Artificial intelligence is evolving at a pace that few organizations feel prepared to match. For HR professionals, the challenge is clear. According to a February 2025 Harvard Business Review Analytic Services survey of 371 leaders already using or considering AI, 72% said focusing on AI has revealed technical skills gaps in their organizations. Yet only 21% reported that HR leadership is closely involved in decisions about their company’s AI strategy, with another 30% moderately involved. Nearly half admitted HR leadership has little or no involvement.

That gap is concerning. HR touches every aspect of a company’s people strategy, and AI is, at its core, about people. Richard Brasser, Managing Director of Carlton Richards and AI expert, says it best: “It is not about technology, it is about helping people succeed. AI is really all about people. We call that generative AI.”

During our recent webinar with Brasser, From Curious to Confident: How to Lead the AI Shift, most participants told us their companies have not yet even started proof of concept projects with AI. That tracks with the HBR survey results. If HR leaders are not involved now, they risk being sidelined in shaping how AI will affect work, talent, and culture.

To lead effectively, HR must first understand one of the most important distinctions in AI: the difference between public and private systems. This knowledge is essential for building policies, identifying risks, and choosing the right use cases that protect people and the organization.

The first dividing line in AI is between public large language models (LLMs) and private, enterprise-grade solutions.

Public AI refers to systems such as ChatGPT, Gemini, or Copilot. These tools are widely available, often free or subscription-based, and trained on massive amounts of publicly available data. Many third-party applications are also built on top of these platforms, sometimes obscuring the fact that data entered is transmitted back to the underlying public model. That means company information may be shared outside your control, even if you think the tool is “enterprise grade.”

Private AI refers to customized, secure systems designed for a specific organization or built by enterprise vendors with safeguards for proprietary information. These solutions can be integrated directly with company systems, structured around internal data, and designed for scale, accuracy, and compliance.

The difference matters because public AI is a powerful tool for creativity and brainstorming, but it is not suited for handling sensitive, confidential, or proprietary company data. Private AI, by contrast, allows organizations to securely harness their own information to drive efficiency, insights, and strategic growth.

The line between public and private AI can be confusing. Many HR leaders assume that if a vendor offers an “enterprise plan” or requires a subscription, the system is private. That is not necessarily the case.

A simple test is to ask whether the AI system can integrate with your company’s internal systems through APIs. If not, it is likely built on a public model that is not designed for enterprise data. Another clue is whether the vendor can describe how your company’s data is stored, secured, and protected from being shared back into the model.

Brasser recommends a framework he calls EPASS for evaluating private AI:

• Enterprise: Designed for organizational use, not consumer-grade experimentation.
• Proprietary: Protects and analyzes your company’s unique data.
• Accurate: Validates outputs rather than generating plausible but false results.
• Scalable: Capable of handling large amounts of complex, unstructured data.
• Secure: Ensures sensitive data is not exposed outside company walls.

If a solution does not meet these standards, it is not truly private.

Public AI tools such as ChatGPT can be an excellent resource for general tasks that do not involve sensitive information. For HR professionals, these include:

• Brainstorming ideas for learning and development programs
• Researching general market trends
• Exploring new HR concepts or approaches
• Drafting non-confidential presentations
• Creating team-building agendas or offsite activities
• Generating sample reports or slide decks
• Supporting learning and professional development

Public AI can also be a great thought partner. Asking a model about a new direction or using it to bounce ideas can accelerate creativity and give HR teams new ways to think about problems. However, HR leaders should never input personal employee data, financial details, compliance information, or other confidential material.

It is also important to note that public AI can generate inaccurate or even fabricated information. ChatGPT, for example, has been known to invent legal cases, complete with citations that look real but are entirely fictional. Any outputs must be reviewed carefully for accuracy.

Private AI shines when organizations need to analyze large amounts of proprietary or sensitive data. For HR, some of the strongest use cases include:

1. Automating Employee Feedback. For a company with tens of thousands of employees and hundreds of thousands of survey responses, private AI can analyze trends, highlight cultural strengths and risks, and identify areas for improvement. This level of analysis would take humans months, but AI can complete it in hours.

2. I-9 Audits and Compliance. HR leaders can use private AI to prepare for audits by ensuring documentation is complete, accurate, and accessible. This reduces compliance risks and administrative burdens.

3. Onboarding and Training. Instead of relying on static intranet pages, private AI can serve as a dynamic assistant. A new employee could ask about time-off policies and not only receive an answer but also be directed to the correct document, video, or timestamp. This creates a more personalized onboarding experience and ensures consistency.

Private AI also opens the door to highly personalized coaching and upskilling. Imagine every employee having access to an AI coach that tracks their learning progress, identifies areas where they need more support, and delivers individualized training. This saves HR time while giving employees more meaningful development opportunities.

The risks of confusing public and private AI are real. For HR, where confidentiality is paramount, sharing employee or organizational data with a public system could create legal, ethical, and reputational consequences. Even if data feels anonymous, once it is in a public model it is out of your control.

There are also risks of bias, misinformation, and lack of transparency. HR leaders must ask tough questions:

1. How does AI treat your people?
2. Are AI-driven processes fair and transparent?
3. How can you ensure ethical use in the workplace?
4. How often should your AI policy be updated?

These are not questions IT or legal can answer alone. They require HR’s leadership because they are fundamentally about people.

SHRM has emphasized that HR should be at the forefront of the AI revolution because AI is human-centered and touches every part of the company. Yet in practice, many HR leaders are still saying, “Just tell me what to do.” That hesitation risks leaving HR behind at a moment when employees, managers, and executives are all looking for guidance.

AI is accelerating so fast that you often do not know what you do not know. The way forward is not to have every technical answer, but to step in as the advocate for people, fairness, and ethical use. Partner with IT and legal to shape policy. Learn the differences between public and private AI. Stress test vendor solutions. And most importantly, lead with the question: how does this technology help our people succeed?

Because in the end, as Richard Brasser reminds us, AI is not about technology. It is about people.

—

The information contained in this site is provided for informational purposes only, and should not be construed as legal advice on any subject matter.