The artificial intelligence (AI) revolution is upon us, with its transformative power evident in diverse sectors. Yet, for all its potential, AI’s success hinges on one crucial factor: effective data management. According to a global study by S&P Global Market Intelligence and WEKA, businesses investing in data management today are poised to become AI leaders tomorrow.
The study revealed an encouraging trend: 69% of respondents have at least one AI project in production, and 28% have achieved enterprise scale. However, a critical challenge remains. Data infrastructure and AI sustainability pose significant obstacles to successful implementation at scale, a problem exacerbated by the rapid advancement of generative AI within businesses throughout 2023.
Among the 1,500 global AI decision-makers surveyed, 32% cited data management as a technological inhibitor to AI/machine learning deployments. This issue outstripped challenges related to security (26%) and compute performance (20%), indicating that many organizations’ current data architectures are ill-equipped to support the AI revolution.
Legacy architectures and data infrastructure were seen as detrimental to sustainability performance by 77% of respondents, with 74% stating that sustainability is a key motivator for moving more workloads to the public cloud. Moreover, 68% expressed concern about the impact of AI/machine learning on their organization’s energy use and carbon footprint.
Going forward, a hybrid approach with multiple deployment locations will be necessary to meet increasing workload demands. Those leveraging the public cloud for AI/machine learning are most likely to adopt a hybrid approach.
Liran Zvibel, Co-founder and CEO at WEKA, highlighted the inadequacy of traditional data infrastructures: “They’re 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.”
Zvibel compared this to expecting battery technologies from the 1990s to power a state-of-the-art electric vehicle. He stressed, “Organizations 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.”
As we navigate the AI revolution, it becomes clear that data management is not just a supporting player but a star performer. Only by addressing the data dilemma can we fully harness the power of AI and shape the future.
