In the past few years, more companies have begun rolling out AI-based platforms capable of helping people with a variety of tasks. Companies all over the world are using AI to automate tasks and free up their employees to tackle the challenging parts of their jobs. An estimated two-thirds of occupational work could eventually be enhanced by robotic process automation.
As you consider incorporating AI into your organization, you’re likely learning more about it. Your first consideration should be whether to use public AI vs private AI. Here, we break down the differences between the two types of AI and explore the pros and cons of each.
Understanding Public AI
Public AI is also known as “open AI.” It refers to any artificial intelligence developed and shared freely without restrictions.
Characteristics and Examples
Public AI’s most defining characteristic is its availability. The term refers to any publicly available AI algorithm that was trained on a large dataset. Public AI refines itself by using publicly accessible data on the internet to learn how to respond to prompts. These AI platforms are also available to a wide range of users, including private sector organizations.
Examples include generative AI platforms such as ChatGPT, customer service chatbots, and Lumen5, which is an AI-powered video generator. These tools leverage existing text, images, videos, and other content available online for machine learning to refine results for each prompt.
Advantages
You can access public AI apps through open platforms, public cloud providers, and public APIs for developers. Many of these platforms already contain machine learning models and libraries built in. So you don’t have to learn how to train them from the ground up.
Since public AI models are open, you are free to evaluate how they work in detail. This helps you look for potential biases and other factors that could make it harder to use AI ethically.
Challenges
Public AI does pose some challenges. Many of these platforms don’t have a formal support team, so you have to navigate multiple message boards when the program isn’t working. You also need in-depth programming skills to customize a public AI platform.
Open AI also potentially collects sensitive data including IP addresses, customer names, and other identifiers to improve machine learning models. The regulatory framework for AI is also relatively new. If you’re working with sensitive customer data, make sure you remove identifying details from your data sets before using it with your AI platforms.
Understanding Private AI
Private AI refers to proprietary systems trained on the specific data of the user or organization. Instead of using publicly available tools to analyze your data and generate reports, you would either hire experts to develop and build a platform or use a tool to build your own AI models. Many private-sector organizations use this model to protect company intel and to customize AI to their needs.
Characteristics and Examples
Private AI’s most defining characteristic is its restricted availability. When you’re working with private AI platforms, you own and control your data. You can also limit its use to authorized users to minimize potential risks.
Examples include personalized electronic health records powered by AI. These tools only evaluate specific patient data to create tailored care and treatment plans. Financial institutions also use private AI to assess risk and perform other financial analyses.
Advantages
Private AI’s biggest advantage is that you control your datasets. Since you can restrict who uses the platform, you know your data is secure.
Private AI is also fully customized to your specific needs. You can easily integrate it with your customer relationship management (CRM) databases and other programs to generate important insights for your business.
Challenges
To make private AI work for your business, you need a team to build and train the models, including language models. Or you need to partner with a company familiar with your industry. For example, if you’re working in aerospace, you will have different needs than a healthcare provider. You need AI platforms that analyze your specific datasets for accurate decision-making processes.
Private AI typically requires sophisticated infrastructure that can be costly to set up.
Security Implications and Governance Considerations
These security and governance considerations will contribute to your decision to choose private vs public AI for your business.
Data Privacy Concerns
Since AI uses large datasets to learn and operate, you risk exposing sensitive data using a public AI platform. These tools may also collect data without notifying you or your customers, so they lose their ability to opt in to sharing their information.
Whether you’re using public or private AI, the system could store data for a long time, meaning your data could be stolen if your AI is hacked.
Regulatory Compliance
Multiple businesses in public sectors and private sectors use AI. Depending on their industry, private-sector organizations aren’t subject to the same regulations, so open AI is less risky. However, if you’re a private company working in finance, healthcare, or another regulated industry, you have to comply with multiple regulations.
Government organizations and other public-sector companies should stick with private AI to keep their data more secure.
Ethical Oversight
In some cases, AI can highlight data biases and use them to inform recommendations. For example, some financial institutions using AI to make decisions on loan applications use historical data to train their models. In many cases, these platforms have made racially biased decisions because of bad input.
Accountability and Transparency
AI isn’t new, but its widespread use is. It isn’t currently scrutinized closely, so it’s up to you to make sure you’re using it ethically. Set up a solid set of governance policies to maintain accountability. Your reputation with customers is on the line, so be transparent about how you’re using AI.
Balancing Public and Private AI in Adoption
If you’re wondering whether to go with private AI vs public AI, know that you can use both in your organization. Use public tools for fast and scalable uses such as market analysis and predictive analysis. Make sure you’re scrubbing any personal information from your datasets.
Use private AI in highly regulated industries and when working with sensitive data. Ask regulatory agencies in your industry to send you guidance on AI restrictions in your field. Finally, create an AI strategy and governance policy before launching any AI-powered platforms.
Secure Your AI Strategy With Surgere
Let Surgere’s industry-leading engineering team work with you to develop a customized AI strategy that meets your needs. We understand how to leverage technology to improve operations. We work with multiple private and public-sector businesses, so we understand your needs. Learn how we can help you develop an AI strategy that makes your business more efficient while staying compliant with regulations. Schedule a call today.
