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Machine Learning: 5 Uses for AI in HR

SmartDreamers Team
3 min. read

It seems like the back-and-forth has been going on for years now. Is AI coming to change everything right now, or is it still in its infancy? Should we all start updating our resumes in case the robots automate our jobs, or should we wait and see what real changes these emerging technologies actually bring? At SmartDreamers, we tend to err on the side of the latter: sure, AI really is emerging as an important piece of the HR-industry’s technological landscape, but so far we’re mostly seeing changes that improve recruiters’ existing capabilities and make their lives easier. What are some examples of AI making life easier? We’re glad you asked!

 

1. Budgeting Advertisements

 

As many of you know, recruitment marketing operates under the assumption that a robust, multi-channel approach to job advertising is the key to spreading your employer brand and attracting high quality hires. After all, your employer brand and EVP (employee value proposition) really only have value as talent acquisition tools insofar as they’re being put out in the world and getting the attention of job candidates. This means that your average recruiter has to take it upon herself to conceive, schedule, design, and budget for various ad campaigns aimed at building up brand gravity, filling open positions, and strengthening her talent pipeline. At its face, this whole process seems to require a lot of guesswork. What’s a reasonable amount to pay per click for an ad campaign? Is it better to place higher bids for a shorter campaign duration, or to risk a lower bid in order to extend your budget? With AI (or, often, with machine learning—which, for our purposes, we’ll think of as a subset of AI more broadly), it’s possible to remove the better part of that guesswork. If you provide information about your campaign, an AI-empowered social media solution could potentially suggest budgets and durations most likely to meet your needs, based on data from previous campaigns.  

 

2. Identifying Demographics

 

The guesswork that we talked about above with regard to setting ad budgets applies equally to something like targeting the right demographic. Sure, you have your candidate personas for each open position, but that doesn’t necessarily mean that you can rattle off the ages, locations, and other demographic information of everyone you’d like your ad to reach. Luckily, in just the same way that AI-empowered platforms can use the success levels of innumerable previous ad campaigns to suggest optimal budgets, they can also theoretically suggest potential demographic targets. This can be particularly critical to success on sites like Facebook, where the ad-targeting options are extremely granular; likewise on platforms like Google, for which your ability to provide content that’s highly relevant to your selected audience will determine how far you’re able to stretch your ads budget. Select the right audience, and your cost efficiency should improve measurably.   

 

3. Designing Ads

 

In much the some vein as the first two applications we identified, AI can also take some of the guesswork out of ad design. At first glance, this might not seem like anything to write home about, but the actual composition of a given ad can have a huge effect on its conversion potential. For instance, if an ad has too much text, most platforms will limit its reach, thereby limiting its value. Likewise, if an ad has a CTA that’s too small or too hard to notice, users will be less inclined to click on it (how the user feels about the landing page the CTA leads to is a whole different story). By giving recruiters some modicum of guidance on these points (based, again, on the results of previous campaigns), AI can help advertisers get the most out of their budget by maximizing each ad’s ability to grab—and make use of—your target persona’s attention.  

 

4. Reporting

 

Now, when it comes to reporting and data analysis, machine learning processes are often already at work in the final results, offering end-users insights that they otherwise might have difficulty arriving at by themselves. But what about when it comes to making that reporting possible in the first place? Technically, this is another area where we’ll be talking about a separate but related technology that sometimes falls under the general AI banner: RPA, or robotic process automation. This involves programming “robots” to perform rote tasks like collecting data off of niche websites and compiling that data into presentable form. Where “true” AI is meant to take over tasks that require some level of human understanding or decision making, RPA steps in where a human is technically required—even though that human is likely to be incredibly bored with the task at hand.

 

As recruitment marketing becomes more data driven, it’s likely that high-quality reporting will become an increasingly indispensable part of recruiters’ jobs. If, for instance, you’re trying to build out your talent pipeline with a series of awareness campaigns across niche channels that might not offer much in the way of reporting, you’ll still want to know how you’re doing and what steps you can take to improve. This AI-adjacent technology makes that possible.  

 

5. Automating Repetitive Tasks

 

Of course, RPA’s applications in recruitment marketing aren’t limited to automating reporting functionality. On the contrary, there’s a whole host of other applications for this emerging technology: scheduling posts on social media sites and other websites that might not have robust infrastructure for doing so, gathering data from ATS and recruitment marketing platforms in order to create visibility between critical recruiting functions, etc. We said above that we see emerging technologies mostly being aimed at improving human capacity and making recruiters smarter and more efficient—and this is why.

 

By helping to cut down on the time and effort it takes to perform simple tasks and create alignment between different recruiting functions, RPA can free up recruiters’ time for more human-centric, creative, and analytical tasks. Since HR is really all about people and human connections, the value of RPA, machine learning, AI, and other technologies that can facilitate those connections can and must have a value for recruiters that’s hard to quantify.

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