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Doch da KI immer mehr Aufgaben übernimmt, können unvollständige Daten und inkonsistente Geschäftsdefinitionen kleine Informationslücken in kostspielige Entscheidungen verwandeln.\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-4709a73c0d",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-34ac7d7b3e","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie 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lesen"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f7c921fe-d10f-4d90-ac38-c79aff7cf132/gulnaz-photo.jpg?quality=85&preferwebp=true","alt":"Gulnaz Sharipova","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Gulnaz Sharipova","Lokale Redakteurin, 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border-top","layout":"SIMPLE","id":"container-4f1bf4afc8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-e1b3a9587c","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThemen rund um Enterprise-KI:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-ad05b64f3c","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/business/ai-council/\"\u003EAI Council\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/business/marketing/\"\u003EKI für Marketing\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/business/enterprise/\"\u003EUnternehmens-KI\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\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-c38b8a3d82","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":{"callout__0":{"id":"text-b285f270aa","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKI IM VERTRIEB DEFINIERT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EKI im Vertrieb wendet prädiktive Modelle, generative Tools und KI-Agenten an, um die Neukundengewinnung, den Geschäftsabschluss, Prognosen und Vertriebsabläufe zu verbessern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-5784c69699","text":"\u003Cp\u003ENeun von zehn Vertriebsteams nutzen KI-Agenten oder planen dies innerhalb von zwei Jahren, so die Studie \u003Ca href=\"https://www.salesforce.com/sales/state-of-sales/\" target=\"_blank\"\u003EState of Sales\u003C/a\u003E 2026 von Salesforce. Dennoch sind die Daten, auf die diese Agenten wahrscheinlich stoßen, möglicherweise noch nicht für eine größere operative Rolle bereit. In der \u003Ca href=\"https://www.validity.com/resource-center/the-state-of-crm-data-management-in-2025\" target=\"_blank\"\u003EUmfrage 2025\u003C/a\u003E von Validity unter 602 CRM-Nutzer:innen und -Stakeholder:innen gaben 76 % an, dass weniger als die Hälfte der CRM-Daten ihres Unternehmens korrekt und vollständig sei, während 37 % von Umsatzverlusten als direkte Folge schlechter Datenqualität berichteten.\u003C/p\u003E\n\u003Cp\u003EZusammen deuten die Ergebnisse auf ein wachsendes Missverhältnis zwischen KI-Ambitionen und den zur Unterstützung verfügbaren Informationen hin. Unvollständige CRM-Daten haben möglicherweise nur begrenzte Auswirkungen, wenn Vertriebsmitarbeiter:innen und Manager:innen manuelle Workflows durch ihr eigenes Fachwissen über Accounts und den Geschäftskontext ergänzen. Da KI jedoch immer mehr Vertriebsaufgaben übernimmt, kann sie sich nicht auf dieses nicht erfasste Wissen verlassen. Lücken im System können dann beeinflussen, welche Accounts Aufmerksamkeit erhalten, wie die Kontaktaufnahme gestaltet wird, ob eine Opportunity richtig gekennzeichnet ist und welche Abschlüsse die Umsatzverantwortlichen erwarten können.\u003C/p\u003E\n\u003Cp\u003EKI kann Vertriebsteams dabei helfen, mehr vom Kontext hinter einem Account oder Deal zu interpretieren, einschließlich Konversationen, Engagement, Produktaktivität und kommerziellem Fortschritt. Sie kann auch Inhalte vorbereiten, Systeme aktualisieren und die Arbeit über den gesamten Umsatzprozess hinweg koordinieren. Die Qualität dieser Ergebnisse hängt davon ab, ob die zugrunde liegenden Daten vernetzt, aktuell und kontrolliert sind und ob das System über genügend Geschäftskontext verfügt, um sie angemessen zu nutzen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-in-sales":{"id":"title-v2-375cb76133","additionalClasses":"anchor-title anchor-title--what-is-ai-in-sales","type":"heading2","lines":["Was ist KI im Vertrieb?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-in-sales_0":{"id":"text-5485541d53","text":"\u003Cp\u003EKI im Vertrieb ist der Einsatz von prädiktiver KI, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/\"\u003Egenerativer KI\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EAgenten-Systemen\u003C/a\u003E, um Vertriebsdaten zu interpretieren, Entscheidungen zu unterstützen und Arbeiten durch Umsatz-Workflows zu führen.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrädiktive KI schätzt, was wahrscheinlich passieren wird:\u003C/strong\u003E Sie identifiziert Muster in Account-, Aktivitäts- und Opportunity-Daten, um Leads zu bewerten, die Konversionswahrscheinlichkeit zu schätzen, Geschäftsrisiken zu identifizieren und den Umsatz zu prognostizieren.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EGenerative KI verwandelt den Vertriebskontext in wertvolle Materialien:\u003C/strong\u003E Sie erstellt Account-Recherchen, personalisierte Kontaktaufnahmen, Konversationszusammenfassungen, Angebote, Coaching-Feedback und andere Inhalte unter Verwendung der ihr zur Verfügung gestellten Informationen und Anweisungen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAgentic AI koordiniert mehrstufige Arbeiten:\u003C/strong\u003E Sie ruft Informationen ab, wählt aus genehmigten Tools aus und setzt eine Vertriebsaufgabe innerhalb der von der Anwendung festgelegten Berechtigungen und Betriebsgrenzen fort.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDiese Funktionen arbeiten oft zusammen. Ein prädiktives Modell könnte einen Account mit einer hohen Konversionswahrscheinlichkeit identifizieren, während ein generatives System Recherchen und eine erste Nachricht vorbereitet, die auf der Branche des Accounts, den jüngsten Aktivitäten und früheren Interaktionen basieren. Ein Agent könnte dann die genehmigte Kontaktaufnahme aufzeichnen, auf eine Antwort warten und die nächste zulässige Aktion koordinieren.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ESehen Sie sich an, wie Jonathan Karon von Anomalo erklärt, wie man automatisierte Datenqualität in Snowflake implementiert:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-ai-in-sales_0":{"id":"embed-e73e062c0a","youtubeVideoId":"gUaimG4dcQM","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"},"title_ai-for-prospecting-and-outreach":{"id":"title-v2-39b83d16fa","additionalClasses":"anchor-title anchor-title--ai-for-prospecting-and-outreach","type":"heading2","lines":["KI für Neukundengewinnung und Kontaktaufnahme"],":type":"snowflake-site/components/title-v2"},"text_ai-for-prospecting-and-outreach_0":{"id":"text-9e6c63743f","text":"\u003Cp\u003EDie Neukundengewinnung beginnt mit einer Entscheidung über die Ressourcenzuweisung: Welche Accounts und Käufer:innen verdienen jetzt die Aufmerksamkeit eines:einer Verkäufer:in. KI kann Teams dabei helfen, diese Entscheidung anhand einer breiteren und aktuelleren Reihe von Signalen zu treffen als eine manuell gepflegte Lead-Liste oder ein statisches Bewertungsmodell.\u003C/p\u003E\n\u003Cp\u003EPrädiktive Modelle bewerten Muster in Account-Attributen, Marketing-Engagement, Produktaktivität, früheren Interaktionen und historischen Verkaufsergebnissen. Wenn neue Aktivitätssignale verfügbar werden, kann sich die Bewertung ändern, um das aktuelle Verhalten des Accounts widerzuspiegeln.\u003C/p\u003E\n\u003Cp\u003EKI kann mehrere Teile der Neukundengewinnung und Kontaktaufnahme unterstützen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ELead- und Account-Scoring:\u003C/strong\u003E Schätzen Sie die Wahrscheinlichkeit ein, dass sich ein Lead oder Account innerhalb eines definierten Zeitraums qualifiziert, konvertiert oder weiterentwickelt.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EErkennung von Kaufsignalen:\u003C/strong\u003E Identifizieren Sie bedeutsame Änderungen beim Engagement, bei der Produktaktivität, bei den Unternehmensmerkmalen oder bei anderen genehmigten Daten.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAccount-Recherche:\u003C/strong\u003E Sammeln und fassen Sie relevante Informationen aus internen Aufzeichnungen und zulässigen externen Quellen zusammen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAnalyse der Käufergruppe:\u003C/strong\u003E Verknüpfen Sie Kontakte, Rollen und Interaktionen, um Vertriebsmitarbeiter:innen zu helfen, zu verstehen, wer den Kauf beeinflussen könnte.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EVorbereitung der Kontaktaufnahme:\u003C/strong\u003E Entwerfen Sie Nachrichten unter Berücksichtigung der Situation des Accounts, früherer Interaktionen und des Ziels der Kommunikation.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEmpfehlungen für die nächstbeste Aktion:\u003C/strong\u003E Ordnen Sie mögliche Aktionen basierend auf der Phase des Accounts, den jüngsten Aktivitäten und den geltenden Vertriebsregeln.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EJede dieser Anwendungen hängt vom aktuellen, vernetzten Account-Kontext ab. Wenn Signale unvollständig, veraltet oder dem falschen Account zugeordnet sind, kann der Workflow den falschen Interessenten priorisieren, das Interesse falsch interpretieren oder eine Aktion empfehlen, die nicht zur Situation passt. Teams sollten auch definieren, welche Informationen für die Neukundengewinnung verwendet werden dürfen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-for-deal-execution-and-enablement":{"id":"title-v2-6c769aa36b","additionalClasses":"anchor-title anchor-title--ai-for-deal-execution-and-enablement","type":"heading2","lines":["KI für Geschäftsabschluss und Enablement"],":type":"snowflake-site/components/title-v2"},"text_ai-for-deal-execution-and-enablement_0":{"id":"text-8864150e32","text":"\u003Cp\u003ESobald eine Opportunity aktiv ist, produziert die Vertriebsarbeit einen kontinuierlichen Informationsfluss. Käufer:innen stellen Fragen, erheben Einwände, stellen Stakeholder:innen vor, überarbeiten Anforderungen und machen Zusagen. Vertriebsmitarbeiter:innen antworten mit Produktinformationen, Preisen, Zeitplänen und Folgeaktionen. Ein Großteil dieses Kontexts taucht in Meetings und Nachrichten auf und gelangt in der Regel nicht sofort in das CRM-System.\u003C/p\u003E\n\u003Cp\u003EKI kann diese Interaktionen in eine vollständigere und sofort nutzbare Opportunity-Historie verwandeln:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EMeeting-Vorbereitung:\u003C/strong\u003E Fassen Sie den Account, die Opportunity, die jüngsten Aktivitäten, offene Fragen und frühere Zusagen vor einem Anruf zusammen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEchtzeit-Unterstützung:\u003C/strong\u003E Rufen Sie genehmigte Produkt-, Preis-, Sicherheits- oder Wettbewerbsinformationen ab, während ein Gespräch läuft.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EKonversationszusammenfassungen:\u003C/strong\u003E Erfassen Sie die Hauptthemen, Käuferanforderungen, Einwände, Entscheidungen und nächsten Schritte aus einem Anruf oder Meeting.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFollow-up-Zusammenfassungen:\u003C/strong\u003E Bereiten Sie kundenorientierte Nachrichten vor, die widerspiegeln, was besprochen wurde und was jeder Teilnehmer zu tun zugestimmt hat.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAngebots- und Kostenvoranschlagsvorbereitung:\u003C/strong\u003E Stellen Sie erstes Material aus genehmigten Produkt-, Preis- und Account-Informationen zusammen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EOpportunity-Aktualisierungen:\u003C/strong\u003E Schlagen Sie Änderungen an CRM-Feldern basierend auf Erkenntnissen aus jüngsten Interaktionen vor.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EVertriebs-Coaching:\u003C/strong\u003E Identifizieren Sie Muster in Konversationen, die Manager:innen und Vertriebsmitarbeiter:innen gemeinsam untersuchen können.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EZusammen reduzieren diese Funktionen die Menge an Geschäftskontext, die zwischen Konversationen, Systemen und Personen verloren geht. 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Diese Kontinuität ist besonders wertvoll in komplexen Verkaufszyklen, an denen mehrere Stakeholder:innen teilnehmen können und Wochen zwischen den Interaktionen vergehen können.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_ai-for-deal-execution-and-enablement_0":{"id":"card-v2-41e65486c3","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Intercom"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Vollständige Customer Story lesen"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--43c070c5-1ab2-4ac0-8a6c-094ab483907c/intercom.png?quality=85&preferwebp=true","alt":"intercom logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EIntercom nutzt Snowflakes AI Data Cloud, Cortex AI und Snowflake Container Services, um Sales Cockpit zu betreiben, eine einheitliche KI-gesteuerte App, die Vertriebsteams hilft, Kundenverhalten und Service-Performance schnell zu verstehen. Sales Cockpit wurde in wenigen Wochen entwickelt und in wenigen Tagen bereitgestellt. Es generiert Kunden-Einblick-Präsentationen in weniger als 10 Minuten statt in 3–4 Stunden, reduziert die Vorbereitung personalisierter ausgehender E-Mails von 40 Minuten auf etwa 30 Sekunden und spart Vertriebsteams mehr als 2.000 Stunden pro Monat – ein geschätzter Vorteil von 1,4 Millionen USD. \u003Ci\u003EDie Statistiken basieren auf internen Daten und Schätzungen von Intercom.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_ai-for-forecasting-and-revenue-intelligence":{"id":"title-v2-3a47d8d26c","additionalClasses":"anchor-title anchor-title--ai-for-forecasting-and-revenue-intelligence","type":"heading2","lines":["KI für Prognosen und Revenue Intelligence"],":type":"snowflake-site/components/title-v2"},"text_ai-for-forecasting-and-revenue-intelligence_0":{"id":"text-23ffcc2a9b","text":"\u003Cp\u003EEin Pipeline-Bericht spiegelt wider, was über jede Opportunity aufgezeichnet wurde: ihr Wert, ihre Phase, ihre Wahrscheinlichkeit, ihr erwartetes Abschlussdatum und ihre Prognosekategorie. KI kann diese Felder mit einer breiteren Beweisgrundlage vergleichen, einschließlich jüngster Aktivitäten, Stakeholder-Beteiligung, Produktnutzung, Angebotsstatus, Vertragsfortschritt und der Art und Weise, wie sich ähnliche Opportunities in der Vergangenheit entwickelt haben.\u003C/p\u003E\n\u003Cp\u003EZu den gängigen Anwendungen gehören:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EOpportunity-Scoring:\u003C/strong\u003E Schätzen Sie die Wahrscheinlichkeit ein, dass ein Deal abgeschlossen wird oder ein anderes definiertes Ergebnis erreicht.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EErkennung von Deal-Risiken:\u003C/strong\u003E Identifizieren Sie ins Stocken geratene Aktivitäten, sich verschiebende Termine, fehlende Stakeholder:innen oder andere Muster, die mit schwächeren Ergebnissen einhergehen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESchätzung des Abschlussdatums:\u003C/strong\u003E Prognostizieren Sie, wann eine Opportunity voraussichtlich abgeschlossen wird, basierend auf ihrem Verlauf und aktuellen Erkenntnissen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPipeline-Inspektion:\u003C/strong\u003E Decken Sie Opportunities auf, deren erfasste Phase oder Prognosekategorie nicht mit der beobachteten Aktivität übereinzustimmen scheint.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAbdeckungsanalyse:\u003C/strong\u003E Vergleichen Sie die verfügbare Pipeline mit Umsatzzielen, Konversionsmustern und der verbleibenden Zeit in der Verkaufsperiode.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESzenarioplanung:\u003C/strong\u003E Modellieren Sie, wie sich Änderungen bei Konversion, Deal-Timing, Kapazität oder Pipeline-Erstellung auf den erwarteten Umsatz auswirken könnten.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUmsatzprognose:\u003C/strong\u003E Schätzen Sie zukünftige Buchungen oder Umsätze anhand von Pipeline, historischer Performance und relevanten Finanzdaten.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EErklärung der Prognose:\u003C/strong\u003E Zeigen Sie, welche Opportunities und Signale am meisten zur Vorhersage oder Änderung beigetragen haben.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDie Genauigkeit von Prognosen hängt von konsistenten Geschäftsdefinitionen in den Bereichen Vertrieb, Finanzen und Revenue Operations ab. Pipeline-Wert, Phase, Abschlussdatum und Prognosekategorie mögen im CRM standardisiert aussehen, aber Teams wenden sie oft je nach Region, Produkt oder Manager:in unterschiedlich an.\u003C/p\u003E\n\u003Cp\u003ETeams benötigen daher verwaltete Definitionen für die Felder und Ergebnisse, die bei der Prognose verwendet werden, sowie eine Aufzeichnung darüber, wann sich diese Definitionen ändern. Die Trainingsdaten, die Modellausgabe und die Prognoseberichte sollten alle denselben semantischen Kontext verwenden, damit die Vorhersage anhand des Geschäftsergebnisses bewertet wird, für dessen Schätzung sie eigentlich entwickelt wurde.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sales-automation-and-ai-sales-agents":{"id":"title-v2-1c4affdba2","additionalClasses":"anchor-title anchor-title--sales-automation-and-ai-sales-agents","type":"heading2","lines":["Vertriebsautomatisierung und KI-Vertriebsagenten"],":type":"snowflake-site/components/title-v2"},"text_sales-automation-and-ai-sales-agents_0":{"id":"text-971cd9b34b","text":"\u003Cp\u003EDie traditionelle Vertriebsautomatisierung verwendet vordefinierte Bedingungen, um bestimmte Aktionen auszulösen. Ein Workflow könnte beispielsweise einen Lead basierend auf dem Gebiet zuweisen oder eine Aufgabe erstellen, wenn eine Opportunity keine kürzliche Aktivität aufweist. Diese Systeme sind effektiv für wiederholbare Prozesse, lassen sich aber nicht leicht anpassen, wenn die geeignete Aktion von anderen Faktoren abhängt.\u003C/p\u003E\n\u003Cp\u003EKI-Agenten können Vertriebsarbeit über mehrere Systeme und Schritte hinweg ausführen und ihre Aktionen anpassen, sobald neue Informationen zu Accounts, Opportunities und Käufer:innen verfügbar werden. Wenn eine Opportunity beispielsweise ins Stocken gerät, hängt die angemessene Reaktion davon ab, was passiert ist. Der:die Käufer:in wartet möglicherweise auf eine Sicherheitsantwort, ein:e Entscheidungsträger:in hat sich möglicherweise zurückgezogen, das Angebot ist möglicherweise abgelaufen oder der CRM-Eintrag ist einfach veraltet. Ein Agent kann diesen Kontext nutzen, um zu bestimmen, ob fehlende Informationen abgerufen, ein Follow-up vorbereitet, die Opportunity aktualisiert oder das Problem an die zuständige Person weitergeleitet werden soll.\u003C/p\u003E\n\u003Cp\u003EVertriebsagenten können Arbeiten unterstützen wie:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EQualifizierung von Interessent:innen:\u003C/strong\u003E Recherchieren Sie einen Account, bewerten Sie ihn anhand genehmigter Kriterien und bereiten Sie die unterstützenden Nachweise für die Überprüfung vor.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMeeting-Vorbereitung:\u003C/strong\u003E Stellen Sie Account-Historie, kürzliche Aktivitäten, Opportunity-Status und ungelöste Fragen zusammen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFollow-up-Koordination:\u003C/strong\u003E Bereiten Sie eine Zusammenfassung vor, erstellen Sie zugewiesene Aufgaben und aktualisieren Sie genehmigte Opportunity-Felder nach einem Meeting.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPipeline-Pflege:\u003C/strong\u003E Identifizieren Sie unvollständige oder inkonsistente Datensätze und fordern Sie Korrekturen an oder schlagen Sie diese vor.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDeal-Inspektion:\u003C/strong\u003E Untersuchen Sie Aktivitäten, Stakeholder-Engagement, offene Verpflichtungen und den kommerziellen Fortschritt vor einem Pipeline-Review.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EWorkflows für Verlängerung und Erweiterung:\u003C/strong\u003E Überwachen Sie Account-Signale, identifizieren Sie relevante Opportunities und koordinieren Sie genehmigte Kontaktaufnahmen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ENext-Best-Action-Workflows:\u003C/strong\u003E Empfehlen oder führen Sie eine zulässige Aktion unter Verwendung des aktuellen Kontexts des Accounts aus.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EVorbereitung des Forecast-Reviews:\u003C/strong\u003E Untersuchen Sie wesentliche Änderungen und stellen Sie die Nachweise zusammen, die Manager:innen zur Bewertung benötigen.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EVertriebsteams können diese Funktionen mit unterschiedlichen Autonomiegraden nutzen. Ein Copilot unterstützt eine interaktive Aufgabe auf begrenzte Weise: Ein:e Vertriebsmitarbeiter:in könnte ihn beispielsweise bitten, einen Account zusammenzufassen, das Ergebnis dann manuell überprüfen und entscheiden, was als Nächstes passiert.\u003C/p\u003E\n\u003Cp\u003EEin Agent kann in einem größeren Teil des Workflows Maßnahmen ergreifen und genehmigte Tools verwenden, um Informationen abzurufen, ein System zu aktualisieren, eine Aufgabe zu erstellen oder Arbeit zur Genehmigung weiterzuleiten. Da das System mehr Arbeit übernimmt, müssen Teams definieren, was es ohne Überprüfung tun darf und wo eine menschliche Genehmigung erforderlich ist.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_sales-automation-and-ai-sales-agents_0":{"id":"text-e2643b36a7","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EHÄUFIGER FALLSTRICK\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ETeams gewähren einem Agenten oft weitreichenden Zugriff, da jede einzelne Aktion risikoarm erscheint. Eine Abfolge zulässiger Aktionen kann jedoch zusammen ein risikoreiches Ergebnis erzeugen, weshalb Teams den gesamten Workflow bewerten und steuern sollten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_what-keeps-ai-sales-workflows-accurate-and-accountable":{"id":"title-v2-6e6581a4d9","additionalClasses":"anchor-title anchor-title--what-keeps-ai-sales-workflows-accurate-and-accountable","type":"heading2","lines":["Was KI-Vertriebsworkflows präzise und nachvollziehbar hält"],":type":"snowflake-site/components/title-v2"},"text_what-keeps-ai-sales-workflows-accurate-and-accountable_0":{"id":"text-84386fa2e5","text":"\u003Cp\u003EDie von KI in Vertriebsanwendungen verwendeten Datensätze müssen verbunden bleiben und konsistente Geschäftsbedeutungen beibehalten, während sie Workflows durchlaufen. Zuverlässige und verantwortungsvolle KI-Systeme hängen auch von klaren Grenzen hinsichtlich des Datenzugriffs, kundenorientierter Aussagen und der Aktionen ab, die ein Agent ausführen darf.\u003C/p\u003E\n\u003Cp\u003EWie Baris Gultekin, VP of Product, AI bei Snowflake, anmerkt: „KI-Agenten sind ein großer Sprung gegenüber herkömmlicher Automatisierung oder Chatbots, aber um sie in großem Maßstab einzusetzen, benötigen Unternehmen ein KI-fähiges Informationsökosystem.“\u003C/p\u003E\n\u003Ch3\u003EVerbinden Sie Account-, Aktivitäts- und Umsatzdaten\u003C/h3\u003E\n\u003Cp\u003EVertriebsentscheidungen stützen sich auf CRM-Datensätze, Marketing-Engagement, Vertriebsaktivitäten, Meeting-Transkripte, Produktnutzung, Support-Interaktionen, Angebote, Verträge und Finanzsysteme. Jede Quelle erfasst einen Teil der Geschäftsbeziehung, oft unter Verwendung unterschiedlicher Kennungen und Aktualisierungspläne.\u003C/p\u003E\n\u003Cp\u003EEine verbundene Account-Historie bietet Modellen und Anwendungen eine konsistente Möglichkeit, Aktivitäten dem:der richtigen Kund:in, dem richtigen Kontakt, der richtigen Opportunity oder der richtigen Einkaufsgruppe zuzuordnen. Ohne diese Verbindung bewertet eine Anwendung möglicherweise einen Account, ohne ein aktives Support-Problem zu berücksichtigen, oder fasst eine Opportunity zusammen, ohne Einblick in das neueste Angebot zu haben.\u003C/p\u003E\n\u003Cp\u003EDie Identitätsauflösung ist besonders wichtig, wenn mehrere Kontakte am Kauf beteiligt sind. Das System muss einzelne Personen unterscheiden und gleichzeitig ihre Beziehung zu demselben Account und derselben Opportunity verstehen. Es sollte auch Änderungen im Laufe der Zeit beibehalten, z. B. wenn ein Kontakt zu einem neuen Unternehmen wechselt oder eine Opportunity in mehrere Kaufvorgänge aufgeteilt wird.\u003C/p\u003E\n\u003Cp\u003EDie Aktualität der Daten sollte der Entscheidung entsprechen. Die jährliche Gebietsplanung kann beispielsweise regelmäßig aktualisierte Account-Informationen verwenden, während die Live-Unterstützung für Opportunities aktuelle Interaktionen, aktuelle Preise und den neuesten Deal-Status erfordert. Teams sollten fehlende Datensätze, verzögerte Aktualisierungen und Änderungen in der Quellenabdeckung überwachen, da jede dieser Ursachen die Ausgaben ohne offensichtlichen Anwendungsfehler verändern kann.\u003C/p\u003E\n\u003Ch3\u003EStandardisieren Sie Vertriebs- und Umsatzdefinitionen\u003C/h3\u003E\n\u003Cp\u003EDie Terminologie erfordert klare, konsistente Definitionen über Anwendungen und Teams hinweg. Beispielsweise kann sich der Deal-Wert auf den jährlichen Vertragswert, den gesamten Vertragswert oder den in der aktuellen Periode erwarteten Umsatz beziehen. Vertriebs-, Finanz- und Revenue-Operations-Teams müssen sich darauf einigen, was jeder Begriff bedeutet, damit Modelle, Berichte und Agenten die richtige Definition für die Aufgabe verwenden.\u003C/p\u003E\n\u003Cp\u003EDer semantische Kontext macht diese Unterscheidungen durch verwaltete Metriken, Ansichten, Metadaten und Beziehungen zwischen Accounts, Kontakten, Opportunities, Produkten und Umsatzereignissen explizit. Er kann Kriterien für den Eintritt in eine Phase, Regeln für Prognosekategorien, Gebietszuweisungen, Qualifizierungsstandards und die Beziehung zwischen Buchungen und realisiertem Umsatz beibehalten.\u003C/p\u003E\n\u003Cp\u003EWichtige Definitionen sollten eine:n benannte:n Eigentümer:in haben, der:die Änderungen genehmigt und Differenzen zwischen Vertrieb, Finanzen, Marketing und Revenue Operations klärt. Wenn sich der Qualifizierungsstandard oder der Prognoseprozess ändert, sollten Teams auch feststellen, ob historische Datensätze noch dasselbe Ergebnis darstellen. Andernfalls behandelt das Modell möglicherweise zwei operative Definitionen so, als wären sie ein konsistentes Label.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-keeps-ai-sales-workflows-accurate-and-accountable_0":{"id":"text-3f7cd79e7c","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EErstellen Sie eine kurze Definitionskarte für jedes Feld, das von einem Modell oder Agenten verwendet wird. Geben Sie die Geschäftsbedeutung, den:die Eigentümer:in, die Berechnung, das Gültigkeitsdatum und etwaige Unterschiede zwischen Regionen oder Produktlinien an.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_what-keeps-ai-sales-workflows-accurate-and-accountable_1":{"id":"text-1602417f67","text":"\u003Ch3\u003EStützen Sie generierte Ausgaben auf genehmigte Vertriebsinformationen\u003C/h3\u003E\n\u003Cp\u003EVertriebsanwendungen verwenden sowohl account-spezifischen Kontext als auch unternehmensweites Quellenmaterial. Der Account-Kontext erklärt die Situation des:der Käufer:in und die Historie der Opportunity. Vom Unternehmen genehmigtes Quellenmaterial legt fest, was über seine Produkte, Preise, Sicherheitspraktiken, Implementierungsanforderungen und Vertragsbedingungen gesagt werden kann.\u003C/p\u003E\n\u003Cp\u003EGenehmigte Informationen sind besonders wichtig bei kundenorientierten Inhalten. Wenn die verfügbaren Quellen die Frage eines:einer Käufer:in nicht beantworten, sollte die Ausgabe das Problem für eine:n Vertriebsmitarbeiter:in oder Fachexpert:in ungelöst lassen, anstatt eine plausibel klingende Antwort zu liefern.\u003C/p\u003E\n\u003Cp\u003EQuelleninformationen benötigen gegebenenfalls eine:n Eigentümer:in, eine Version und einen Gültigkeitszeitraum. Der Abruf sollte aktuelles Material priorisieren und gleichzeitig genügend Historie beibehalten, um bereits während des Deals gemachte Zusagen zu erklären.\u003C/p\u003E\n\u003Ch3\u003ESetzen Sie Zugriff und zulässige Nutzung durch\u003C/h3\u003E\n\u003Cp\u003EVertriebsworkflows enthalten personenbezogene Daten, Account-Strategien, Anrufaufzeichnungen, Preise, Produktnutzung und Vertragsdetails. Diese Datensätze unterliegen weiterhin den Anforderungen an Zugriff und zulässige Nutzung, wenn eine KI-Anwendung sie abruft oder zu einer neuen Ausgabe kombiniert.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-security/rbac/\"\u003ERollenbasierte Zugriffskontrollen\u003C/a\u003E (RBAC) schränken ein, welche Benutzer:innen und Dienstidentitäten kundenbezogene oder kommerziell sensible Informationen einsehen können. Klassifizierung und Maskierung schützen sensible Felder, während Berechtigungen für Gebiete, Account-Teams und Deal-Rooms weiter einschränken können, wer auf eine bestimmte Opportunity zugreifen kann.\u003C/p\u003E\n\u003Cp\u003EDie Anwendung muss auch den Zweck und die Bedingungen respektieren, unter denen Daten erfasst wurden. Die Genehmigung zur Nutzung von Informationen für den Kundensupport oder die Produkt-Analytics berechtigt das Unternehmen nicht automatisch dazu, sie für das Sales-Targeting oder andere Zwecke zu verwenden. Verträge, regionale Datenschutzanforderungen und interne Richtlinien können einschränken, wie Aktivitätsdaten und Anrufaufzeichnungen analysiert werden dürfen.\u003C/p\u003E\n\u003Cp\u003EAudit-Datensätze sollten zeigen, auf welche Daten die Anwendung zugegriffen hat, welche Richtlinien angewendet wurden und wie die Ausgabe in den Vertriebsworkflow gelangt ist. Dies wird besonders wichtig, wenn eine Empfehlung die Account-Priorisierung beeinflusst, ein:e Kund:in generierte Kommunikation erhält oder ein Agent ein System of Record ändert.\u003C/p\u003E\n\u003Ch3\u003EBeschränken Sie Agentenberechtigungen und kundenorientierte Aktionen\u003C/h3\u003E\n\u003Cp\u003EAgentenbasierte Vertriebsworkflows benötigen zusätzlich zu Datenzugriffskontrollen Berechtigungen auf Aktionsebene.\u003C/p\u003E\n\u003Cp\u003EDer Workflow sollte definieren:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EAuf welche Accounts, Opportunities und Datenquellen der Agent zugreifen darf\u003C/li\u003E\n\u003Cli\u003EWelche CRM-Felder er vorschlagen oder aktualisieren darf\u003C/li\u003E\n\u003Cli\u003EWelche Kommunikationskanäle er nutzen darf\u003C/li\u003E\n\u003Cli\u003EOb ausgehende Nachrichten die Genehmigung der Vertriebsmitarbeiter:innen erfordern\u003C/li\u003E\n\u003Cli\u003EWelche Produkt-, Preis- oder Vertragsaussagen er einbeziehen darf\u003C/li\u003E\n\u003Cli\u003EWelche kommerziellen Änderungen die Genehmigung von Management, Finanz- oder Rechtsabteilung erfordern\u003C/li\u003E\n\u003Cli\u003EWann der Agent anhalten, um Klärung bitten oder die Aufgabe eskalieren muss\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDienstidentitäten, Tool-Berechtigungen, Genehmigungsschwellenwerte und Stoppbedingungen definieren die operativen Grenzen des Agenten. Die Anwendung sollte nur die für die zugewiesene Aufgabe erforderlichen Daten und Tools erhalten, anstatt eines umfassenden Zugriffs auf den Vertriebs-Stack. Teams benötigen außerdem Traces, die zeigen, welche Informationen der Agent abgerufen, welche Tools er aufgerufen und welche Aktionen er abgeschlossen hat.\u003C/p\u003E\n\u003Cp\u003EDiese Kontrollen wahren die menschliche Verantwortung für Kundenbeziehungen und daraus resultierende kommerzielle Entscheidungen, während der Agent einen größeren Teil der damit verbundenen Recherche, Koordination und Dokumentation übernehmen kann.\u003C/p\u003E\n\u003Ch3\u003EOutputs anhand von Vertriebsergebnissen bewerten\u003C/h3\u003E\n\u003Cp\u003EEine zuverlässige Bewertung beginnt mit einem Dataset, das die Vertriebsarbeit widerspiegelt, auf die das System stoßen wird, einschließlich häufiger Fälle, schwieriger Fälle und Situationen, in denen die richtige Reaktion darin besteht, anzuhalten oder zu eskalieren. Erwartete Outputs, zulässige Aktionen und Akzeptanzkriterien sollten vor dem Testen definiert werden, damit das System an einem konsistenten Standard und nicht an den Eindrücken der Prüfer:innen gemessen wird.\u003C/p\u003E\n\u003Cp\u003EDie Bewertungsmethode hängt dann von der jeweiligen Fähigkeit ab. Prädiktive Modelle erfordern Metriken wie Kalibrierung, Ranking-Qualität und Performance über relevante Segmente hinweg. Generative Systeme benötigen Tests auf sachliche Richtigkeit, Verankerung in genehmigten Quellen, Vollständigkeit und Einhaltung der erforderlichen Sprache. Agenten müssen auch hinsichtlich des von ihnen gewählten Pfads bewertet werden: ob sie die geeigneten Tools auswählen, die richtigen Daten verwenden, innerhalb ihrer Berechtigungen bleiben, sich von Fehlern erholen und die Stoppbedingungen der Aufgabe erfüllen.\u003C/p\u003E\n\u003Cp\u003EErgebnisse auf Komponentenebene reichen jedoch nicht aus, wenn mehrere Modelle, Abrufschritte und Tools zu einem Workflow beitragen. Teams sollten auch End-to-End-Bewertungen durchführen, die erfassen, ob der Kontext zwischen den Schritten erhalten bleibt und ob ein früher Fehler eine spätere Entscheidung oder Aktion verändert.\u003C/p\u003E\n\u003Cp\u003ENach der Bereitstellung helfen Produktions-Traces, Korrekturraten, Eskalationen und Ergebnisdaten dabei, aufzuzeigen, ob sich die Performance ändert, wenn sich Vertriebsdaten, Produkte und Betriebsregeln weiterentwickeln. Diese Beobachtungen sollten in das Bewertungsset zurückfließen, damit die Tests weiterhin die vom System ausgeführte Arbeit repräsentieren.\u003C/p\u003E\n\u003Ch3\u003EDatenqualität und Bias überwachen\u003C/h3\u003E\n\u003Cp\u003EVertriebsdaten spiegeln sowohl das Käuferverhalten als auch die früheren Entscheidungen des Unternehmens wider. Historische Ergebnisse werden davon geprägt, welche Accounts in das CRM aufgenommen wurden, wie Gebiete zugewiesen wurden, welche Leads weiterverfolgt wurden und wie konsequent die Vertriebsmitarbeiter:innen ihre Aktivitäten dokumentiert haben. Ein Modell, das mit diesen Datensätzen trainiert wurde, lernt möglicherweise die frühere Aufmerksamkeitsverteilung des Unternehmens zusammen mit Mustern der Kundennachfrage.\u003C/p\u003E\n\u003Cp\u003EUnvollständige Aktivitäten führen zu einer weiteren Verzerrung. Erfolgreiche Vertriebsmitarbeiter:innen, die nur wichtige Meilensteine aufzeichnen, scheinen Deals möglicherweise mit weniger Interaktionen voranzutreiben als solche, die jeden Anruf und jede Nachricht dokumentieren. Accounts, die über informelle Kanäle verwaltet werden, wirken möglicherweise weniger engagiert als Accounts, deren Aktivitäten automatisch erfasst werden.\u003C/p\u003E\n\u003Cp\u003EDie Behebung dieser Probleme beginnt mit aktuellen, kontrollierten Quelldaten und dokumentierten Ergebnisdefinitionen, erfordert aber auch aktives Testen. Teams sollten die Performance über Gebiete, Account-Gruppen, Akquisitionsquellen und andere relevante Segmente hinweg vergleichen; untersuchen, ob fehlende Daten die Vorhersage verändern; und überprüfen, wie sich Modellempfehlungen auf die zukünftigen Daten auswirken, die für das Training verwendet werden.\u003C/p\u003E\n\u003Cp\u003EWenn ein System ungleichmäßige Ergebnisse liefert, sollten Teams die Datensätze, Labels, Entscheidungsregeln und Betriebsprozesse untersuchen, die diese hervorgebracht haben. Eine menschliche Überprüfung am Ende des Workflows korrigiert kein Scoring- oder Priorisierungsmuster, das wiederholt bestimmt, welche Accounts überhaupt erst Aufmerksamkeit erhalten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-for-sales-runs-on-snowflake":{"id":"title-v2-bd35a2771a","additionalClasses":"anchor-title anchor-title--why-ai-for-sales-runs-on-snowflake","type":"heading2","lines":["Warum KI für den Vertrieb auf Snowflake läuft"],":type":"snowflake-site/components/title-v2"},"text_why-ai-for-sales-runs-on-snowflake_0":{"id":"text-5e8d9d3209","text":"\u003Cp\u003ESnowflake bietet Vertriebs-, Revenue-Operations-, Daten- und Finanzteams eine gemeinsame Umgebung für die Arbeit mit CRM-, Aktivitäts-, Produkt-, Support- und Umsatzdaten sowie für die Anwendung von prädiktiver, generativer und Agentic AI, ohne für jeden Vertriebs-Workflow eine separate Datenarchitektur erstellen zu müssen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E unterstützt prädiktive Modelle, die auf kontrollierten Daten in Snowflake erstellt und ausgeführt werden. Klassifizierung kann Workloads wie Lead-, Account- oder Opportunity-Scoring unterstützen, indem Muster erlernt werden, die mit definierten historischen Ergebnissen verknüpft sind. Prognosefunktionen können zukünftige numerische Werte aus Zeitreihendaten vorhersagen, während umfassendere Funktionen die Entwicklung und den Betrieb benutzerdefinierter Modelle ermöglichen, wenn der Workflow dies erfordert.\u003C/p\u003E\n\u003Cp\u003EDa das Modell dort ausgeführt wird, wo sich die zugrunde liegenden Daten bereits befinden, können Teams Scores und Vorhersagen aus verbundenen CRM-, Aktivitäts- und Produktdaten aktualisieren, ohne eine separate Scoring-Kopie pflegen zu müssen. Das Unternehmen bestimmt weiterhin das Zielergebnis, die Trainingsdaten, die Bewertungskriterien und wie die Vorhersage in den Vertriebsprozess einfließt.\u003C/p\u003E\n\u003Cp\u003ECortex AI Functions wenden Sprach- und multimodale Modelle auf in Snowflake gespeicherte Informationen an. Vertriebs- und Datenteams können diese Funktionen nutzen, um Besprechungsprotokolle zusammenzufassen, Einwände zu klassifizieren, Anforderungen oder Aktionspunkte zu extrahieren und Sammlungen von Notizen oder Dokumenten über SQL-basierte Daten-Workflows zu analysieren. Diese Outputs können mit strukturierten Account- und Opportunity-Daten für Reporting, Coaching oder nachgelagerte Anwendungen verknüpft werden.\u003C/p\u003E\n\u003Cp\u003EFür Workflows, die Informationen untersuchen und über mehrere Schritte hinweg fortgesetzt werden müssen, können \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E Cortex Analyst für strukturierte Daten, Cortex Search für unstrukturierte Informationen und benutzerdefinierte Tools kombinieren, die mit einem genehmigten Geschäftsprozess verbunden sind. Eine Vertriebsanwendung könnte diese Funktionen nutzen, um eine Opportunity zu prüfen, relevante Interaktionen und Dokumente abzurufen, eine Empfehlung vorzubereiten und die resultierende Arbeit an ein anderes System weiterzuleiten.\u003C/p\u003E\n\u003Cp\u003EDer Zugriff auf Cortex Agents wird über die Role-Based Access Control von Snowflake geregelt, einschließlich Berechtigungen für den Agenten und die Objekte, die seine Tools verwenden. Die für die Anwendung konfigurierten Rollen, Tools und Berechtigungen bestimmen, welche Informationen der Agent abrufen und welche Aktionen er ausführen kann. Agenten-Monitoring-Datensätze können zusätzliche Transparenz in Bezug auf Anfragen und die zugrunde liegende Tool-Nutzung bieten.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E bietet Governance über die Daten- und KI-Workloads hinter diesen Anwendungen hinweg. Klassifizierung, Maskierung, Role-Based Access Control und Lineage bleiben mit den zugrunde liegenden Account-, Konversations- und Umsatzdaten verbunden und helfen Unternehmen dabei, zu kontrollieren, wer und was auf sensible Vertriebsinformationen zugreifen darf.\u003C/p\u003E\n\u003Cp\u003EZusammen ermöglichen diese Funktionen es Unternehmen, KI über verschiedene Vertriebsanwendungsfälle hinweg einzusetzen und gleichzeitig die Daten, Geschäftsdefinitionen und Richtlinien unter diesen Workflows konsistent zu halten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-ai-for-sales-runs-on-snowflake_0":{"id":"text-558948134b","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EErfolgreiche Vertriebs-KI erfordert mehr als nur ein leistungsfähiges Modell. Sie erfordert vernetzte Kunden- und Umsatzdaten, gemeinsame Geschäftsdefinitionen, genehmigtes Quellmaterial und Kontrollen, die sicherstellen, dass jede Empfehlung und Aktion fundiert, zulässig und nachvollziehbar ist.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-ai-in-sales","text_what-is-ai-in-sales_0","yt_what-is-ai-in-sales_0","title_ai-for-prospecting-and-outreach","text_ai-for-prospecting-and-outreach_0","title_ai-for-deal-execution-and-enablement","text_ai-for-deal-execution-and-enablement_0","card_v2_ai-for-deal-execution-and-enablement_0","title_ai-for-forecasting-and-revenue-intelligence","text_ai-for-forecasting-and-revenue-intelligence_0","title_sales-automation-and-ai-sales-agents","text_sales-automation-and-ai-sales-agents_0","callout_sales-automation-and-ai-sales-agents_0","title_what-keeps-ai-sales-workflows-accurate-and-accountable","text_what-keeps-ai-sales-workflows-accurate-and-accountable_0","callout_what-keeps-ai-sales-workflows-accurate-and-accountable_0","text_what-keeps-ai-sales-workflows-accurate-and-accountable_1","title_why-ai-for-sales-runs-on-snowflake","text_why-ai-for-sales-runs-on-snowflake_0","callout_why-ai-for-sales-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 aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-cf4c22d536",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-0a9768ca4f","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn diesem Leitfaden\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-12fea9977a","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-ai-in-sales\"\u003EWas ist KI im Vertrieb?\u003C/li\u003E\u003Cli data-anchor=\"ai-for-prospecting-and-outreach\"\u003EKI für Neukundengewinnung und Kontaktaufnahme\u003C/li\u003E\u003Cli data-anchor=\"ai-for-deal-execution-and-enablement\"\u003EKI für Geschäftsabschluss und Enablement\u003C/li\u003E\u003Cli data-anchor=\"ai-for-forecasting-and-revenue-intelligence\"\u003EKI für Prognosen und Revenue Intelligence\u003C/li\u003E\u003Cli data-anchor=\"sales-automation-and-ai-sales-agents\"\u003EVertriebsautomatisierung und KI-Vertriebsagenten\u003C/li\u003E\u003Cli data-anchor=\"what-keeps-ai-sales-workflows-accurate-and-accountable\"\u003EWas KI-Vertriebsworkflows präzise und nachvollziehbar hält\u003C/li\u003E\u003Cli data-anchor=\"why-ai-for-sales-runs-on-snowflake\"\u003EWarum KI für den Vertrieb auf Snowflake läuft\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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gestellte Fragen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-218e5042fe","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EIhre häufigsten Fragen zu KI im Vertrieb, beantwortet von Snowflake-Expert:innen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-039386b84c","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Was ist der Unterschied zwischen prädiktiver, generativer und Agentic AI im Vertrieb?","richText":"\u003Cp\u003EPrädiktive KI schätzt Ergebnisse wie Lead-Konvertierung, Deal-Risiko und zukünftigen Umsatz. Generative KI erstellt Materialien wie Account-Recherchen, Outreach, Anrufzusammenfassungen und Angebote. Agentic AI koordiniert mehrstufige Aufgaben und nutzt genehmigte Daten und Tools, um eine Situation zu untersuchen und zulässige Aktionen unter menschlicher Aufsicht auszuführen.\u003C/p\u003E"},{"title":"Wird KI Vertriebsmitarbeiter:innen ersetzen?","richText":"\u003Cp\u003EKI kann die Zeit reduzieren, die Vertriebsmitarbeiter:innen mit der Recherche von Accounts, der Dateneingabe, der Zusammenfassung von Besprechungen, der Vorbereitung erster Entwürfe und der Überprüfung der Pipeline verbringen. Sie kann ihnen auch dabei helfen, relevante Signale zu identifizieren und Informationen während eines Deals abzurufen. Aber der Vertrieb hängt weiterhin vom menschlichen Urteilsvermögen ab.\u003C/p\u003E"},{"title":"Welche Daten benötigen Sie, bevor KI für den Vertrieb Ergebnisse liefert?","richText":"\u003Cp\u003EKI für den Vertrieb benötigt vernetzte, aktuelle und kontrollierte Daten über Accounts, Kontakte, Opportunities, Aktivitäten und Ergebnisse. Das Unternehmen sollte in der Lage sein, diese Datensätze dem richtigen Account und der richtigen Opportunity zuzuordnen, zu definieren, was Felder und Metriken bedeuten, und zu identifizieren, welche Quellen kundenorientierte Aussagen regeln.\u003C/p\u003E"},{"title":"Wie unterscheidet sich KI für den Vertrieb von KI für das Marketing?","richText":"\u003Cp\u003EBeide Funktionen nutzen prädiktive, generative und Agentic AI, wenden sie jedoch auf unterschiedliche Teile der Kundenbeziehung an. Marketing-KI identifiziert und bindet in der Regel Zielgruppen über Kampagnen, Kanäle und Customer Journeys hinweg ein. Vertriebs-KI arbeitet direkter mit einzelnen Accounts, Kontakten und Opportunities durch Prospecting, Outreach, Deal Execution, Coaching und Forecasting.\u003C/p\u003E"},{"title":"Wie fangen Sie mit KI für den Vertrieb an?","richText":"\u003Cp\u003EBeginnen Sie damit, das CRM mit den für den ausgewählten Workflow erforderlichen Aktivitäts- und Ergebnisdaten zu verbinden, und definieren Sie dann Zugriffskontrollen, Geschäftsdefinitionen und Bewertungskriterien, bevor Sie KI anwenden. Wählen Sie einen abgegrenzten Anwendungsfall mit einem messbaren Ergebnis. Lead-Scoring kann testen, ob prädiktive KI die Account-Priorisierung verbessert, während Anrufzusammenfassungen eine direkte Möglichkeit bieten, den generierten Output mit der ursprünglichen Konversation zu vergleichen. Nachdem das Pilotprojekt eine zuverlässige Aufgaben-Performance und ein nützliches Vertriebsergebnis gezeigt hat, kann das Unternehmen den Workflow auf zusätzliche Daten, Aktionen und Agentic-Funktionen ausweiten.\u003C/p\u003E"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1467213961":{"id":"flexible-column-container-bef7a9aed4","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f93d6da559",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-e18327dfeb","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["KI-Ressourcen 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Classification"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"resource_chip_3":{"id":"content-chip-067f745c49","tagText":"WEBINAR","tagColor":"#EEBDD3","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/webinars/thought-leadership/ai-for-gtm-leaders-transforming-marketing-and-sales-performance-2026-07-15/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"On-Demand ansehen"},"headline":{"id":"title","type":"heading5","lines":["AI for GTM Leaders: Transforming Marketing and Sales 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entdecken"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-ab7bb0e6a0","additionalClasses":"hub-explore-topics-header__subheadline","text":"\u003Cp\u003EDetaillierte Einblicke in jeden Aspekt der künstlichen 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