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Snowflake:2026年人工智能与数据展望报告:代理型AI崛起:催生新型企业与新型劳动力(中译版)(32页).pdf

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1、AI+DATA PREDICTIONS 2026The rise of agentic AI will create a new kind of enterprise and a new kind of workerThe Year of Agents and EcosystemsIt seems like every new year is labeled a“breakthrough year for enterprise AI.”And pretty much every year is.Just looking at the recent past,2024 was the year

2、businesses got creative,experimenting with explosively hot generative AI technologies to varying levels of initial success.By 2025,the focus was on ROI and proving that all the hype and FOMO wasnt getting in the way of solid business value.(Countless surveys,including one published by Snowflake,foun

3、d that many AI-forward organizations were measuring solid return on their gen AI investments.)So here comes 2026,and another evolution in enterprise-class artificial intelligence.Two things,broadly,are on the horizon:extending ROI from the level of individual projects to comprehensive,strategic AI e

4、cosystems,and agentic AI.The former reflects a maturing of the enterprise itself,as the CDO and other leaders integrate individual successes into a data and AI strategy that helps every team and contributor perform better.The latter reflects a maturing of the technology,as large language models evol

5、ve into large reasoning models,and as these LRMs become increasingly capable of reliably taking action with less human supervision.This years report considers the ever-shifting state of AI technology,the outlook and opportunities for enterprise adoption,and the challenges,particularly around securit

6、y and data governance.It draws from in-depth conversations with more than a dozen leaders and experts at Snowflake.Click through the summaries for an expanded view of these topics:The AI landscapeChoke points and paths of progress Feedback loops will improve agents EUregulationsmayadvance,nothinder,

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根据《AI + Data Predictions 2026》报告,以下是全文关键点: 1. **AI发展趋势**:2026年,企业AI将重点从单个项目扩展到全面的AI生态系统,并迎来“代理AI”的兴起。 2. **AI挑战**:评估、优化和安全性是代理AI发展的关键挑战。 3. **数据策略**:数据治理和高质量数据是AI成功的关键。 4. **AI与劳动力**:员工需掌握AI协作和沟通技能,以及战略思维。 5. **AI在行业中的应用**: - **零售**:生成AI将改变客户360度数据收集方式,并推动个性化推荐。 - **金融服务**:企业将回归数据优先思维,关注AI投资的回报率。 - **制造业**:AI将在制造业中发挥核心作用。 核心数据: - 2027年,AI编码助手将使开发者效率提高33%。
企业生态崛起?" "2026年,AI助手将如何改变工作?" "数据驱动,AI如何助力企业转型?"
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