Global study reveals accelerated AI adoption amid data challenges

In a recent comprehensive global study commissioned by WEKA and conducted by S&P Global Market Intelligence, it has been unveiled that enterprises and research organizations are rapidly embracing artificial intelligence (AI) to create new value propositions. However, data infrastructure challenges and AI sustainability pose hurdles to widespread successful implementation. The study’s findings, released as part of the 2023 Global Trends in AI report by S&P Global, shed light on the current state of AI adoption and its evolving landscape.

The extensive research draws insights from a vast survey encompassing more than 1,500 AI practitioners and decision-makers across medium to large enterprises and research institutions in regions including APAC, EMEA, and North America. This comprehensive survey is one of the largest of its kind, capturing a global perspective on AI trends and challenges.

“The meteoric rise of data and performance-intensive workloads like generative AI is forcing a complete rethink of how data is stored, managed, and processed. Organizations everywhere now have to build and scale their data architectures with this in mind over the long term,” said Nick Patience, senior research analyst at 451 Research, part of S&P Global Market Intelligence.

“Although it is still the early days of the AI revolution, one of the overarching takeaways from our 2023 Global Trends in AI study is that data infrastructure will be a deciding factor in which organizations emerge as AI leaders.1 Having a modern data stack that efficiently and sustainably supports AI workloads and hybrid cloud deployments is critical to achieving enterprise scale and value creation.”

Key observations from the AI adoption study encompass:

  • Accelerated AI Adoption and Use Cases: A remarkable 69% of the survey participants acknowledged having at least one AI project in production. Additionally, 28% reported achieving enterprise scale, signifying the widespread implementation of AI projects that drive substantial business value. Moreover, the role of AI has evolved from a cost-saving mechanism to a revenue generator, with 69% of respondents harnessing AI/ML to forge new revenue streams.
  • Data Management as the Leading Technical Hindrance: The study identifies data management as the most prominent technological obstacle to AI/ML deployments, cited by 32% of participants. This surpasses challenges related to security (26%) and compute performance (20%). This emphasizes the need for more data architectures to support the AI revolution.
  • Shift from Cost-Savings to Topline Growth: A paradigm shift is evident in the focus of enterprise AI use cases, with 69% of respondents concentrating on cultivating new revenue drivers and value creation, as opposed to a cost reduction-centric approach (31%).
  • Hybrid Approach for Workload Demands: As AI initiatives mature, a hybrid approach alongside multiple deployment locations has emerged to support the demands of AI/ML workloads. Organizations are deploying AI/ML workloads across diverse locations, encompassing public clouds, enterprise data centers, and edge sites.
  • Sustainability Concerns and Cloud Adoption: 68% of participants expressed concerns over the energy usage and carbon footprint associated with AI/ML. Notably, 74% identified sustainability as a critical factor motivating the transition of workloads to the public cloud.
  • Impact of Aging Data Infrastructures: A substantial 77% of respondents indicated that their data architectures directly influence their sustainability performance. This underscores the need for organizations to revamp their data and infrastructure strategies to lead effectively with AI.

In a rapidly evolving landscape where AI is reshaping industries and pushing boundaries, the challenges and opportunities identified by this study offer valuable insights for businesses seeking to harness the potential of AI while navigating complex data and sustainability issues. As enterprises continue to embrace AI, the lessons from this study will guide achieving successful AI integration and transformation.


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