AI Technology in Recruitment Automation
In my earlier blog I outlined the The Rise and Rumble of Intelligent Recruitment Automation. In this blog I go through the technologies that are used in Intelligent Recruitment Automation.
| Part 2 of the blog series on Intelligent Recruitment Automation
The recruitment process is one in which people play the key role more than in any other business domain. People that you potentially want to hire appreciate it when you resonate your human side as an individual and as a company as a whole. If we look at outreach for an example, the most effective outreach is still almost always one with a creative and personalized approach. But some activities are just very hard to do effectively with our human brains. Take processing millions of talent profiles, filtering big sets of data down to relevant information and remembering (or storing) data.
A key characteristic that useful intelligent automations share is richness of data. Data obviously helps to make better decisions, but just data doesn’t bring you anywhere. You need the right technologies and the right application of those technologies to make sense out of data and put it to work effectively. Below is an outline of the different technologies used in Intelligent Recruitment Automation so you can better apply these technologies yourself and to make a better informed decision in working with third party suppliers.
The buzz around AI
Artificial Intelligence (AI) is an umbrella term of several artificially intelligent technologies like Natural Language Processing (NLP), Machine Learning (ML) and Predictive Analytics. These technologies are generally aimed at mimicking the smart things we do with our human brains. Artificial means unnatural or made by human beings. Intelligence literally means the ability to learn, understand and make judgments based on reasoning.
In all industries AI is changing the way people create, grow and run businesses and support their work related activities. In the recruitment industry, AI is getting more and more popular and fortunately not only more popular but also more effective.
The increased accessibility of these technologies is driven by more clear applications for AI and an increased user friendliness of third party tools. But it is important to say that intelligent automation technologies do not always yield the desired results in recruitment. Highly generalized algorithms can be causing depersonalization and bias, the black box effect can cause machines doing things that are hard to trace back and control and some systems simply break because of technological and process dependencies.
Because AI is often overgeneralized in all the hype it is not commonly understood. Below I have given my description of the underlying technologies of AI which I hope can help to better understand what they are.
Data scraping
Data scraping is a general term referring to the extraction of data from external online sources. A common type of data scraping is (web) crawl…
