Private AI vs. Public AI: Exploring the Next Frontier

Generative AI (GenAI) is transitioning from a cutting-edge innovation to a universally leveraged workplace tool in a remarkably short span of time. Today, 62% of professionals utilize ChatGPT and similar platforms at work—doubling adoption within a year. The rapid growth trajectory of GenAI remains unabated for the foreseeable future.

As businesses move into mainstream adoption, issues of privacy, content ownership, and security are emerging. The rising conversation around public vs. private data—and the guardrails required to manage AI in the workplace—are driving companies to review and institute business policies to address potential exposures. Going forward, businesses must leverage the competitive edge GenAI offers while embracing cautious, private AI implementation.

Private AI: Integrating with IT Systems and Data

Tools like ChatGPT and Microsoft Copilot—as well as the use of large language models such as Claude—have become synonymous with GenAI, yet they only represent a subset of the broader capabilities enterprise-wide AI implementation will soon demand. These widely used models, commonly referenced as public AI, are trained on data derived from public internet sources (including websites) and open records (ex: Library of Congress), little of which provide a competitive advantage to you or your business.

Here’s an example of how businesses use public AI in software development:

Public AI offers a simple solution for fairly general requests—think “Translate this code from Python to C++” or “Generate ad copy using these parameters.” It relies on the specificity of your prompts and publicly available information to produce responses.

On the other side of the spectrum, private AI algorithms are trained with proprietary datasets—such as confidential customer information and sales metrics—that an organization cannot entrust to third-party platforms. This enables tailored results that are only accessible by the organization that owns the model. Imagine typing in a query—for example, “Estimate our projected growth if we reduce our customer churn rate by 5%”—and getting results that are exclusively accurate for your business.

Integrating the most valuable corporate asset—company data—with artificial intelligence opens the door to a wider audience of corporate users, abstracted away from the complexities of programming languages and SQL queries. This requires the complex endeavor of private systems integration and AI model training, yet the payoff is valuable: More people are empowered to make highly intelligent decisions, faster.

Private AI offers a powerful way of gaining insights and fine-tuning content to your exact needs, with your data, all while maintaining total confidentiality. It significantly improves privacy by keeping sensitive information within the secure confines of your infrastructure. It enables compliance with even the strictest data protection regulations—such as GDPR and HIPAA—reducing legal and reputational risks associated with breaches and data mishandling. As private AI evolves, its capabilities will increasingly include robust access controls and powerful security integrations.

Data Accuracy in Private AI vs. Public AI

The wide breadth of data that public models receive can make them prone to false or misleading results, otherwise known as AI hallucinations. Without control over data sources or filtering capabilities, getting accurate and high-quality outputs can be a tedious process—and you still won’t receive fully personalized results. Directly leveraging public AI responses for commercial purposes can also pose copyright and other legal risks without careful research and citation.

Using private AI models, businesses can carefully review training data to ensure diverse, high-quality, and well-structured inputs. This allows for optimal and custom-tailored GenAI responses. Furthermore, by implementing human oversight processes on the backend, organizations can proactively prevent biased results that can’t easily be eliminated when using public AI.

Implementing Private AI

Public AI can certainly be the ideal choice for projects that require large-scale, non-proprietary data—for instance, when conducting research into a new market or generating ideas for a campaign. However, it carries significant limitations for businesses and will increasingly be affected by regulations in the future. It’s no wonder why businesses are shifting toward private AI, which can continuously mold itself to your unique goals and demands.

So, how can you begin strengthening your operations and efficiency with private AI? Consider a hybrid if not on-premises solution, allowing for secure implementation close to your most critical data.

Partnering with a cutting-edge, business transformation leader can help. At Capstone IT Solutions, we’re continuously innovating new ways to future-proof your applications through the integration of cloud, container, and private AI technologies. Our approach enables a sophisticated blend of scalability, security, and intelligent personalization—building your digital resilience and unlocking new opportunities to support customers.

Future-proof your IT infrastructure with private AI. Discover how Capstone IT Solutions can help.