Private AI should have a starring role in your AI strategy

C-suite executives are facing mounting pressure to not only showcase current AI initiatives but also to envision bold new possibilities. So, the burning question is, which platform is most likely to bring these ambitions to life?

The public cloud is a sound choice for simple GenAI proofs of concept (POCs) and pilots that use common data. However, organizations are likely to lean toward private AI platforms for instances where use cases involve sensitive, sovereign or mission-critical data. The verdict is not about satisfying the urge to be a first adopter of an emerging trend, but the need to align LLM behavior with business and operational goals. 

It pays to adopt a private enterprise AI solution strategy

Private AI is an extremely compelling proposition for businesses paying thousands of dollars a month once they bring AI projects to fruition. A recent survey reported that the average monthly AI spend in 2024 was $62,964 (rising by 36% in 2025). When consumption of GenAI services grows to exceptionally high levels, public cloud costs rise rapidly. That said, investing in a complex infrastructure before establishing its value can prove just as expensive.

Getting GenAI right on Private AI

Building enterprise AI solutions takes organizational common sense and persistence. It requires a different approach to compute, as well as how you store and curate data. By starting with more straightforward tasks, you can familiarize yourself with the technology in a less-risky environment, gaining quick wins using simpler use cases like document summarization or decision-making. 

Strong leadership and a precise focus are essential for driving the project with cross-functional teams that are formed to fulfill the intent of the GenAI charter. Private AI infrastructure helps lighten the load while you develop GenAI use cases. That's because it delivers prebuilt platforms and accelerators (to help move from pilot to production quickly), built-in data governance and security.

Whether running on-prem or in a hybrid cloud, you benefit from a standardized environment that protects intellectual property and digital assets.

More benefits, less risk

This means you can leverage GenAI’s decision-making capacity without risking data breaches or unauthorized access. You gain unrestricted data volume and processing capabilities to support large data loads, real-time data processing and large-scale simulations, as well as AI model customization. In addition, you can change or fine-tune models to meet specific business challenges.

Working with a partner significantly reduces costs. This is particularly true for expenses involving inference, fine-tuning, deployment and maintenance, and for acquiring the expertise to comply with private AI infrastructure security regulations.

Fluent in business impact

All these capabilities are part of the DXC Private AI solution. Our specialist teams have decades of experience in perfecting data center deployment, training and customizing reference architectures. And we’re experts in starting small and scaling dynamically, while developing strong partnerships with leading AI providers.

Also, our unique collaboration with NVIDIA delivers significant economies of scale. DXC performs a TCO analysis to see which Private AI solution is appropriate for specific stages of the client journey. Depending on the Private AI solution, it’s essential to have sufficient opportunities for running large-scale AI inference workloads to justify the CapEx or OpEx.