GenAI Adoption Soars; Infrastructure Gaps Obstruct Success

The Rising Adoption of Generative AI: Challenges and Infrastructure

Enterprises are increasingly adopting generative AI (GenAI). However, substantial hurdles persist. Most organizations lack the required infrastructure and policies for successful implementation.

A new study by the Enterprise Strategy Group, sponsored by Hitachi Vantara, reveals that 97% of enterprises rank GenAI among their top five priorities. U.S. companies are 35% more likely to prioritize GenAI compared to their European counterparts.

Importance of Robust Infrastructure

The survey included over 800 IT professionals, business leaders, and data practitioners. It highlighted the key role of infrastructure in GenAI adoption. Enterprises aim to increase accuracy (56%), stay current with technology (47%), and meet compliance requirements (43%).

Despite these goals, only 37% of respondents are confident in their company’s infrastructure. C-level executives are 1.3 times more likely than lower-level employees to see room for improvement.

Cross-Functional Collaboration and Data Security

According to Hitachi, 88% of organizations have initiated cross-functional collaboration among data science, IT, and business teams. This aims to utilize GenAI more effectively. Ensuring data security and quality is crucial for infrastructure and storage.

Ayman Abouelwafa, CTO at Hitachi Vantara, stressed the need for a robust infrastructure to unlock the power of GenAI. Jason Hardy, CTO of AI at Hitachi Vantara, believes MSPs can address this by leveraging their products effectively.

Security and Cost Concerns in GenAI

Organizations focus on security, cost, and data quality for GenAI projects. However, security challenges like data privacy, misuse, unauthorized access, and confidentiality are prevalent. To combat inaccurate responses, 86% of organizations are considering retrieval-augmented generation (RAG) solutions.

96% of companies are shifting towards open-source models to find cost-effective GenAI options. As their skill levels improve, they are six times more likely to switch to proprietary models for a competitive edge.

For further information, read more about AI ecosystems for MSPs and how they can develop effective strategies with AI.

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