Generative AI has moved well beyond early experimentation. For service providers, the opportunity lies in helping customers turn AI from a talking point into a practical business capability. But scaling GenAI isn’t easy. As more organisations move pilots into production, many are finding that legacy infrastructure, governance gaps, and fragmented data are slowing progress.
The gap between interest and readiness
Demand is growing fast, but production-scale AI is bringing new pressures that most businesses haven’t factored in and aren’t ready for. To help your customers, you need to address these three core issues: security and compliance, reliable performance, and data integration across mixed environments.
Without the right foundation in place, pilots can stall before they deliver any real value.
Infrastructure must evolve
Scaling GenAI requires more than adding capacity. You need to support GPU-heavy workloads, high-throughput storage, and container-based environments that can handle modern AI applications. A software-defined approach is key. It helps abstract hardware complexity, speeds up deployment, and gives you more flexibility to adopt new models and technologies without constant redesign.
The importance of data sovereignty
As AI workloads spread across cloud, edge, and on-premises environments and form part of core operations, control over data and models is becoming a strategic priority, with many organisations seeing their trained models as highly valuable intellectual property.
To take advantage of the GenAI opportunity, you need to start delivering secure, scalable, and sovereign-ready platforms while focusing on modern infrastructure, strong governance, and flexible operating models.
Reach out for more information and advice on how you can start increasing revenue, navigating the GenAI enterprise transition with Nutanix.
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