New Market Research Finds Up to 20% of AI Initiatives Fail Without Intelligent Data Infrastructure - Seite 2

“This IDC White Paper further solidifies that companies need intelligent data infrastructure to scale AI responsibly and boost the rate of AI initiative success,” said Jonsi Stefansson, Senior Vice President and Chief Technology Officer at NetApp. “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% respectively.
• 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.

“Infrastructure decisions made during the design and planning process of AI Initiatives must factor in architecture flexibility,” said Ritu Jyoti Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice, Global AI Research Lead, at IDC. “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. This requires a flexible, unified approach to storage, a common control plane, and management tools that make it seamless for data scientists and developers to consume data with MLOps integrations.”

Effective Data Governance and Security Processes Drive AI Success

The IDC White Paper found that:

• The inability for AI Emergents to progress is often due to a lack of standardized governance policies and procedures; only 8% of AI Emergents have completed and standardized these across all AI projects, compared to 38% of AI Masters.
• While 51% of AI Masters reported they have standardized policies in place that are rigorously enforced by an independent group in their organization, only 3% of AI Emergents claim this.

The study found that effective data governance and security are crucial indicators of organizational maturity in AI initiatives. Managing data responsibly and securely remains a key issue for enterprises, because AI stakeholders often try to shortcut security processes to accelerate development. Feedback from organizations that have become more successful at delivering positive outcomes from their AI initiatives demonstrates that governance and security are not merely cost centers but vital enablers of innovation. By prioritizing security, data sovereignty, and regulatory compliance, organizations can mitigate risk in their AI and GenAI initiatives and ensure that their data engineers and scientists can focus on maximizing efficiency and productivity.