Enterprises are investing in generative AI because it is quickly becoming a core part of their workloads and a practical way to drive productivity, automation, and new digital experiences. According to research from Informa TechTarget’s Enterprise Strategy Group, **30% of respondents already have GenAI use cases in production**, not just in proof-of-concept or experimental phases. This shows that GenAI is moving beyond pilots into day-to-day operations. Organizations see GenAI as a way to: - Improve productivity across teams. - Automate repetitive or knowledge-heavy tasks. - Support innovation in areas like content creation, code generation, and customer engagement. Budgets are following this trend. A meaningful share of organizations already report **GenAI budgets of $1 million or more**, and many expect those budgets to increase in the near term. Overall, enterprises believe GenAI will deliver **significant or transformative productivity gains**, which is why they are prioritizing it in their technology roadmaps.

As organizations move GenAI from pilots into production, several practical challenges emerge: 1. **Privacy and security** A large portion of organizations name **data privacy and security as a top GenAI risk**. They need to ensure: - Sensitive and proprietary data does not leak outside the organization. - Access to data and models is tightly controlled. - AI usage aligns with internal security policies. 2. **Regulatory compliance and governance** Many organizations say **regulatory compliance** is a key concern. They need ways to: - Govern how GenAI models are trained and used. - Demonstrate compliance with industry and regional regulations. - Maintain auditability and control over AI outputs. 3. **Cost and technical complexity** Cost is a major factor, with **many organizations citing cost as a primary GenAI challenge**. At the same time, **technical complexity** is a barrier, especially when: - Integrating GenAI into existing applications and workflows. - Managing and fine-tuning large language models (LLMs). - Scaling infrastructure efficiently. 4. **Infrastructure fit and performance** A significant share of organizations say **they need to change or upgrade their supporting infrastructure before they can move forward with GenAI**. They must balance: - Performance requirements for training and inference. - The need to incorporate their own enterprise data (which many agree is important for GenAI success). - The right mix of public and private cloud resources. In short, enterprises want to use fine-tuned LLMs on their own data, but they need the right infrastructure, governance, and cost model to do it safely and efficiently.

VMware Private AI Foundation with NVIDIA is designed to give enterprises an AI-specific infrastructure platform that fits within their existing environment while addressing security, performance, and cost concerns. Here’s how it helps: 1. **Enable privacy, security, and compliance** The platform builds on **VMware Cloud Foundation** and **NVIDIA AI Enterprise** to: - Keep GenAI workloads and data within a controlled, private environment. - Protect proprietary data from leaking outside the organization. - Support compliance requirements by centralizing control over infrastructure, data access, and model usage. 2. **Flexible, private-cloud–based GenAI infrastructure** The solution supports a **private cloud approach**, combining: - The scale and agility often associated with public cloud. - The security and performance benefits of a private environment. This helps organizations reimagine how they deploy GenAI—on infrastructure that aligns with their existing operations and governance models. 3. **Choice of models, tools, and frameworks** The platform is designed to provide a **broad and continually updated range of AI models, tools, and frameworks**, so teams can: - Use and fine-tune LLMs on their own enterprise data. - Stay current with evolving GenAI practices without constant re-platforming. 4. **Simplified deployment and optimized costs** VMware Private AI Foundation with NVIDIA includes **advanced capabilities to streamline GenAI deployment**, which helps organizations: - Stand up and manage GenAI models more easily. - Optimize resource usage and total cost of ownership (TCO). 5. **High performance for AI workloads** The platform’s integrated software and hardware stack is tuned for GenAI performance. A recent Broadcom benchmark study shows that **VMware and NVIDIA can deliver AI workload performance on par with bare metal**, helping organizations: - Run demanding GenAI workloads efficiently. - Support use cases like code generation, contact center optimization, content creation, advanced information retrieval, and agentic AI. For enterprises looking to scale GenAI while maintaining control over data, security, and costs, VMware Private AI Foundation with NVIDIA offers a way to rethink their AI infrastructure without starting from scratch.