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aem-Grid--default--12","columnClassNames":{"flexible_column_container":"aem-GridColumn aem-GridColumn--default--12","related_content":"aem-GridColumn aem-GridColumn--default--12"},"id":"main-content",":type":"snowflake-site/components/container",":items":{"flexible_column_container":{"id":"flexible-column-container-5e51f3e99e","propertiesId":"snowflake-blog-template-main-container","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":true,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-acc337af14",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_hero":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_hero":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-b95204687b",":type":"snowflake-site/components/container",":items":{"blog_hero":{"id":"blog-hero-84dc2246d7","showClaude":true,"showChatGpt":true,"authors":[],"image":{"id":"image-12b17cec91","height":"720","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ea163c17-eb6e-4d4e-95d4-e978d31f8878/ver2-blog-agentic-ai-1680x720.png?quality=85&preferwebp=true","alt":"Accelerate 2026 Agentic AI","isLcpImage":false,"width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"7","publicationDate":"APR 30, 2026","tag":{"tagText":"Retail   Grande distribution","tagColor":"#29B5E8"},"title":{"lines":["Du pilote à la production : comment les leaders du retail et de l’industrie concrétisent l’IA agentique"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12"},"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-832f7bcb23","text":"\u003Cp\u003ELa phase d’expérimentation est terminée. À Accelerate 2026, le message des leaders était limpide : l’avenir de l’IA n’est plus un exercice théorique, c’est une nécessité opérationnelle, et les entreprises les plus avancées l’intègrent déjà au cœur de leurs fonctions de base. Dans le seul secteur du retail, \u003Ca href=\"https://resources.nvidia.com/en-us-state-of-ai-report-2026/retail-state-of-ai-report\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E58 % des entreprises déploient activement l’IA\u003C/a\u003E. Les consommateurs achètent de plus en plus via des agents qui n’ont jamais visité le site web de la marque. Et dans le secteur de l’industrie, certaines entreprises déploient des agents alimentés par l’IA comme bases de connaissances vivantes et interrogeables, afin de compenser la perte de savoir institutionnel alors que \u003Ca href=\"https://bringbackthetrades.org/press-release/closing-the-skilled-trades-gap-could-unlock-325-6-billion-in-gdp-nationwide/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Edes millions de postes qualifiés dans les métiers techniques devraient rester vacants d’ici 2030\u003C/a\u003E.\u003Cbr\u003E\n\u003Cbr\u003E\nPour les leaders du retail comme du secteur de l’industrie, le choix est clair : l’IA est une priorité opérationnelle stratégique. Les organisations présentes à Accelerate 2026 ne se contentaient pas de parler de projets pilotes : elles présentaient des workflows agentiques prêts pour la production, qui transforment leurs modèles économiques en temps réel.\u003C/p\u003E\n\u003Ch3\u003ELa grande bascule : de la recherche aux agents\u003C/h3\u003E\n\u003Cp\u003ELes rapports Snowflake 2026 Data Trends sont sans ambiguïté : nous sommes entrés dans l’ère de l’IA agentique. Dans le retail, le modèle traditionnel du commerce digital est bouleversé, car les consommateurs privilégient de plus en plus la conversation plutôt que la recherche. Comme l’a souligné Shanthi Rajagopalan, Global Head of Strategy for Retail & Consumer Goods chez Microsoft, lors de notre executive panel avec Microsoft à NRF 2026 : « Dans le monde traditionnel du retail, nous avons toujours pensé aux magasins et à l’immobilier en termes d’emplacement, d’emplacement, d’emplacement. Et avec le passage au digital et à l’e-commerce, tout a tourné autour de la recherche, de la recherche, de la recherche. » Pourtant, comme l’a ajouté Rajagopalan : « Ce que nous observons vraiment aujourd’hui, c’est une nouvelle rupture : les consommateurs se tournent de plus en plus vers des interfaces conversationnelles pour découvrir des produits et acheter. »\u003C/p\u003E\n\u003Cp\u003ECette nouvelle réalité, cette transformation profonde des processus et des normes du retail, exige un changement tout aussi majeur dans la façon dont les acteurs du retail gèrent les données. « Nous sommes désormais dans une situation où les acheteurs nous livrent beaucoup plus d’informations sur qui ils sont et ce qu’ils veulent », explique Rajagopalan. « En tant que secteur, nous devons vraiment comprendre comment traduire ces intentions exprimées en langage naturel en métadonnées exploitables. »\u003C/p\u003E\n\u003Cp\u003EDans le secteur de l’industrie, la mutation est tout aussi profonde : on passe de feuilles de calcul statiques et de données cloisonnées à une IA intégrée aux systèmes embarqués. Ici, l’IA n’est pas simplement un outil : elle devient le seul plan de succession viable pour une main-d’œuvre confrontée à une vague massive de départs liée au vieillissement. Les études anticipent ce qu’Accelerate 2026 a confirmé : dans ces salles, les leaders ne lançaient pas des pilotes, ils réfléchissaient sérieusement à la manière de devenir des organisations plus intelligentes, plus rapides et plus efficaces grâce à des cas d’usage d’IA ciblés.\u003C/p\u003E\n\u003Ch3\u003ERetail : l’essor du commerce agentique et la course à la visibilité\u003C/h3\u003E\n\u003Cp\u003ELe commerce agentique est la grande mutation de l’année. Dans \u003Ca href=\"https://www.youtube.com/watch?v=5n8qdKvXtAg\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eune récente interview\u003C/a\u003E, Michele Fisher, Global Director de la stratégie commerciale pour Retail and Consumer Goods chez Microsoft, a expliqué pourquoi les retailers doivent repenser la façon dont les consommateurs les trouvent, afin de rester visibles dans la transition de l’omnicanal vers le commerce agentique. Les marques qui n’ont pas conçu leur espace sémantique se retrouvent invisibles aux yeux des agents qui achètent pour le compte des consommateurs.\u003C/p\u003E\n\u003Cp\u003EShalion, leader des études de marché, a démontré l’intérêt de se préparer à cette évolution majeure du comportement des consommateurs grâce à sa couche d’IA conversationnelle, baptisée Maestro. Comme l’a expliqué Alejo Buxeres Solder, CDO chez Shalion, le véritable facteur différenciant n’est pas le LLM lui-même, qu’il estime désormais banalisé, mais la couche sémantique Snowflake gouvernée qui le sous-tend. Cette architecture permet aux brand managers de poser des questions complexes, comme « Pourquoi ma part de marché a-t-elle chuté en Espagne la semaine dernière ? », et d’obtenir immédiatement des réponses étayées par les données. \u003C/p\u003E\n\u003Cp\u003ELes retailers se concentrent désormais sur l’Answer Engine Optimization (AEO) et la Generative Engine Optimization (GEO), qui remplacent le SEO traditionnel axé sur les mots-clés pour gagner en visibilité auprès des agents d’achat. Ces retailers essaient, pour ainsi dire, de se voir à travers les yeux de l’agent. Ils optimisent leurs contenus autour de questions clés, plutôt que de mots-clés, afin que leurs produits remontent plus efficacement auprès de ces agents. Et comme l’a souligné Kelly Thompson, anciennement Chief Merchant chez Walmart, chaque cas d’usage d’IA doit être quantifiable, qu’il s’agisse de générer des revenus, de réduire les coûts ou d’améliorer la productivité, avant d’y consacrer la moindre ressource. Comme dans le secteur de l’industrie, passer des pilotes à des applications transformationnelles d’IA centrées sur le business est une priorité clé pour les retailers.\u003C/p\u003E\n\u003Ch3\u003EIndustrie : transformer les processus grâce aux opérations agentiques\u003C/h3\u003E\n\u003Cp\u003EL’analyse qualité traditionnelle examine les défauts isolément, en évaluant séparément des variables cloisonnées, et passe ainsi à côté des effets de réseau où les causes premières impliquent plusieurs facteurs qui interagissent. Le temps qu’un fabricant remonte d’un défaut à un fournisseur ou à une étape de processus spécifique, plusieurs jours se sont déjà écoulés. \u003C/p\u003E\n\u003Cp\u003ETripp Smith, Field CTO de Snowflake pour le secteur de l’industrie, a présenté une solution de traçabilité des processus par Graph Neural Network (GNN), une approche qui modélise l’ensemble du processus de fabrication, fournisseurs, matériaux, postes et défauts, comme un réseau interconnecté. Désormais, une analyse des causes premières qui prenait autrefois plusieurs jours ne prend plus que quelques minutes, avec la capacité de prédire le risque de défaut avant même le début de la production.\u003C/p\u003E\n\u003Cp\u003EDans le secteur de l’industrie, les enjeux se mesurent en disponibilité et en précision. Smith a illustré la puissance de GraphSage, un réseau neuronal exécuté sur des GPU NVIDIA, qui traite toute la supply chain, fournisseurs, postes, matériaux et défauts, comme un graphe interconnecté.\u003C/p\u003E\n\u003Cp\u003EEn unifiant les données IT et OT dans un espace de noms unique, des entreprises comme WolfSpeed et Lindt Chocolate ont dépassé l’analyse de variables indépendantes. WolfSpeed a déployé avec succès 12 agents Snowflake Intelligence, transformant des tâches d’analyste qui prenaient une semaine en analyses de causes instantanées. Cette capacité à remonter en quelques secondes d’un défaut à un fournisseur ou à un ajustement de processus spécifique illustre parfaitement la puissance de la fabrication autonome dans le secteur de l’industrie.\u003C/p\u003E\n\u003Cp\u003ELa même bascule s’opère dans les services industriels. \u003Ca href=\"https://www.snowflake.com/en/news/press-releases/united-rentals-empowers-frontline-decision-making-with-business-intelligence-agent-built-on-snowflake/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EUnited Rentals\u003C/a\u003E, qui gère des flottes d’équipements dans plus de 1 600 agences, a déployé un Business Intelligence Agent sur Snowflake Intelligence. Les responsables d’agence et les équipes régionales peuvent ainsi poser en langage naturel des questions sur leurs données opérationnelles et financières, en temps réel, tout en éliminant le goulot d’étranglement de l’analyse manuelle qui ralentissait autrefois les décisions sur le terrain.\u003C/p\u003E\n\u003Ch3\u003EUn avantage agentique démontré dans chaque secteur \u003C/h3\u003E\n\u003Cp\u003ELe retail et le secteur de l’industrie ne sont pas les seuls secteurs à connaître des transformations profondes grâce à l’IA. Cette dynamique est devenue universelle :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EServices financiers :\u003C/b\u003E des essaims d’agents dédiés aux asset managers réduisent les workflows de gestion de portefeuille et d’analyse des risques, de plusieurs semaines à quelques instants.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESanté et sciences de la vie :\u003C/b\u003E Innovalon fait passer l’examen de dossiers médicaux de 200 pages de plusieurs semaines à quelques minutes grâce à l’autorisation préalable agentique.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPublicité, médias et divertissement :\u003C/b\u003E Warner Music Group utilise Cortex Code pour permettre aux équipes d’analystes de créer des tableaux de bord et des vues sémantiques sans l’aide des équipes d’ingénierie.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESecteur public : \u003C/b\u003Edes agences comme le Township of King mettent en lumière des gains opérationnels, par exemple la conformité de la maintenance de l’éclairage public, auparavant noyés parmi des centaines de KPI.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003ECe que les leaders du secteur font différemment\u003C/h3\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIls définissent d’abord les résultats :\u003C/b\u003E ils définissent les cas d’usage selon les trois catégories évoquées par Kelly Thompson, génération de revenus, réduction des coûts et amélioration de la productivité, avant de choisir la moindre technologie.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ELes données comme prérequis :\u003C/b\u003E ils considèrent la préparation des données, espaces de noms unifiés et couches sémantiques gouvernées, comme un prérequis au déploiement d’agents, et non comme un projet ultérieur.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPriorité aux workflows spécifiques :\u003C/b\u003E ils commencent par des workflows spécifiques et à forte valeur ajoutée, comme un catalogue produit ou une archive de commentaires d’équipe, plutôt que par des stratégies d’IA généralistes.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMesure opérationnelle :\u003C/b\u003E ils évaluent l’IA agentique à l’aune d’un résultat business quantifiable, et non d’un KPI technologique.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003EComme l’a souligné Luv Kothari, General Manager for Forward Deployed AI Engineering chez Snowflake, les organisations qui réussissent obtiennent un impact sur les résultats en trois à six mois, en passant de requêtes tactiques à des opérations stratégiques pilotées par des agents.\u003C/p\u003E\n\u003Ch3\u003ELa voie à suivre\u003C/h3\u003E\n\u003Cp\u003ELa phase d’expérimentation est terminée. L’ère de l’exécution commence.\u003C/p\u003E\n\u003Cp\u003EPour voir le commerce agentique en production, regardez la \u003Ca href=\"https://www.snowflake.com/accelerate/retail/?utm_cta=website-accelerate26-hub-page\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Esession Retail Accelerate à la demande\u003C/a\u003E et découvrez Maestro en action. Regardez la \u003Ca href=\"https://www.snowflake.com/accelerate/manufacturing/?utm_cta=website-accelerate26-hub-page\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Esession consacrée au secteur de l’industrie\u003C/a\u003E pour voir comment Tripp Smith a retracé un défaut lors d’une démo GraphSage en direct. Téléchargez ensuite les eBooks Tendances Data 2026 \u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-manufacturing/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EIndustrie\u003C/a\u003E et \u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-retail-and-consumer-goods/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ERetail et grande distribution\u003C/a\u003E pour identifier les points essentiels à connaître sur l’avenir de l’IA dans ces secteurs.\u003C/p\u003E\n\u003Cp\u003ELa question n’est plus de savoir si votre organisation adoptera l’IA agentique. Elle est de savoir à quelle vitesse vous pouvez l’opérationnaliser pour prendre la tête.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETéléchargez l’eBook Data Trends 2026 pour votre secteur d’activité :\u003C/b\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-retail-and-consumer-goods/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Retail et grande distribution\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-manufacturing/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Industrie\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-financial-services/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Services financiers\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/resources/ebook/data-trends-2026-advertising-media-and-entertainment/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Secteur de la publicité, des médias et du divertissement\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/ebook/data-trends-2026-healthcare-and-life-sciences/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Santé et sciences de la vie\u003C/a\u003E (en anglais)\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/ebook/data-trends-2026-public-sector/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ETendances Data 2026 : Secteur public\u003C/a\u003E (en 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