Which AI tools should you choose for HR? A time-saving overview

There’s no shortage of AI tools. New models and applications promising to transform the way we work appear almost every day. The challenge is knowing which ones can genuinely save your HR team time and which ones simply look impressive in a demo. This article takes a task-based approach because the most important question when choosing an AI tool isn’t “Which tool should I use?” but “What do I need it for?”

One question came up repeatedly before our AI in HR webinar: Which AI tools should we use? Which ones actually prove their value in everyday HR work? And where should we start without getting overwhelmed by endless testing? From our conversations with HR practitioners, we also know that the AI landscape in Polish HR departments is highly fragmented. One person uses ChatGPT, another works with Gemini, and a third is testing Copilot. Often, the choice comes down to whichever tool is easiest to access or most frequently recommended. This variety shows that HR professionals are curious and ready to experiment – which is encouraging. At the same time, it highlights the need for a common framework: which tool to use for which task, and how to use it safely.

Start with the question “What For?”, not “Which Tool?”

The most common trap when choosing AI is looking for one tool that can do everything. AI works differently from an HRM system, where a single platform can support an entire process. Different AI models have different strengths, process language differently, and perform better on certain types of tasks.

General-purpose large language models (LLMs) such as ChatGPT, Claude, Gemini, and Copilot are well suited to writing, text analysis, and communication. If your company works primarily within the Google ecosystem, Gemini may be a natural choice because it integrates with Gmail and Docs. In a Microsoft 365 environment, Copilot plays a similar role and can work with company documents within the Microsoft ecosystem.

Alongside these general-purpose tools is a growing category of specialized solutions – from presentation and video creation to data analysis and training materials. So instead of asking “Which AI should I use?”, start with the task you want to accomplish. Do you want to write a job ad, analyze survey data, or prepare a video for new hires? Once you define the task, choosing the right tool becomes much easier.

Flexible HRM software supported by AI

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Specialized tools by task

For HR departments, specialized AI tools can be particularly useful when creating onboarding and training materials and when analyzing HR data.

Onboarding and training materials

Preparing high-quality onboarding materials used to take weeks. Today, a significant part of this work can be completed in just a few hours by combining several specialized tools:

• Gamma – generates presentations and training materials from text. Provide an outline, and the tool can suggest the structure, layout, and visuals — an alternative to building slides manually in PowerPoint.
• Loom – lets you record short instructional videos, such as guides to company systems for new employees. Record once, and the material can be reused across multiple onboarding processes.
• ElevenLabs – generates natural-sounding voiceovers from text, allowing you to create audio and video materials without a recording studio.
• HeyGen – creates videos featuring digital avatars. For organizations with a large number of roles, this can help scale personalized onboarding content without recording every video separately.

Analytics and working with HR Data

ChatGPT’s file-analysis capabilities can be useful for working with HR data – provided the data is prepared correctly. In many cases, the biggest challenges arise during data preparation rather than from the technology itself. A few simple rules can make a significant difference:

• Use the right file format – XLS files with complex formatting, merged cells, and formulas can lead to reading errors. Converting the data to CSV, a plain-text format, makes it easier for the model to process.
• Use one column for one data type – mixing numbers and text in the same column can confuse the model. For missing data, use NA or null instead of leaving fields blank.
• Remove the “noise” – replace formulas with values and remove merged cells, empty rows, colors, and icons. The cleaner the dataset, the fewer opportunities there are for errors.
• Use “programmer-style” headers – short names without special characters or spaces, such as salary_level, department, and hire_year, instead of long descriptive labels like “average salary Q1 2025.”

When general-purpose tools aren’t enough

All the tools described above share one fundamental limitation: they don’t know your organization. Each time you use them, you have to provide the necessary context from scratch. And when you’re working with real employee data, public tools may not be appropriate because the data can be processed outside the company’s controlled environment. This is where the distinction between using AI alongside your HR system and using AI within your HR system becomes important.

The first approach can speed up individual tasks. The second can change how entire HR processes work. In HRM Productive24AI, AI assistants and agents work directly with your organization’s data – including performance reviews, employment history, OKR goals, and training results. Instead of relying only on generic patterns, they can work with the real context of a specific organization and its employees.

In practice, this creates three key differences:

• Data security – access to information is controlled through the company’s role structure, sensitive data can be anonymized, and the system can run on-premise or in a private cloud, keeping data within the organization’s infrastructure.
• Quality of recommendations – AI works with data about a specific employee or candidate, allowing its suggestions to reflect the actual situation rather than a generic or statistical average.
• Integration with the process – the assistant works directly within system forms, tasks, and reports instead of requiring users to constantly copy information between separate tools.

A few examples of assistants available in HRM Productive24AI show what this can look like in everyday work:

• Candidate Scoring Assistant – analyzes candidate profiles and matches them against role requirements using ATS data, giving recruiters an organized recommendation instead of requiring them to review a stack of CVs manually.
• Performance Review Assistant – supports the preparation and summarization of performance reviews using data from the company’s review cycle.
• Knowledge Base Assistant – answers employee questions using company documentation, including regulations, procedures, and FAQs, rather than relying on generic information.
• Employee Development Assistant – suggests personalized career paths and training recommendations based on an employee’s profile, competencies, and goals.

The system is also fully configurable. Assistants can be adapted to an organization’s processes, structure, and way of working. That’s an important difference from off-the-shelf tools, which often require organizations to adapt their processes to the software.

AI Agents and Assistants in the HRM system

AI that works in your context and understands your organization. It runs on real data in a secure environment.

How to choose the right tools

Whether you’re considering a public language model, a specialized AI tool, or an integrated solution, it’s worth asking three questions before making a decision.

1. Is the Data I’ll Be Processing Safe in This Tool?

With anonymized or fictitious data, most tools can be used for testing and experimentation with relatively little risk. The situation changes significantly when employees’ or candidates’ personal data is involved. In that case, your options should generally be limited to approved tools with appropriate data-processing safeguards, including solutions that guarantee processing within the EU or run on the company’s own infrastructure.

Before using a tool, check three things:

• Where is the data physically processed — inside or outside the EU?
• Is the content you enter used to train or improve the provider’s models?
• Does the provider offer an appropriate data processing agreement?

If the answers are unclear or difficult to find, treat that as a red flag.

2. Is the Tool Approved by IT?

If you don’t know, ask before you start. It’s a conversation that’s much better to have at the beginning than after a security incident.

It’s also worth changing the way you think about IT. The IT department isn’t a gatekeeper to work around. It’s a partner that can answer many of the security and data-processing questions you need to resolve. Your organization may already have a list of approved tools, business licenses for popular AI models, or plans for secure AI infrastructure – things HR may simply not know about. One conversation can save weeks of independent testing and give your team access to capabilities the company has already invested in.

And if no such list exists, your question could be the starting point for creating one. That’s valuable, too.

3. Can the result be measured?

Before rolling out an AI tool, define what will demonstrate that it works. The metric doesn’t have to be sophisticated – it simply needs to be concrete and comparable over time.

• Time – how long does a task take before and after implementation? For example, preparing a monthly report could drop from 6 hours to 1.5.
• Quality – how many errors, corrections, or items returned for completion are there?
• Throughput – how many cases can the team handle in the same amount of time? For example, how many recruitment processes can run in parallel?
• Experience – how satisfied are the people involved in the process? Even a short survey after onboarding can provide useful insight.

Without a metric, conversations about scaling and budget will rely on impressions rather than evidence. With a metric, you gain something more valuable: a way to talk about AI with management. “Tool X reduced report preparation time by 70%” is a much stronger argument for further investment than the team’s general feeling that the tool is useful.

Starter list: where to begin

If you’re just starting to organize AI use within your team, the list below can serve as a practical starting point:

• Writing job ads, emails, and document templates → ChatGPT or Claude (anonymized or fictitious data only)
• Analyzing HR data from Excel → ChatGPT with file analysis, after preparing the data in CSV format
• Onboarding materials – presentations → Gamma
• Onboarding materials – instructional videos → Loom or HeyGen
• Working with employee data, including reviews, development, and recruitment → AI assistants in HRM Productive24AI

With AI, the goal isn’t to test the largest number of tools as quickly as possible. It’s to find the tools that solve your specific problems and then give your team time to develop effective ways of working with them. The most well-known tool will always be less valuable than the one your people actually use – and that delivers measurable results.

See how AI can support your HR processes!

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