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

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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