AI practitioners reveal top enterprise adoption challenges

Enterprise AI adoption faces significant challenges as organizations work to implement and scale artificial intelligence solutions, according to recent IDC survey findings of over 1,200 AI practitioners worldwide.

Key survey findings: The research identified three major obstacles hindering AI implementation across organizations of varying AI maturity levels.

• 63% of respondents indicated their organizations need major improvements or complete overhauls in storage infrastructure to support AI workloads properly
• Data access limitations due to infrastructure constraints emerged as the primary reason for AI project failures
• Only 20% of organizations have implemented mature, centralized policies for AI data governance and security

Infrastructure challenges: Storage bottlenecks consistently slow down AI model training and development across enterprises.

• NetApp is partnering with NVIDIA to qualify solutions specifically designed for model training
• The company is optimizing its ONTAP system for SuperPOD qualification, which is currently in testing
• New scalable data infrastructure has been announced that can handle both large foundational model development and smaller inferencing needs

Data accessibility solutions: Organizations are implementing unified data storage approaches to address access limitations.

• A single data and control plane spans edge, data center, and cloud environments
• Native support for all data formats enables efficient data movement between on-premises and cloud resources
• Integration with popular data science tools like AWS SageMaker, Google Vertex, and Azure ML Studio improves accessibility

Security and governance framework: NetApp is developing comprehensive solutions to address the lack of mature data governance policies.

• Policy-driven security at the data layer employs AI/ML models to detect threats in real-time with 99%+ precision
• New tools are being developed to help data scientists work more efficiently while maintaining compliance
• Solutions focus on data discovery, curation, security compliance, and workflow integration

Future implications: While organizations are making progress in addressing AI implementation challenges, the survey reveals significant gaps in infrastructure, data access, and governance that must be bridged for successful enterprise AI adoption. The development of specialized solutions and partnerships between technology providers suggests a maturing ecosystem that will help organizations overcome these obstacles more effectively.