnungen von Suchkriterien, Modellversionen und Prüfentscheidungen.\u003C/p\u003E\n\u003Ch4\u003EManagement regulatorischer Änderungen\u003C/h4\u003E\n\u003Cp\u003EDocument Intelligence kann neue regulatorische Texte mit Richtlinien und Kontrollen vergleichen, während Agenten eine erste Folgenabschätzung erstellen und Folgearbeiten weiterleiten. Gerichtsbarkeit, Gültigkeitsdatum und Ausnahmen müssen mit der Analyse verknüpft bleiben, wobei die rechtliche Prüfung für die Interpretation beibehalten wird.\u003C/p\u003E\n\u003Ch4\u003EAktenverwaltung und Legal Operations\u003C/h4\u003E\n\u003Cp\u003EKI kann eingehende Anfragen klassifizieren, Arbeiten nach Fachwissen und Kapazität weiterleiten, Aktenkosten prognostizieren und Abrechnungsanomalien identifizieren. Konsistente Aktenkodierung und Abrechnungsdaten unterstützen Kennzahlen wie Aufnahmezeit, Budgetabweichung, Ausgaben für externe Rechtsberater und Workload-Transparenz.\u003C/p\u003E\n\u003Ch4\u003EWissensmanagement und Unterstützung bei der Entwurfserstellung\u003C/h4\u003E\n\u003Cp\u003EEin berechtigungsgesteuerter Assistent kann genehmigte Vorlagen, Klauseln, Leitfäden und frühere Arbeitsprodukte abrufen, bevor er einen ersten Entwurf erstellt. Klare Verantwortlichkeiten und Lebenszykluskontrollen verhindern, dass veraltete Vorlagen oder ersetzte Leitfäden in die aktuelle Arbeit einfließen.\u003C/p\u003E\n\u003Cp\u003EKI-generierte rechtliche Analysen sollten von einer qualifizierten Fachkraft überprüft werden, bevor sie für Rechtsberatung, Einreichungen oder andere wichtige Entscheidungen verwendet werden.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle im Bildungswesen\u003C/h3\u003E\n\u003Ch4\u003EIntelligentes Tutoring und Lernunterstützung\u003C/h4\u003E\n\u003Cp\u003EIntelligente Tutorensysteme können Erklärungen, Übungsfragen und Feedback anhand von Lehrplaninhalten und früherer Performance an den Fortschritt eines Lernenden anpassen. Institutionen benötigen eine Abstimmung mit genehmigten Lernzielen und sollten Beherrschung und Fortschritt bewerten, anstatt nur das Engagement.\u003C/p\u003E\n\u003Ch4\u003EAnalytics für Studienerfolg und -bindung\u003C/h4\u003E\n\u003Cp\u003EPrädiktive Modelle können Anwesenheit, Kursaktivität, Bewertungs-Performance und Beratungshistorie kombinieren, um Studierende zu identifizieren, die möglicherweise Unterstützung benötigen. Ein nützlicher Score ist mit einer definierten Intervention verbunden, enthält verständliche Einflussfaktoren und wird über Studierendengruppen hinweg überwacht, wobei Bindung, Abschluss und Reaktion auf Kontaktaufnahme praktische Kennzahlen liefern.\u003C/p\u003E\n\u003Ch4\u003EAutomatisierte Benotung und Feedback\u003C/h4\u003E\n\u003Cp\u003EKI kann objektive Arbeiten bewerten, Antworten mit einer Rubrik vergleichen und formatives Feedback für die Überprüfung durch Dozent:innen vorbereiten. Offene Arbeiten erfordern repräsentative Beispiele, Rubrikabgleich und Eskalation bei mehrdeutigen Fällen, insbesondere wenn sich das Ergebnis auf Abschlussnoten oder den Fortschritt auswirkt. Unternehmen sollten eine angemessene Aufsicht durch Pädagog:innen für folgenschwere Entscheidungen in Bezug auf Benotung, Einstufung, Fortschritt und Unterstützung von Studierenden beibehalten.\u003C/p\u003E\n\u003Ch4\u003EInstitutionelles Wissen und Richtlinienunterstützung\u003C/h4\u003E\n\u003Cp\u003EEin Assistent für natürliche Sprache kann Fragen anhand von Handbüchern, Richtlinien und Programmanforderungen beantworten. Da die richtige Antwort von Standort, Immatrikulationsjahr, Programm oder Mitarbeitertyp abhängen kann, benötigt das System Versionskontrolle, zielgruppenspezifischen Zugriff und Zitate der geltenden Richtlinie.\u003C/p\u003E\n\u003Ch4\u003EForschungsverwaltung und Fördermittelmanagement\u003C/h4\u003E\n\u003Cp\u003EAgenten können Finanzierungsmöglichkeiten klassifizieren, Anforderungen vergleichen, Compliance-Dokumente zusammenstellen und Meldefristen verfolgen, während Document Intelligence Bedingungen aus Vergabematerialien extrahiert. Vorbereitungszeit für Vorschläge, verpasste Fristen und administrativer Workload liefern praktische Kennzahlen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-use-cases-built-on-behavioral-and-event-data":{"id":"title-v2-816315d5e8","additionalClasses":"anchor-title anchor-title--ai-use-cases-built-on-behavioral-and-event-data","type":"heading2","lines":["KI-Anwendungsfälle basierend auf Verhaltens- und Ereignisdaten"],":type":"snowflake-site/components/title-v2"},"text_ai-use-cases-built-on-behavioral-and-event-data_0":{"id":"text-e5c49810c6","text":"\u003Cp\u003EUnternehmen in den Bereichen Handel, Werbung, Medien und Cybersicherheit verlassen sich auf großvolumige Aufzeichnungen darüber, was Kund:innen, Zielgruppen, Systeme und Geräte im Laufe der Zeit tun. Diese Anwendungen erfordern eine Identitätsauflösung über Geräte und Kanäle hinweg, eine an das Entscheidungsfenster angepasste Ereignisverarbeitung, Einwilligungs- und Zweckkontrollen sowie Feedbackschleifen, die einen Klick, eine Warnung oder eine Anomalie von dem Ergebnis unterscheiden, das dem Unternehmen tatsächlich wichtig ist.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in Handel und E-Commerce\u003C/h3\u003E\n\u003Ch4\u003EBedarfsprognose\u003C/h4\u003E\n\u003Cp\u003EBedarfsprognosemodelle kombinieren Transaktionshistorie, Preisgestaltung, Werbeaktionen, Saisonalität, Inventar und externe Signale, um die Nachfrage nach Produkt, Standort und Kanal zu schätzen. Produkt- und Standorthierarchien müssen Substitutionen, Sortimentsänderungen und neue Produkte widerspiegeln, wobei Verfügbarkeit, Preisnachlässe, Verderb und Lagerumschlag praktische Kennzahlen liefern.\u003C/p\u003E\n\u003Ch4\u003ESortiments- und Bestandsoptimierung\u003C/h4\u003E\n\u003Cp\u003EOptimierungsmodelle übersetzen Bedarfsprognosen in Lager- und Zuteilungsentscheidungen unter Berücksichtigung von Regal-, Lieferanten-, Margen- und Kapazitätsbeschränkungen. Sie müssen auch Interaktionen im gesamten Sortiment abbilden, da das Hinzufügen eines Produkts oder einer Werbeaktion die Nachfrage an eine andere Stelle verlagern kann.\u003C/p\u003E\n\u003Ch4\u003EPersonalisierungs- und Empfehlungs-Engines\u003C/h4\u003E\n\u003Cp\u003EEmpfehlungs-Engines ordnen Produkte oder Inhalte anhand von Browsing-, Kauf-, Such- und Produktdaten, während eine 360-Grad-Kundensicht die Konsistenz über E-Commerce, Filialen, Service und Marketing hinweg unterstützt. Verfügbarkeit, Marge, Einwilligung, Merchandising-Prioritäten und Kundenermüdung sollten das Ergebnis neben dem prognostizierten Interesse prägen.\u003C/p\u003E\n\u003Ch4\u003EPreis- und Werbeaktionsoptimierung\u003C/h4\u003E\n\u003Cp\u003EKI kann die Nachfragereaktion, die Kannibalisierung von Werbeaktionen und das Timing von Preisnachlässen schätzen, sodass Händler Umsatz-, Margen- und Bestandsszenarien vergleichen können. Empfehlungen erfordern eine fortlaufende Neukalibrierung, da Wettbewerbsverhalten, Lieferengpässe und Kundenstimmung die historische Elastizität verändern können.\u003C/p\u003E\n\u003Ch4\u003EOptimierung von Handelsmedien\u003C/h4\u003E\n\u003Cp\u003EHandelsmediennetzwerke können First-Party-Einkaufsverhalten nutzen, um Zielgruppen aufzubauen, die Performance zu prognostizieren und Werbekontakte mit Verkäufen zu verknüpfen, während \u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-a-data-clean-room/\"\u003EData Clean Rooms\u003C/a\u003E die Kollaboration mit Marken unterstützen, ohne rohe Kundendatensätze offenzulegen. Vereinbarte Definitionen für Exposure, Conversion und Inkrementalität sind für eine zuverlässige Messung erforderlich.\u003C/p\u003E\n\u003Ch4\u003EFilialbetrieb und kassenloser Checkout\u003C/h4\u003E\n\u003Cp\u003EHandelsanwendungen von Computer Vision und Sensorfusion können kassenlosen Checkout, Regalüberwachung, Warteschlangenerkennung und Verlustprävention unterstützen, während Prognosen die Arbeitskräfte an den Traffic und den Fulfillment-Bedarf anpassen. Filiallayout, Beleuchtung und Kundenverhalten erfordern eine lokale Kalibrierung und eine klare Ausnahmebehandlung.\u003C/p\u003E\n\u003Ch4\u003ESupply Chain und Fulfillment-Orchestrierung\u003C/h4\u003E\n\u003Cp\u003EKI kann Lieferantenverzögerungen vorhersagen, Fulfillment-Standorte auswählen und Lieferoptionen basierend auf Bestand, zugesagtem Datum und Kosten empfehlen. Der aktuelle Bestand ist entscheidend, da veraltete Daten zu geteilten Lieferungen, Stornierungen oder verpassten Zusagen an anderer Stelle im Netzwerk führen können.\u003C/p\u003E\n\u003Ch4\u003EProduktinhalte und Commerce-Automatisierung\u003C/h4\u003E\n\u003Cp\u003EGenerative KI kann Attribute aus Lieferantendateien extrahieren, Beschreibungen erstellen, Inhalte übersetzen und unvollständige Katalogeinträge identifizieren, während multimodale Modelle Produktbilder mit aufgelisteten Attributen vergleichen. Die menschliche Überprüfung sollte sich auf regulierte Behauptungen, Markensprache und hochwertige Produkte konzentrieren.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EErfahren Sie mehr über KI im Handel \u003E\u003C/a\u003E\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in der Werbung\u003C/h3\u003E\n\u003Ch4\u003EZielgruppenerstellung und -erweiterung\u003C/h4\u003E\n\u003Cp\u003EKI kann Zielgruppensegmente anhand von Verhaltensdaten, Kaufsignalen, Content-Interaktionen und Kampagnenreaktionen identifizieren und diese dann mit Lookalike-Modellen erweitern. Das zur Wertdefinition gewählte Ergebnis – Klicks, qualifizierte Leads, Käufe oder langfristiger Kundenwert – prägt die resultierende Zielgruppe.\u003C/p\u003E\n\u003Ch4\u003EMediaplanung und Budgetzuweisung\u003C/h4\u003E\n\u003Cp\u003EPrädiktive Modelle können Reichweite, Konversion und Grenzertrag über alle Kanäle hinweg prognostizieren und Werbetreibenden dabei helfen, zusätzliche Ausgaben zu vergleichen. Da Kanaldaten unterschiedliche Identifikatoren, Attributionsfenster und Reporting-Methoden verwenden, ist eine konsistente Messung erforderlich, bevor die Performance sinnvoll verglichen werden kann.\u003C/p\u003E\n\u003Ch4\u003EGebots- und Kampagnenoptimierung\u003C/h4\u003E\n\u003Cp\u003EKI kann Gebote, Platzierungen und Pacing entsprechend der prognostizierten Reaktion und den Kampagnenzielen anpassen. Ausgabenlimits, Inventarqualität und verbotene Platzierungen bleiben explizite Kontrollmechanismen, während inkrementelle Konversionen und Gewinne stärkere Maßstäbe bieten als nur kostengünstige Impressions.\u003C/p\u003E\n\u003Ch4\u003ECreative Intelligence und Generierung\u003C/h4\u003E\n\u003Cp\u003EMultimodale KI kann kreative Attribute klassifizieren, sie mit der Performance verknüpfen und Variationen für unterschiedliche Zielgruppen oder Formate generieren. Marken-, Rechts- und Rechtekontrollen bleiben notwendig, und Experimente sollten die Wirkung des Creatives von anderen Kampagnenänderungen isolieren.\u003C/p\u003E\n\u003Ch4\u003EMessung und Attribution\u003C/h4\u003E\n\u003Cp\u003EKI kann den Kampagnenbeitrag über fragmentierte Customer Journeys hinweg schätzen, während Data Clean Rooms es Werbetreibenden, Publishern und Einzelhändlern ermöglichen, Expositions- und Ergebnisdaten unter kontrollierten Bedingungen zu vergleichen. Holdouts, Inkrementalitätstests und klar formulierte Annahmen helfen Führungskräften, die Attribution zu interpretieren, ohne die Präzision überzubewerten.\u003C/p\u003E\n\u003Ch4\u003EBetrug und Inventarqualität\u003C/h4\u003E\n\u003Cp\u003EDie Anomalieerkennung kann ungültigen Traffic, Bot-Aktivitäten, Domain-Spoofing und verdächtige Konversionsmuster identifizieren, während die Graphenanalyse verwandte Websites, Geräte oder Konten aufdeckt. Aktuelle Ereignisdaten und das Feedback von Analyst:innen sind notwendig, da sich das Betrugsverhalten als Reaktion auf die Erkennung ändert.\u003C/p\u003E\n\u003Ch4\u003EDatenschutzbewusste Kollaboration\u003C/h4\u003E\n\u003Cp\u003EData Clean Rooms können es Unternehmen ermöglichen, Daten abzugleichen und zu analysieren und gleichzeitig die direkte Offenlegung von Rohkundendaten zu begrenzen. Identitätsregeln, Aggregationsschwellenwerte und zulässige Verwendungszwecke müssen vor der Analyse definiert werden; die technische Umgebung kann bei der Durchsetzung konfigurierter Regeln helfen, aber Unternehmen bleiben für die Definition und Kontrolle der zulässigen Verwendungszwecke verantwortlich.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-advertising/\"\u003EErfahren Sie mehr über KI in der Werbung \u003E\u003C/a\u003E\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in Medien und Unterhaltung\u003C/h3\u003E\n\u003Ch4\u003EContent-Empfehlung und -Entdeckung\u003C/h4\u003E\n\u003Cp\u003EEmpfehlungs-Engines bewerten Programme, Artikel, Musik oder Spiele anhand von Publikumsverhalten, Sitzungskontext und Katalog-Metadaten. Genaue Informationen über Genre, Besetzung, Thema, Rechte und Format helfen dabei, ein Gleichgewicht zwischen unmittelbarer Relevanz und Entdeckung, Vielfalt und langfristigem Engagement herzustellen.\u003C/p\u003E\n\u003Ch4\u003EAudience Analytics und Churn-Prognosen\u003C/h4\u003E\n\u003Cp\u003EPrädiktive Modelle können Engagement-Muster identifizieren, die mit Kündigungen, Upgrades oder reduzierter Nutzung einhergehen, sodass Medienunternehmen Kundenbindungsangebote oder Content-Empfehlungen maßschneidern können. Die Intervention sollte getestet werden, um festzustellen, ob sie das Verhalten ändert, anstatt davon auszugehen, dass jede:r abwanderungsgefährdete Abonnent:in gehalten werden kann.\u003C/p\u003E\n\u003Ch4\u003EContent-Nachfrage und -Beauftragung\u003C/h4\u003E\n\u003Cp\u003EKI kann Publikumstrends, Katalog-Performance, Suchverhalten und Marktsignale analysieren, um Akquise, Programmgestaltung und Beauftragung zu informieren. Die historische Performance könnte vertraute Konzepte begünstigen, daher sollte das Ergebnis die Portfolioanalyse unterstützen, während das redaktionelle und strategische Urteilsvermögen intakt bleibt.\u003C/p\u003E\n\u003Ch4\u003EWerbeertrags- und Inventaroptimierung\u003C/h4\u003E\n\u003Cp\u003EModelle können das verfügbare Inventar, die Zusammensetzung des Publikums und die Nachfrage prognostizieren und dann dabei helfen, Impressions auf direkte Zusagen, programmatische Nachfrage und interne Werbung aufzuteilen. Rechte, Häufigkeit und Kampagnenbeschränkungen müssen aktuell bleiben, wobei der Umsatz pro Impression, die Auslastungsrate und die Lieferung gemäß den Zusagen nützliche Maßstäbe darstellen.\u003C/p\u003E\n\u003Ch4\u003EContent-Metadaten und Archive Intelligence\u003C/h4\u003E\n\u003Cp\u003EMultimodale KI kann Video, Audio und Bilder transkribieren, klassifizieren und taggen, wodurch Archive nach Thema, Person, Szene oder gesprochener Phrase durchsuchbar werden. Rechteinformationen müssen mit jedem Asset verknüpft bleiben, damit eine verbesserte Auffindbarkeit nicht zu einer unbefugten Nutzung führt.\u003C/p\u003E\n\u003Ch4\u003ELokalisierung und Barrierefreiheit\u003C/h4\u003E\n\u003Cp\u003EKI kann bei Transkription, Übersetzung, Untertitelung, Synchronisation und Audiodeskription helfen, während sich Spezialist:innen auf kulturelle Anpassung, Timing und Qualität konzentrieren. Die Evaluierung sollte Sprache, Dialekt, Genre, Hintergrundaudio, Barrierefreiheit und das Publikumserlebnis berücksichtigen.\u003C/p\u003E\n\u003Ch4\u003EProduktions- und Postproduktions-Workflows\u003C/h4\u003E\n\u003Cp\u003EKI kann Filmmaterial organisieren, Transkripte durchsuchen, Kontinuitätsprobleme identifizieren und grobe Schnitte oder Visual-Effects-Assets generieren, während Agenten den Überprüfungsstatus und die Versionen koordinieren. Unveröffentlichter Content erfordert strenge Zugriffs- und Rechtekontrollen, wobei der frühe Wert oft aus schnellerer Protokollierung, Abruf und Koordination resultiert.\u003C/p\u003E\n\u003Ch4\u003ERechte, Tantiemen und Content-Schutz\u003C/h4\u003E\n\u003Cp\u003EKI kann die Content-Nutzung mit Eigentumsnachweisen abgleichen, unlizenzierte Verbreitung identifizieren und Anomalien im Tantiemen-Reporting erkennen. Da Rechte je nach Gebiet, Plattform und Zeitraum variieren, sind genaue Beziehungen zwischen Assets, Verträgen und Eigentümer:innen erforderlich, bevor das System Zahlungs- oder Durchsetzungsentscheidungen unterstützen kann.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_ai-use-cases-built-on-behavioral-and-event-data_0":{"id":"card-v2-55d6fdeaa8","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer Story: Luminate"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},"image":{"id":"image","height":"234","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--91a067a6-5d47-4e6d-9fdc-9bace95c71f0/luminate%25403x.png?preferwebp=true&quality=85","alt":"Luminate logo","lazyEnabled":true,"isLcpImage":false,"width":"416",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELuminate nutzt Snowflake als Kern seiner Entertainment-Datenplattform, um täglich mehr als 3,5 TB an Daten aus den Bereichen Musik, Film und Fernsehen zu vereinheitlichen und Produkte wie die Billboard-Musikcharts und die Streaming-Originals-Rankings von Variety zu betreiben. Mit der skalierbaren Data Lake-Architektur von Snowflake, Secure Data Sharing, Snowpark und Snowpark ML steigerte Luminate die tägliche Datenverarbeitung um 334 %, führt Marktberichte über Nacht statt über einen ganzen Monat hinweg aus und kann fundiertere branchenübergreifende Erkenntnisse für Labels, Studios, Netzwerke und Partner gewinnen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"text_ai-use-cases-built-on-behavioral-and-event-data_1":{"id":"text-eb5b62e821","text":"\u003Ch3\u003EKI-Anwendungsfälle in Sports Analytics\u003C/h3\u003E\n\u003Ch4\u003ESpieler-Performance und Workload-Management\u003C/h4\u003E\n\u003Cp\u003EIn Verbindung mit professioneller medizinischer Beratung und dem Urteilsvermögen von Trainer:innen können Modelle für maschinelles Lernen Entscheidungen über Trainingsintensität, Rotation und die Rückkehr ins Spiel unterstützen. Die Daten müssen mit der richtigen Athlet:in, der richtigen Sitzung und dem richtigen Wettbewerbskontext verknüpft bleiben. Performance-Maßstäbe können je nach Position, Gegner und Spielstand erheblich variieren, sodass Vergleiche mehr als nur eine einzige aggregierte Punktzahl erfordern.\u003C/p\u003E\n\u003Ch4\u003ESpielstrategie und Gegneranalyse\u003C/h4\u003E\n\u003Cp\u003EKI kann Play-by-Play-Aufzeichnungen, Tracking-Daten und Videos analysieren, um taktische Muster, Matchup-Tendenzen und Änderungen im Verhalten des Gegners zu identifizieren. Trainer:innen und Analyst:innen können diese Erkenntnisse nutzen, um Spielpläne vorzubereiten, Aufstellungen zu bewerten oder zu verstehen, wie eine Strategie unter bestimmten Bedingungen funktioniert.\u003C/p\u003E\n\u003Ch4\u003EScouting und Talentbewertung\u003C/h4\u003E\n\u003Cp\u003ETeams können KI nutzen, um die Performance von Spieler:innen über Ligen, Wettbewerbsniveaus und Spielstile hinweg zu vergleichen. Computer Vision und Ereignisdaten können dabei helfen, Bewegungen, Entscheidungsfindung und rollenspezifische Merkmale aufzudecken, die in herkömmlichen Statistiken möglicherweise nicht auftauchen. Eine zuverlässige Bewertung erfordert eine Normalisierung über verschiedene Umgebungen hinweg. Wettbewerbsqualität, Teamrolle, Spielzeit und Datenverfügbarkeit können Spieler:innen sonst vergleichbarer erscheinen lassen, als sie es tatsächlich sind.\u003C/p\u003E\n\u003Ch4\u003EFan-Engagement und Personalisierung\u003C/h4\u003E\n\u003Cp\u003EEine 360-Grad-Kundensicht kann Ticketing, digitales Engagement, Merchandise, Aktivitäten am Veranstaltungsort und Content-Konsum miteinander verbinden, um relevantere Fan-Erlebnisse zu unterstützen. Empfehlungssysteme können Content, Angebote oder Kommunikation entsprechend der Beziehung des Fans zum Team oder Event personalisieren. Einwilligung und Kanalpräferenz sollten bestimmen, wie diese Informationen verwendet werden.\u003C/p\u003E\n\u003Ch4\u003ETicketing und Anwesenheitsprognosen\u003C/h4\u003E\n\u003Cp\u003EPrognosemodelle können die Nachfrage nach Event, Bereich und Vertriebskanal anhand von Spielplan, Gegner, Timing, Preisgestaltung und historischer Anwesenheit schätzen. Diese Informationen können die Inventarzuweisung, Personalbesetzung und Werbeplanung unterstützen. Externe Ereignisse, Wetter und Planänderungen können sich ebenfalls schnell auf die Nachfrage auswirken.\u003C/p\u003E\n\u003Ch4\u003EVeranstaltungsort- und Event-Betrieb\u003C/h4\u003E\n\u003Cp\u003EKI kann das Eintrittsvolumen, die Nachfrage nach Konzessionen, den Personalbedarf und die Bewegung von Menschenmengen an einem Veranstaltungsort prognostizieren. Computer Vision und Sensordaten können auch dabei helfen, Staus oder betriebliche Probleme während eines Events zu erkennen. Da sich die Layouts von Veranstaltungsorten und Event-Profile unterscheiden, benötigen Modelle einen standortspezifischen Kontext und klar definierte Eskalationsverfahren. Wartezeiten, Personalabweichungen, Inventarverschwendung und die Reaktion auf Vorfälle bieten praktische Maßstäbe.\u003C/p\u003E\n\u003Ch4\u003EContent-Produktion und Medienbetrieb\u003C/h4\u003E\n\u003Cp\u003EGenerative und multimodale KI kann dabei helfen, Highlights zu taggen, Videoarchive zu durchsuchen, Clips vorzubereiten und lokalisierten oder personalisierten Content zu erstellen. Rechte, Sponsoring-Verpflichtungen und Vertriebsfenster müssen mit jedem Asset verknüpft bleiben, damit eine schnellere Produktion nicht zu einer unbefugten Nutzung führt.\u003C/p\u003E\n\u003Ch3\u003EKI-Anwendungsfälle in der Cybersicherheit\u003C/h3\u003E\n\u003Cp\u003EKI-gestützte Sicherheitstools können eine Reihe von Anwendungsfällen unterstützen, von der Erkennung von Bedrohungen und der Analyse von Schwachstellen bis hin zur Priorisierung von Warnmeldungen und der Unterstützung bei der Reaktion auf Vorfälle. Da diese Tools falsch-positive und falsch-negative Ergebnisse liefern können, sollten sie innerhalb klar definierter Berechtigungen, Validierungsverfahren und Anforderungen an die menschliche Überprüfung betrieben werden.\u003C/p\u003E\n\u003Ch4\u003EBedrohungs- und Anomalieerkennung\u003C/h4\u003E\n\u003Cp\u003EMaschinelles Lernen kann ungewöhnliche Aktivitäten bei Identitäts-, Endpunkt-, Netzwerk-, Applikations- und Cloud-Ereignissen identifizieren, einschließlich Kombinationen, die nicht mit einer bekannten Signatur übereinstimmen. Da sich normales Verhalten ändert, benötigen Modelle eine fortlaufende Neukalibrierung und sollten anhand von Erkennungsabdeckung, falsch-positiven Ergebnissen und der Zeit bis zur Triage gemessen werden.\u003C/p\u003E\n\u003Ch4\u003EIdentitäts- und Zugriffsrisiko\u003C/h4\u003E\n\u003Cp\u003EKI kann unwahrscheinliche Reisen, anormale Nutzung von Berechtigungen, ungewöhnliche Dienstkonto-Aktivitäten und Zugriffsmuster identifizieren, die nicht mit der Rolle eines Benutzers oder einer Benutzerin übereinstimmen, während die Graphenanalyse Pfade zu sensiblen Systemen aufdeckt. Beschäftigungsstatus, Berechtigungen, Geräte- und Authentifizierungsverlauf helfen dabei, legitime, aber ungewöhnliche Aktivitäten von Missbrauch zu unterscheiden.\u003C/p\u003E\n\u003Ch4\u003EWarnmeldungs-Triage und -Untersuchung\u003C/h4\u003E\n\u003Cp\u003ESicherheitsagenten können Beweise aus Protokollen, Threat Intelligence und früheren Fällen sammeln, den Ereignisablauf zusammenfassen und Untersuchungsschritte empfehlen. Tool-Berechtigungen sollten die Konsequenz der Aktion widerspiegeln, mit unterschiedlichen Genehmigungsschwellenwerten für das Abrufen von Beweisen, das Deaktivieren eines Kontos oder das Isolieren eines Hosts.\u003C/p\u003E\n\u003Ch4\u003EErkennung von Phishing und Social Engineering\u003C/h4\u003E\n\u003Cp\u003ESprach- und multimodale Modelle können E-Mail-Content, Absenderverhalten, Links, Anhänge und Identitätsdiebstahl-Signale auswerten und Kampagnen identifizieren, die den Wortlaut variieren, während sie dieselbe Infrastruktur nutzen. Feedback aus bestätigten Vorfällen verbessert die Erkennung, wenn sich Angreifer:innen anpassen.\u003C/p\u003E\n\u003Ch4\u003EPriorisierung von Schwachstellen\u003C/h4\u003E\n\u003Cp\u003EKI kann Schweregrad, Exploit-Aktivität, Asset-Exposition, Geschäftskritikalität und bestehende Kontrollen kombinieren, um Behebungsarbeiten zu priorisieren. Ein genaues Asset-Inventar und Eigentumsverhältnisse sind Voraussetzungen, da Teams einen Befund nicht beheben können, ohne zu wissen, ob die Komponente bereitgestellt ist und wem sie gehört.\u003C/p\u003E\n\u003Ch4\u003EErkennung von Datenverlust und Insider-Risiken\u003C/h4\u003E\n\u003Cp\u003EVerhaltensmodelle können ungewöhnliche Downloads, Freigabeaktivitäten, Repository-Zugriffe oder die Bewegung sensibler Informationen identifizieren, während Klassifizierung und Lineage zeigen, um welche Art von Daten es sich handelt. Anomalien allein sollten nicht als Beweis für böswillige Absichten gewertet werden; Datenschutzkontrollen, Untersuchungsverfahren und der geschäftliche Kontext bleiben weiterhin erforderlich.\u003C/p\u003E\n\u003Ch4\u003EAnalyse von Malware und Threat Intelligence\u003C/h4\u003E\n\u003Cp\u003EKI kann Malware klassifizieren, Indikatoren extrahieren, technische Berichte zusammenfassen und Kampagnen mit Infrastruktur oder Taktiken verknüpfen. Da Threat Intelligence unvollständig, widersprüchlich oder irreführend sein kann, sollten die Ergebnisse die Quellenangabe, das Konfidenzniveau und die Zeitsensibilität beibehalten.\u003C/p\u003E\n\u003Ch4\u003EValidierung von Security Posture und Kontrollen\u003C/h4\u003E\n\u003Cp\u003EAgenten können Konfigurationsnachweise sammeln, diese mit Richtlinien vergleichen und Kontrolllücken in Cloud- und Software-as-a-Service-Umgebungen identifizieren, um anschließend Anleitungen zur Behebung oder einen überprüfbaren Change Request zu erstellen. Die Ergebnisse sind am nützlichsten, wenn sie einem:einer bestimmten Verantwortlichen, einer Konfiguration und einer Richtlinienausnahme mit Ablaufdatum zugeordnet werden können.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EErfahren Sie mehr über KI in der Cybersicherheit\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-industry-ai-runs-on-snowflake":{"id":"title-v2-697abc7faf","additionalClasses":"anchor-title anchor-title--why-industry-ai-runs-on-snowflake","type":"heading2","lines":["Warum Branchen-KI auf Snowflake läuft"],":type":"snowflake-site/components/title-v2"},"text_why-industry-ai-runs-on-snowflake_0":{"id":"text-d04c910b73","text":"\u003Cp\u003EDie Anwendungen in diesem Verzeichnis nutzen unterschiedliche Daten, Modelle und Betriebsprozesse, teilen jedoch mehrere grundlegende Implementierungsanforderungen: kontrollierten Zugriff auf aktuelle Unternehmensdaten, ausreichend Kontext für deren korrekte Interpretation und einen gesteuerten Weg von der Analyse zur Aktion.\u003C/p\u003E\n\u003Cp\u003ESnowflake führt strukturierte, semistrukturierte und unstrukturierte Daten mit KI-Funktionen in einer Umgebung zusammen, die Sicherheits- und Governance-Kontrollen unterstützt. \u003Ca href=\"https://www.snowflake.com/de/product/features/cortex/\"\u003ECortex AI\u003C/a\u003E umfasst Funktionen zur Erstellung von Agenten, die Unternehmensdaten nutzen, unterstützte Dokumente und multimodale Inhalte analysieren sowie natürlichsprachlichen Zugriff auf kontrollierte Informationen bieten können. Snowflake ML bietet Funktionen, die verschiedene Phasen prädiktiver Machine-Learning-Workflows unterstützen, darunter Feature Engineering, Modellentwicklung, Bereitstellung und Monitoring.\u003C/p\u003E\n\u003Cp\u003EÜber den \u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003EHorizon Catalog\u003C/a\u003E und zugehörige Snowflake-Funktionen können Unternehmen Kontrollen wie rollenbasierten Zugriff, Maskierungsrichtlinien, Lineage und Datenqualitätsüberwachung innerhalb unterstützter Workflows anwenden. Semantische Modelle und kontextbezogene Metadaten können KI-Systemen helfen, Geschäftsdefinitionen und -beziehungen konsistenter zu nutzen, anstatt sich nur auf isolierte Tabellen oder Dokumente zu verlassen.\u003C/p\u003E\n\u003Cp\u003ESecure Data Sharing und Snowflake Data Clean Rooms können die kontrollierte Zusammenarbeit unterstützen und gleichzeitig die Notwendigkeit verringern, separate Kopien sensibler Daten auszutauschen.\u003C/p\u003E\n\u003Cp\u003EMit kontrollierten Daten, semantischem Kontext und einer vorhandenen KI-Infrastruktur können Teams gemeinsame Funktionen in branchenspezifischen Anwendungen wiederverwenden und sie gleichzeitig an den jeweiligen Anwendungsfall anpassen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_intro_0","title_what-shapes-ai-outcomes-across-industries","text_what-shapes-ai-outcomes-across-industries_0","title_ai-use-cases-built-on-sensitive-regulated-data","text_ai-use-cases-built-on-sensitive-regulated-data_0","card_v2_ai-use-cases-built-on-sensitive-regulated-data_0","text_ai-use-cases-built-on-sensitive-regulated-data_1","title_ai-use-cases-built-on-machine-and-operational-data","text_ai-use-cases-built-on-machine-and-operational-data_0","card_v2_ai-use-cases-built-on-machine-and-operational-data_0","title_ai-use-cases-built-on-documents-and-language","text_ai-use-cases-built-on-documents-and-language_0","title_ai-use-cases-built-on-behavioral-and-event-data","text_ai-use-cases-built-on-behavioral-and-event-data_0","card_v2_ai-use-cases-built-on-behavioral-and-event-data_0","text_ai-use-cases-built-on-behavioral-and-event-data_1","title_why-industry-ai-runs-on-snowflake","text_why-industry-ai-runs-on-snowflake_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn 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