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New Study Reveals Generative AI Has Eclipsed Other AI Applications In the Enterprise Fueling a New Cohort of AI Leaders and Cloud Providers

New Study Reveals Generative AI Has Eclipsed Other AI Applications In the Enterprise Fueling a New Cohort of AI Leaders and Cloud Providers

新研究显示,生成式人工智能已在企业中超越了其他人工智能应用,推动了一批新的人工智能领袖和云服务提供商。
PR Newswire ·  09/11 12:00

MONTREAL and CAMPBELL, Calif., Sept. 11, 2024 /PRNewswire/ -- From ALL IN 2024: WEKA, the AI-native data platform company, and S&P Global Market Intelligence unveiled the findings of their second annual Global Trends in AI report. The global study, conducted by S&P Global Market Intelligence and commissioned by WEKA, surveyed over 1500 AI practitioners and decision-makers to understand the underlying trends influencing AI adoption and implementation. It also provides insights into the key practices of organizations now emerging as leaders in the AI revolution.

蒙特利尔和加利福尼亚州坎贝尔,2024年9月11日 /PRNewswire/ — 来自2024年的全部:人工智能原生数据平台公司WEKA和标普全球市场情报公布了其第二份年度全球人工智能趋势报告的调查结果。这项全球研究由标普全球市场情报局进行,受WEKA委托,对1500多名人工智能从业者和决策者进行了调查,以了解影响人工智能采用和实施的潜在趋势。它还提供了对现已成为人工智能革命领导者的组织的关键实践的见解。

"One of the most striking takeaways from our 2024 Trends In AI study is the astonishing rate of change that's taken place since the onset of ChatGPT 3 and the first wave of generative AI models reached the market in early 2023. In less than two years, generative AI adoption has eclipsed all other AI applications in the enterprise, defining a new cohort of AI leaders and shaping an emergent market of specialty AI and GPU cloud providers," said John Abbott, principal research analyst at 451 Research, part of S&P Global Market Intelligence. "We can now see a direct correlation forming with those with a higher degree of AI maturity and increased revenue, operating efficiencies, and faster time to market for product innovation."1

“我们《2024年人工智能趋势》研究中最引人注目的收获之一是,自ChatGPT 3问世和第一波生成式人工智能模型于2023年初进入市场以来,发生了惊人的变化速度。标普全球市场情报旗下451 Research的首席研究分析师约翰·阿伯特说,在不到两年的时间里,生成式人工智能的采用使企业中的所有其他人工智能应用黯然失色,定义了新的人工智能领导者群体,塑造了专业人工智能和GPU云提供商的新兴市场。“我们现在可以看到,与那些人工智能成熟度更高、收入和运营效率更高、产品创新上市时间更快的公司形成了直接的相关性。” 1

Additionally, the new report underscored that, although AI is now more widely implemented in global organizations, obstacles remain in deploying AI successfully at scale. Data architectures were a reoccurring theme in this year's report, defining the first wave of emerging AI leaders while many enterprises still struggle to scale. GPU availability was another commonly cited challenge, and regional disparities persist, suggesting global AI demand is outpacing access to AI accelerators and GPUs needed to power AI projects. Many organizations have successfully embraced AI infrastructure-as-a-service offerings from hyperscale cloud providers and an emergent market of new AI and GPU cloud markets to overcome this supply-demand gap and fuel their generative AI initiatives.

此外,新报告强调,尽管人工智能现在已在全球组织中得到更广泛的实施,但在成功大规模部署人工智能方面仍然存在障碍。数据架构是今年报告中反复出现的主题,它定义了第一波新兴的人工智能领导者,而许多企业仍在努力扩大规模。GPU 的可用性是另一个常被提及的挑战,地区差异仍然存在,这表明全球人工智能需求正在超过为人工智能项目提供动力所需的人工智能加速器和 GPU 的使用量。许多组织已经成功地采用了超大规模云提供商以及新的人工智能和GPU云市场的新兴市场提供的人工智能基础设施即服务产品,以克服这种供需缺口并推动其生成式人工智能计划。

Key findings of the 2024 Trends In AI report include:

《2024年人工智能趋势》报告的主要发现包括:

AI Applications Are Increasingly Pervasive In the Enterprise

AI 应用程序在企业中越来越普遍

  • 33% of survey respondents have reached enterprise scale, with AI projects being widely implemented and driving significant business value, up from 28% last year.
  • North America leads in enterprise AI adoption, with 48% of North American respondents indicating that AI is widely implemented, compared to APAC (26%) and EMEA (25%).
  • Product improvement and operational effectiveness are key investment drivers, with organizations leveraging AI to improve product or service quality (42%), target increased revenue growth (39%), improve workforce productivity (40%) and IT efficiencies (41%), and accelerate their overall pace of innovation (39%).
  • 33%的受访者已达到企业规模,人工智能项目得到广泛实施并推动了可观的商业价值,高于去年的28%。
  • 北美在企业人工智能采用率方面处于领先地位,48%的北美受访者表示人工智能已得到广泛应用,而亚太地区(26%)和欧洲、中东和非洲(25%)。
  • 产品改进和运营效率是关键的投资驱动力,各组织利用人工智能来改善产品或服务质量(42%),以增加收入增长(39%)为目标,提高员工工作效率(40%)和 IT 效率(41%),并加快整体创新步伐(39%)。

Generative AI Has Rapidly Eclipsed Other AI Applications

生成式 AI 已迅速超过其他 AI 应用程序

  • An astonishing 88% of organizations are actively investigating generative AI, far outstripping other AI applications such as prediction models (61%), classification (51%), expert systems (39%) and robotics (30%).
  • Generative AI adoption is exploding: 24% of organizations say they already see generative AI as an integrated capability deployed across their organization. 37% have generative AI in production but not yet scaled. Just 11% are not investing in generative AI at all.
  • 令人惊讶的是,88%的组织正在积极研究生成式人工智能,远远超过预测模型(61%)、分类(51%)、专家系统(39%)和机器人(30%)等其他人工智能应用程序。
  • 生成式人工智能的采用呈爆炸式增长:24% 的组织表示,他们已经将生成式人工智能视为组织内部署的集成能力。37% 的组织已在生产中但尚未扩大规模。只有11%的人根本没有投资生成式人工智能。

Many AI Projects Fail to Scale — Legacy Data Architectures Are the Culprit

许多 AI 项目无法扩展——传统数据架构是罪魁祸首

  • On average, organizations have 10 AI projects in the pilot phase and 16 in limited deployment, but only six are deployed at scale.
  • Data quality is the top challenge when moving AI projects into production.
  • The most frequently cited technological inhibitors to AI/ML deployments are storage and data management (35%)—significantly greater than computing (26%), security (23%), and networking (15%). This is evidence that weak data foundations impede many organizations' AI projects.
  • 平均而言,组织有 10 个 AI 项目处于试点阶段,16 个处于有限部署阶段,但只有六个是大规模部署的。
  • 将人工智能项目投入生产时,数据质量是最大的挑战。
  • 最常被提及的妨碍 AI/ML 部署的技术抑制因素是存储和数据管理(35%),明显高于计算(26%)、安全性(23%)和网络(15%)。这证明薄弱的数据基础阻碍了许多组织的人工智能项目。

GPU Availability Continues To Be Constrained, Shaping Infrastructure Decision-Making

GPU 可用性继续受到限制,影响基础设施决策

  • Four in 10 organizations suggest access to AI accelerators is a leading consideration in their infrastructure decision-making, and 30% cite GPU availability among their top three most serious challenges in moving AI models into production.
  • Key channels for companies to access GPUs: respondents leverage hyperscale public clouds (46%) and – increasingly – GPU cloud service providers (32%) for model training.
  • 十分之四的组织表示,获得人工智能加速器是其基础设施决策的主要考虑因素,30%的组织认为,在将人工智能模型投入生产过程中,GPU的可用性是他们面临的三大最严重的挑战。
  • 公司接入 GPU 的关键渠道:受访者利用超大规模公共云(46%)以及越来越多的 GPU 云服务提供商(32%)进行模型训练。

Concerns About AI's Environmental Impact Persist But Are Not Slowing Adoption

对人工智能环境影响的担忧仍然存在,但并未减缓采用的速度

  • Nearly two-thirds (64%) of organizations say they are concerned about the impact of AI/machine learning (ML) projects on their energy use and carbon footprint; 25% indicate they are very concerned.
  • 42% of organizations indicated that they have invested in energy-efficient IT hardware/systems to address the potential environmental impacts of their AI initiatives over the past 12 months. Of those, 56% believe this has had a "high" or "very high" impact.
  • 近三分之二 (64%) 的组织表示,他们担心人工智能/机器学习 (ML) 项目对其能源使用和碳足迹的影响;25% 的组织表示他们非常担心。
  • 42% 的组织表示,在过去的12个月中,他们投资了节能的IT硬件/系统,以应对其人工智能计划对环境的潜在影响。其中,56%的人认为这产生了 “高” 或 “非常高” 的影响。

"Like the internet, the smartphone, and cloud computing before it, AI represents a paradigm shift that will leave an indelible mark on business and society and is already defining a new generation of industry leaders and disruptors," said Liran Zvibel, cofounder and CEO at WEKA. "Unlike past technology transitions, AI's adoption and maturation are growing with unprecedented velocity. The findings of S&P Global's 2024 Trends In AI report underscore that the first wave of AI leaders is already scaling their competitive advantage by accelerating organizational and product innovation with faster time to market, positively impacting their bottom line. Those who are less AI mature are at risk of falling behind. To survive and thrive in the AI era, organizations must find trusted technology partners to help them cross the chasm and ensure they can agilely adapt to whatever the future brings."

WEKA联合创始人兼首席执行官Liran Zvibel表示:“与之前的互联网、智能手机和云计算一样,人工智能代表着一种模式转变,它将在商业和社会上留下不可磨灭的印记,并且已经定义了新一代的行业领导者和颠覆者。”“与过去的技术过渡不同,人工智能的采用和成熟正以前所未有的速度增长。标普全球《2024年人工智能趋势》报告的调查结果突出表明,第一波人工智能领导者已经通过加快组织和产品创新,缩短上市时间,从而扩大其竞争优势,对他们的利润产生了积极影响。那些不太成熟的人有落后的风险。为了在人工智能时代生存和发展,组织必须找到值得信赖的技术合作伙伴,以帮助他们跨越鸿沟,并确保他们能够敏捷地适应未来的一切。”

To read the full 2024 Global Trends in AI study from S&P Global Market Intelligence, visit .

要阅读标普全球市场情报部门发布的《2024年全球人工智能趋势》研究报告全文,请访问。

Research Methodology
The findings in S&P Global Market Intelligence's 2024 Global Trends in AI report draw from a survey fielded in Q2 2024 of 1,519 AI/ML decision makers/influencers in enterprises, research organizations, and AI providers building AI technologies, products, and solutions. The study prioritized respondents with AI/ML projects in pilots and production environments across the following industries: aerospace and defense, automotive, energy/oil and gas, finance, government, healthcare, higher education, IT and services, life sciences, manufacturing, media/entertainment, telecommunications, transportation and logistics, and utilities. The report also draws on contextual knowledge of additional research conducted by S&P Global.

研究方法
标普全球市场情报局的《2024年全球人工智能趋势》报告中的调查结果来自于2024年第二季度对构建人工智能技术、产品和解决方案的企业、研究机构和人工智能提供商中的1,519名人工智能/机器学习决策者/影响者进行的一项调查。该研究优先考虑在以下行业的试点和生产环境中开展人工智能/机器学习项目的受访者:航空航天和国防、汽车、能源/石油和天然气、金融、政府、医疗保健、高等教育、信息技术和服务、生命科学、制造、媒体/娱乐、电信、运输和物流以及公用事业。该报告还借鉴了标普全球开展的其他研究的背景知识。

About WEKA
WEKA is architecting a new approach to the enterprise data stack built for the AI era. The WEKA Data Platform sets the standard for AI infrastructure with a cloud and AI-native architecture that can be deployed anywhere, providing seamless data portability across on-premises, cloud, and edge environments. It transforms legacy data silos into dynamic data pipelines that accelerate GPUs, AI model training and inference, and other performance-intensive workloads, enabling them to work more efficiently, consume less energy, and reduce associated carbon emissions. WEKA helps the world's most innovative enterprises and research organizations overcome complex data challenges to reach discoveries, insights, and outcomes faster and more sustainably – including 12 of the Fortune 50. Visit to learn more, or connect with WEKA on LinkedIn, X, and Facebook.

关于 WEKA
WEKA 正在为专为 AI 时代构建的企业数据堆栈设计一种新的方法。WEKA 数据平台采用可在任何地方部署的云和 AI 原生架构,为人工智能基础设施设定了标准,可跨本地、云和边缘环境提供无缝的数据可移植性。它将传统的数据孤岛转变为动态数据管道,以加速 GPU、AI 模型训练和推理以及其他性能密集型工作负载,使它们能够更高效地工作,消耗更少的能源,减少相关的碳排放。WEKA帮助世界上最具创新性的企业和研究组织克服复杂的数据挑战,以更快、更可持续的方式获得发现、见解和成果,其中包括财富50强中的12家。访问以了解更多信息,或在 LinkedIn、X 和 Facebook 上与 WEKA 联系。

WEKA and the WEKA logo are registered trademarks of WekaIO, Inc. Other trade names used herein may be trademarks of their respective owners.

WEKA 和 WEKA 徽标是 WekaIO, Inc. 的注册商标。此处使用的其他商品名称可能是其各自所有者的商标。

1 451 Research, part of S&P Global Market Intelligence, Discovery Report "Global Trends in AI," August 2024

1 451 Research,标普全球市场情报的一部分,《探索报告:人工智能全球趋势》,2024年8月

SOURCE WekaIO

来源 WekaIO

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