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aem-Grid--default--12","columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-67baf8eddf","text":"\u003Cp\u003E여러 산업 분야의 많은 기업들이 실험적인 AI 시대에서 실제 구현의 시대로 전환하고 있습니다. 아직 AI 솔루션을 테스트 및 평가하고 있는 기업들조차도 단순히 실현 가능한 기술보다는 조만간 측정 가능한 ROI를 창출하게 될 기술에 총력을 기울이고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake의 금융 서비스 부문 제품 마케팅 이사인 Samuel Lee는 이렇게 말합니다. “이제는 단순히 AI라는 이유만으로 회사 리더들의 승인을 얻기란 힘든 일입니다. 그보다는 AI 구현을 통해 실질적인 가치를 창출해야 하고, 리더들은 AI 투자를 수치화하고 더 광범위한 비즈니스 임팩트를 실현해야 하는 압박에 직면하게 될 것입니다.”\u003C/p\u003E\r\n\u003Cp\u003E이것이 바로 지난 3월에 열린 Snowflake의 산업 포커스 이벤트인 Accelerate Financial Services and Manufacturing의 핵심 메시지였습니다. Snowflake의 파트너인 Amazon Web Services(AWS)와 함께 개최한 동 이벤트는 전문가들과 리더들이 데이터 및 AI 구현을 가로막는 장애물을 극복하고, AI를 자신 있게 확장하고, 성공적인 비즈니스 성과를 거두는 방법을 알아볼 수 있는 귀중한 기회였습니다. \u003C/p\u003E\r\n\u003Cp\u003E각 세션에서는 최신 산업 동향, 혁신적인 사용 사례 및 성공적인 엔터프라이즈 데이터 및 AI 전략을 수립하기 위한 여러 접근법을 다뤘습니다. 또한 Snowflake 전문가, 고객 및 파트너들이 AI 전략 최적화를 위한 전략적 인사이트 및 실질적인 팁을 제공하고 주요 사용 사례 및 모범 사례와 관련된 데모를 선보였습니다.\u003C/p\u003E\r\n\u003Cp\u003E이번 이벤트에서 도출된 7가지 주요 시사점을 살펴보면 다음과 같습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E데이터 클라우드 전략은 운영에 AI를 적용하려는 기업이 거쳐야 할 첫 번째 단계임.\u003C/b\u003E AWS의 산업 솔루션 GTM 글로벌 책임자인 Magnus Akesson은 “고급 AI/ML를 활용하는 것은 으레 조직적이고 체계적인 방식으로 모든 데이터를 클라우드로 가져오는 것에서부터 시작된다"라고 지적했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E고급 AI 기능의 접근성이 운영 작업을 혁신하고 간소화하고 있음. \u003C/b\u003ENorthwestern Mutual의 기술 선임 이사인 Debanit Das는 "특히 문서 처리와 같은 작업에서 더 많은 운영 효율적인 기능이 사용되고 있다"라면서 이렇게 설명합니다. “이전에는 비정형 데이터를 처리하려면 수작업이 필요했기 때문에 너무나 많은 시간과 수고를 들여야 했지만 이제는 LLM과 ML 기능을 사용해 자동화가 가능하기 때문에 좀 더 비용 효율적으로 비정형 데이터를 활용할 수 있습니다. 물론 이전에도 시맨틱 분석과 패턴 인식 모델을 구현하는 것은 가능했지만, 전담 연구 개발 팀이 필요한 탓에 비용이 많이 들었습니다. 그렇지만 이제는 약간의 프롬프트 엔지니어링만 뒷받침 되면 이러한 기능에 손쉽게 액세스할 수 있습니다."\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E업계 리더들이 새롭게 부상하는 기술을 채택함에 따라 규정 준수가 최우선 과제로 떠오르고 있음.\u003C/b\u003E AWS의 생성형 AI 및 데이터 솔루션 전략 및 아키텍처 글로벌 책임자인 Lorraine Knerr은 “많은 금융 서비스 기업이 생성형 AI나 에이전트 자동화처럼 AI를 많이 활용하고 있다”면서 “특히 데이터 개인정보 보호 측면에서 이와 관련된 엄격한 규제 조사에도 대비가 되어 있기를 원한다"고 밝혔습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAI 모델이 점점 더 정교해지면서 포괄적인 거버넌스로의 전환이 부상하고 있음.\u003C/b\u003E Capital One의 데이터 거버넌스 및 개인정보 보호 엔지니어링 VP인 Awah Teh는 “모델 거버넌스는 이전부터 있었지만 이제는 AI 모델을 훈련하는 데 들어가는 데이터뿐만 아니라 모델에서 나오는 응답에 대한 거버넌스를 관리하는 관점도 존재한다”고 지적했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E선도 기업들은 공급망의 주요 혁신에 적응하기 위해 다면적 전략을 구현하고 있음. \u003C/b\u003EDeloitte의 AI 전략 및 엔지니어링 리더인 Saurabh Vijayvergia는 "디지털 트랜스포메이션을 수용하고, 협업이 강화된 공급업체 관계를 촉진하며, 애자일하고 견실한 제조를 구현하고 탄력적이고 유연한 네트워크를 개발하는 것"이 이러한 전략에 포함된다고 밝혔습니다. 또한 “조직의 가장 중요한 주제 중 하나는 데이터 협업을 통해 가치 사슬 전반에서 효율성과 통합을 개선하는 것”이라면서 “가치 사슬 전반에서 데이터를 원활하게 공유함으로써 조직은 공급망의 모든 측면에 대한 가시성 인사이트를 향상하여 잠재적인 공급 차질을 사전에 식별하고 완화할 수 있다"고 강조했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E새로운 기술을 적용하기 전에 데이터의 정렬 여부를 반드시 확인해야 함.\u003C/b\u003E GXO 물류 데이터 엔지니어링 및 고급 데이터 사이언스(AI/ML) 부문 선임 부사장 Ramin Rastin은 이렇게 밝혔습니다. “기술 도구는 훌륭합니다. 하지만 [현재 개선 중인] 프로세스를 보면, 데이터 거버넌스 프로세스를 설정하고 모든 데이터를 인벤토리화하는 데이터 관리 프로세스를 마스터합니다. 우선은 이 프로세스부터 수정해야 합니다. 왜냐하면 Snowflake와 같은 파트너와 함께 글로벌 데이터 레이크를 시작하면 해당 도구를 온보딩하는 것이 훨씬 더 쉽고, 빠르고, 민첩해지기 때문입니다. 그리고 AI 도구를 갖춘 해당 플랫폼에서 얻게 되는 결과는 훨씬 더 많은 의미를 갖게 될 것입니다.” \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E생성형 AI는 이미 제조 공급망을 재편하고 있음.\u003C/b\u003E Snowflake의 제조 산업 글로벌 GTM 리드인 Tim Long은 “생성형 AI는 수요 예측, 프로덕션 프로세스 개선 또는 재고 감축 등 제조 조직 전반에서 사용되고 있다”라고 지적하면서 다음과 같이 설명했습니다. “제조 분야 전반에서 생성형 AI가 장애와 근본 원인을 파악하는 데 도움이 되는 선도적인 기법으로 등장하고 있습니다. 또한 생성형 AI는 인지 공급망을 강화하여 최종 고객 제품을 최대한 효율적으로 제공할 수 있도록 지원하고 있습니다.” \u003C/p\u003E\r\n\u003Cp\u003E아쉽게도 이번 이벤트에 직접 참석하지 못하셨다면 온디맨드 비디오 \u003Ca href=\"https://www.snowflake.com/accelerate/financial-services/?utm_cta=website-blog-accelerate-2025-preevent-promo-accelerate-finserv\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EAccelerate Financial Services\u003C/a\u003E와 \u003Ca href=\"https://www.snowflake.com/accelerate/manufacturing/?utm_cta=website-blog-accelerate-2025-preevent-promo-accelerate-manu\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EAccelerate Manufacturing\u003C/a\u003E를 통해 만나보시기 바랍니다.   \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 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Page"}}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_hero","responsivegrid_content","responsivegrid_premium_content_banner","container_author_chip"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-9fe09b08b2",":items":{"blog_table_of_content":{"id":"blog-table-of-content-67a63f4e19",":type":"snowflake-site/components/blog/blog-table-of-content","tableOfContents":[]}},":itemsOrder":["blog_table_of_content"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":true,"isActiveTOC":false},"related_content":{"id":"related-content-fb635e86a7","relatedContent":[{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"금융 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