The Revolution Will Begin Eventually (Maybe): AI and Recruiting

Here’s the latest prediction concerning the future of AI, based on a survey of the leading researchers, conducted by Oxford University: the experts predict there’s a 50 percent chance that AI will be better than humans at more or less everything in about 45 years. Apparently they’ve learned from Nostradamus, who made a lot of predictions that have supposedly all come true. The secret is to be vague on the details and timing.

Making a specific prediction is usually a bad idea. That’s why the Mayans lost so much credibility when the world didn’t end in 2012. That’s especially true in technology. Video phones were first introduced in 1964 and were supposed to become common in a few years. Flying cars were supposed to have become commonplace long before now. The same goes for AI. The first practical demonstration of it occurred in 1956. Then it was predicted that we were 40-50 years away from AI becoming better than humans. And as the survey mentioned above proves,  that prediction turned out to be true; we are 40-50 years away from AI becoming better than humans. In 40-50 years it may still be true.

The HR and recruiting technology world is abuzz with predictions about how AI will revolutionize the space. Predictions run the gamut from recruiters being completely replaced to not a huge change occurring. So what are likely to see? First, we should be clear that there’s no such thing as AI. True AI means a software system having the flexible, general-purpose intelligence of the type which allows an individual to learn to complete a vast range of tasks. That does not exist anywhere. No software available today is capable of mimicking even the intelligence of a three-year old. What is labelled AI is machine learning — the ability of certain software products to change and improve at what they do when exposed to new data, without being explicitly programmed. Of course the term “AI” sounds so much better than “machine learning.” The 2001 movie titled “AI” likely wouldn’t have been much of a success if it had been titled “machine learning.” Maybe a movie about Wall-e going to college?

Predictions are that AI systems will outperform humans in the next 10 years in tasks such as translating languages, writing high school essays, and driving trucks. These aren’t exactly profound predictions since the technologies have already been demonstrated. What these tasks have in common is that the technology focuses on a narrowly defined task. Make no mistake: it’s still a very complex undertaking to get a truck from one place to another, but it’s well understood what’s needed. So it will be for machine learning and recruiting.


Recruiting will be affected by AI through “multiplicity” — diverse groups of people and machines working together to solve problems. An article in the Wall Street Journal mentions that solving complex problems requires training machine learning algorithms using data from a varied group of humans to demonstrate appropriate responses to varying situations. This is how the algorithms for a driverless car are trained. In recruiting, the two areas that would benefit the most from machine learning are predicting a candidate’s motivation and fit.

Predicting Motivation

Evaluating motivation is about improving sourcing, which is typically a low-yield, labor-intensive business. Every recruiter knows that reaching out to candidates who have not applied often produces few results because of low response rates. However, a machine learning system can identify people who are more likely to to consider a solicitation for a job; in other words, those who are more motivated to change jobs or accept a new one. There’s an abundance of data on social networks and other places that can be tapped for this purpose. For example, Google’s Timeline tracks your every move (check it out) and can be used to accurately determine a person’s commute. A candidate with a long commute is more likely to respond to a solicitation than someone who has a short one, especially if the former travels through heavy traffic.

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Combine a candidate’s travel information with other data — such as remarks posted on social networks that can be indicative of their engagement levels in their current job — and you’ll very likely boost your response rate. Currently Google doesn’t let anyone see other people’s timeline, but that’s by no means guaranteed to continue. The cell-phone providers all have the same data as well and already sell it for targeted marketing campaigns.  

Evaluating Fit

Knowing how well a candidate may fit in is another aspect of recruiting that will benefit from AI. The challenge today is one of defining the culture of the organization, which may not be the same everywhere. It’s easier to know if a candidate will fit in with a group using data from social networks. Using the profiles of people in a team, it’s possible to predict if a person will make friends with them. That’s a good proxy for evaluating fit and eliminates any need to define the culture.

Machine learning can also predict what the impact of adding a person is to a team’s productivity. It may substantially increase the team’s overall productivity or have no effect at all. It may even be negative. This can also be turned around to figure out what kind of a candidate should be hired so that a team can achieve certain goals.

The Revolution

The impact of revolutions can take a long time to be realized, and they often are not what anyone expected. There are more recruiters today than before the creation of job boards, applicant tracking systems, and social networks — all of which were predicted to eliminate the need for recruiters. AI will change the work recruiters do but not eliminate the need. Given the combination of a recruitment marketing system, an ATS, and assessments, it’s already possible to automate every step of the recruiting process. That recruiters haven’t suffered the fate of the dinosaurs isn’t because the work can’t be automated, but because it fundamentally involves human interaction. AI can help, but only up to a point. The algorithms need continuous input for improvement. The WSJ article also mentions that if people stopped providing input, these systems would quickly become outdated and would deteriorate. Of course, that may not be true in 40-50 years.

Raghav Singh, director of analytics at Korn Ferry Futurestep, has developed and launched multiple software products and held leadership positions at several major recruiting technology vendors. His career has included work as a consultant on enterprise HR systems and as a recruiting and HRIT leader at several Fortune 500 companies. Opinions expressed here are his own.


6 Comments on “The Revolution Will Begin Eventually (Maybe): AI and Recruiting

  1. Hmmm Raghav not so sure if your reading of AI and recruitment future totally right. Let’s face it at least 50 to 70% of what takes place in the initial stages and all the way up to 1st interview and including all the practical co-ordination is fairly straight forward and in AI/cognitive sense piece of cake. Just on that basis many in the industry (ask the usual.suspects) are in agreement that the minute various chatbots and co-ordination systems able to they will be able to replace many a mundane recruitment tasks. Over time and with the use of the ‘acceleration of return’ principles systems will become cleverer and cleverer. Right now for instance a range of 1st generation automated systems for corporate recruitment are undergoing.trial (this is 1st hand knowledge) and if these just able to do 50% of what they intended to that will render 25% of work done by humans in corporate recruitment redundant. The future is far closer than anyone think and able to comprehend. As for past predictions not coming true, perhaps, but we are with current developments into territory like never seen before.

  2. Thanks Rahgav. Enjoyed the perspective. Lots of thoughts to discuss – agree and disagree but this one statement caught my eye more than others “There are more recruiters today than before the creation of job boards, applicant tracking systems, and social networks —”. I’m not confident I know that statement to be true given lots of variables but I don’t know it isn’t either and so if we assume it is true….that recruiting requires more labor for each hire than it did 25 years ago, it seems to me that we are in need of a deeper analysis as to why more efficiencies have not been gained given the stacks upon stacks of technology solutions employers are paying for today. One possibility is that our culture is so set to human interaction controlling the employer’s decision that we’ve failed to test the viability of automated decisions that favor the candidate being in charge. I think I’ll look for some data

    1. I tend to agree that there’s a cultural bias towards applying human interaction to a process that doesn’t always require it. It’s been known since at least the 1940s that applying a regression model to predict job performance is a far more effective solution to making hiring decisions than the typical hiring process. Yet we persist in hiring people with mechanisms that are far from optimal. I expect that machine learning systems will incrementally improve hiring activities, likely starting with jobs that require limited skills – truck drivers, customer service and retail for example. Situations where it is difficult to justify the value of any human interaction with the candidate.

  3. Hi Raghav,

    Good to hear from you. I appreciate you making the distinction between AI “Artificial Intelligence” and Machine Learning aka AS or “Artificial Stupidity”. While I share your skepticism on the development of true AI any time soon, I don’t think true AI is needed to make a significant difference in many areas, including recruiting.
    The following tools (in non-sourcing areas) exist RIGHT NOW:
    1) Chatbots which largely eliminate the need for scheduler/coordinators, and which (with a little tweaking) could provide a very effective means of creating, developing, and maintaining talent communities/pipelines

    2) SW which quickly and automatically evaluates “hard skills” against a job description using semantic search, greatly reducing the need for recruiters’ eyeballing resumes. (While not a trivial problem, I see nothing which would prevent this from being adapted into a powerful SOURCING ENGINE.)

    3) SW which quantifies “soft skills” against optimal organizational and job profiles based on 3+M analyzed profiles Not LI Profiles).

    These tools eliminate much of the routine drudge-work that we do, leaving us to do the higher-touch, higher value added recruiting work, like mentoring, counselling, streamlining/improving hiring processes, and CLOSING. All well and good EXCEPT:
    1) When you take away the “grunt stuff”- there isn’t all that much of the “good stuff” left, and often companies ether think they don’t need someone to do it AT ALL, or think they can do it themselves.
    2) In all probability, many recruiters who are very good at the “grunt stuff” probably can’t do the “good stuff” very well.

    So will most of our jobs go away soon? I don’t think so, and here’s why:
    Our good old friend the GAFIS Principle! The GAFIS Principle states that while companies usually say they are logical, profit-maximizing entities, in reality the great majority of important decisions are made due to the Greed, Arrogance, Fear, Ignorance/Incompetence, and//or Stupidity of the chief decision-makers. How does that translate to our discussion? Well, as long as a large, bloated recruiting organization is seen as a sign of importance rather than an indicator of inefficiency/waste, there will be incentives for organizations (with the money) to avoid automating (“through-sourcing”), eliminating inefficiencies which reduce headcount (“no-sourcing”), or sending it away to a lower-cost venue (“out-sourcing”). At a very deep level, it’s a lot easier for an SVP to show they have “big hands” with a stable full of eager young recruiting “bros” and “sisses” than it does with an invisible SW application.Quite often, this SVP and other high-level decision-makers justify their need for huge numbers of onsite people using some vague, pseudo-humanistic statements like “it improves productivity” or “there’s a clear need for human-human interaction” without either clearly defining what they mean or showing objective, peer-reviewed studies which validate their point(s). (Not quite “alternative facts” but headed in that general direction…)

    In summary: if I were a young person thinking about recruiting today, I’d think about learning the things which really can’t be “transourced” (no-sourced, through-sourced, and out-sourced) for the near-mid-term future, particularly: LEARNING TO CLOSE.


    Keith Halperin, 415.672.7326

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