Comparing AI Tools for People Teams: What to Use, How to Use It, and Why Data Protection Comes First
Introduction: AI Is Reshaping How People Teams Work
From writing performance reviews to automating survey summaries, AI tools are changing the way People Teams operate. Tools like ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), and Copilot (Microsoft) are now easily accessible—and often already embedded in the platforms HR uses every day.
The promise is clear: save time, simplify tasks, and support better decisions. But using AI in HR isn’t just about picking the most powerful tool. It’s also about understanding what happens to your data, especially when the data you’re handling involves people.
HR manages some of the most sensitive and regulated data in any company: compensation, demographics, health, performance, disciplinary actions, and more. Using AI responsibly means knowing how these tools work—and where your data goes.
A Look at the Leading AI Tools
| Tool | Built By | Best For HR Use Cases | Integration Strength | Key Data Privacy Controls |
|---|---|---|---|---|
| ChatGPT | OpenAI | Writing assistance, policy drafts, HR chatbots | Integrates broadly via API, Slack, Notion | Enterprise plans offer opt-out from training |
| Gemini | Email replies, summaries, Sheets formulas | Seamless with Google Workspace | Admin-level control via Workspace settings | |
| Claude | Anthropic | Long document summarization, ethical AI use | Integrates with Notion, Slack | Designed with privacy-by-default approach |
| Copilot | Microsoft | Embedded in Word, Excel, Teams | Deep Microsoft 365 integration | Data stays within Microsoft tenant |
Each tool offers slightly different strengths, but none of them are HR-specific by default. That’s why the People Team’s role is to test, adapt, and apply these tools carefully—especially when real employee data is involved.
What “Training the Model” Really Means
One of the most misunderstood aspects of AI use is how data is used to “train” models.
- Training a model means feeding it large datasets to help it learn patterns.
- Some models like ChatGPT or Claude were trained on internet-scale data.
- By default, when you use free or basic versions, your inputs may be stored and used to improve the model unless you opt out or have an enterprise agreement.
In HR, this creates a serious risk. Uploading internal policy documents, performance reviews, or employee feedback into a free AI tool could unintentionally expose confidential information to future versions of the model.
That’s why your employee data should never be used to train public AI models.
Use AI to Accelerate, Not to Expose
There’s a difference between using AI to generate outputs (e.g., draft a job description) and using it to learn from your historical employee data.
Here’s how to stay on the right side of responsible use:
Safe Examples:
- Using AI to rephrase a public job description
- Generating a generic email template for onboarding
- Summarizing anonymized survey themes (offline)
Risky Examples:
- Pasting real resignation emails into a public tool
- Uploading engagement survey verbatims with names
- Feeding payroll or compensation data into AI prompts
The safest way to use these tools is to keep employee data out of them, unless you’re working in a private, enterprise-secure environment with full governance in place.
Questions to Ask Before Using AI in Your People Team
- Where does the data go once I press "Enter"?
- Does the vendor store it? Train their models on it?
- Can I control who has access to AI-generated outputs?
- Do you have versioning, audit trails, or export control?
- Have I removed all sensitive or personal information?
- Redact names, roles, emails, and any confidential info.
- Is this task repetitive and templated—or nuanced and risky?
- Use AI to support routine tasks, not final decisions.
- Do I have an enterprise-level agreement in place?
- If not, avoid sharing anything that isn’t fully public.
Balancing Innovation with Caution
AI tools are here to stay, and their capabilities are only improving. But HR’s role is not to chase every shiny object—it’s to balance innovation with accountability.
- Experiment with AI in low-risk scenarios: policy drafts, calendar invites, general summaries.
- Avoid using AI where the data is sensitive, regulated, or personally identifiable—unless it's in a secure environment with vendor assurances.
- Educate your HR team on what tools are approved and how to use them.
- Control access, especially when using tools embedded in Microsoft or Google environments.
Final Thought: Trust Is the Real Product
People teams work hard to build trust—between leadership and employees, between teams, across geographies. That trust can be eroded in seconds if personal data is exposed or misused.
AI can be a powerful ally, but only if used thoughtfully. The right tool isn't just the smartest—it's the safest.
Choose tools that respect your data. Use them to scale your impact, not your risk. And always ask: is this helpful, and is it safe?