It has been a year since generative AI surged into the mainstream, accelerating the growth of enterprise AI. According to the International Data Corp, annual spending on AI — including generative AI — is projected to rise from US$175.9 billion ($235.4 billion) in 2023 to US$509.1 billion in 2027, boasting a CAGR of 30.4%.
Enterprises heavily invest in AI strategies to seize opportunities and avoid lagging behind competitors. Yet, as they ramp up AI adoption, many also acknowledge the importance of ensuring their strategy is sustainable and responsible.
Another growing area of concern is data management and protection. After all, data is the heart of AI. To fulfil the promise of AI, enterprises must capture data from the right sources and feed it into the right models.
One of the most significant challenges enterprises encounter when implementing their AI strategies is maximising the value of their AI models while safeguarding their data from exposure or risks. Many enterprises are now exploring private AI, which entails creating an environment exclusively tailored to a specific organisation’s use.
Private AI versus public AI
Before the rise of generative AI and large foundation models, AI models typically remained private by default. Each model had to undergo training on private data tailored to the specific use cases of individual enterprises. It is only with the advent of foundation models, like the large language models utilised by ChatGPT, that public AI has become feasible. Foundation models can undergo fine-tuning to accommodate various use cases, enabling different users or enterprises to utilise and exchange the same models.
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Private AI is gaining prominence as organisations increasingly acknowledge the drawbacks of solely relying on public AI for their AI strategies. Even if unfamiliar with the term, they recognise the necessity for private AI.
Three reasons why private AI is right for enterprises
Using private AI can provide many opportunities for enterprises to optimise their AI strategies. They include:
See also: Responsible AI starts with transparency
1. Protect your proprietary data
By feeding your business’s proprietary data into public AI models, you unintentionally agree to its public disclosure. This means trusting a third party with your sensitive data without assurance of adequate safeguards. Furthermore, there’s a risk of granting your competitors direct access to the business insights gleaned from your data through this process.
Having a private data architecture grants you control over your data, ensuring it remains protected and used solely for the benefit of your company.
2. Reduce your regulatory risk
We are navigating through an era marked by growing regulatory complexity as governing bodies across the globe establish new mandates governing how enterprises handle data collection, storage, transfer and processing. For global companies, adhering to data sovereignty requirements and specific data life cycle management regulations can be especially challenging. How can enterprises ensure compliance with the law while still accessing the vast data necessary for their AI models?
A private AI approach can help. Enterprises can design their models and data architectures to give themselves end-to-end control over their data. This includes specifying exactly what equipment is used to store and move the data, what physical locations the data is stored in, who has access to the data and for what purposes. In short, you won’t have to outsource your compliance responsibilities to a third party, as you would if using public AI models.
3. Optimise performance and cost-efficiency
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When enterprises feed their proprietary data into public AI models, the data and the models typically reside in different environments. For instance, public AI models are often hosted in a public cloud environment. Every time the enterprise moves data between its environment and the public cloud, it can cause delay and egress charges—especially if they don’t have an interconnection partner to help optimise performance.
Enterprises can design their private AI environments to minimise these issues. This could mean building their data architecture so that AI models and data warehouses are adjacent. Doing so will ensure a consistent, low-latency flow of data. Also, since the data never leaves the internal data architecture, the enterprise will never have to pay a third party to move its data.
Infrastructure requirements
AI represents a groundbreaking technology with distinct infrastructure needs. Enterprises cannot fully reap the benefits of AI if they persist in relying solely on traditional IT infrastructure. Public AI services are attractive because they offer a convenient and swift path to entry without the need to construct a dedicated AI infrastructure from scratch.
Enterprises should build their private infrastructure once they are ready to scale their AI strategies for important reasons. Let’s discuss what that infrastructure should look like.
Firstly, having a private AI environment doesn’t necessarily imply exclusion from public clouds altogether. You should tap into public cloud resources for many reasons, such as connecting with an AI Model as a service vendor hosted there.
The key is connecting to public clouds on your terms. This means building a cloud-adjacent data architecture, where you maintain custody over your data while also being able to move it into the cloud on demand via dedicated, private network connections.
Secondly, even when enterprises build private AI infrastructure, they don’t have to do it alone. They can connect to various partners and service providers to get the agility and flexibility they need from their AI infrastructure.
With access to the right digital ecosystem partners in the right places, enterprises can deploy the network, cloud, and software-as-a-service (SaaS) services they need to scale their AI infrastructure quickly and evolve it over time to keep up with the business’s changing needs.
Finally, building a private AI environment requires the flexibility to capture valuable data wherever it’s generated worldwide. You also need to position AI workloads in the locations that best meet their density and latency requirements — not just the best locations where you have infrastructure available.
Ruth Faller is the vice president for corporate development and strategy at Equinix
