Category: Talent Technology

We’re hearing about artificial intelligence everywhere these days. It’s constantly in the news with developments like self-driving cars. If you’re interacting with a company through a website chat window, chances are you’re talking to a computer.

Now, applications of AI like machine learning and natural language processing are transforming talent acquisition. The right AI tools can streamline the sourcing and recruiting process, providing a better candidate experience and keeping your organization ahead in the tight talent landscape.

When you think about AI, think of artificial intelligence as the umbrella term. Put most simply, it is a branch of computer science that involves computers doing things normally done by people. Different applications of AI can play different roles throughout the talent acquisition process. To attract and hire the best talent, employers need a process with a strong candidate experience and a partner with the expertise to identify the best person for a job. To accomplish that, it’s important to understand the different applications of AI and how they can optimize every step of a candidate’s journey.

Machine Learning

Machine learning is the next step in artificial intelligence, where computers are able to learn how to do something without being specifically programmed how to do that one thing. Machine learning develops algorithms, which are procedures or processes for solving problems.

Examples of machine learning are everywhere. Email spam filters learn how to identify spam depending on context and subject. Facebook’s photo tagging algorithm learns to recognize faces based on previous tags.

Machine learning can have impacts throughout the talent acquisition process. When applied to the sourcing process, machine learning technology can identify active and passive candidates within seconds of a job requisition being posted. There is so much publicly available information about which candidates are likely to join a company or leave their current job.  This saves time for the recruiter and can provide valuable information including how likely a person is to leave their current job or how well their past experience meets the job description.

When it comes to the application process, machine learning technology can help create a personalized experience for the candidate. By analyzing the information a candidate shares online, the technology can provide them with job openings that fit their interests and experience.

Natural Language Processing

Natural language processing (NLP) is a way for computers to understand and interpret human language. It’s particularly difficult because human language is never as uniform and simple as code. When people talk and write, they have regional differences, use metaphors and display emotional cues that impact the meaning of what they’re saying.  When people interact with AI, natural language processing is important for creating a positive interaction. A chatbot needs to understand what people are saying to respond properly. A virtual assistant needs to process and respond to the speech of people with different accents and dialects.

NLP should play a large role throughout talent acquisition. Starting with the process of crafting job descriptions, NLP can help write a description that attracts more strong candidates. In sourcing, an AI tool needs to have NLP to search for quality candidates and understand the information they share through social media and other online resumes.

Additionally, chatbots can play a role in the application process, reducing the need for long application forms, and perform some candidate prescreening. This can free up more time for recruiters, but it can also improve the candidate experience. Business Insider reports that people are growing more comfortable with, and in some cases even prefer, interacting with a chatbot over another person. To accomplish this and provide a good candidate experience, natural language processing is necessary.

Predictive Analytics

Predictive analytics finds patterns in data and then uses those patterns or models to attempt to predict the future. Predictive analytics can’t tell you what will happen, but it shows what is likely to happen based on past trends. It works by taking large sets of data and then applying artificial intelligence technology to see trends and patterns that would be difficult, time-consuming or possibly impossible for humans to accomplish alone.

Predictive analytics can make an impact throughout the entire hiring process. The ability to make predictions and suggest a certain course of action can give employers a better understanding of factors like how long it will take to fill a position or what kind of salary and benefits package will be required to secure a successful candidate.

When it comes to candidate success, predictive analytics can use candidate assessments to predict how successful a person will be in a given role and even how long they may stay with the company. Armed with that information, organizations can make better hiring decisions.

Robotic Processing Automation

Robotic processing automation (RPA) is the process of using software to automate simple business tasks. According to the Wall Street Journal, RPA works by following simple rules to mimic the way that people interact with different business applications. By doing this, the technology is able to automate manual processes that would be tedious or time consuming for a worker.

RPA can automate many of the administrative tasks that can take up a lot of a recruiter’s time. When a job requisition opens, RPA can automate the keystrokes required to submit information and post jobs to sources that require manual submission. Additionally, it can automate the distribution of job openings notifications to community organizations. This can ensure a job posting reaches as many candidates as possible and save recruiters’ time for more valuable tasks, like engaging with the strongest candidates.

RPA can also play a role after a candidate accepts an offer. The technology can automate many of the post-offer activities that clear candidates for hire, like collecting more information from candidates, submitting forms and processing pre-employment checks.

To learn more about how PeopleScout is using AI in Affinix and how it can help you get ahead, schedule a candidate experience evaluation.

Today, PeopleScout released Affinix™, a proprietary technology solution designed to help its clients connect with the best talent faster through a candidate experience that mimics the simplicity and usability of the best consumer websites.

Affinix is a mobile-first, cloud-based platform that creates a consumer-like candidate experience and streamlines the sourcing process. Embedded within PeopleScout’s talent solutions, Affinix delivers speed and scalability while leveraging artificial intelligence, recruitment marketing, machine learning, predictive analytics and other emerging technologies with one point ATS and VMS integration and single sign-on.

Affinix simplifies the fragmented talent acquisition technology ecosystem by deploying best-in-class technology to create a seamless experience for both the candidate and hiring manager. Built on Amazon Web Services (AWS) Cloud infrastructure for stability, scalability and security and engineered for flexibility and future enhancement – Affinix integrates with ATS and VMS technology to support compliance and provide insights across a client’s total workforce.

Affinix balances human expertise and automation, enabling PeopleScout to enhance talent intelligence by better leveraging the power of data gleaned from engaging millions of candidates and contingent associates every year.

Affinix boasts powerful capabilities including:

• Artificial intelligence and other emerging technologies that extend across the platform, streamlining the sourcing and screening process and identifying the top talent quickly. Affinix sources strong passive and active candidates within seconds of an open job requisition.
• Digital and social recruitment marketing to reach candidates where they are and enhance an organization’s employer brand through customized ads, optimized job descriptions, personalized landing pages, career portals and more. Recruitment marketing elevates job postings with robust content and campaign management.
• A mobile-first application process designed to mimic the simplicity and usability of the best consumer websites. The platform is mobile-first for both the candidate and the hiring manager, ensuring engagement from any device at any time throughout the application, scheduling and screening process.
• Video interviews and digital assessments that cut days off the hiring process by using data analytics and machine learning to identify and rank the best candidates. This simplifies the screening process, allowing PeopleScout to share top candidates with hiring managers faster.
• Predictive analytics and machine learning extend across the hiring process to enable PeopleScout to better understand top talent behaviors and predict factors such as cultural fit, willingness to change companies and future tenure potential. Over time, this improves hiring speed and accuracy by more quickly identifying, screening and assessing top talent.

To learn more about how Affinix will add speed, scalability and personalization to our talent solutions, please read our press release and watch our new PeopleScout Now to Next and Meet Affinix videos.

• Read our Affinix press release
• Visit our homepage to watch our PeopleScout Now to Next story
• Visit our Affinix page to watch our Meet Affinix video

Data analytics is a valuable tool for talent acquisition professionals, as specific data can be used to predict things like the success of a candidate or the time it will take to fill a position. It can also help build the type of candidate-centered hiring experience that’s necessary to stay competitive in this tight talent market.

However, sophisticated artificial intelligence and massive sets of data can make data analytics seem overwhelming and confusing. In order to understand what this technology can do, you need to have a basic understanding of what goes into the process. We’ve put together some definitions for the terms you need to know to start to take advantage of data analytics in your organization.

Big Data:

Big data is a simple sounding term, but experts say defining it can be surprisingly complicated. Forbes argues there could be as many as 12 different ways to interpret the phrase. Some of these explanations are practical like, “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.” Other definitions focus on the forces that drive the use of these large data sets like, “a new attitude by businesses, non-profits, government agencies, and individuals that combining data from multiple sources could lead to better decisions.”

When we’re talking about how data can be used in recruiting, think about big data as the raw material, the large sets of information, that can be used to make predictions and inferences about candidates, employees and staffing needs.

Small Data:

Data doesn’t have to be big to be useful. For many organizations, smaller sets of internal data can provide critical insights. According to the Harvard Business Review, small data is data of a manageable size that is already somewhat organized, and it mostly comes from your own data systems. Most organizations have been using small data for years.

Data Analytics:

Big data and small data are useless unless you have a way to analyze it; that’s where data analytics comes in. An article in the International Journal of Information Management calls data analytics the “efficient processes to turn high volumes of fast-moving and diverse data into meaningful insights.” There are a few types of data analytics – descriptive, predictive and prescriptive.

Descriptive Analytics:

Descriptive analytics describe what happened in the past. Descriptive analytics software combs through large or small data sets and produces useful information about trends and patterns. According to Information Week, the purpose of descriptive analytics is simply to summarize what happened.

Predictive Analytics:

Predictive analytics is the next step. It uses data to find patterns and then uses those models to attempt to predict the future. Predictive analytics can’t tell you what will happen, but it shows what is likely to happen based on past trends.

Another way to look at it, according to PC Magazine, is that predictive analytics is something you can do with AI, machine learning and deep learning. Predictive analytics takes large sets of data and then applies these different forms of technology to see trends and patterns that would be difficult, time-consuming or possibly impossible for humans to accomplish alone.

Prescriptive Analytics:

Prescriptive analytics is a type of predictive analytics that goes a step further. Rather than just predicting what could happen in the future, prescriptive analytics goes so far as to recommend one or more courses of action, as well as the likely outcomes of those decisions, according to Information Week.

Not only can prescriptive analytics predict what is likely to happen, it can also predict the best course of action for a person to take to get a particular outcome. Think about prescriptive analytics as predictive analytics with the ability to make a decision.

AI, Machine Learning and Deep Learning:

Artificial intelligence is an umbrella term. Put most simply, it is a branch of computer science that involves computers doing things normally done by people.

We use AI to perform data analysis because having humans process and analyze all the information would be overwhelming, time-consuming, expensive and in most cases, nearly impossible.

Machine learning and deep learning are the next steps in artificial intelligence, where computers are able to learn how to do something without being specifically programmed how to do that one thing. Machine learning develops algorithms, which are procedures or processes for solving problems. Deep learning is a type of AI that mimics the way the human brain works.

This is especially important to predictive and prescriptive analytics, where the goal is to look beyond what happened in the past and find ways to apply it to what might happen in the future.

What Can Data Analytics Do?

Data analytics technology has applications throughout the entire sourcing and recruiting process. The ability to make predictions and suggest a certain course of action can give employers a better understanding of factors like how long it will take to fill a position or what kind of salary and benefits package will be required to secure a successful candidate. When it comes to candidate success, predictive analytics can use candidate assessments to predict how successful a person will be in a given role and even how long they may stay with the company. Armed with that information, organizations can make better hiring decisions.

If you’re in the business of working with people, at first, data analytics can seem cold, but when applied correctly, it can actually make the hiring process more personal. Candidates leave data behind whenever they go online, log in to a social media profile or make Google search. Marketers are already tapping into that information to personalize and target content. Talent acquisition professionals can make that same data work to customize the sourcing and recruiting process.

We’ll have more on what data analytics can do for the industry and what issues to watch for on the blog in the coming weeks. To learn more about AI, machine learning and deep learning, read our blog post about what they mean to recruiting.

Data analytics is one of the top seven tech trends impacting the talent acquisition industry. To learn more about some of the possible impacts and the six other trends, download our ebook: Seven Tech Trends Shaping the Talent Landscape.

In popular culture, artificial intelligence is the stuff of science fiction from HAL 9000 to Wall-E. AI has also worked its way into our daily lives with virtual assistants like Siri and Alexa. Technological advancements like driverless cars make headlines. Now, artificial intelligence has the potential to transform the hiring process.

Defining the Terms

If you aren’t entrenched in technology, trying to understand the mechanisms behind AI can be headache-inducing.

Artificial intelligence: Artificial intelligence is the umbrella term. Put most simply, it is a branch of computer science that involves computers doing things normally done by people.

The definition of exactly what we, as a culture, call artificial intelligence changes over time. For example, Microsoft Excel’s ability to complete mathematical calculations doesn’t seem like artificial intelligence in 2017, but when it came out in 1985, that’s what it felt like.

Now, virtual assistant programs like Siri and Alexa feel like artificial intelligence because we can talk to them and they respond in the most human way we can expect from computers. Looking forward though, it’s likely the artificial intelligence of the future will make these current iterations look simple.

Machine Learning: Machine learning is the next step in artificial intelligence, where computers are able to learn how to do something without being specifically programmed how to do that one thing. Machine learning develops algorithms, which are procedures or processes for solving problems.

Examples of machine learning are everywhere. Email spam filters learn how to identify spam depending on context and subject. Facebook’s photo tagging algorithm learns to recognize faces based on previous tags.

Machine learning has a lot of potential. According to the Harvard Business Review, corporate investment in artificial intelligence is expected to triple in 2017, and experts expect the talent acquisition industry to see major impacts from machine learning advancements.

Deep Learning: Deep learning is one step deeper in AI. It’s a subsection of machine learning that uses computers designed to mimic the way the human brain works.

According to Forbes, deep learning is already common in everyday life. It’s used by Google in image and voice recognition. Netflix uses it to recommend shows, and Amazon uses it to predict your purchases.

Predictive analytics: Predictive analytics uses data to find patterns and then uses those patterns to attempt to predict the future. Another way to look at it, according to PC Magazine, is that predictive analytics is something you can do with AI, machine learning and deep learning. Predictive analytics takes large sets of data and then applies these different forms of technology to see trends and patterns that would be difficult, time-consuming or possibly impossible for humans to accomplish alone.

What’s Possible Now?

These technological advancements are already making an impact on the talent acquisition industry. New technology solutions are developed every day on the cutting edge of what AI can accomplish.

One major application for AI identified by Harvard Business Review is sifting through job applications and selecting the best candidates for the next step in the process. Applicant tracking systems already search through resumes, but AI advances can make them better, moving beyond keyword searches. The impact of AI can start even earlier in the hiring process, like using AI to source candidates by searching through social media profiles.

AI helps remove bias in the hiring process. AI structured interviews also help recruiters and hiring managers focus on relevant skills, and AI interview analysis uses data analytics to predict how successful a candidate will be at a position. TLNT further explores the ability of technology to sort through resumes, looking only at relevant information rather than social cues that may sway recruiters or hiring managers.

AI improves the candidate experience as it becomes a part of the entire hiring process. Studies already show that people prefer chatbots over humans for customer relations. That could apply to multiple stages, from applications to initial interviews and scheduling. AI also makes sure candidates don’t get lost in the process, an issue that’s frustrating for candidates and time consuming for recruiters. At PeopleScout, we use AI technology that allows candidates to feel like they’re talking with a real person and lets them to apply through a conversation instead of a long, impersonal process.

The interview process is also seeing the impact of artificial intelligence. Digital interviewing can allow for live or on-demand video interviews and can also use artificial intelligence to provide reports that analyze verbal response, intonation and nonverbal communication.

Where Do We Go Next?

The question of what is possible in the talent acquisition industry through artificial intelligence is the most difficult question to answer because the possibilities are constantly evolving.

On a different level, AI will change the jobs the talent acquisition industry needs to fill. AI skills will become indispensable. Transportation companies are already looking for workers to maintain driverless fleets, rather than drivers to sit behind the wheel. New industries and job titles demanding new skill sets will emerge and demand innovation from talent acquisition.

AI is one of the top trends impacting the talent acquisition industry. To learn more about how it could transform recruiting, check out our AI in Recruiting Handbook for Talent Acquisition Leaders.

The shifting technology landscape can seem overwhelming, but new developments happening every day are taking over the recruiting industry one step at a time. Smart companies need to stay on top of the trends and make changes at the right time. Here’s seven we’re watching.

1. Artificial intelligence

Artificial intelligence has been the topic of books and movies for decades, but AI is ubiquitous in our society and now transforming the recruiting industry. According to the Society for Human Resource Management, it isn’t a case of robots putting humans out of work. Instead, recruiters use technology to make themselves more efficient and effective.

AI can sort through resumes, narrowing a large pool of applicants down to the top candidates, but its role in recruiting is expanding to even more steps in the process. Some AI programs can conduct interviews, and facial recognition technology and voice analysis can help recruiters understand candidates’ emotional intelligence and truthfulness by analyzing facial expressions and vocal tones in video and phone interviews.

 2. Deep learning

Think about deep learning as AI on steroids. While some AI can sound like humans, deep learning seeks to create computers that can “think” like humans. IBM’s Watson already uses some deep learning technology.

What could this mean for recruiting? Watson is already tackling the challenge of matching patients with clinical trials in a healthcare setting. Imagine if similar technology could be used to match candidates with job openings, whether the right person is actively job-hunting or not. TLNT identifies that as a real possibility. Deep learning also gives computers the ability to recognize patterns humans might miss. Applied to recruiting, that pattern recognition could mean the ability to make better hiring decisions.

3. Virtual Assistants

Artificial intelligence is already invading our lives. Virtual assistants leaped from our phones with software like Apple’s Siri and Microsoft’s Cortana to our kitchen counters with the growing popularity of devices like Amazon’s Alexa and Google Home. Business Insider even reports that Apple is working on its own device, dubbed an “Amazon Echo killer,” and that the trend is here to stay.

Reports that Amazon alone has sold more than 11 million Echo devices show that people are growing more comfortable than ever interacting with virtual assistants. Interviews could happen between Alexa and a candidate without anyone even picking up the phone according to ERE. Virtual assistants could take a role even earlier in the process, feeding candidates information about positions and companies. If AI could handle this piece of candidate engagement, it could free up time for recruiters to focus more on the late stages of the process, like crafting an offer letter.

4. Predictive analytics

Predictive analytics may be the closest recruiters can get to truly looking into the future. The field runs information about the past through data analysis and statistical techniques to make predictions about the future. According to PC Magazine, predictive analytics has already made a massive impact on customer relationship management, but the implications go even further.

Some hospitals already use predictive analytics to forecast patient demand and optimize staffing, like NorthShore University. If you consider the ability to predict staffing needs a “macro” application of predictive analytics, the technology has more “micro” implications for recruiters as well. According to this podcast featuring Greta Roberts, the co-founder and CEO of Talent Analytics, the technology can predict how a candidate will perform in a job including factors like making sales numbers, staying at a company long term or even driving without accidents.

5. Neuroscience

The talent acquisition industry has used the science of psychology in recruiting for decades. Consider neuroscience the next step. The well-known Myers Briggs personality test is 75-years-old. While neuroscience profiles don’t have the same place in pop culture, they can tell recruiters even more about a candidate.

Neuroscience profiles can provide a more objective look into a candidate’s strengths and weaknesses over a psychological test. When it comes to finding candidates, lessons from neuroscience can even help your recruitment messaging reach the right people.

6. Internet of Things

The internet as we know it, the world wide web, is only 25 years old, and in just more than a generation, it’s gone from dial-up to the dial on your thermostat. The Internet of Things, or the interconnected computers inside everyday objects, makes us more connected than ever before.

For recruiters, this means the types of in-demand jobs and the candidates needed to fill them are changing rapidly, according to Recruiting Daily, as programming skills like HTML5, iOS and Android represent huge growth. The growing interconnectedness also means candidates expect more of their life to take place online but off a traditional computer – including their job hunt.

7. Driverless cars

The most well-known “thing” on the Internet of Things might be the self-driving car, with both Google and Uber regularly making the news for their tests and industry experts predicting they’ll be common and affordable by 2025.

The change will impact more than your commute, as more than 3.5 million people in America making their living as truck drivers. Recruiters focused on hiring truck drivers need to be prepared for the change because as the industry transforms, they need to find the candidates who can be successful in the shift. While the demand for drivers may go down, the search for candidates who can build and maintain driverless fleets will provide a new challenge.

With so many changes on the horizon, what is PeopleScout doing to be a leader in innovation and technology? Read more about the strategic steps we’re taking to set our clients up for success.If you’d like to learn more about these seven trends and how to evaluate the question of when to buy into the latest trends to minimize risk and stay ahead of the competition, download our ebook: Seven Tech Trends Shaping the Talent Landscape.