Recent advances in Large Language Models (LLMs) are changing the staffing industry, albeit slowly. Do not fear — it is still fundamentally a people business, run by humans focused on helping companies and individuals. But in some areas, these models are now making staffing firms more efficient by handling repetitive and tedious tasks. It is offering a glimpse of the way it may change how these companies are organized.

In this article, we peer past the hype to explore AI’s impact on staffing and how some of the biggest companies are capitalizing on this emerging technology.

We have no affiliation with the tools mentioned in this article, and offer them simply as examples of what is now commercially available.

6 ways staffing firms are using AI in their operations

AI adoption in the staffing industry is unfolding at two speeds, fast and slow, depending on whether the company is large or medium-to-small.

Broadly, 40% of staffing firms have not yet integrated AI into their operations according to our first-ever staffing opportunities report. Larger companies with advanced recruiting departments are much more likely to already be using it, whereas the medium and small-sized companies are understandably more cautious, though it’s not for a lack of interest or awareness — they want to observe and consider before they act.

Nearly all staffing associations are actively educating their members on AI, preparing them for the technological shift. Among those using the technology, 29% have already replaced some temporary workers with automation for various use cases, such as the following.

1. Matching people to roles

One example of AI in action is talent role matching. At the largest scale, since 2022, Amazon has been using an AI-driven system called Automated Applicant Evaluation (AAE) technology to sort applications and match them to corporate and warehouse roles. This has significantly sped up the process and as a result, they don’t have to hire as many people again. For companies that cannot afford to develop their own technology, these sorts of features are showing up in the applicant tracking software they already use.

However, we should note that these tools are not without fault. According to a Bloomberg experiment, GPT3.5 failed a bias test by favoring names from some demographics over others. This experiment raises concerns about fairness when using such automated tools and shows there’s still more work to be done before they can be widely adopted without human supervision.

2. Producing job descriptions and content

As you’re probably aware, creating thorough job descriptions takes considerable time and effort. It takes 65% of HR professionals at least 2 hours to create one, according to one survey. Multiply that time by the number of new job descriptions you have to create in a week and add the time needed to gather or write accompanying content to aid candidates, and you can see why many staffing firms are turning to AI for this task.

As just one example of a company solving for this, the startup Phenom can help companies craft personalized articles and then match these articles to candidates to interest them in the role.

3. Reaching out to potential candidates

Staffing firms are using AI-powered tools for sourcing candidates, screening, and initial outreach, a process that is usually time-consuming and labor-intensive. They do this with tools like Recruitbot, which allows them to access a database of millions of candidates and send bulk-personalized outreach emails. This saves recruiters time and increases engagement with prospects, as the messages are tailored to the individual at scale.

4. To interview candidates with chatbots

When L’Oreal started to get over a million applicants per year for about 15,000 positions, it turned to an AI chatbot called Mya — now acquired by The Stepstone Group — to save time in the first stage of the process. It answered routine queries and took care of other rote tasks. The result? Recruiters saved over 200 hours on one internship program that received 12,000 applicants for 80 spots.

Another surprising benefit, according to a L’Oreal representative, was the increased diversity. It helped them hire candidates they might have previously passed over for one reason or the other.

5. To analyze talent data

We’re also seeing companies using AI to analyze and interpret massive volumes of candidate data that would take a human eons to review. These insights can help recruiters make more informed decisions and ensure they are matching the right people with the right roles, as it can more objectively assess why some recruits did or did not work out.

AI recruiting software CVVIZ has an analytics feature that measures different aspects of the hiring process itself, and shares the recruitment metric insights that help guide recruiter’s actions.

6. To manage candidates/new hires

Perhaps one of the most critical areas where AI is showing up is in people management. As we said earlier, staffing is still a people business, staffing firms know that relationships still matter, and the data proves this. According to a 2023 talent trends report, candidates are 73% more likely to keep working with a firm if the process is positive, i.e. the recruiter is attentive and responsive.

Companies are using onboarding tools like Enboarder to build connections with hires through automated and personalized onboarding.

There are more use cases, but those are the top six. And now they come with a caveat. Before you can realize the same benefits these companies are seeing, you need to have quality data and systems in place to make use of AI, which as we explore next, is not a simple matter.

The data challenge: garbage in, garbage out

AI is only as good as the data you feed it. That’s why you should have an up-to-date enterprise resource planning (ERP) system and applicant tracking system (ATS). If your systems aren’t updated, they’ll collect incorrect or outdated data, leading AI to make inaccurate judgments and take ineffective (or counterproductive) actions.

Unfortunately, 30% of private companies have not updated their ERP systems in the past five years. And one in ten of them, in the past decade. If you want to begin experimenting with AI, this is the best place to start — investigating your data and internal AI readiness.

For help implementing new technologies or auditing your existing implementations, please reach out to myself, BJ Hoffman, or Michael Napolitano.