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artificielle",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-3e8d0529e4-item-dc030db719","link":{"valid":true,"url":"/fr/artificial-intelligence/business/"},"active":false,"current":false,"title":"L’IA en entreprise",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-3e8d0529e4-item-b216a683bf","link":{"valid":true,"url":"/fr/artificial-intelligence/business/sales/"},"active":true,"current":true,"title":"IA pour les ventes",":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-ee1359894a","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-c26b230c07",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-2c45c6d026","additionalClasses":"hub-hero__headline","type":"heading1","lines":["IA pour les ventes :  cas d'usage, agents et socle de données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-8141c5ff77","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EL’IA peut aider les équipes commerciales à identifier les comptes prometteurs, à faire progresser les transactions et à prévoir les revenus plus efficacement. Mais à mesure que l’IA prend en charge une part croissante des tâches, des données incomplètes et des définitions métier incohérentes peuvent transformer de légers manquements d’information en décisions coûteuses.\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-6e29488dab",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-fe55467edb","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","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-b3824a99e5","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, 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":["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-ccb19c66b0",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-74935c7989","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-919a79783e","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-fabbf22996",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-b86d5d40c7","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003ESujets liés à l’IA en entreprise :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-c258bf233a","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/business/ai-council/\"\u003EComité d’IA\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/business/marketing/\"\u003EIA pour le marketing\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/business/enterprise/\"\u003EIA d’entreprise\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-9433cd9121","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-075dfa452b","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDÉFINITION DE L’IA POUR LES VENTES\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDans le domaine des ventes, l’IA s’appuie sur des modèles prédictifs, des outils génératifs et des agents d’IA pour optimiser la prospection, l’exécution des transactions, les prévisions et les opérations commerciales.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-6e339aee0b","text":"\u003Cp\u003ESelon l’étude 2026 \u003Ca href=\"https://www.salesforce.com/sales/state-of-sales/\" target=\"_blank\"\u003EState of Sales\u003C/a\u003E de Salesforce, neuf équipes commerciales sur dix utilisent des agents d’IA ou prévoient de le faire d’ici deux ans. Cependant, les données que ces agents traitent risquent de ne pas être prêtes pour assumer un rôle opérationnel plus étendu. Dans l’enquête \u003Ca href=\"https://www.validity.com/resource-center/the-state-of-crm-data-management-in-2025\" target=\"_blank\"\u003E2025\u003C/a\u003E de Validity menée auprès de 602 utilisateurs et parties prenantes de CRM, 76 % des répondants ont déclaré que moins de la moitié des données CRM de leur organisation était exacte et complète, tandis que 37 % ont signalé des pertes de revenus directement dues à une mauvaise qualité des données.\u003C/p\u003E\n\u003Cp\u003EPris ensemble, ces résultats révèlent un écart croissant entre les ambitions en matière d’IA et les informations disponibles pour les concrétiser. Des données CRM incomplètes peuvent avoir un impact limité lorsque les commerciaux et les managers complètent les workflows manuels avec leur propre connaissance pratique des comptes et du contexte des transactions. Cependant, à mesure que l’IA prend en charge une part accrue du travail commercial, elle ne peut pas s’appuyer sur ce savoir non documenté. Des lacunes dans le système peuvent alors influencer les comptes ciblés en priorité, la formulation des prises de contact, la qualification exacte d’une opportunité et les prévisions de revenus sur lesquelles les responsables peuvent compter.\u003C/p\u003E\n\u003Cp\u003EL’IA peut aider les équipes commerciales à mieux analyser le contexte d’un compte ou d’une transaction, y compris les conversations, l’engagement, l’utilisation des produits et la progression commerciale. Elle peut également rédiger du contenu, mettre à jour les systèmes et coordonner les actions à chaque étape du processus d’affaires. La qualité de ces résultats dépend du caractère connecté, récent et gouverné des données sous-jacentes, ainsi que de la capacité du système à disposer de suffisamment de contexte métier pour les utiliser de manière appropriée.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-in-sales":{"id":"title-v2-58422387f9","additionalClasses":"anchor-title anchor-title--qu-est-ce-que-l-ia-pour-les-ventes","type":"heading2","lines":["Qu’est-ce que l’IA pour les ventes ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-in-sales_0":{"id":"text-b109258472","text":"\u003Cp\u003EDans les ventes, l’IA désigne l’utilisation de l’IA prédictive, de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/generative-ai/\"\u003El’IA générative\u003C/a\u003E et de \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003Esystèmes agentiques\u003C/a\u003E pour interpréter les données commerciales, faciliter la prise de décision et exécuter les tâches au sein des workflows de vente.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EL’IA prédictive estime ce qui est susceptible de se produire :\u003C/strong\u003E Elle identifie des tendances dans les données de comptes, d’activités et d’opportunités afin d’attribuer un score aux leads, d’estimer la probabilité de conversion, d’évaluer les risques liés aux transactions et d’effectuer les prévisions des revenus.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EL’IA générative transforme le contexte commercial en contenus valorisables :\u003C/strong\u003E Elle produit des recherches sur les comptes, des messages prospectifs personnalisés, des synthèses de conversations, des propositions commerciales, des conseils de coaching et d’autres contenus en s’appuyant sur les informations et consignes fournies.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EL’IA agentique coordonne les tâches à plusieurs étapes :\u003C/strong\u003E Elle extrait des informations, sélectionne les outils approuvés et poursuit une tâche commerciale dans le respect des autorisations et des limites opérationnelles fixées par l’application.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECes capacités fonctionnent souvent en synergie. Un modèle prédictif peut identifier un compte présentant une forte probabilité de conversion, tandis qu’un système génératif prépare une synthèse documentaire et un premier message basés sur le secteur d’activité du compte, ses activités récentes et ses interactions passées. Un agent peut ensuite enregistrer la prise de contact approuvée, surveiller les réponses et coordonner la prochaine action autorisée.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ERegardez Jonathan Karon, d’Anomalo, expliquer comment mettre en œuvre une qualité des données automatisée au sein de Snowflake :\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-a8e882b05a","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-v2-4a67dbfc46","additionalClasses":"anchor-title anchor-title--l-ia-pour-la-prospection-et-la-prise-de-contact","type":"heading2","lines":["L’IA pour la prospection et la prise de contact"],":type":"snowflake-site/components/title-v2"},"text_ai-for-prospecting-and-outreach_0":{"id":"text-70c766adc4","text":"\u003Cp\u003ELa prospection commence par une décision d’allocation des ressources : déterminer quels comptes et acheteurs méritent l’attention immédiate d’un commercial. L’IA peut aider les équipes à prendre cette décision en exploitant un ensemble de signaux plus vaste et plus récent qu’une liste de leads gérée manuellement ou un modèle de notation statique.\u003C/p\u003E\n\u003Cp\u003ELes modèles prédictifs analysent les schémas récurrents parmi les attributs des comptes, l’engagement marketing, l’activité produit, les interactions passées et l’historique des ventes. À mesure que de nouveaux signaux d’activité apparaissent, le score peut évoluer pour refléter le comportement actuel du compte.\u003C/p\u003E\n\u003Cp\u003EL’IA peut intervenir à plusieurs étapes de la prospection et du démarchage :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ENotation des leads et des comptes :\u003C/strong\u003E Évaluer la probabilité qu’un lead ou un compte se qualifie, se convertisse ou progresse sur une période donnée.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDétection des signaux d’achat :\u003C/strong\u003E Identifier les changements significatifs en matière d’engagement, d’activité produit, de caractéristiques organisationnelles ou d’autres données autorisées.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERecherche sur les comptes :\u003C/strong\u003E Rassembler et synthétiser les informations pertinentes à partir de dossiers internes et de sources externes autorisées.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAnalyse du groupe d’achat :\u003C/strong\u003E Associer les contacts, les rôles et les interactions pour aider les commerciaux à identifier les personnes influençant l’achat.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPréparation de la prospection :\u003C/strong\u003E Rédiger des messages en tenant compte de la situation du compte, des interactions passées et de l’objectif visé.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERecommandations de la meilleure action suivante :\u003C/strong\u003E Classer les actions possibles selon l’avancement du compte, son activité récente et les règles commerciales applicables.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EChacune de ces applications dépend d’un contexte de compte à jour et connecté. Lorsque les signaux sont incomplets, obsolètes ou associés au mauvais compte, le workflow risque de prioriser un prospect non pertinent, de mal interpréter l’intérêt ou de recommander une action inadaptée. Les équipes doivent également définir les informations dont l’utilisation est autorisée pour la prospection.\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-ae60102d67","additionalClasses":"anchor-title anchor-title--l-ia-pour-conclure-et-accompagner-les-ventes","type":"heading2","lines":["L'IA pour conclure et accompagner les ventes"],":type":"snowflake-site/components/title-v2"},"text_ai-for-deal-execution-and-enablement_0":{"id":"text-0e0731c16c","text":"\u003Cp\u003EUne fois l’opportunité active, le travail commercial génère un flux continu d’informations. Les acheteurs posent des questions, soumettent des objections, font intervenir des parties prenantes, révisent leurs exigences et prennent des engagements. Les commerciaux y répondent en fournissant des détails sur les produits, les tarifs, les échéanciers et les actions de suivi. Une grande partie de ce contexte émerge au cours de réunions et d’échanges de messages, et n’est généralement pas intégrée immédiatement dans le système de CRM.\u003C/p\u003E\n\u003Cp\u003EL’IA peut transformer ces interactions en un historique d’opportunités plus complet et utilisable immédiatement :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EPréparation des réunions :\u003C/strong\u003E Synthétiser les données relatives au compte, à l’opportunité, aux activités récentes, aux questions en suspens et aux engagements passés avant un échange.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAssistance en temps réel :\u003C/strong\u003E Récupérer les informations validées sur les produits, les tarifs, la sécurité ou la concurrence pendant la conversation.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERésumés de conversations :\u003C/strong\u003E Relever les sujets clés, les exigences des acheteurs, les objections, les décisions et les prochaines étapes à l’issue d’un appel ou d’une réunion.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERécapitulatifs de suivi :\u003C/strong\u003E Préparer des messages destinés aux clients récapitulant les points abordés et les engagements de chaque participant.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPréparation des propositions et devis :\u003C/strong\u003E Rassembler les éléments initiaux à partir d’informations validées sur le produit, la tarification et le compte.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMises à jour des opportunités :\u003C/strong\u003E Suggérer des modifications des champs du CRM à partir d’éléments issus des interactions récentes.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECoaching commercial :\u003C/strong\u003E Dégager des tendances à travers les conversations pour permettre aux managers et aux commerciaux de les analyser ensemble.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEnsemble, ces fonctionnalités réduisent la perte de contexte d’une transaction entre les conversations, les systèmes et les personnes. Les informations issues d’une réunion permettent d’alimenter le suivi, de mettre à jour la fiche de l’opportunité, de préparer l’interaction suivante et d’offrir aux managers une vue plus récente de l’avancement de la transaction. Cette continuité s’avère particulièrement précieuse lors de cycles de vente complexes, où plusieurs parties prenantes interviennent et où des semaines peuvent s’écouler entre chaque interaction.\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-v2-1cb0b7a16d","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EEtam, le détaillant de lingerie et de mode français, utilise l'AI Data Cloud de Snowflake pour connecter et partager des données avec Salesforce (CRM) afin de personnaliser l'expérience client. Grâce à Cortex AI, Etam réalise des recommandations alimentées par l'IA et des prévisions de demande jusqu'à 18 mois à l'avance. Cette approche a permis d'améliorer la précision des prévisions de 8 à 15 % pour les achats et la planification de la demande, avec un retour sur investissement prévu de 5 à 7 millions d'euros pour deux cas d'usage seulement. Sophie Buresi, Data Director chez Etam, déclare : « Snowflake était le choix évident. Nous avons été vraiment impressionnés par la feuille de route produit et ses capacités d'IA émergentes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Etam"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/customers/all-customers/case-study/etam/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire le témoignage complet du client"},"image":{"id":"image","alt":"Etam","lazyEnabled":true,"isLcpImage":false,":type":"snowflake-site/components/image"},"type":"content-card"},"title_ai-for-forecasting-and-revenue-intelligence":{"id":"title-v2-a49d228bac","additionalClasses":"anchor-title anchor-title--l-ia-pour-les-previsions-et-l-intelligence-des-revenus","type":"heading2","lines":["L’IA pour les prévisions et l’intelligence des revenus"],":type":"snowflake-site/components/title-v2"},"text_ai-for-forecasting-and-revenue-intelligence_0":{"id":"text-e6d8adf7a3","text":"\u003Cp\u003EUn rapport de pipeline reflète ce qui a été enregistré pour chaque opportunité : sa valeur, son étape, sa probabilité, sa date de clôture prévue et sa catégorie de prévision. L’IA peut comparer ces champs avec un ensemble plus large d’éléments probants, notamment l’activité récente, la participation des parties prenantes, l’utilisation des produits, l’état des propositions, l’avancement des contrats et l’évolution historique d’opportunités similaires.\u003C/p\u003E\n\u003Cp\u003ELes applications courantes incluent :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ENotation des opportunités :\u003C/strong\u003E Estimer la probabilité qu'une affaire soit conclue ou atteigne un autre résultat défini.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDétection des risques sur les affaires :\u003C/strong\u003E Identifier les activités au point mort, les glissements de calendrier, l’absence de parties prenantes ou d’autres schémas associés à des résultats inférieurs aux prévisions.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEstimation de la date de clôture :\u003C/strong\u003E Prédire la date de clôture probable d’une opportunité en fonction de sa progression et des données actuelles.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EInspection du pipeline :\u003C/strong\u003E Faire ressortir les opportunités dont l’étape enregistrée ou la catégorie de prévision semble incohérente avec l’activité observée.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAnalyse de la couverture :\u003C/strong\u003E Comparer le pipeline disponible avec les objectifs de chiffre d’affaires, les modèles de conversion et le temps restant sur la période commerciale.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPlanification de scénarios :\u003C/strong\u003E Modéliser l’impact des variations de conversion, de calendrier des deals, de capacité ou de création de pipeline sur les revenus attendus.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrévision des revenus :\u003C/strong\u003E Estimer les réservations ou les revenus futurs à l’aide du pipeline, des performances historiques et des données financières pertinentes.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EExplication des prévisions :\u003C/strong\u003E Indiquer les opportunités et les signaux qui ont le plus contribué à la prédiction ou au changement.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EL'exactitude des prévisions dépend de définitions métier cohérentes entre les ventes, la finance et les opérations de revenus. La valeur du pipeline, l’étape, la date de clôture et la catégorie de prévision peuvent sembler standardisées dans le CRM, mais les équipes les appliquent souvent différemment selon les régions, les produits ou les managers.\u003C/p\u003E\n\u003Cp\u003ELes équipes ont donc besoin de définitions gouvernées pour les champs et les résultats utilisés dans les prévisions, ainsi que d’un historique des modifications de ces définitions. Les données d’entraînement, les résultats des modèles et les rapports de prévision doivent tous utiliser le même contexte sémantique afin que la prédiction soit évaluée par rapport au résultat métier qu’elle devait estimer à l’origine.\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-edb6c64b6a","additionalClasses":"anchor-title anchor-title--automatisation-des-ventes-et-agents-d-ia-commerciaux","type":"heading2","lines":["Automatisation des ventes et agents d’IA commerciaux"],":type":"snowflake-site/components/title-v2"},"text_sales-automation-and-ai-sales-agents_0":{"id":"text-042c9336c1","text":"\u003Cp\u003EL’automatisation classique des ventes s’appuie sur des conditions prédéfinies pour déclencher des actions spécifiques. Un workflow peut par exemple attribuer un prospect en fonction du secteur géographique, ou créer une tâche lorsqu’une opportunité ne présente aucune activité récente. Ces systèmes s’avèrent efficaces pour les processus répétitifs, mais ils s’adaptent difficilement lorsque l’action appropriée dépend d’autres facteurs.\u003C/p\u003E\n\u003Cp\u003ELes agents d’IA peuvent exécuter des tâches commerciales à travers plusieurs systèmes et étapes, en adaptant leurs actions au fur et à mesure que de nouvelles informations sur les comptes, les opportunités et les acheteurs deviennent disponibles. Lorsqu’une opportunité stagne, par exemple, la réponse adéquate dépend de ce qui s’est passé. L’acheteur attend peut-être une réponse sur la sécurité, un décideur a pu cesser de participer, la proposition a pu expirer ou la fiche CRM est tout simplement obsolète. Un agent peut s’appuyer sur ce contexte pour déterminer s’il doit récupérer des informations manquantes, préparer un suivi, mettre à jour l’opportunité ou transmettre le problème à la personne concernée.\u003C/p\u003E\n\u003Cp\u003ELes agents commerciaux peuvent prendre en charge des tâches telles que :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EQualification des prospects :\u003C/strong\u003E Analyser un compte, l’évaluer par rapport à des critères approuvés et préparer les éléments justificatifs pour examen.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPréparation des réunions :\u003C/strong\u003E Rassembler l’historique du compte, l’activité récente, le statut de l’opportunité et les questions en suspens.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECoordination du suivi :\u003C/strong\u003E Préparer un récapitulatif, créer les tâches attribuées et mettre à jour les champs d’opportunité approuvés après une réunion.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMaintenance du pipeline :\u003C/strong\u003E Identifier les fiches incomplètes ou incohérentes et demander ou proposer des corrections.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EInspection des deals :\u003C/strong\u003E Examiner l’activité, l’engagement des parties prenantes, les engagements en cours et l’avancement commercial avant une revue de pipeline.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EWorkflows de renouvellement et d’expansion :\u003C/strong\u003E Surveiller les signaux des comptes, identifier les opportunités pertinentes et coordonner les actions de prospection approuvées.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EWorkflows de la meilleure action suivante :\u003C/strong\u003E Recommander ou exécuter une action autorisée en utilisant le contexte actuel du compte.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPréparation de la revue des prévisions :\u003C/strong\u003E Analyser les changements significatifs et rassembler les éléments dont les managers ont besoin pour les évaluer.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELes équipes commerciales peuvent utiliser ces fonctionnalités à différents niveaux d’autonomie. Un copilote assiste une tâche interactive de manière limitée : Un commercial peut par exemple lui demander de résumer un compte, puis examiner manuellement le résultat et décider de la suite à donner.\u003C/p\u003E\n\u003Cp\u003EUn agent peut intervenir sur une plus grande partie du workflow, en utilisant des outils approuvés pour récupérer des informations, mettre à jour un système, créer une tâche ou transmettre un travail pour approbation. À mesure que le système prend en charge une part plus importante du travail, les équipes doivent définir ce qu’il est autorisé à faire sans révision et les cas où l’approbation humaine est requise.\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-396c2a861c","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELes équipes accordent souvent aux agents un accès étendu, car chaque action individuelle semble présenter un risque faible. Néanmoins, la succession d’actions autorisées peut aboutir à un résultat à haut risque. Les équipes doivent donc évaluer et gouverner l’ensemble du workflow.\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-fd522efeee","additionalClasses":"anchor-title anchor-title--omment-fiabiliser-les-workflows-de-vente-ia","type":"heading2","lines":["Comment fiabiliser les workflows de vente IA"],":type":"snowflake-site/components/title-v2"},"text_what-keeps-ai-sales-workflows-accurate-and-accountable_0":{"id":"text-dccab42a28","text":"\u003Cp\u003ELes enregistrements utilisés par l’IA dans les applications de vente doivent rester connectés et conserver une signification métier cohérente au fil des workflows. Les systèmes d'IA fiables et responsables dépendent également d'un encadrement clair de l'accès aux données, des affirmations adressées aux clients et des actions qu'un agent est autorisé à effectuer.\u003C/p\u003E\n\u003Cp\u003EComme le souligne Baris Gultekin, Vice-président Produit & IA de Snowflake, « les agents d'IA représentent un bond majeur par rapport à l'automatisation traditionnelle ou aux chatbots, mais pour les déployer à grande échelle, les entreprises ont besoin d'un écosystème d'information prêt pour l'IA. »\u003C/p\u003E\n\u003Ch3\u003EConnecter les données relatives aux comptes, aux activités et aux revenus\u003C/h3\u003E\n\u003Cp\u003ELes décisions commerciales s’appuient sur les enregistrements CRM, l’engagement marketing, l’activité commerciale, les comptes-rendus de réunion, l’utilisation des produits, les interactions de support, les devis, les contrats et les systèmes financiers. Chaque source capture une partie de la relation commerciale, souvent à l’aide d’identifiants et de fréquences de rafraîchissement différents.\u003C/p\u003E\n\u003Cp\u003EL’historique connecté des comptes offre aux modèles et aux applications un moyen cohérent d’associer chaque activité au client, au contact, à l’opportunité ou au groupe d’achats adéquat. Sans cette connexion, une application risque de scorer un compte sans tenir compte d’un problème de support en cours, ou de résumer une opportunité sans avoir de visibilité sur la toute dernière proposition commerciale.\u003C/p\u003E\n\u003Cp\u003ELa résolution d’identité s’avère particulièrement importante lorsque plusieurs contacts interviennent dans l’achat. Le système doit pouvoir distinguer chaque personne tout en comprenant son lien avec le compte et l’opportunité concernés. Il doit également conserver l’historique des changements, comme la mutation d’un contact vers une autre entreprise ou la division d’une opportunité en plusieurs cycles d’achat.\u003C/p\u003E\n\u003Cp\u003ELa fraîcheur des données doit correspondre à la prise de décision. La planification annuelle des secteurs de vente peut, par exemple, utiliser des informations de compte rafraîchies périodiquement, tandis que l’assistance en direct sur une opportunité nécessite des interactions récentes, les tarifs actuels et le statut le plus récent de la transaction. Les équipes doivent surveiller les enregistrements manquants, les mises à jour retardées et les variations de couverture des sources, car chacun de ces facteurs peut modifier les résultats sans déclencher d’erreur explicite dans l’application.\u003C/p\u003E\n\u003Ch3\u003ENormaliser les définitions des ventes et des revenus\u003C/h3\u003E\n\u003Cp\u003ELa terminologie requiert des définitions claires et cohérentes dans l’ensemble des applications et des équipes. Par exemple, la valeur d’une transaction peut désigner la valeur annuelle du contrat, la valeur totale du contrat ou le chiffre d’affaires prévu pour la période en cours. Les équipes chargées des ventes, de la finance et des opérations de revenus doivent s’accorder sur la signification de chaque terme afin que les modèles, les rapports et les agents utilisent la bonne définition pour la tâche à accomplir.\u003C/p\u003E\n\u003Cp\u003ELe contexte sémantique rend ces distinctions explicites grâce à des métriques gouvernées, des vues, des métadonnées et des relations entre les comptes, les contacts, les opportunités, les produits et les événements de chiffre d’affaires. Il permet de préserver les critères de changement d’étape, les règles des catégories de prévision, les attributions de secteurs, les normes de qualification ainsi que la relation entre les réservations et le chiffre d’affaires comptabilisé.\u003C/p\u003E\n\u003Cp\u003ELes définitions importantes doivent être confiées à un responsable désigné, chargé de valider les modifications et d’arbitrer les divergences entre les ventes, la finance, le marketing et les opérations de revenus. Lorsque les normes de qualification ou le processus de prévision changent, les équipes doivent également déterminer si les enregistrements historiques correspondent toujours au même résultat. Sinon, le modèle risque de traiter deux définitions opérationnelles comme s’il s’agissait d’une seule étiquette cohérente.\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-98d4b01f1d","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ECréez une fiche de définition courte pour chaque champ utilisé par un modèle ou un agent. Indiquez la signification métier, le propriétaire, le mode de calcul, la date d’effet ainsi que les éventuelles divergences selon les régions ou les gammes de produits.\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-69c4746bc4","text":"\u003Ch3\u003EAncrer les résultats générés dans des informations commerciales approuvées\u003C/h3\u003E\r\n\u003Cp\u003ELes applications de vente s’appuient à la fois sur le contexte spécifique du compte et sur les documents sources de l’entreprise. Le contexte du compte explique la situation de l’acheteur et l’historique de l’opportunité. Les documents sources approuvés par l’entreprise établissent ce qui peut être avancé sur ses produits, ses tarifs, ses pratiques de sécurité, ses exigences de mise en œuvre et ses conditions contractuelles.\u003C/p\u003E\r\n\u003Cp\u003EDes informations approuvées s’avèrent particulièrement essentielles dans les contenus destinés aux clients. Lorsque les sources disponibles ne répondent pas à la question d’un acheteur, le résultat doit laisser le sujet en attente pour un commercial ou un expert métier, plutôt que de fournir une réponse plausible mais incertaine.\u003C/p\u003E\r\n\u003Cp\u003ELes informations sources nécessitent un propriétaire, une version et une période de validité, le cas échéant. La recherche d’informations doit privilégier les documents récents tout en conservant suffisamment d’historique pour expliquer les engagements déjà pris au cours de la transaction.\u003C/p\u003E\r\n\u003Ch3\u003EAppliquer les contrôles d’accès et les autorisations d’utilisation\u003C/h3\u003E\r\n\u003Cp\u003ELes workflows de vente contiennent des données personnelles, des stratégies de compte, des enregistrements d’appels, des tarifs, des données d’utilisation des produits et des détails contractuels. Ces enregistrements restent soumis à des exigences d’accès et d’utilisation autorisée lorsqu’une application d’IA les extrait ou les combine dans un nouveau résultat.\u003C/p\u003E\r\n\u003Cp\u003ELe \u003Ca href=\"https://www.snowflake.com/fr/data-governance/data-security/rbac/\"\u003Econtrôle d’accès basé sur les rôles\u003C/a\u003E (RBAC) limite les utilisateurs et les identités de service pouvant consulter les informations sensibles au niveau du client ou sur le plan commercial. La classification et le masquage protègent les champs sensibles, tandis que les autorisations basées sur le secteur géographique, l’équipe de compte ou la « deal room » peuvent restreindre davantage l’accès à une opportunité spécifique.\u003C/p\u003E\r\n\u003Cp\u003EL’application doit également respecter la finalité et les conditions dans lesquelles les données ont été collectées. L’autorisation d’utiliser des informations pour le support client ou l’analyse des produits ne permet pas automatiquement à l’entreprise de les exploiter pour du ciblage commercial ou à d’autres fins. Les contrats, les exigences régionales en matière de confidentialité et les politiques internes peuvent restreindre la manière dont les données d’activité et les enregistrements d’appels sont analysés.\u003C/p\u003E\r\n\u003Cp\u003EEn France et dans l'UE, les données personnelles utilisées dans les workflows de vente (contacts, enregistrements d'appels, prospection commerciale) sont soumises au \u003Ca href=\"https://www.snowflake.com/fr/data-governance/regulatory-compliance/gdpr/\"\u003ERéglement Général sur la Protection des Données\u003C/a\u003E (RGPD). La Commission Nationale de l'Informatique et des Libertés (CNIL) a publié des recommandations spécifiques sur la prospection commerciale et l'enregistrement d'appels, que les outils de vente basés sur l'IA doivent respecter. Cela inclut l'obligation de disposer d'une base légale valide pour le traitement des données (article 6 du RGPD) avant d'utiliser des données personnelles pour générer des messages de prospection ou analyser des appels.\u003C/p\u003E\r\n\u003Cp\u003ELes enregistrements d’audit doivent indiquer les données auxquelles l’application a accédé, les politiques appliquées et la façon dont le résultat a intégré le workflow de vente. Cela devient particulièrement important lorsqu’une recommandation influe sur la priorité d’un compte, qu’un client reçoit une communication générée automatiquement ou qu’un agent modifie un système d’enregistrement.\u003C/p\u003E\r\n\u003Ch3\u003ELimiter les autorisations des agents et les actions destinées aux clients\u003C/h3\u003E\r\n\u003Cp\u003ELes workflows de vente basés sur des agents nécessitent des autorisations au niveau des actions, en plus des contrôles d’accès aux données.\u003C/p\u003E\r\n\u003Cp\u003ELe workflow doit définir :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003ELes comptes, opportunités et sources de données auxquels l’agent peut accéder\u003C/li\u003E\r\n\u003Cli\u003ELes champs du CRM qu’il peut proposer ou mettre à jour\u003C/li\u003E\r\n\u003Cli\u003ELes canaux de communication qu’il peut utiliser\u003C/li\u003E\r\n\u003Cli\u003ESi les messages sortants nécessitent l’approbation du représentant\u003C/li\u003E\r\n\u003Cli\u003ELes déclarations relatives aux produits, à la tarification ou aux contrats qu’il peut inclure\u003C/li\u003E\r\n\u003Cli\u003ELes modifications commerciales qui nécessitent l’approbation de la direction, des finances ou du service juridique\u003C/li\u003E\r\n\u003Cli\u003EÀ quel moment l’agent doit s’arrêter, demander des éclaircissements ou faire remonter la tâche\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELes identités de service, les autorisations des outils, les seuils d’approbation et les conditions d’arrêt définissent le périmètre d’intervention de l’agent. L’application ne doit recevoir que les données et les outils nécessaires à la tâche assignée, plutôt qu’un accès étendu à la stack de vente. Les équipes ont également besoin de traces indiquant quelles informations l’agent a récupérées, quels outils il a appelés et quelles actions il a effectuées.\u003C/p\u003E\r\n\u003Cp\u003ECes contrôles préservent la responsabilité humaine dans les relations clients et les décisions commerciales importantes, tout en permettant à l’agent de prendre en charge une plus grande part de la recherche, de la coordination et de la tenue des dossiers qui s’y rapportent.\u003C/p\u003E\r\n\u003Ch3\u003EÉvaluer les résultats par rapport aux performances commerciales\u003C/h3\u003E\r\n\u003Cp\u003EUne évaluation fiable commence par un jeu de données qui reflète le travail de vente auquel le système sera confronté, y compris les cas courants, les cas difficiles et les situations où la bonne réponse consiste à s’arrêter ou à faire remonter le problème. Les résultats attendus, les actions autorisées et les critères d’acceptation doivent être définis avant les tests, afin que le système soit évalué par rapport à une norme homogène plutôt qu’aux impressions des évaluateurs.\u003C/p\u003E\r\n\u003Cp\u003ELa méthode d’évaluation dépend ensuite de la capacité. Les modèles prédictifs nécessitent des mesures telles que la calibration, la qualité du classement et la performance sur les segments pertinents. Les systèmes génératifs nécessitent des tests portant sur l’exactitude factuelle, l’ancrage dans des sources approuvées, l’exhaustivité et le respect du langage requis. Les agents doivent également être évalués sur le cheminement qu’ils empruntent : s’ils sélectionnent les outils appropriés, utilisent les bonnes données, respectent leurs autorisations, se remettent des erreurs et satisfont aux conditions d’arrêt de la tâche.\u003C/p\u003E\r\n\u003Cp\u003ECependant, les résultats au niveau des composants ne suffisent pas lorsque plusieurs modèles, étapes de récupération et outils contribuent à un seul workflow. Les équipes doivent également effectuer des évaluations de bout en bout pour vérifier si le contexte est conservé d’une étape à l’autre et si une erreur précoce modifie une décision ou une action ultérieure.\u003C/p\u003E\r\n\u003Cp\u003EAprès le déploiement, les traces de production, les taux de correction, les remontées et les données de résultat permettent de déterminer si la performance évolue à mesure que les données de vente, les produits et les règles d’exploitation changent. Ces observations doivent être réintégrées dans l’ensemble d’évaluation afin que les tests continuent de représenter le travail effectué par le système.\u003C/p\u003E\r\n\u003Ch3\u003ESurveiller la qualité des données et les biais\u003C/h3\u003E\r\n\u003Cp\u003ELes données de vente reflètent à la fois le comportement des acheteurs et les décisions antérieures de l’entreprise. Les résultats historiques sont façonnés par les comptes qui ont été saisis dans le CRM, la manière dont les territoires ont été attribués, les leads qui ont été suivis et la régularité avec laquelle les représentants ont consigné leur activité. Un modèle entraîné sur ces enregistrements peut apprendre à la fois la manière dont l'organisation orientait son attention par le passé et les régularités de la demande client.\u003C/p\u003E\r\n\u003Cp\u003EUne activité incomplète crée une autre distorsion. Un représentant performant qui ne consigne que les étapes majeures peut sembler faire avancer les transactions avec moins d’interactions qu’un représentant qui documente chaque appel et chaque message. Les comptes gérés via des canaux informels peuvent paraître moins engagés que ceux dont l’activité est capturée automatiquement.\u003C/p\u003E\r\n\u003Cp\u003EPour résoudre ces problèmes, il faut commencer par des données sources actuelles et gouvernées, ainsi que par des définitions de résultats documentées, mais cela nécessite également des tests actifs. 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Une révision humaine à la fin du workflow ne corrigera pas un modèle de notation ou de priorisation qui détermine de manière répétée quels comptes reçoivent de l’attention en premier lieu.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-for-sales-runs-on-snowflake":{"id":"title-v2-5d60c1f4a3","additionalClasses":"anchor-title anchor-title--pourquoi-l-ia-pour-les-ventes-s-execute-sur-snowflake","type":"heading2","lines":["Pourquoi l’IA pour les ventes s’exécute sur Snowflake"],":type":"snowflake-site/components/title-v2"},"text_why-ai-for-sales-runs-on-snowflake_0":{"id":"text-f2d4baaac0","text":"\u003Cp\u003ESnowflake offre aux équipes des ventes, des opérations de revenus, des données et des finances un environnement partagé pour exploiter les données CRM, d'activité, de produits, d'assistance et de revenus, et déployer l'IA prédictive, générative et agentique sans créer une architecture de données distincte pour chaque workflow de vente.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E prend en charge les modèles prédictifs créés et exécutés sur des données gouvernées dans Snowflake. La classification peut prendre en charge des charges de travail telles que le scoring des leads, des comptes ou des opportunités en apprenant les régularités associées à des résultats historiques définis.  Les fonctions de prévision peuvent prédire des valeurs numériques futures à partir de données de séries chronologiques, tandis que des capacités plus larges permettent de développer et d’exploiter des modèles personnalisés lorsque le workflow l’exige.\u003C/p\u003E\n\u003Cp\u003EÉtant donné que le modèle s’exécute là où résident déjà les données sous-jacentes, les équipes peuvent mettre à jour les scores et les prédictions à partir des données connectées du CRM, d’activité et de produits sans maintenir une copie de notation distincte. L’entreprise détermine toujours le résultat cible, les données d’entraînement, les critères d’évaluation et la manière dont la prédiction s’intègre au processus de vente.\u003C/p\u003E\n\u003Cp\u003ELes fonctions Cortex AI appliquent des modèles linguistiques et multimodaux aux informations stockées dans Snowflake. Les équipes des ventes et des données peuvent utiliser ces fonctions pour résumer les transcriptions de réunions, classer les objections, extraire les exigences ou les mesures à prendre et analyser des ensembles de notes ou de documents via des workflows de données basés sur SQL. Ces résultats peuvent être joints à des données structurées de comptes et d’opportunités pour la création de rapports, le coaching ou des applications en aval.\u003C/p\u003E\n\u003Cp\u003EPour les workflows qui nécessitent d’examiner des informations et de se poursuivre sur plusieurs étapes, \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E peut combiner Cortex Analyst pour les données structurées, Cortex Search pour les informations non structurées et des outils personnalisés connectés à un processus métier approuvé. Une application de vente pourrait utiliser ces capacités pour examiner une opportunité, récupérer les interactions et les documents pertinents, préparer une recommandation et acheminer le travail qui en résulte vers un autre système.\u003C/p\u003E\n\u003Cp\u003EL’accès à Cortex Agents est régi par le contrôle d’accès basé sur les rôles de Snowflake, y compris les privilèges sur l’agent et les objets utilisés par ses outils. Les rôles, les outils et les autorisations configurés pour l’application déterminent quelles informations l’agent peut récupérer et quelles actions il peut effectuer. Les enregistrements de surveillance de l’agent peuvent offrir une visibilité supplémentaire sur les requêtes et l’utilisation sous-jacente des outils.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E étend la gouvernance à l’ensemble des workloads de données et d’IA qui sous-tendent ces applications. 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Elle nécessite des données client et de revenus connectées, des définitions métier partagées, des documents sources approuvés et des contrôles qui garantissent que chaque recommandation et action est fondée, autorisée et responsable.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-3163c856ae",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-bf6f7d0472","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EDans ce guide\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-d04c4d712f","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"qu-est-ce-que-l-ia-pour-les-ventes\"\u003EQu'est-ce que l'IA pour les ventes ?\u003C/li\u003E\r\n\u003Cli data-anchor=\"l-ia-pour-la-prospection-et-la-prise-de-contact\"\u003EL'IA pour la prospection et la prise de contact\u003C/li\u003E\r\n\u003Cli data-anchor=\"l-ia-pour-conclure-et-accompagner-les-ventes\"\u003EL'IA pour conclure et accompagner les ventes\u003C/li\u003E\r\n\u003Cli data-anchor=\"l-ia-pour-les-previsions-et-l-intelligence-des-revenus\"\u003EL'IA pour les prévisions et l'intelligence des revenus\u003C/li\u003E\r\n\u003Cli data-anchor=\"automatisation-des-ventes-et-agents-d-ia-commerciaux\"\u003EAutomatisation des ventes et agents d'IA commerciaux\u003C/li\u003E\r\n\u003Cli data-anchor=\"comment-fiabiliser-les-workflows-de-vente-ia\"\u003EComment fiabiliser les workflows de vente IA\u003C/li\u003E\r\n\u003Cli data-anchor=\"pourquoi-l-ia-pour-les-ventes-s-execute-sur-snowflake\"\u003EPourquoi l'IA pour les ventes s'exécute sur Snowflake\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-8270306148","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes réponses des experts Snowflake à vos questions fréquentes sur l’IA pour les ventes.\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-da01f97b7e","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Quelle est la différence entre l’IA prédictive, générative et agentique dans les ventes ?","richText":"\u003Cp\u003EL’IA prédictive estime des résultats tels que la conversion des leads, le risque des transactions et les revenus futurs. L’IA générative crée des contenus tels que la recherche sur les comptes, les messages de prospection, les résumés d’appels et les propositions. L’IA agentique coordonne des tâches à plusieurs étapes, en utilisant des données et des outils approuvés pour examiner une situation et exécuter des actions autorisées sous supervision humaine.\u003C/p\u003E"},{"title":"L’IA remplacera-t-elle les commerciaux ?","richText":"\u003Cp\u003EL’IA peut réduire le temps que les commerciaux passent à faire des recherches sur les comptes, à saisir des données, à résumer des réunions, à préparer des brouillons initiaux et à inspecter le pipeline. Elle peut également les aider à identifier des signaux pertinents et à récupérer des informations lors d’une transaction. Mais les ventes dépendent toujours du jugement humain.\u003C/p\u003E"},{"title":"De quelles données avez-vous besoin avant que l’IA pour les ventes ne produise des résultats ?","richText":"\u003Cp\u003EL’IA pour les ventes nécessite des données gouvernées, actuelles et connectées concernant les comptes, les contacts, les opportunités, les activités et les résultats. L’entreprise doit être capable d’associer ces enregistrements au compte et à l’opportunité appropriés, de définir la signification des champs et des métriques, et d’identifier les sources qui gouvernent les affirmations destinées aux clients.\u003C/p\u003E"},{"title":"En quoi l’IA pour les ventes diffère-t-elle de l’IA pour le marketing ?","richText":"\u003Cp\u003ELes deux fonctions utilisent l’IA prédictive, générative et agentique, mais elles l’appliquent à différentes parties de la relation client. L’IA pour le marketing identifie et engage généralement les audiences à travers les campagnes, les canaux et les parcours clients. L'IA commerciale opère plus directement sur les comptes, contacts et opportunités individuels, à travers la prospection, l'approche commerciale, la conclusion des affaires, le coaching et la prévision.\u003C/p\u003E"},{"title":"Comment faire ses premiers pas avec l’IA pour les ventes ?","richText":"\u003Cp\u003ECommencez par connecter le CRM aux données d’activité et de résultats requises pour le workflow sélectionné, puis définissez les contrôles d’accès, les définitions métier et les critères d’évaluation avant d’appliquer l’IA. Choisissez un cas d’usage délimité avec un résultat mesurable. Le lead scoring peut tester si l’IA prédictive améliore la priorisation des comptes, tandis que les résumés d’appels offrent un moyen direct de comparer le résultat généré avec la conversation source. 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