"Privacy, choice and control": Building a private AI framework
"Enterprises can gain a flexible, modular infrastructure that prevents vendor lock-in and ensures ongoing compatibility."
Enterprise AI has seen unprecedented growth over the past year, largely due to generative AI’s mainstream emergence. Companies and governments are developing AI strategies to capitalise on the potential benefits and respond to competitive pressures. The priority now is creating strategic approaches that maximise AI’s potential while ensuring security, data sovereignty, and regulatory compliance.
The European generative AI market is forecast to grow by approximately 42% between 2024 and 2030, contributing to the global market’s upward trajectory. Private AI provides organisations with a platform to securely manage AI models, control data interactions, and future-proof their technology infrastructure. By promoting local data storage, supporting economic growth, and offering flexibility for evolving regulations, private AI enables businesses to adopt advanced AI technologies. It ensures privacy, choice, and control while adapting to changing needs. For organisations, it drives productivity, while helping countries lead the way in the global AI landscape.
You can upgrade without a complete system overhaul
Customers consistently express uncertainty about AI adoption, primarily due to widespread misconceptions about the technological and financial barriers to entry. The prevailing narrative that AI implementation demands significant public cloud infrastructure has created unnecessary intimidation and hesitation among businesses looking to leverage advanced technologies. Many have asked: why should we invest time and money in AI technologies hosted in the public cloud just to be told by a regulator or a cloud provider outside their country or region that they suddenly need to change tactics?
The simple answer: You don’t have to, and this is where private AI comes into play. Private AI models are inherently adaptable, integrating global regulatory considerations directly into their core architecture. By maintaining granular data traceability, these models enable organisations to proactively comply with emerging data sovereignty requirements. Unlike the hasty public cloud migrations of the past, where companies adopted technologies without strategic foresight, private AI represents a more considered, cost-effective, mission-adaptable approach to technological infrastructure.
The AI landscape is dynamic and rapidly expanding, with new technology vendors constantly emerging. Private AI offers enterprises a flexible, modular infrastructure that prevents vendor lock-in and ensures ongoing compatibility with evolving technologies. Organisations can now build AI platforms designed to integrate smoothly with open-source tools and APIs.
Additionally, product 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.
What is the best application for private AI?
To understand how to use private AI tools for your organisation, look at the use cases around you first. Private AI is already transforming how organisations tackle data privacy and compliance challenges across multiple sectors. In financial services for instance, banks are leveraging this technology to process sensitive information securely, enabling advanced fraud detection and customer analysis while adhering to strict regulatory standards. By keeping data out of public cloud environments, these institutions are able to get maximum value out of their data, while maintaining robust protection and operational efficiency.
Similarly, law enforcement agencies are using private AI to revolutionise investigative processes. Advanced language models help analyse vast volumes of case data, uncovering critical connections and accelerating case resolutions with unprecedented precision, all while ensuring strict control over sensitive information.
Customer contact centres represent another compelling use case, where private AI enhances backend operations to support human agents. Rather than replacing customer interactions, these AI systems enable faster, more accurate responses, improving ticket resolution rates and overall productivity while allowing for complete data privacy and compliance.
These practical applications demonstrate private AI's transformative potential: delivering tangible business benefits like increased productivity, value, and cost efficiency, without sacrificing the fundamental need for data security and regulatory compliance.
Private AI represents more than a technological trend - it's a fundamental reimagining of how businesses can better offer intelligent solutions. By seamlessly integrating robust regulatory compliance with dynamic innovation, this approach has become essential for enterprises navigating today's complex digital landscape.
Next steps for Private AI
Private AI offers a compelling combination of cutting-edge intelligence with robust security measures. It allows businesses to harness powerful computational tools while maintaining complete control over sensitive information. Beyond mere compliance, it also creates opportunities for competitive differentiation.
As market demands evolve and digital transformation accelerates, private AI provides the agility needed to adapt quickly while building sustainable technological foundations. This approach doesn’t just solve today’s problems – it positions organisations to capitalise on future opportunities with a secure, scalable AI infrastructure designed for long-term growth and innovation.
Joe Baguley is EMEA CTO at Broadcom
