Why Private AI is becoming the preferred choice for enterprise AI deployment

Many companies once felt that AI was out of their reach, believing they needed extensive infrastructure and vast resources to make it work. They were told that scaling AI required significant investments in public cloud platforms, which meant the risk of vendor lock-in and surrendering some control over their data. However, the landscape has shifted dramatically. Companies are realizing that they have a choice and don't need to rely solely on public cloud providers to access the benefits of AI-Private AI is changing the game.

In the past year, we've seen a surge in demand for Private AI solutions, driven by organizations that want to innovate while keeping data privacy and security at the forefront. Customers who initially hesitated to invest in AI due to security and privacy risks or compliance concerns are now actively seeking Private AI solutions that allow them to maintain control over their data without sacrificing the power of advanced analytics.

Rising demand for Private AI

Generative AI's emergence triggered a wave of excitement, but as the dust settled, enterprises quickly realized they needed more control over their data. This desire for control, particularly over how enterprise data is used, has made Private AI increasingly important.

Private AI refers to artificial intelligence systems that are developed, deployed, and managed within an organization's own infrastructure or a secure environment. It enables businesses to take advantage of AI capabilities while keeping sensitive data private and maintaining full control over its processing and storage. This is particularly important in Europe, where rigorous standards like GDPR are in place. Private AI enables organizations to comply with these regulations while retaining ownership and full governance over their data, which is of course a crucial requirement for many businesses.

The unpredictability of future AI legislation is another factor driving the adoption of Private AI. Companies are hesitant to make substantial investments in public cloud AI platforms that may soon be constrained by new regulations. Private AI offers a way to safeguard these investments, enabling companies to adapt to shifting legal landscapes without being overly dependent on external vendors.

Cost and energy efficiencies are also a significant motivator. Running AI workloads in private or hybrid environments is often more economical than storing data in public cloud environments. Some customers, for example, have reported substantial savings when deploying Private AI. In fact, in a recent IDC survey, 60% of respondents cited on-premises AI as lower in cost or equal in cost to public cloud AI services.

Scaling with confidence

Broadcom's approach to Private AI is underpinned by a commitment to open standards, flexibility, and efficiency. Unlike proprietary AI platforms that often create silos, the VMware portfolio is designed to integrate smoothly with open-source tools and APIs. This approach helps organisations avoid vendor lock-in and supports interoperability across various systems.

Capabilities such as advanced resource scheduling and memory management allow for the dynamic allocation of GPU and hardware resources between production and research tasks, ensuring optimal performance while keeping costs in check. This flexibility allows businesses to expand their AI capabilities without having to invest in a lot of extra hardware.

Our partnerships with industry leaders like NVIDIA and AMD further enhance our Private AI solutions. These collaborations de-risks AI projects, enabling organisations to scale with confidence and ensure robust performance across diverse deployment environments.

Private AI in the field

There are many instances of organizations already using Private AI to address specific challenges tied to data security, privacy and compliance. Let's take a look at financial services as an example - an industry that is at the forefront of Private AI adoption, with banks and financial institutions using the technology to securely process sensitive data. This ensures compliance with stringent regulations while improving operational efficiency. Processes like fraud detection and customer analysis are enhanced, all while keeping sensitive information protected from public cloud exposure.

The public sector provides another example where law enforcement agencies are using Private AI to manage and analyze large volumes of case data. Advanced language models streamline investigations by revealing critical connections within records, accelerating case resolution and enhancing the efficiency of police work and public safety. This capability is invaluable for maintaining control over highly sensitive information.

Private AI is also being used in customer service, particularly in contact centers. Instead of deploying AI to interact with customers directly, many companies are using AI in back-end operations to assist human agents. This approach leads to quicker and more precise responses, boosting ticket resolution rates and productivity while maintaining strict data privacy. Businesses can retain complete control over customer interactions, ensuring they meet compliance requirements.

These examples underscore Private AI's ability to deliver clear business advantages, from reducing costs to boosting organizational productivity and effectiveness, without compromising data security and privacy.

Shaping the future of technological innovation

Private AI more than lives up to the hype - it's a critical shift in how organizations deploy intelligent solutions. By bringing together regulatory compliance with scalable innovation, it has become indispensable for modern businesses.

In a world that demands privacy and flexibility, Private AI is shaping the future of technological progress. How is your organization positioning itself in this evolving landscape? We'd love to hear your perspective.

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