How to use AI in HR: A practical guide to getting started

If you’re feeling behind on AI, you’re not alone.

While the conversation around AI in HR has exploded, many teams are still figuring out what it actually means for their day-to-day work and where to even begin. 38% of HR professionals are in the discussions or evaluation stage, and nearly half of C-suite leaders believe their organizations are moving too slowly on implementation.  

But here’s the good news: getting started might be easier than you think. You don’t need to roll out a full-scale AI strategy overnight. All it takes is a clear starting point and the confidence to take that first step. 

In this practical guide, we’ll focus on the essentials: how to set your team up for success, how to get started with artificial intelligence in HR, and what to look for in an AI-powered tool. 

What your team needs before getting started

Before you take the first step on your AI journey, there’s one stakeholder you absolutely need to get on board: your team.

Laying the groundwork internally is just as important as choosing the right tool. Your colleagues need to feel informed, supported, and confident about what’s coming. Otherwise, you risk implementing a shiny new solution just for it to collect dust. Or worse, you might face active resistance from people who don’t trust, understand, or feel part of the process.

Here’s how to set your team up for success:

Clear communication

Let your team know why you're introducing AI. Is it to save time? Improve fairness? Reduce bottlenecks? If people understand the purpose, they’re far more likely to engage with the process.

Defined roles and responsibilities

Make it clear who’s leading the rollout, who owns tool selection, and who’s expected to test and provide feedback. A little up-front structure goes a long way.

A safe space to ask questions

AI can feel intimidating, especially for those who haven’t worked with it before. Create space for honest questions, and be prepared to challenge assumptions (both overly optimistic and overly cautious).

Pilot users with early access

Start small. Give a few trusted users (with varying skill levels) early access to a tool or feature and gather real feedback before scaling. Their experience will help shape a smoother implementation and land you internal advocates.

Leadership alignment

If your C-suite is backing AI, make sure they’re visible and vocal about their support. When leaders treat AI adoption as a strategic priority, the rest of the team tends to follow suit.

As Malin Gustafsson, CPO and AI enthusiast at Talentech puts it: “AI works best when it’s not just introduced, but understood. The teams that succeed are the ones who prepare their people, not just their platforms.”

Phase 1: Lay the groundwork (Weeks 1-4)

Start with clarity. What are your team’s biggest hurdles? Is it screening CVs, answering repeat questions, or keeping candidate comms on track? Choose one small, high-friction task as your test case.

These are the tasks you should aim to have achieved by the end of this phase: 

• Audit your current HR workflows: Map out your recruitment or HR processes step by step. Where does time get wasted? Where do tasks pile up? These pain points are perfect candidates for AI support.
  
  
• Align on your goal (e.g. save time, improve consistency, reduce bias): Make sure everyone involved knows what success looks like and why you're doing this. Setting a clear objective will help you evaluate tools and measure results later.
  
  
• Explore lightweight AI tools that integrate easily: Look for tools built for HR teams, not generic AI platforms. Prioritize options that are simple to activate, easy to train on, and play nicely with your existing systems.
  
  
• Involve key stakeholders from day one: Whether it’s recruiters, HR business partners, or hiring managers, loop them in early. Their input will shape better decisions, and their buy-in will speed up adoption.

Phase 2: Test a single use-case (Weeks 5–8)

Once you’ve picked your first AI use case, it’s time to put it into practice. This phase is all about learning on a small scale with low risk and extensive feedback. Focus on testing, not perfection.

Plan to check off the following:

• Roll out one use case with a small team: Start with something contained, like generating job ads, screening CVs, or using a chatbot for candidate FAQs. Limit it to a specific location, team, or recruiter group so you can monitor closely.
  
  
• Provide basic training and context: Make sure your pilot users know what the tool does, how it works, and why you’re testing it. A short demo or cheat sheet can go a long way toward building confidence.
  
  
• Track results from day one: Look for signals like time saved, faster response times, fewer manual steps, or better candidate experience. Even anecdotal wins matter at this stage, so don’t forget to write them down.
  
  
• Gather feedback in real time: Talk to the people using the tool weekly. What’s working? What’s clunky? What would make it easier? Their insight will be critical to making smart decisions in Phase 3.
  
  
• Set expectations that this is a test: Be clear that this isn’t a full rollout. Framing it as an experiment makes it safer to try, easier to critique, and more likely to succeed long-term.

Phase 3: Build internal capability (Weeks 9–12)

You’ve got some early results. Now, it’s time to turn that momentum into confidence. Phase 3 is about strengthening team knowledge, building trust in the process, and laying the foundation for the actual implementation. 

Don’t forget to: 

• Share your pilot results widely: Don’t keep the good news to yourself. Present early wins in a team meeting or internal update.
  
  
• Offer short, targeted training: Focus on what the wider team needs to know to feel confident, not just how the tool works, but what it helps minimise (manual admin, unclear workflows, etc.). 
  
  
• Keep monitoring performance: Track how the tool performs as usage grows. Are results still positive? Are new issues emerging? Use this data to refine your approach before a wider rollout.
  
  
• Make space for pushback: Not everyone will be sold right away, and that’s okay. Create space for honest feedback, answer tough questions, and adjust where needed. 

Phase 4: Scale with purpose (Weeks 13+)

• Expand to new use cases and workflows: Once you’ve nailed your first implementation, look at other areas that could benefit from AI efficiency. Think: interview support, preboarding, employee surveys, or internal mobility insights. Use what you’ve learned to prioritise what’s next.
  
  
• Maintain regular check-ins and audits: AI isn’t a “set and forget” solution. Build in regular reviews, both performance-based (what’s working?) and ethical (is it still fair, transparent, and aligned with your goals?).
  
  
• Integrate AI into your long-term HR strategy: Don’t treat it as a one-off initiative. Factor AI into your annual planning, budget decisions, and digital roadmap. Think beyond the quick wins and ask yourself: how can this support our broader HR goals?
  
  
• Keep investing in your people: As your AI capabilities grow, so should your team’s confidence. Offer regular training, create internal champions, and keep the conversation open.
  
  
• Avoid automation for automation’s sake: Just because a task can be automated doesn’t mean it should be. When deciding whether to involve AI, don’t forget to ask yourself: Does this improve the experience for candidates, employees, and the HR team?

What to look for in AI tools for HR

Now that we’ve covered the how, the next big question is what to use to get there. With so many tools on the market, it’s easy to get distracted by shiny features or big promises. But here’s the thing: not all AI is built for HR. 

The best tools are the ones that match your needs, integrate with your systems, and support your people, rather than just automating your tasks.

Here’s what to look out for: 

HR-first design

Choose tools made for recruiters and HR pros, not retrofitted from other industries. That means clear use cases, practical workflows, and people-focused UX.

Integration-ready

Your AI should fit into your existing stack, not create more work. Bonus points for tools that connect easily with your ATS, HRIS, and communication platforms.

Data security and GDPR compliance

Especially in HR, where personal information is everywhere, your AI tool should meet strict data protection standards. Look for solutions that are GDPR-compliant, store data securely, and offer transparency on how information is processed, used, and retained.

Explainability

If you can’t explain how the tool makes decisions, you can’t build trust. Look for vendors who offer transparency and auditability.

Ongoing testing and bias checks

Ethical AI isn’t a one-time feature. Your provider should be testing regularly for fairness, neutrality, and accountability.