Private AI vs Public AI: 4 Key Facts

Artificial intelligence (AI) has reached a tipping point in the public consciousness. Much of this has been driven by technology developments related to large language models (LLMs) and the release of generative AI tools, including ChatGPT from OpenAI. However, for enterprises shaping forward-looking AI strategy, a critical part of the conversation that needs to be addressed is the issue of private AI vs. public AI. It’s important to understand the data privacy and regulatory implications of each approach, especially for public organizations or private sector companies in heavily regulated industries. Let’s explore both approaches to better understand the potential impact of each. 

Before we go further, let’s define the two terms. Public AI refers to any kind of publicly available artificial intelligence algorithm that trains on a wide set of data, typically pulled from users or customers. ChatGPT is an example of public AI—it was trained on publicly available data from across the internet, such as text articles, images, and videos. 

Public AI can also refer to any algorithm that uses datasets that are not private to a specific user or organization. Providers of public AI will often train their models  using customer data to improve their own services. This often means that, as a customer, your own data is not fully private to you. And, in fact, these organizations may be using your data to improve AI algorithms for your competitors. For example, if you use AI to predict when to purchase digital advertising space, the AI provider may use your information on purchase pricing and win/loss for ad buys to update their own algorithm. This means any of your competitors who use that same AI vendor get the benefit of an algorithm trained on your data. 

Private AI refers to the practice of training algorithms on data specific to one user or organization. In other words, if you use machine learning systems to train a model on a data set, such as a set of documents like invoices or tax forms, that model is used only for your organization and will not be used by the platform vendor to train their own models. And the benefit is that you’re not helping create the collective intelligence that could help one of your competitors. 

Private AI can be built using two methods: The first involves hiring in-house experts, such as a team of data scientists, engineers, and software developers, to build and support AI models without external involvement. The second method involves using a platform to build machine learning models that enable AI capabilities that pull from your own private data and ensure that data will never be used to train a more widely available algorithm. This second approach reduces the need to hire an extensive team to build and maintain the models and infrastructure required to run private AI models while still retaining your data privacy.

The truth is, both models offer benefits and drawbacks. Here are four things you should know about private AI vs. public AI:

Private AI scores points in the realm of digital privacy. Private AIs can also offer strong security, as long as the company follows security best practices. 

Here’s why: With private AI, your enterprise data remains yours. By hosting models trained or fine tuned using private data and restricting its usage to your organization only, you guarantee that your data remains private and you reap all the benefits of optimizing the model for your use case.  In comparison, with public AI, you are essentially sharing your private data with the AI provider, who will store it online and use it for further learning.

Public AI vendors rarely give you control over how you want the algorithm to run. The AI will have been trained on pre-existing data—sometimes customer data—to optimize the algorithm. This means that the AI won’t necessarily be tailored to your organization or use case and may require more human intervention than private AI would. 

Private AI offers greater control, allowing you to customize your AI model to your specific organization. This ensures greater model accuracy and, in the event of data drift, lets you update your algorithms over time. 

Public AI models can be more cost-effective than private AI in general, particularly if you do not have a team with AI expertise in house. You’re paying to leverage pre-trained models and cloud resources from the public cloud providers.

In-house private AI models typically require more of an investment than public cloud options. If you aren’t using a platform, the in-house approach requires a team of experts—data scientists, data engineers, and software engineers—to build the infrastructure and the AI models. This can quickly grow expensive. Plus, infrastructure builds and maintenance can add to the tab. However, taking a platform approach to private AI, which does not require a team of experts, offsets these costs.

Public AI typically allows you to use AI services quickly because they rely on pre-trained models and readily available services. With a private, in-house AI model, it takes time to collect data, develop the model, test it, and validate it before deploying. Then, there’s a significant amount of in-house IT work required to deploy the models into production.

However, if you use a platform approach that allows you to build a private model, then you can often deploy a fully trained AI model in just minutes.

Of course, you don’t have to completely choose between private and public AI. You can keep the benefits of both when you take a platform approach. A process automation platform like Appian lets you build your own private data models more quickly on a strong, secure platform. In fact, using a platform like Appian means using AI as part of a wider hyperautomation approach. You’ll have access to multiple automation tools (including robotic process automation (RPA) and low-code) to automate entire processes. AI is a powerful capability, but it’s not a silver bullet—you’ll need a blend of technologies to keep up with the pace of modern business.