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Blackwood"}},{"authorImage":{"id":"image-5a875f60ca","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dba9205b-35b1-49d2-a314-a05a603a5f0b/swati-humbad.jpg?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"800",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e7761b3665","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/swati-humbad/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Swati Humbad"}}],"image":{"id":"image-f1f9eeddac","height":"4500","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ee60c6c-379f-4ec7-a140-50f660a5f053/30-general.jpg?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"8000",":type":"snowflake-site/components/image"},"timeToRead":"12","publicationDate":"OCT 23, 2026","tag":{"tagText":"AI e ML","tagColor":"#C54985"},"title":{"lines":["Oltre l’adozione: come misurare se l’AI sta cambiando il lavoro che conta"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","blog_text_1956837561":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-10d6bbf8ec","text":"\u003Cp\u003EPensa a due organizzazioni che registrano entrambe 1.000 interazioni con l’AI in una settimana. Nella prima, la maggior parte di quelle interazioni consiste in spiegazioni e ricerche di informazioni. Nella seconda, le persone creano pipeline di dati, analizzano anomalie ed eseguono workflow multifase.\u003C/p\u003E\n\u003Cp\u003ELa maggior parte delle dashboard AI aziendali attribuirebbe a queste due organizzazioni lo stesso punteggio. La prima generazione di metriche per gli sviluppatori AI misurava accesso e attività: quante persone hanno una licenza, quante sono attive e quanti prompt inviano. Ma queste metriche non possono dirti se l’AI sta generando risultati di lavoro significativi.\u003C/p\u003E\n\u003Cp\u003ERispondere a questa domanda significa monitorare per cosa le persone usano l’AI e se questo cambia nel tempo. Ecco il framework che usiamo per misurare il valore dell’AI e cosa abbiamo scoperto applicandolo a due dei nostri team.\u003C/p\u003E\n\u003Ch2\u003EUn framework in quattro parti per misurare il valore dell’AI\u003C/h2\u003E\n\u003Ch3\u003E1. Partire dal lavoro che l’AI sta svolgendo\u003C/h3\u003E\n\u003Cp\u003EChiedere a un assistente di spiegare un’istruzione SQL è una richiesta diversa dal chiedere a un agente AI di creare una pipeline o investigare un errore. Raggruppa il lavoro svolto dall’AI in tre livelli:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ELavoro di base: \u003C/b\u003El’AI spiega il codice, risponde a domande e aiuta a comprendere un concetto non familiare.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESviluppo applicato:\u003C/b\u003E l’AI scrive e modifica codice, esplora i dati, risolve problemi e crea report o applicazioni.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ELavoro agentico: \u003C/b\u003Esi tratta di un processo multifase. L’AI ragiona sul contesto, usa strumenti e contribuisce a portare a termine un risultato invece di rispondere a una singola domanda.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EQuesta suddivisione descrive il lavoro, non il volume, e questo ti offre un elemento da verificare. Se l’AI sta creando capacità reale, le ore risparmiate dovrebbero riflettersi nel livello agentico.\u003C/p\u003E\n\u003Ch3\u003E2. Misurare adozione e AI fluency separatamente\u003C/h3\u003E\n\u003Cp\u003ELa domanda successiva è se l’utilizzo a maggior valore si sia diffuso oltre a poche persone. Due dimensioni rispondono a questa domanda:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EAdozione: \u003C/b\u003Emisura quanto ampiamente l’AI viene utilizzata nella popolazione idonea.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAI fluency: \u003C/b\u003Emostra quanto sia diventato sofisticato il lavoro che le persone delegano all’AI.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EInsieme, queste dimensioni descrivono quattro posizioni.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-416acbf468","height":"1068","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--03ad0138-c69e-480b-ba5f-6330ecf8a2e6/ai-fluency-adoption.png?quality=85&preferwebp=true","alt":"ai fluency","lazyEnabled":true,"isLcpImage":true,"width":"1664",":type":"snowflake-site/components/image"},"blog_text_1956837561":{"id":"blog-text-1a274d9d3b","text":"\u003Cp\u003E\u003Ci\u003EFigura 1. Misurazione di adozione e AI fluency. \u003C/i\u003E\u003C/p\u003E\n\u003Ch3\u003E3. Usare i benchmark di settore per il contesto\u003C/h3\u003E\n\u003Cp\u003EUn numero isolato è difficile da tradurre in azioni concrete. Se il 35% degli sviluppatori idonei utilizza attivamente un agente AI per il coding, questo dato da solo non ti dice se il 35% sia un buon risultato. Dipende da cosa fanno le organizzazioni comparabili, e le aspettative variano in base al settore. Un’azienda software e un assicuratore regolamentato non partono dalla stessa base di riferimento.\u003C/p\u003E\n\u003Cp\u003EIl confronto con organizzazioni simili ti offre una visione relativa su entrambe le dimensioni. Invece di limitarsi a indicare che l’adozione è al 35%, può dirti se questo ti colloca tra le organizzazioni emergenti, in crescita, competenti o leader del tuo settore.\u003C/p\u003E\n\u003Cp\u003ELo stesso vale per l’AI fluency. Un’organizzazione può avere un’adozione ampia ma poco lavoro agentico. Un’altra può avere una manciata di utenti che svolgono un lavoro sofisticato che non si è diffuso. Questi due risultati richiedono risposte diverse.\u003C/p\u003E\n\u003Ch3\u003E4. Osservare il divario tra i tuoi utenti migliori e tutti gli altri\u003C/h3\u003E\n\u003Cp\u003EI tuoi utenti più avanzati sono il segnale da osservare.\u003C/p\u003E\n\u003Cp\u003ENella maggior parte delle organizzazioni, alcune persone individuano workflow AI efficaci prima dei colleghi. Imparano a delegare attività più ampie, fornire un contesto migliore e combinare strumenti. Considera questi pattern come una roadmap, non come una raccolta di aneddoti.\u003C/p\u003E\n\u003Cp\u003ELa domanda pratica è come trasferire un comportamento da una persona a 100. Per noi hanno funzionato quattro tattiche:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EFai in modo che i power user insegnino, non solo che facciano demo: \u003C/b\u003Eun workshop pratico guidato da una persona del team che ottiene già risultati è più efficace di una sessione di formazione del fornitore. Verifica se l’utilizzo settimanale si mantiene un mese dopo, non se la sala era piena. Un workshop che cambia le abitudini è diverso da uno che produce solo un picco.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ETrasforma i loro workflow in ricette e template: \u003C/b\u003Equando qualcuno risolve bene un problema, fallo scrivere come qualcosa che un collega possa eseguire, non solo leggere. Il test è se la persona successiva riesce a eseguirlo senza fare domande all’autore.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EOrganizza uno show-and-tell periodico: \u003C/b\u003Eprevedi uno spazio fisso in cui le persone presentano un workflow che hanno costruito, incluse le cose che non hanno funzionato. I fallimenti si diffondono meglio dei successi, perché indicano a tutti gli altri quali problemi vale la pena affrontare.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ERendi la libreria facilmente trovabile: \u003C/b\u003Eè il passaggio che la maggior parte dei team salta. Quando hai decine di workflow riutilizzabili, il limite non è più la creazione ma la scoperta, e una libreria che nessuno può cercare è una libreria che nessuno riutilizza.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EL’obiettivo è diffondere le pratiche comprovate da poche persone a molte, e il meccanismo conta più dell’entusiasmo.\u003C/p\u003E\n\u003Ch2\u003ECosa fare con la risposta\u003C/h2\u003E\n\u003Cp\u003ELa misurazione ha senso solo quando indica un’azione, non semplicemente uno stato. Esistono tre posizioni comuni, e ciascuna suggerisce un diverso corso d’azione:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESofisticato ma limitato: \u003C/b\u003Ei primi utilizzatori usano agenti AI per lavori avanzati, mentre la maggior parte delle persone idonee non ha ancora iniziato. Riduci gli attriti di accesso, condividi esempi interni ed espandi l’enablement.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAmpio ma superficiale: \u003C/b\u003Emolte persone usano l’AI, soprattutto per spiegazioni e generazione di codice semplice. Mostra loro come delegare attività più ampie attraverso esempi, template e formazione pratica.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAmpio e sofisticato: \u003C/b\u003Eaumentare ulteriormente l’adozione non è più la priorità. Individua i workflow ripetibili, crea agenti AI riutilizzabili e misura risultati come la velocità di sviluppo o l’efficienza operativa.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELe stesse metriche possono portare a consigli diversi, in base alla tua situazione di partenza.\u003C/p\u003E\n\u003Ch2\u003EApplicare il framework ai nostri team\u003C/h2\u003E\n\u003Cp\u003EAbbiamo dedicato due trimestri all’utilizzo di questo framework su Snowflake CoCo, il nostro assistente di sviluppo AI agentico, all’interno di Snowflake. Due team con attività molto diverse sono arrivati a risultati simili, e in entrambi i casi i vantaggi sono derivati da workflow agentici piuttosto che da un utilizzo di base.\u003C/p\u003E\n\u003Ch3\u003EData science di prodotto: automatizzare un runbook diagnostico\u003C/h3\u003E\n\u003Cp\u003EIl nostro team di data science di prodotto gestisce migliaia di pipeline di dati. Nel primo trimestre del FY27, quelle pipeline hanno richiesto attenzione 960 volte, un dato normale su questa scala. Ogni caso richiedeva a un ingegnere esperto circa 30 minuti di diagnosi. Questo corrisponde a circa 160 ore al mese di tempo di esperti dedicate alla diagnosi. \u003C/p\u003E\n\u003Cp\u003EOra CoCo esegue questa diagnosi. Nel corso del Q1 e del Q2, lavorando al fianco del team, CoCo ha: \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EMigliorato più di 500 pipeline\u003C/li\u003E\n\u003Cli\u003EFatto il merge di centinaia di PR\u003C/li\u003E\n\u003Cli\u003ERidotto il tempo dall’incidente a una correzione testata e verificabile a meno di 16 minuti\u003C/li\u003E\n\u003Cli\u003ERidotto sprechi di centinaia di crediti ogni giorno.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EOttimizzare una pipeline costosa è utile ma raramente urgente, quindi è il tipo di lavoro che aspetta all’infinito. Un agente AI che propone queste modifiche come prassi non ha bisogno di una settimana tranquilla per occuparsene.\u003C/p\u003E\n\u003Cp\u003EIl team aveva già svolto il lavoro importante prima che CoCo entrasse in gioco. Disponevano di un runbook diagnostico rigoroso, scritto nel corso di centinaia di incidenti e sufficientemente generale da risultare utile per arrivare al nocciolo di molti tipi diversi di incidenti. Questa disciplina è il motivo per cui gli incidenti venivano risolti in modo coerente. Il runbook era già abbastanza valido da poter essere scalato. Quello che mancava era un modo per eseguirlo senza dover aspettare che qualcuno fosse disponibile.\u003C/p\u003E\n\u003Cp\u003ECosì il team ha affidato il runbook a CoCo, facendolo attivare da un avviso invece che da una pressione di tasto. Stessi passaggi, stesso standard, ma veniva eseguito secondo i tempi dell’incidente e non quelli del team.\u003C/p\u003E\n\u003Cp\u003EQuesto è lo schema del passaggio dalla frontiera al mainstream nella sua forma più letterale. Un metodo che apparteneva a un solo team è diventato un workflow che funziona in autonomia. Se un metodo è chiaro abbastanza da poter formare una nuova risorsa, è chiaro abbastanza da poter essere affidato all’AI. E se attualmente esiste solo nell’esperienza di un esperto, metterlo per scritto ha valore di per sé.\u003C/p\u003E\n\u003Ch3\u003ECome funziona la revisione\u003C/h3\u003E\n\u003Cp\u003ECoCo propone, le persone decidono. Ogni modifica arriva sotto forma di pull request, revisionata e approvata da un responsabile designato. Il merge e il deployment restano in mano al team.\u003C/p\u003E\n\u003Cp\u003EQuesto confine è intenzionale e si sposta a seconda di dove si trova il lavoro. L’attenzione di chi revisiona diventa la risorsa da gestire, e un team che produce più proposte di quante ne possa leggere ha spostato il proprio collo di bottiglia, non lo ha eliminato. In cambio, gli ingegneri hanno trasferito 160 ore di diagnosi nella decisione finale, la parte che effettivamente richiedeva il loro intervento.\u003C/p\u003E\n\u003Cp\u003ELa lezione pratica: progettare il lavoro in modo che il suo risultato sia sempre qualcosa che una persona può revisionare. La revisione diventa il controllo, ed è questo che rende semplice eseguirlo su pipeline di produzione.\u003C/p\u003E\n\u003Ch3\u003EMarketing: dall'11,6% di fiducia al 70% di utilizzo settimanale\u003C/h3\u003E\n\u003Cp\u003ELa prova più evidente che questo approccio si generalizza è arrivata da una parte dell’azienda che non ha quasi nessun ingegnere. L’organizzazione marketing Snowflake conta quasi 600 persone, la maggior parte più a proprio agio con uno slogan di campagna che con un’istruzione SQL.\u003C/p\u003E\n\u003Cp\u003ENel febbraio 2025, un sondaggio interno al team marketing del Nord America ha rilevato che l'11,6% si sentiva molto sicuro nell’usare l’AI nel proprio ruolo. Quando Snowflake CoCo è arrivato come research preview alla fine di quell’anno, otto addetti marketing del Nord America lo usavano settimanalmente. \u003C/p\u003E\n\u003Cp\u003EA maggio 2026, l’utilizzo settimanale di CoCo nel team marketing globale aveva raggiunto il 70%. Separatamente, il 93% ha dichiarato di utilizzare strumenti di AI ogni giorno. Il primo dato è un comportamento osservato, il secondo è autodichiarato: per questo partiamo dal primo.\u003C/p\u003E\n\u003Cp\u003ECosa hanno creato:\u003C/p\u003E\n\u003Cp\u003EUna pipeline di esportazione per motion graphics che ha trasformato un’intera settimana di lavoro per il Summit in 20 minuti.\u003C/p\u003E\n\u003Cp\u003EUn’app di reporting win-loss su CoCo e Streamlit che pianifica i colloqui e genera il deck trimestrale per la leadership. Tre ore al mese di identificazione dei candidati più una settimana di lavoro sul deck sono ora automatizzate per circa il 90%.\u003C/p\u003E\n\u003Cp\u003EUn motore di automazione per le richieste in entrata che gestisce da 2000 a 3000 risposte email giornaliere alle campagne globali senza alcun intervento umano, sostituendo un acquisto software già pianificato.\u003C/p\u003E\n\u003Cp\u003EDa allora, gli addetti marketing hanno creato più di 600 skill personali e a livello di progetto, e da 100 a 200 ne hanno costruite di proprie da zero. L’utente mediano utilizza sette skill a settimana. \u003C/p\u003E\n\u003Cp\u003EEntrambe le dimensioni sono cambiate. L’adozione è passata da una piccola minoranza sicura di sé a un utilizzo settimanale del 70%, e l’AI fluency è passata dal fare domande alla creazione di workflow che funzionano senza la presenza di chi li ha creati. Una dashboard che monitorasse solo l’adozione ne avrebbe colto solo metà.\u003C/p\u003E\n\u003Cp\u003EQuesto risultato è arrivato grazie agli AI Day trimestrali, agli hackathon regionali, a una newsletter e a sessioni di office hours, tutti gestiti dai colleghi stessi.\u003C/p\u003E\n\u003Ch2\u003EPerché il contesto della piattaforma è importante\u003C/h2\u003E\n\u003Cp\u003EQualsiasi assistente AI capace può leggere un messaggio di errore. Questi risultati si confermano in produzione perché CoCo lavora dall’interno della piattaforma dati, dove il contesto necessario per una vera correzione è già disponibile.\u003C/p\u003E\n\u003Cp\u003ELegge il catalogo dati e il lineage, verifica se i dati sono aggiornati e individua quali parti dell’azienda dipendono da una determinata tabella. Nell’uso interattivo funziona con le autorizzazioni della persona che lo utilizza, quindi il suo ambito corrisponde al loro. È questo ancoraggio che permette a un runbook scritto per le persone di essere trasferito a un agente AI.\u003C/p\u003E\n\u003Cp\u003EAbbiamo pubblicato la misurazione invece di limitarci ad affermarla. Snowflake AI Research ha creato \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/data-eng-bench-data-engineering-agent-benchmark/\"\u003Edata-eng-bench\u003C/a\u003E, un benchmark aperto di 103 task reali di data engineering, valutati in base al funzionamento della pipeline risultante. 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