New research spearheaded by NetApp, in partnership with IDC, has unveiled key insights about the evolving role of artificial intelligence (AI) in the enterprise sector. The study highlights the unique challenges and business benefits at various levels of AI maturity and offers insight into the successful strategies used by leading organisations to scale AI responsibly.

The IDC White Paper, titled "Scaling AI Initiatives Responsibly: The Critical Role of an Intelligent Data Infrastructure", featured over 1,200 global decision makers involved in AI initiatives in IT operations, data science, data engineering and software development. The report revealed that up to 20% of AI initiatives fail due to the lack of intelligent data infrastructure, underscoring the importance of such infrastructure in implementing successful AI projects.

The study identified different levels of AI maturity, from AI Emergents who are just starting their AI journeys, to AI Masters who have optimised their data infrastructures for transformational AI initiatives. According to the findings, AI Masters facilitate easy access to corporate datasets with minimal preparation and design a unified, hybrid, multi-cloud environment that supports various data types and access methods. On the other hand, AI Emergents often face difficulties due to a lack of standardised governance policies and procedures, with only 8% having standardised these across all AI projects, compared to 38% of AI Masters.

The survey also found that nearly two-thirds (63%) of all respondents believe their storage needs to be majorly improved or completely overhauled to be optimised for AI. Only 14% of respondents felt their current storage situation required no improvements.

According to Jonsi Stefansson, Senior Vice President and Chief Technology Officer at NetApp, "companies need intelligent data infrastructure to scale AI responsibly and boost the rate of AI initiative success." He further explained that with intelligent data infrastructure, "companies have the flexibility to access any data, anywhere with integrated data management to ensure data security, protection, and governance."

The research also highlighted the importance of data infrastructure flexibility for successful AI initiatives. AI Masters reported having instant availability of their structured data (48%) and unstructured data (43%), while AI Emergents only had 26% and 20% respectively. Furthermore, the study emphasised that effective data governance and security processes drive AI success. A staggering 38% of AI Masters have standardised governance policies and procedures across all AI projects, compared to a mere 8% of AI Emergents.

The report ultimately stresses that as AI workflows become increasingly crucial to almost every industry, assessing the efficiency of resource use in AI model creation becomes a key measure of AI maturity. The study found that 43% of AI Masters have clearly defined metrics for assessing resource efficiency in AI model development that were standardised across all AI projects compared to only 9% of AI Emergents.