How AI is Revolutionizing The Recruitment and Hiring Process


The time-consuming and often frustrating world of recruiting and hiring could use some automation. Headhunters and interviewers alike should be spending their time looking for talent and analyzing skills, not struggling with making new tools and working the bugs out of old ones. Here are a few ways that artificial intelligence (AI) and machine learning are changing the recruiting and hiring processes.

Analyzing The Perfect Fit With Better Data

What does "the perfect fit" mean? There are a lot of arbitrary details and wishful thinking that get in the way of this answer, and data analytics can help clear your 

At the most basic level, the perfect candidate for a job usually fills these criteria:

  • Knowledge of the subject at a specific level.
  • Salary demands that fit the workload.
  • Ability and desire to execute given responsibilities.

Most of the other details depend on the business. You may have a company climate that requires a specific mindset, such as being able to work without supervision or under heavy supervision depending on your leadership style. Some companies may have fast and loose behavior rules, and some jobs inherently pull from talent pools that expect certain social norms and expectations. While being politically correct is at the front of most companies, it may not be a big deal in your team. Such specifics shouldn't be at the top of your search, but they matter. How can you add those kinds of details to a hiring expert's plate and expect fast, accurate results? Automation is the answer.

As your team looks through candidates, it's better to absorb as many applications that fit the most basic requirements for analysis. As AI and machine learning improve, you can sift through applications to figure out how candidates performed at other jobs, how they respond to your interview questions, and the kinds of jobs they're used to. After analyzing these profiles, you can hand the more specific questions to human recruiters and interviewers. They can ask questions that are more specific to the business, log the answers, and load those details into your hiring system to train the AI even better. Building these custom datasets for machine learning at the business level is a key part of the future of AI. Be ready to document and feed your system with onboarding statistics if you want to take charge of the future.


Guiding The Search And The Searchers

How do you find the perfect candidate in a sea of others? How can the perfect candidate--or even good enough candidates--find you in a sea of other businesses and projects?

At the moment, job searching is all about search engines. You put in a term, you get results about those terms. With a lot of search engines, you can get related searches with a series of well-programmed guesses.The trick behind Google and Bing is more than artificial intelligence. Machine learning professionals can both train AI to think about what a search actually means and force manual associations. There are even real people who rate how these results look from a human perspective, but based on the search company’s guidelines.
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What does forcing a manual association mean? If a search engine can't figure out that "help desk" usually means "technical support", a technician can manually set technical support searches to show up for help desk.

The work doesn't end there. In the world of Information Technology (IT), help desk is a specific type of technical support. Some people may specifically want to work in a place where they answer calls for general IT support, such as resetting passwords or unlocking accounts.They may also want to be part of a help desk for a company that treats help desk as a secretary or operator office geared towards fixing problems. Do you necessarily want technical support to show up for these jobs?

The World Of Hiring Is Changing For Humans And Machines Alike

Making relevant connections isn't easy, and not even Google or Bing have perfect ways of finding the answer. AI is still growing, and machine learning professionals can train their systems by using datasets based on what real job searchers and job creators think.That can be hard, since even real people have trouble agreeing to or understanding how certain terms are related. In the end, it's all about research, feeding new data, and making the process smarter every day. Speak with a business data professional to discuss other ways to improve the hiring process with new, useful technology.