New Market Research Finds Up to 20% of AI Initiatives Fail Without Intelligent Data Infrastructure

NetApp (NASDAQ: NTAP), the intelligent data infrastructure company, today unveiled insights from its latest report on the evolving landscape of AI in the enterprise. The IDC White Paper, sponsored by NetApp, “Scaling AI Initiatives Responsibly: The Critical Role of an Intelligent Data Infrastructure*,” reveals the various challenges and business benefits at different levels of AI maturity and provides insights into the successful strategies adopted by leading organizations in their efforts to responsibly scale AI and GenAI workloads. By spotlighting actionable approaches, the report aims to help organizations avoid common pitfalls, ensuring that their AI initiatives are not one of the 20% that are likely to fail. The report also introduces a detailed AI maturity model developed to assess organizational progress based on their approach to AI, from AI Emergents and AI Pioneers, to AI Leaders and AI Masters

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.