WEKA和S&P Global Market Intelligence发布了第二届年度全球AI趋势报告，该报告调查了1500多位AI从业人员和决策者，以了解影响AI采用和实施的潜在趋势。报告显示，生成式AI的采用速度已超过其他所有企业AI应用，并正在塑造一个新的专业AI和GPU云提供商市场。报告还强调了尽管AI在全球组织中得到更广泛的实施，但仍存在成功大规模部署AI的障碍。数据架构是今年报告中反复出现的主题，定义了第一波新兴AI领导者，而许多企业仍在努力扩展。GPU可用性是另一个经常被提及的挑战，而且地区差异仍然存在，表明全球AI需求超过了为AI项目提供动力的AI加速器和GPU的获取。许多组织已成功采用来自超大规模云提供商的AI基础设施即服务产品，以及新兴的AI和GPU云市场，以克服这种供需差距并推动他们的生成式AI计划。

🤔 **生成式AI的快速崛起**：报告显示，生成式AI在企业中的采用率已超过其他所有AI应用，88%的组织正在积极研究生成式AI，而预测模型、分类、专家系统和机器人等其他AI应用的调查率分别为61%、51%、39%和30%。生成式AI的采用率正在迅速上升，24%的组织表示他们已经将生成式AI作为一项集成功能部署到整个组织中，37%的组织已将生成式AI投入生产，但尚未扩展。只有11%的组织完全没有投资生成式AI。

🚧 **数据架构的挑战**：尽管AI在企业中得到广泛实施，但许多组织仍然难以扩展AI项目。数据架构是今年报告中反复出现的主题，定义了第一波新兴AI领导者，而许多企业仍在努力扩展。报告发现，在将AI项目投入生产时，数据质量是最主要的挑战，存储和数据管理是AI/ML部署中最常被提及的技术障碍，占35%，远高于计算（26%）、安全（23%）和网络（15%）。这表明，薄弱的数据基础阻碍了许多组织的AI项目。

⚡ **GPU可用性的限制**：报告指出，GPU可用性是另一个经常被提及的挑战，许多组织将GPU可用性列为将AI模型投入生产的三大最严重挑战之一。四分之一的组织表示，获取AI加速器是其基础设施决策中的主要考虑因素。组织利用超大规模公共云（46%）和GPU云服务提供商（32%）来进行模型训练，并且越来越多地依赖于GPU云服务提供商。

🌎 **AI环境影响的关注**：报告显示，近三分之二（64%）的组织表示他们担心AI/机器学习（ML）项目对能源使用和碳足迹的影响，25%表示他们非常担心。42%的组织表示，他们在过去12个月中投资了节能的IT硬件/系统，以解决其AI计划的潜在环境影响。其中，56%认为这产生了“高”或“非常高”的影响。

🚀 **AI领导者与颠覆者**：WEKA联合创始人兼首席执行官Liran Zvibel表示：“AI就像之前的互联网、智能手机和云计算一样，代表着一场将对商业和社会产生不可磨灭影响的范式转变，它已经定义了新一代的行业领导者和颠覆者。与过去的科技转型不同，AI的采用和成熟正以前所未有的速度增长。S&P Global 2024年AI趋势报告的调查结果表明，第一波AI领导者已经通过加速组织和产品创新，以更快的上市时间来扩大其竞争优势，从而对他们的底线产生积极影响。那些AI成熟度较低的企业有落后的风险。为了在AI时代生存和繁荣，组织必须找到值得信赖的技术合作伙伴来帮助他们跨越鸿沟，并确保他们能够灵活地适应未来的任何挑战。”

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.

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“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.”

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.

Key findings of the 2024 Trends In AI report include: 

AI Applications Are Increasingly Pervasive In the Enterprise

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%).

Generative AI Has Rapidly Eclipsed Other AI Applications

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.

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

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.

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

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.

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

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.

“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.”

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