How to Conduct Actually Good AI Experiments as a Recruiter

We talk a lot about the future of work and the incredible potential — and proven impact — of AI. But as companies grapple with incorporating AI into their workflows, many are mistaking correlation for causation. And that can lead to big, costly mistakes.

This shouldn't be about guessing what's working — rather, it should be about gaining the confidence you need to strategically integrate AI into your recruiting process.

The Foundation for AI Success: Insights from Laszlo Bock

To kick things off, let's look at some insights from Laszlo Bock, the former SVP of People Operations at Google and a two-time founder. His perspective on "The Impact of AI on the Future of Work" offers fundamental building blocks that prepare you for AI success.

First up, and we can't stress this enough: Clean and centralize your data.

It's the classic "garbage in, garbage out" scenario. Accurate, accessible data is fundamental to your success today and especially tomorrow. If your data isn't accurate and accessible, any AI you throw at it will just amplify those existing flaws.

The Power of Good Statistics and Experiments

Next, Bock emphasizes the need for people teams and recruiters to get really good at statistics and running high-quality experiments, always keeping in mind that correlation does not equal causation.

Bock argues that a lot of popular HR programs are actually based on correlation rather than true causation.

Think about referral bonuses. Google, for example, doubled its referral bonus at one point, and guess what? It had no impact on hiring.
Or consider fitness incentives. People who sign up for those are likely already health-minded. The incentive might correlate with their fitness, but it's probably not the direct cause of their healthy habits.
And here's a big one we've all been talking about: return to office mandates. Hybrid work has been proven to make people happiest, improves retention, reduces real estate costs, and has zero demonstrable impact on productivity. Yet, many companies are still pushing for a full return. This is a classic example of assuming causation where there's only correlation, or worse, ignoring clear data.

We also know that AI excels at certain tasks — but it completely falls short in others.

Remember Klarna? They laid off 22% of their workforce last December to replace them with AI, only to restart hiring human customer service agents in May. Why? Because the AI agents simply weren't up to snuff. This isn't to say AI is bad, but it highlights the crucial need for smart experimentation before making drastic decisions based on a false hypothesis.

So, How Do You Conduct an Actually Good AI Experiment?

This brings us to the core of this post: how do you conduct an actually good AI experiment as a recruiter?

Tie it to tangible business value: Your experiments shouldn't just be about cool tech. They need to directly align with broader business goals. How do your key recruiting metrics — like time-to-hire, quality of hire, or candidate satisfaction — impact the bottom line?
Identify areas of high potential AI impact: Don't just blindly apply AI everywhere. Audit your entire recruiting process. Where are your personal bottlenecks? Where might bias exist? Where are you spending a disproportionate amount of time on repetitive tasks? These are your high-potential areas.
Formulate specific, testable hypotheses: This is crucial. A weak hypothesis would be, "AI will make recruiting better." That's too vague. A good hypothesis is specific and measurable, like: "Implementing an AI-powered sourcing tool will increase the number of qualified candidates in the pipeline by 15% compared to traditional sourcing methods." See the difference?
A/B test: This is your best friend in experimentation. Maintain a control group — your standard way of doing things — and introduce a treatment group, which is where you introduce the AI element.

When you're A/B testing, keep these things in mind:

Sample size: Make sure you have enough data points for statistically significant results.
Randomization: Assign subjects to control and treatment groups randomly to avoid bias.
Isolation of variables: Only change one thing—the AI tool—between your groups. If you change multiple things, you won't know what caused the outcome.
Defined start and end points: Know when your experiment begins and when it ends.
Clearly defined metrics for measuring success: How will you know if your hypothesis was right? What numbers are you looking at?

After the experiment, don't forget to:

Merge quantitative insights with qualitative observations: Numbers tell one part of the story. But don't forget the human element. Talk to your recruiters, hiring managers, and even candidates. How do they feel about the changes? What are their experiences?
Test across other variables/drill down: Once you have some initial findings, don't stop there. Test across different segments, industries, or job titles. See if the AI performs differently in various contexts.

The Human Element: Trailblazers and Learning

Beyond the experiments themselves, there are two more critical components for long-term AI success in recruiting.

First, have a dedicated AI trailblazer. This is someone in your organization who is genuinely committed to understanding how AI works within the context of your specific organizational needs and outputs. They should be reporting on that information weekly, constantly iterating and refining your approach.

Second, invest in learning and development. Teach your people how to learn. The days of being able to predict exactly what skills your workforce will need years down the line are behind us. The pace of change with AI demands that your team is adaptable and continuously upskilling.

Wrapping Up

Conducting truly good AI experiments isn't just about adopting new tech; it's about making informed, strategic decisions that drive real business value. By embracing data, rigorous testing, and a continuous learning mindset, recruiters can confidently navigate the AI landscape and shape the future of talent acquisition.

Ready to learn more about how Loxo's AI and Agentic AI can transform your recruitment workflow? You know where to find us.