Generative AI's mainstream debut has accelerated enterprise AI growth in the last year. Companies are aggressively pursuing AI strategies, driven by potential benefits and competitive pressures. In fact, IDC predicts AI spending to grow from $175.9 billion in 2023 to $509.1billion in 2027 (30.4% CAGR).

However, while fighting for competitive advantage, organisations are often not able to innovate freely owing to global regulatory requirements. Additionally, companies are also mindful of the need for sustainable and ethical AI implementation as they accelerate adoption. In fact, data management and security are emerging as critical concerns. Data forms the core of AI systems, and companies must carefully source and utilise data to power their AI models effectively. This presents a significant challenge: how can businesses maximise AI potential while safeguarding their data?

Private AI, custom-built for exclusive organisational use, is emerging as a potential solution - allowing enterprises to maintain control over their AI models and the data they feed into them.This move is a step in the right direction. And here are three ways Private AI puts organisations in the driving seat for innovation.

1. Unparalleled levels of control

With businesses becoming more and more data-led, it's even more important that they know where their data is being held at all times. Private AI gives organisations the freedom to choose where to store their data and then build their AI Large Language Models (LLMs). This could range from on-premises environments to private clouds, at the edge or across multiple public clouds. This approach ensures that IT teams aren't reliant on one cloud service provider and limited by their data management policies. It is an enabler for companies to have access to all the software, infrastructure and capabilities of hyperscalers - without the associated concerns of how these large cloud providers will manage and use confidential data sets.

Private AI also allows organisations to bring AI to the data, rather than vice versa. Enterprises have complete ownership of their AI models and can control how much data they feed the technology and where/when to limit access. This allows for real-time management of all new technologies and innovations added to their tech infrastructure, making sure IT teams can track their spend on new technology as well as measure any return on investment.

Innovation with AI often involves data being pushed and stored beyond national boundaries - usually under the control of a US-based hyperscaler. Private AI brings access back and allows for the creation of a local virtuous economy. For example, a local UK hospital developing an AI model to accelerate cancer research will be using data locally stored in a UK data centre/ private cloud where it will be feeding the local economy, paying local taxes, complying with the local regulations and even employing and teaching local experts. This brings a whole host of opportunities not just to the organisation looking to adopt a Private AI model, but to the surrounding community as well.

Security is also a big consideration here. Private AI models are trained on proprietary data, which stays within the organisation's control, mitigating risks of unauthorised access or data breaches. This is particularly crucial for industries like construction, healthcare, finance, media, and legal services, where sensitive or creative data is needed to train machine learning models. Using private AI models built with the company's own data also ensures compliance and practicality.

1. Ease of navigating the regulatory landscape

Currently, innovation is largely focused on the other side of the pond given the convoluted AI regulatory landscape in Europe. In recent news, Meta announced that it is going to limit its AI roll out in Europe owing to increased and unpredictable regulations.

Acting as an antidote to this exodus of innovation, Private AI provides the opportunity for companies to digitally transform within the existing regulatory landscape while also driving local economies and delivering against national interest.

The regulatory landscape for AI is as nascent as the technology itself, and as such, the future impact of these regulations isn't fully known. As a result, any AI innovation done at the enterprise level should also be open and interoperable. Given the trackable nature of the data used in Private AI models, organisations can ensure that all data sovereignty standards are met. This will futureproof any new technology against new regulation, while also taking away any roadblocks for organisations looking to use AI for tech change.

1. Futureproofing tech infrastructures

Companies don't have to make the same mistakes they made with the rush to move to public cloud. The AI landscape is evolving and there are more AI providers that will enter the market beyond the top providers.

The use of Private AI ensures that the next phase of infrastructure built by enterprises is flexible and mouldable - to avoid vendor lock-in and future proof its compatibility with evolving technologies and AI providers. You don't want to end up with another legacy system to navigate three years down the line!

When it comes to AI, there is no longer any reason to debate trade-offs in choice, privacy, and control. Private AI empowers organisations with all three, enabling them to accelerate AI adoption while futureproofing their AI infrastructure. Sustainable innovation is not just about safeguarding data; it is about strategically unifying AI innovation with compliance and data privacy. This approach empowers businesses to responsibly harness the power of AI, paving the way for a more secure and innovative future.

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