Data Management revealed as top challenge in AI revolution

According to S&P and WEKA, those who leverage the public cloud to run AI/ML are most likely to leverage a hybrid approach, as opposed to those who do not use the public cloud
A study conducted by S&P and WEKA found that investing in modern data is critical for scaling AI successfully, but half of businesses face cost barriers

According to a new global study conducted by S&P Global Market Intelligence and commissioned by WEKA, the businesses that can invest in data management now will become AI leaders in the long run.

69% of survey respondents have at least one AI project in production, with 28% already reaching enterprise scale.

Whilst AI adoption in enterprises and research organisations seeking to create new value propositions is accelerating, the study suggests that data infrastructure and AI sustainability challenges present barriers to implementing it successfully at scale. It highlights how these challenges have increased due to generative AI advancing very rapidly within businesses throughout 2023.

AI adoption continues to increase, but enterprise-scale remains a challenge

WEKA announced these findings from its joint research with S&P in which 1500 global AI decision makers were surveyed. It identifies the opportunities and obstacles that organisations have encountered in their AI journeys and the unique motivators for global AI adoption across industries. It also provides insights into what steps organisations will need to take to succeed with AI in the future.

32% of those surveyed cited data management as a technological inhibitor to AI/ML deployments. In addition, outweighing challenges for security (26%) and compute performance (20%) are evidence that many organisations’ current data architectures are unfit to support the AI revolution.

77% of respondents believe legacy architectures and data infrastructure impact their sustainability performance, with 74% of total respondents saying that sustainability is an important or critical motivator for moving more workloads to the public cloud.

68% also indicated that they were concerned with the impact AI/ML has on their organisation’s energy use and carbon footprint. 

As AI initiatives develop further, a hybrid approach and multiple deployment locations are needed to support workload demands. According to S&P and WEKA, those who leverage the public cloud to run AI/ML are most likely to leverage a hybrid approach, as opposed to those who do not use the public cloud.

“Traditional data infrastructures are having a direct, negative impact on their ability to use AI efficiently and sustainably at scale because they weren’t developed with modern performance-intensive workloads or hybrid cloud and edge modalities in mind,” said Liran Zvibel, Co-founder and CEO at WEKA. 

“Just as you wouldn’t expect to use battery technologies developed in the 1990s to power a state-of-the-art electric vehicle, like a Tesla, you can’t expect data management approaches designed for last century’s data challenges to support next-generation applications like generative AI. 

“Organisations that build a modern data stack designed to support the needs of AI workloads that seamlessly span from edge to core to cloud will emerge as the leaders and disruptors of the future.”  

 

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