GEC NEWS WIRENetApp’s latest report, sponsored by IDC, explores the AI landscape in enterprises. Titled “Scaling AI Initiatives Responsibly: The Critical Role of an Intelligent Data Infrastructure,” the report discusses challenges, benefits, and successful strategies at different AI maturity levels. It aims to help organizations scale AI and GenAI workloads responsibly, avoiding common pitfalls and ensuring the success of their AI initiatives.
Intelligent Data Infrastructure is the Foundation of AI Success
The IDC White Paper found that:
- AI Masters optimize their data infrastructure for transformational AI initiatives by facilitating easy access to corporate datasets with minimal preparation and designing a unified, hybrid, multicloud environment that supports various data types and access methods.
- AI Masters have more ambitious AI goals and yet experience data-related failures including infrastructure-based data access limitations (21%), compliance limitations (16%), and insufficient data (17%).
- AI Emergents note similar challenges but also experience budget constraints (20% Emergents vs 9% AI Masters), insufficient data for model training (26% vs 17%) and business restrictions on data access (28% vs 20%).
According to the findings, organizations need an intelligent data infrastructure in order to scale AI initiatives responsibly. Where a company falls on the AI maturity scale is determined by the level of infrastructure they have in place that will not only drive the long-term success of AI projects, but also of their associated business outcomes. Those organizations that are just beginning or have recently begun their AI journey typically have disparate data architectures or plans for a more unified architecture, while AI Leaders and AI Masters are likely already executing on a unified vision. As a result, organizations with the most AI experience are failing less.
Jonsi Stefansson, Senior Vice President and Chief Technology Officer at NetApp, said, “With intelligent data infrastructure in place, companies have the flexibility to access any data, anywhere with integrated data management to ensure data security, protection, and governance and adaptive operations that can optimize performance, cost and sustainability.”
Data Infrastructure Flexibility is Crucial for Data Access and AI Initiative Success
The IDC White Paper found that:
- 48% of AI Masters report they have instant availability of their structured data and 43% of their unstructured data, while AI Emergents have only 26% and 20%
- AI Masters (65%) and AI Emergents (35%) reported their current data architectures can seamlessly integrate their organization’s private data with AI Cloud services.
According to the research, AI Masters know that their data architecture and infrastructure for transformational AI initiatives must offer ease of access to corporate data sets without any—or with only minor—preparation or preprocessing.
Ritu Jyoti Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice, Global AI Research Lead, at IDC, said, “The dynamic nature of data inputs to AI and GenAI workstreams means easy access to distributed and diverse data—both structured and unstructured data sets with varying characteristics—is critical.”
