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azienda",":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-c030536d36","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-610c0f98fd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_221741058":{"id":"text-bca9204ced","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuida di riferimento\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-e28b13bafe","additionalClasses":"hub-hero__headline","type":"heading1","lines":["AI in azienda oggi: come le aziende trasformano i progetti di AI in risultati in produzione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-aea165c895","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EI casi d’uso dell’AI in azienda non mancano, ma molte iniziative non raggiungono gli obiettivi una volta superata la fase pilota. La differenza tra sperimentazione e valore concreto per l’azienda dipende dal sistema che circonda il modello: dati affidabili, flussi di lavoro governati, supervisione umana e cicli di feedback che migliorano nel tempo.\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 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-58dac20d2c","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?preferwebp=true&quality=85","lazyEnabled":true,"alt":"David Gaule","isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_221741058","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-74ae051c56",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-5ae5af7350","youtubeVideoId":"CXoca9hDdaQ","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-3692ae5e14","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-776e4fdc88",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-a0103535e6","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sull’AI in azienda:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-3ffc9c42d0","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\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-d1682eaf3a","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-3c8bf90e72","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAI IN AZIENDA: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’intelligenza artificiale in azienda consiste nell’utilizzo di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Esistemi di AI\u003C/a\u003E per analizzare dati, interpretare informazioni, generare contenuti e intraprendere azioni nei flussi di lavoro aziendali. È particolarmente utile nei processi in cui il lavoro dipende dal riconoscimento di schemi, dalla comprensione del contesto, dalla previsione dei risultati o dalla sintesi di informazioni su una scala o con una velocità difficili da gestire manualmente.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-bf93b01aef","text":"\u003Cp\u003ELe aziende non hanno certo ambizioni limitate in materia di AI. Ma trasformare queste ambizioni in valore in produzione si è rivelato più difficile del previsto. Il \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.osservatori.net/blog/artificial-intelligence/artificial-intelligence-journey-nelle-imprese\"\u003Ereport MIT del 2025 \u003Ci\u003EState of AI in Business\u003C/i\u003E\u003C/a\u003E ha rilevato che, nonostante investimenti aziendali nell’AI generativa per 30-40 miliardi di dollari, il 95% delle organizzazioni non registrava alcun ritorno misurabile. La diagnosi del report non era che i modelli fossero incapaci: “La maggior parte dei progetti fallisce a causa di flussi di lavoro fragili, dell’assenza di apprendimento contestuale e del mancato allineamento con le operazioni quotidiane”. Anche in Italia emergono difficoltà analoghe nel trasformare l’adozione dell’AI in valore misurabile. Secondo l’\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.osservatori.net/blog/artificial-intelligence/artificial-intelligence-journey-nelle-imprese\"\u003EOsservatorio Artificial Intelligence del Politecnico di Milano, nel 2025\u003C/a\u003E il 71% delle grandi imprese italiane aveva avviato almeno un progetto di AI e il 60% almeno un’iniziativa di AI generativa. Tuttavia, per circa un’azienda italiana su tre che ha avviato progetti di AI, quantificare preventivamente il ritorno sull’investimento (ROI) rimaneva una sfida e solo una grande impresa su cinque utilizzava l’AI in modo pervasivo nelle diverse funzioni aziendali.\u003C/p\u003E\r\n\u003Cp\u003EQuesta lacuna è il punto di partenza per comprendere l’AI in azienda oggi. L’AI può supportare un’ampia gamma di attività, dall’analisi dei dati e dall’automazione alla intelligenza documentale, alle previsioni, al supporto alle decisioni e ai flussi di lavoro con Agentic AI. A determinare se queste iniziative producono valore non è il solo caso d’uso, ma il grado di aderenza di ciascuna di esse al problema aziendale, il collegamento a dati affidabili, una governance adeguata e la misurazione rispetto ai risultati realmente importanti per l’organizzazione.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-in-business":{"id":"title-v2-0d17429c8e","additionalClasses":"anchor-title anchor-title--what-is-ai-in-business","type":"heading2","lines":["Che cos’è l’AI in azienda?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-in-business_0":{"id":"text-2faf547e28","text":"\u003Cp\u003EL’utilizzo dell’AI in azienda comprende diversi approcci. Il \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E addestra i modelli su dati storici per classificare, assegnare punteggi, ordinare o fare previsioni, ed è alla base della maggior parte dei casi d’uso predittivi, dal rischio di abbandono al rilevamento delle anomalie. Nei flussi di lavoro aziendali, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003El’AI generativa\u003C/a\u003E utilizza spesso \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Emodelli linguistici di grandi dimensioni\u003C/a\u003E (LLM) per recuperare, comprendere, riassumere e redigere informazioni, risultando quindi adatta ai problemi in cui l’input o l’output non è strutturato. L’analisi predittiva rileva segnali prospettici dai dati storici e operativi quando un’organizzazione deve agire prima che un risultato sia pienamente visibile.\u003C/p\u003E\r\n\u003Cp\u003ELe capacità che l’AI offre per affrontare i problemi aziendali si sono ampliate più rapidamente di quanto la maggior parte delle organizzazioni sia riuscita ad assimilare. Lo stesso vale per la tecnologia. Lo sviluppo recente più significativo è l’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-agents/\"\u003EAgentic AI\u003C/a\u003E: sistemi in grado di pianificare e svolgere sequenze di azioni tramite strumenti approvati e nell’ambito di autorizzazioni definite, eseguendo query sui dati, recuperando documenti, chiamando sistemi esterni e redigendo risposte.\u003C/p\u003E\r\n\u003Cp\u003ECiò che accomuna tutti questi approcci è la dipendenza dal sistema che circonda il modello. Le pipeline di dati, i controlli degli accessi, il livello semantico, il processo di valutazione, il percorso di distribuzione e le integrazioni del flusso di lavoro che circondano il modello sono gli elementi che determinano se un’iniziativa di AI produce risultati in produzione. Sono anche gli aspetti in cui la maggior parte delle implementazioni investe troppo poco.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_benefits-of-using-ai-in-business":{"id":"title-v2-e0d6dde4bd","additionalClasses":"anchor-title anchor-title--benefits-of-using-ai-in-business","type":"heading2","lines":["Vantaggi dell’utilizzo dell’AI in azienda"],":type":"snowflake-site/components/title-v2"},"text_benefits-of-using-ai-in-business_0":{"id":"text-e6cbdf9f0a","text":"\u003Cp\u003EL’AI aumenta l’efficienza, riduce i costi e accelera l’acquisizione di insight, ma il vantaggio principale consiste nel liberare le persone da attività ripetitive e tediose, permettendo loro di applicare il proprio giudizio alle decisioni che plasmano l’azienda.\u003C/p\u003E\n\u003Cp\u003EQuando l’AI viene implementata correttamente, con dati adeguati, un processo in grado di agire sui risultati prodotti dal modello e un ciclo di feedback che lo migliora nel tempo, cambia anche l’economia delle attività che in precedenza non potevano scalare. Ecco alcuni esempi:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ELa revisione delle frodi, che richiedeva a un team di analisti di esaminare manualmente gli avvisi, può essere eseguita continuamente su ogni transazione.\u003C/li\u003E\n\u003Cli\u003ELe previsioni della domanda, che richiedevano settimane di lavoro al team di pianificazione, possono aggiornarsi automaticamente all’arrivo di nuovi segnali.\u003C/li\u003E\n\u003Cli\u003ELe interazioni con i clienti, che richiedevano la ricerca manuale della cronologia dell’account, delle policy pertinenti e la stesura di una risposta, possono essere preparate in pochi secondi per la revisione umana.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_benefits-of-using-ai-in-business_0":{"id":"card-v2-6aba6b4d68","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003ESpark New Zealand utilizza Snowflake per unificare le proprie funzionalità di gestione dei dati e alimentare Brain, una piattaforma decisionale basata sull’AI che contribuisce a proporre il messaggio giusto al cliente giusto al momento giusto. Grazie all’accesso ai dati in batch e in tempo reale tramite Snowflake, Spark può prevedere con maggiore precisione quali clienti sono prossimi all’acquisto e personalizzare le esperienze di marketing, mentre la stessa data foundation di AI e machine learning si estende ad aree come la forza lavoro e la supply chain. Il risultato: prestazioni delle campagne fino a 20 volte superiori.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: Spark New Zealand"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/video/spark-nz-2/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Guarda la customer story"},"image":{"id":"image","height":"1080","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--5e83a558-d490-49b2-8020-398fec67a76d/spark-nz-logo-nav.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Spark New Zealand","isLcpImage":false,"width":"2520",":type":"snowflake-site/components/image"},"type":"content-card"},"title_how-businesses-use-artificial-intelligence":{"id":"title-v2-e87b4f9483","additionalClasses":"anchor-title anchor-title--how-businesses-use-artificial-intelligence","type":"heading2","lines":["Come le aziende utilizzano l’intelligenza artificiale"],":type":"snowflake-site/components/title-v2"},"text_how-businesses-use-artificial-intelligence_0":{"id":"text-4899d50be9","text":"\u003Cp\u003EL’AI si applica alle attività aziendali in diversi modi. Le sezioni seguenti descrivono sei degli approcci più comuni, anche se la maggior parte dei sistemi utilizzati nel mondo reale ne combina più di uno.\u003C/p\u003E\n\u003Ch3\u003EBusiness intelligence\u003C/h3\u003E\n\u003Cp\u003ELa business intelligence tradizionale si basava su report e dashboard, utili per monitorare metriche note, ma lente nel rispondere a domande non previste al momento della creazione della dashboard. Ogni domanda successiva che esula dalle viste esistenti richiede un analista, l’esecuzione di una query e un’attesa.\u003C/p\u003E\n\u003Cp\u003EL’analisi dei dati in linguaggio naturale governata cambia questa dinamica: chi opera nell’azienda pone domande in linguaggio comune e riceve risposte derivate da metriche approvate e definizioni aziendali, senza dover inviare una richiesta o attendere un report personalizzato. Chi si trova più vicino a una decisione può analizzarla direttamente, modificando sia la velocità sia la qualità dell’analisi che la supporta.\u003C/p\u003E\n\u003Ch3\u003EAutomazione AI\u003C/h3\u003E\n\u003Cp\u003ELa maggior parte dei flussi di lavoro aziendali include un passaggio in cui l’azione successiva dipende dall’interpretazione del contenuto di una richiesta, anziché dall’applicazione di una regola fissa. Ad esempio, una richiesta di approvvigionamento richiede la lettura del testo contrattuale per determinare se un fornitore è idoneo. Una richiesta di assistenza da parte di un cliente richiede la comprensione della cronologia dell’account per determinare quale risposta sia appropriata.\u003C/p\u003E\n\u003Cp\u003EQuesti passaggi rallentano i processi non perché siano difficili, ma perché assemblare il contesto corretto richiede tempo. L’automazione basata sull’AI risolve questo problema svolgendo il lavoro di raccolta che precede la valutazione: identifica le informazioni necessarie, le recupera da fonti strutturate e non strutturate, applica la logica aziendale e prepara una raccomandazione per la revisione. Il passaggio umano rimane, ma arriva con il contesto già assemblato.\u003C/p\u003E\n\u003Ch3\u003EElaborazione del linguaggio naturale e intelligenza documentale\u003C/h3\u003E\n\u003Cp\u003EUna parte significativa delle conoscenze aziendali risiede in forma non strutturata, ad esempio in contratti, policy, trascrizioni delle interazioni con l’assistenza, report di ricerca e documenti normativi. Gli LLM, spesso abbinati a sistemi di ricerca semantica o di recupero delle informazioni, rendono accessibili questi contenuti in modi che la ricerca per parole chiave non consente: riassumendo documenti lunghi, estraendo clausole specifiche, confrontando versioni e rispondendo a domande basate su un corpus di conoscenze interne.\u003C/p\u003E\n\u003Cp\u003EPer le organizzazioni che gestiscono grandi volumi di documenti, in ambito legale, compliance, finanziario o di assistenza clienti, il lavoro che in precedenza richiedeva a persone esperte una lettura e una sintesi approfondite può essere preparato per la revisione in una frazione del tempo. La qualità dell’output dipende dalla qualità e dall’aggiornamento delle fonti sottostanti, nonché dal fatto che il sistema sia connesso a contenuti mantenuti e governati, anziché lasciati accumulare e deteriorarsi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-businesses-use-artificial-intelligence_0":{"id":"card-v2-8793322807","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EZoom utilizza Snowflake Cortex AI per sviluppare app di AI generativa di livello enterprise sulla propria data foundation Snowflake. Grazie a governance, privacy e sicurezza integrate nell’AI Data Cloud Snowflake, Zoom può portare i carichi di lavoro AI sui propri dati, creare strumenti come un chatbot in linguaggio naturale per i team a contatto con i clienti e consentire al personale di accedere più rapidamente a insight affidabili per prendere decisioni migliori.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer story: Zoom"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/it/customers/all-customers/video/zoom/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Guarda la customer story"},"type":"content-card"},"text_how-businesses-use-artificial-intelligence_1":{"id":"text-4d22736fca","text":"\u003Ch3\u003EAnalisi predittiva e supporto alle decisioni\u003C/h3\u003E\n\u003Cp\u003EL’analisi predittiva è più utile quando un’organizzazione deve agire prima che un risultato sia pienamente visibile. I modelli ML addestrati su dati storici possono stimare il rischio di abbandono prima che un cliente lasci l’azienda, segnalare transazioni simili a frodi prima che una perdita sia confermata e prevedere la domanda prima di dover prendere decisioni sulle scorte. L’output del modello, ovvero un punteggio, una classificazione o una probabilità, modifica ciò che chi prende la decisione sa nel momento in cui deve agire.\u003C/p\u003E\n\u003Cp\u003EL’utilità di questo segnale dipende dalla qualità dei dati su cui si basa, dalla precisione del modello addestrato su tali dati e dal fatto che il flusso di lavoro associato sia strutturato per agire in base all’output prodotto.\u003C/p\u003E\n\u003Ch3\u003EAgentic AI\u003C/h3\u003E\n\u003Cp\u003EI sistemi Agentic AI eseguono sequenze di azioni, come interrogare database, recuperare documenti, chiamare sistemi esterni ed eseguire task, per raggiungere un obiettivo definito. Un agente AI per gli approvvigionamenti non si limita a segnalare una richiesta perché chi acquista la analizzi. Recupera il testo contrattuale pertinente, verifica la cronologia della spesa rispetto alla policy, conferma lo stato di approvazione del fornitore e presenta una raccomandazione con le prove già raccolte.\u003C/p\u003E\n\u003Cp\u003EQuesta autonomia modifica in modo significativo i requisiti di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E. I limiti entro i quali un agente AI può accedere alle informazioni e agire senza approvazione umana non sono considerazioni progettuali facoltative: determinano se il sistema è sicuro da implementare in un processo aziendale con conseguenze rilevanti.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-businesses-use-artificial-intelligence_0":{"id":"text-3d70915c34","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003ECONSIGLIO RAPIDO\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EInizia dal flusso di lavoro, non dal modello. Definisci la decisione, il processo o il risultato che il sistema AI deve migliorare, quindi risali ai dati, alla governance, alle integrazioni e ai passaggi di revisione necessari per supportarlo.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_ai-in-the-enterprise":{"id":"title-v2-6680a22f1d","additionalClasses":"anchor-title anchor-title--ai-in-the-enterprise","type":"heading2","lines":["L’AI nelle aziende"],":type":"snowflake-site/components/title-v2"},"text_ai-in-the-enterprise_0":{"id":"text-0f69e63c6b","text":"\u003Cp\u003ELa maggior parte delle organizzazioni ha avviato i propri programmi di intelligenza artificiale allo stesso modo: una manciata di progetti, ciascuno affidato a un team diverso, con decisioni autonome sull’accesso ai dati, sulla selezione dei modelli e sui criteri per definire un output valido. Questo approccio produce risultati: i singoli workflow migliorano e la produttività aumenta, ma non è scalabile.\u003C/p\u003E\n\u003Cp\u003ECi si accorge di non poter accedere a dati che un altro team utilizza già. Le funzionalità vengono sviluppate due volte perché nessuno sapeva dell’esistenza delle prime versioni. I modelli arrivano in produzione senza risposte chiare a domande fondamentali: quale versione è in esecuzione, con quali dati è stata addestrata, chi l’ha approvata e chi è responsabile se l’output è errato.\u003C/p\u003E\n\u003Cp\u003ELe organizzazioni che hanno superato questo ostacolo hanno smesso di considerare l’enterprise AI una raccolta di progetti e hanno iniziato a considerarla un modello operativo. Questo significa disporre di un’infrastruttura dati condivisa con controlli di accesso coerenti, definizioni semantiche che forniscono ai sistemi AI un contesto aziendale accurato, framework per agenti con limiti definiti su ciò che è possibile recuperare e sulle azioni che è possibile eseguire senza approvazione umana, oltre a pratiche di valutazione che misurano la qualità dell’output dopo la distribuzione, non solo prima. In un programma maturo di enterprise AI, la governance non è un controllo da superare prima del lancio. Fa parte del funzionamento del sistema.\u003C/p\u003E\n\u003Cp\u003ELa fase successiva dell’enterprise AI sarà definita dalla capacità delle organizzazioni di integrare efficacemente l’AI nei workflow in cui vengono prese le decisioni aziendali, collegandola a dati affidabili, facendola operare entro limiti chiari e producendo output su cui i team possano agire e di cui possano assumersi la responsabilità.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/enterprise-ai/\"\u003EScopri di più: enterprise AI \u003E\u003C/a\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-for-small-business":{"id":"title-v2-c80bc2eac5","additionalClasses":"anchor-title anchor-title--ai-for-small-business","type":"heading2","lines":["L’AI per le piccole imprese"],":type":"snowflake-site/components/title-v2"},"text_ai-for-small-business_0":{"id":"text-ca108ae705","text":"\u003Cp\u003ELe piccole imprese non sono una versione ridotta delle aziende più grandi. Devono affrontare vincoli diversi: meno risorse tecniche, un’infrastruttura dati meno formalizzata e tempi più limitati per cicli di implementazione prolungati. Hanno anche vantaggi che spesso mancano alle organizzazioni aziendali più grandi.\u003C/p\u003E\n\u003Cp\u003EIl vantaggio più significativo è la vicinanza. Chi supervisiona un progetto di intelligenza artificiale per una piccola impresa si trova di solito abbastanza vicino al processo sottostante da poter valutare direttamente l’output. Ad esempio, un retailer di piccole dimensioni che sta testando la previsione delle scorte capisce nel giro di una stagione se il modello riduce l’eccesso di scorte o crea nuovi problemi. Questo ciclo diretto di feedback elimina l’ambiguità che rende lenta e soggetta a dinamiche politiche la valutazione dell’enterprise AI.\u003C/p\u003E\n\u003Cp\u003ELa vicinanza accorcia anche il ciclo decisionale. Quando un progetto di intelligenza artificiale non produce i risultati attesi, una piccola impresa può modificarlo o interromperlo senza dover coinvolgere un ampio comitato di governance, avviare una trattativa con un fornitore o coordinare più team. Questa rapidità è un vantaggio, purché l’ambito del progetto sia stato definito in modo sufficientemente circoscritto e con guardrail adeguati.\u003C/p\u003E\n\u003Cp\u003EIl rigore nella definizione dell’ambito determina spesso il successo o il fallimento dei progetti di intelligenza artificiale nelle piccole imprese. Le organizzazioni che ottengono valore iniziano con un workflow, verificano che i dati siano disponibili e definiscono in anticipo in cosa consista il miglioramento. Quelle che incontrano difficoltà cercano di risolvere troppe esigenze contemporaneamente, scoprono che i dati non sono pronti e non hanno un modo chiaro per misurare se sia cambiato qualcosa.\u003C/p\u003E\n\u003Cp\u003EI guardrail sono fondamentali, ma spesso vengono trascurati. Le piccole imprese tendono a investire troppo poco nella definizione di ciò che il sistema AI è autorizzato a fare, dei dati a cui può accedere e di chi deve esaminare i suoi output prima che raggiungano un cliente o contribuiscano a una decisione. Questi limiti sono necessari a qualsiasi livello di scala.\u003C/p\u003E\n\u003Cp\u003EUn modello che produce raccomandazioni imprecise sulle scorte, un assistente rivolto ai clienti che utilizza prezzi obsoleti o un’automazione che agisce su dati a cui non avrebbe dovuto accedere possono creare problemi più difficili da risolvere di quanto sarebbe stato prevenirli. Definire responsabilità chiare, fonti di dati governate e un processo di revisione per gli output con conseguenze rilevanti previene rischi non necessari.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-business-strategy":{"id":"title-v2-3f43ce90a1","additionalClasses":"anchor-title anchor-title--ai-business-strategy","type":"heading2","lines":["Strategia di business per l’intelligenza artificiale"],":type":"snowflake-site/components/title-v2"},"text_ai-business-strategy_0":{"id":"text-bdd8b4df82","text":"\u003Cp\u003ELa domanda iniziale di intelligenza artificiale supera quasi sempre la capacità di un’organizzazione di implementarla, governarla e misurarla efficacemente. I progetti che procedono senza una valutazione o una pianificazione rigorose tendono a consumare risorse senza produrre i risultati attesi e spesso creano invece nuovi problemi. Una strategia solida inizia individuando ciò che merita un investimento immediato, ciò che richiede prima dati o governance migliori e ciò che non dovrebbe essere portato avanti.\u003C/p\u003E\n\u003Cp\u003EUn framework di valutazione utile considera quattro dimensioni:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EValore aziendale:\u003C/strong\u003E Quale decisione, processo o struttura dei costi cambierebbe se il progetto funzionasse, e il cambiamento sarebbe abbastanza significativo da giustificare l’investimento?\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFattibilità:\u003C/strong\u003E I dati, le competenze e le integrazioni necessari sono disponibili oggi oppure il progetto dipende da uno stato futuro che non è ancora stato realizzato?\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERischio:\u003C/strong\u003E In che modo l’AI interagisce con dati regolamentati, comunicazioni con i clienti o risultati finanziari, e quale revisione è necessaria prima del passaggio in produzione?\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPotenziale di riutilizzo:\u003C/strong\u003E Il progetto produce un’infrastruttura condivisa, come definizioni semantiche, servizi di recupero o modelli di governance, che riduce il costo e la complessità del progetto successivo?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELa misurazione deve essere definita prima dell’implementazione. Ad esempio, le prestazioni di un assistente per il supporto possono essere misurate in base ai tempi di risoluzione, al tasso di escalation e all’adozione da parte degli agenti, mentre quelle di un workflow di previsione possono essere valutate in base all’accuratezza della previsione, alla durata del ciclo di pianificazione e alla capacità della previsione di modificare effettivamente le decisioni che intendeva informare. Definire in anticipo la metrica impone il confronto sull’ambito che distingue i progetti di intelligenza artificiale utili da quelli costosi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_ai-business-strategy_0":{"id":"text-4e1d0713c9","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EL’ERRORE COMUNE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’AI non va trattata come uno strumento indipendente, ma come parte di un processo operativo. Anche un modello avanzato può non creare valore se i dati di input sono incompleti, gli output non raggiungono il workflow corretto o nessuno è responsabile di esaminare e migliorare nel tempo le prestazioni.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-and-when-to-establish-an-ai-council":{"id":"title-v2-a966d23643","additionalClasses":"anchor-title anchor-title--how-and-when-to-establish-an-ai-council","type":"heading2","lines":["Come e quando istituire un comitato per l’AI"],":type":"snowflake-site/components/title-v2"},"text_how-and-when-to-establish-an-ai-council_0":{"id":"text-c86da69ab9","text":"\u003Cp\u003ELa maggior parte delle organizzazioni raggiunge un punto in cui l’adozione dell’AI procede più rapidamente delle relative linee guida. Il personale sperimenta gli strumenti, i team sviluppano workflow e integrano l’AI nei sistemi aziendali. Tuttavia, le domande su ciò che è consentito, su ciò che è sicuro e su cosa costituisca un buon caso d’uso ricevono risposte incoerenti, oppure nessuna risposta. Un comitato per l’AI serve a colmare questa lacuna.\u003C/p\u003E\n\u003Cp\u003ELa funzione principale del comitato per l’AI è favorire l’adozione: aiutare il personale a comprendere quali strumenti sono disponibili, come utilizzare l’AI in modo responsabile nel proprio contesto specifico e a chi rivolgersi in caso di domande. In pratica, questo significa demo dal vivo, sessioni di consulenza, hackathon, training basato sui ruoli, librerie di prompt e presentazioni di casi d’uso.\u003C/p\u003E\n\u003Cp\u003ESono necessari più formati, perché il personale affronta l’adozione dell’AI partendo da livelli di esperienza diversi. Alcune persone hanno bisogno di ispirazione. Altre hanno bisogno di supporto pratico. Gli esempi forniti da colleghi e colleghe sono particolarmente efficaci: quando il personale vede persone che svolgono attività simili alle proprie e utilizzano l’AI con successo, riesce a immaginare più chiaramente come questa tecnologia possa essere utile nel proprio lavoro.\u003C/p\u003E\n\u003Cp\u003EGli strumenti operativi del consiglio devono essere abbastanza concreti da risultare utili nel momento in cui si prende una decisione: elenchi degli strumenti approvati, linee guida per la gestione dei dati, criteri di valutazione iniziale dei casi d’uso, fasi di revisione obbligatorie per gli output ad alto rischio ed esempi documentati di come si utilizza correttamente l’AI nella pratica.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/business/ai-council/\"\u003EScopri di più: Consiglio per l’AI \u003E\u003C/a\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-business-use-cases-and-software-applications":{"id":"title-v2-5c4f499a27","additionalClasses":"anchor-title anchor-title--ai-business-use-cases-and-software-applications","type":"heading2","lines":["Casi d’uso aziendali dell’AI e applicazioni software"],":type":"snowflake-site/components/title-v2"},"text_ai-business-use-cases-and-software-applications_0":{"id":"text-fd0367d5d2","text":"\u003Cp\u003EL’AI crea valore aziendale attraverso casi d’uso specifici, come la previsione della domanda, il rilevamento delle frodi, la prevenzione dell’abbandono dei clienti, l’analisi dei contratti e la pianificazione delle scorte, ciascuno collegato a un processo destinato a cambiare e a un risultato misurabile. Capire quali casi d’uso sono realistici, quali richiedono dati che ancora non esistono e quali produrrebbero il cambiamento più significativo è l’aspetto in cui la pianificazione dell’AI svolge generalmente il lavoro più importante.\u003C/p\u003E\n\u003Cp\u003ELe app sono il livello software che rende operativi i casi d’uso: motori di raccomandazione, rilevatori di anomalie, assistenti per i documenti, interfacce di analisi dei dati in linguaggio naturale e AI conversazionale. La scelta dell’app dipende dai requisiti del caso d’uso, dai dati disponibili, dalla decisione da supportare e dal workflow in cui deve inserirsi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-build-ai-on-snowflake":{"id":"title-v2-9b738e58b3","additionalClasses":"anchor-title anchor-title--why-build-ai-on-snowflake","type":"heading2","lines":["Perché sviluppare l’AI su Snowflake?"],":type":"snowflake-site/components/title-v2"},"text_why-build-ai-on-snowflake_0":{"id":"text-2739917f4e","text":"\u003Cp\u003EIl modello è solo un componente. La capacità di generare valore in produzione dipende da tutto ciò che lo circonda: i dati utilizzati per l’addestramento, le feature su cui si basa, la governance che controlla ciò a cui può accedere, il monitoraggio che rileva i cambiamenti nel contesto e il workflow che agisce sui suoi output.\u003C/p\u003E\n\u003Cp\u003ESnowflake riunisce questi elementi in un ambiente governato, in cui dati, autorizzazioni, artefatti dei modelli, servizi di retrieval e logica applicativa operano con controlli di accesso coerenti.\u003C/p\u003E\n\u003Cp\u003EFunzionalità principali:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-analyst/\"\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/a\u003E\u003Cstrong\u003E:\u003C/strong\u003E Text-to-SQL governato sulle viste semantiche, per fornire risposte alle domande in linguaggio naturale basate su definizioni aziendali approvate anziché sulla logica delle colonne non elaborate.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E Recupera il contesto pertinente dai dati non strutturati per le pipeline \u003Ca href=\"https://www.snowflake.com/it/fundamentals/rag/\"\u003ERAG\u003C/a\u003E e i workflow agentici, mantenendo il retrieval collegato a fonti di contenuti governate.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/#0\"\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E\u003C/a\u003E\u003Cstrong\u003E:\u003C/strong\u003E Combina input strutturati e non strutturati nei workflow agentici, generando SQL e recuperando il contesto dei documenti in un unico processo governato anziché tramite strumenti separati.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECortex AISQL:\u003C/strong\u003E Integra le funzioni di AI direttamente nei workflow SQL, consentendo di applicare il machine learning ai dati senza spostarli al di fuori dell’ambiente governato.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESnowflake Intelligence:\u003C/strong\u003E Interfaccia conversazionale per lavorare con i dati aziendali e la logica di business senza esportare i dati in strumenti in cui i controlli di accesso e il data lineage non vengono mantenuti.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/model-management\"\u003E\u003Cstrong\u003ESnowflake Model Registry\u003C/strong\u003E\u003C/a\u003E\u003Cstrong\u003E:\u003C/strong\u003E Gestisce i modelli con controllo delle versioni, metadati e stato della distribuzione, consentendo di rispondere alle domande più importanti in produzione: quale modello è in esecuzione, quali dati lo hanno addestrato e cosa è cambiato rispetto alla versione precedente.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EUn’esperienza di analisi dei dati self-service, un workflow di automazione, un modello predittivo e un assistente rivolto alla clientela possono sembrare iniziative separate, ma si basano su molti degli stessi asset sottostanti: dati affidabili, definizioni semantiche, percorsi di retrieval, controlli di accesso, data lineage e monitoraggio. Quando questi asset sono condivisi, ogni nuovo progetto parte con una parte maggiore del modello operativo già predisposta e si riduce la distanza tra un prototipo funzionante e un sistema di produzione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-path-to-reliable-ai-in-business":{"id":"title-v2-fffd5d07a4","additionalClasses":"anchor-title anchor-title--the-path-to-reliable-ai-in-business","type":"heading2","lines":["Il percorso verso un’AI aziendale affidabile"],":type":"snowflake-site/components/title-v2"},"text_the-path-to-reliable-ai-in-business_0":{"id":"text-ba763a5d22","text":"\u003Cp\u003EOttenere risultati corretti dall’AI in un contesto aziendale richiede di risolvere simultaneamente due problemi. La tecnologia deve essere adeguata al problema: occorre scegliere l’approccio corretto, addestrarlo con dati accurati e produrre output sufficientemente precisi per poter agire. Anche il sistema che la circonda deve essere sviluppato per supportarla.\u003C/p\u003E\n\u003Cp\u003ELa maggior parte delle iniziative di AI che non raggiungono gli obiettivi ha investito troppo poco in uno dei due aspetti. Le iniziative che generano valore progressivamente, in cui ogni nuovo progetto si basa su ciò che ha stabilito il precedente, considerano entrambi requisiti essenziali fin dall’inizio.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-path-to-reliable-ai-in-business_0":{"id":"text-77679feecc","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELe organizzazioni che ottengono risultati duraturi dalle iniziative di AI collegano fin dall’inizio i propri sistemi di AI a dati affidabili, processi governati, responsabilità chiare e risultati misurabili.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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