Job Board Journalist: Should Job Boards Care About AI?

Peter Weddle

Advances in artificial intelligence and machine learning are now arriving at a breath-taking pace. That’s even if you discount the hype coming from the posers in the market. So, should job boards care? Some will, of course, say no – after all, they do nothing for recruitment advertising. Others, however, will say yes for one or both of two reasons: first, these technologies could become competitors for scare recruiting dollars, and second, they can significantly improve job board operations and thus their financial performance. The yeasayers are going to smoke the other guys.

As with everything else these days, there’s little agreement on what is or isn’t artificial intelligence. For the purposes of this post, I’m going to limit my definition to the subset of AI called machine learning. The IBM Journal of Research & Development defines it as “a field of computer science that gives computers the ability to learn without being explicitly programmed.” Said another way, machine learning occurs when a computer can – all by itself – use the data from its previous operations and other sources to learn something that is helpful in future operations. In humans, we call it the wisdom that’s acquired from experience.

So, what can machine wisdom do for job boards? It can help them improve both the job seeker’s and the recruiter’s experience.

Improving the Job Seeker’s Performance

There are already a number of machine learning-based chatbots on the market that can help candidates negotiate an employer’s application process. They include Olivia from Paradox.ai, Mya from MyaSystems and ARI from TextRecruit. Drawing on what they’ve learned serving previous applicants, these systems can answer candidate questions and guide them through the steps they will have to complete to be considered for an opening.

Job seekers have many questions before they even get to an employer’s site, however, and that’s where machine learning can differentiate a job board and build loyalty with its passive and active visitors. A chatbot career counselor, for example, could help a job seeker identify their options at any point in their career, assess their readiness for a job move or even give them a sense of how they would fare against others in the market at any point in time.

Machine learning could also be used to help job seekers with the one task they’re least able to do well – searching the job database to find openings that are appropriate for them. A system that learns from the experience of all job seekers as they search a job board’s database can help each job seeker do a more targeted and thorough examination of the openings in that database. In essence, it puts the lessons learned by every previous job seeker in the hands of each new one, helping them to avoid missing opportunities and wasting time on inappropriate ones.

Improving the Recruiter’s Experience

Recruiters have questions just as job seekers do, so here again machine learning-based chatbots can be helpful. They could be deployed, for example, to take an employer’s recruitment advertising budget, target demographics and geolocations and provide the optimal campaign – including job postings, featured postings, direct messaging and brand ads – based on the experience of previous advertisers. They could also provide guidance on which days and times of day to execute their campaign for the best results. And, they could do it all 24 hours a day, 7 days a week with a personable, human-like interface.

In addition, machine learning-based systems could help recruiters conduct a more fruitful search a job board’s resume database. Such systems could learn the variations in human expression and then use that data to make connections between disparate terms and identify overlapping competencies among different occupations. In effect, they could transform even the most inexperienced recruiter into a database search savant, ensuring they don’t miss potentially great candidates or waste their time on those who are unlikely to make the grade.

Yes, it’s true that future AI and machine learning based systems could pose a threat to job boards, but for the present and the foreseeable future, they’re more likely to be a competitive advantage, whether they’re developed in-house or introduced via a partnership. In either case, it’s important that job boards invest the time to understand the technology and explore how best to deal with it.

Food for thought,
Peter

P.S. To learn even more about how AI and machine learning could be helpful to your site, attend the TAtech Leadership Summit on AI & Machine Learning in Talent Acquisition. It’s coming up on February 12-13 in Scottsdale, Arizona.

TAprose and Job Board Journalist by Peter Weddle are brought to you by TAtech: The Association for Talent Acquisition Solutions.

Mark Your Calendars! TAtech’s 2018 events include:

• February 12-13, 2018 Scottsdale, Arizona USA: The TAtech Leadership Summit on AI/Machine Learning in Talent Acquisition – the only conference totally focused on the capabilities and impact of AI/ML/NLP in recruitment.

• March 13-14, 2018 Dublin, Ireland: TAtechEurope 2018 – the premier event for recruitment advertising and technology thought & business leaders in Europe, the Middle East and Africa.

• April 18-19, 2018 Las Vegas, Nevada USA: The TAtech Spring Congress & Meetup – a unique conference designed to maximize opportunities for B2B networking, trending topic discussions and the exploration of partnerships and business opportunities.

• June 5-6, 2018 Minneapolis, Minnesota USA: The TAtech Leadership Summit on Programmatic Ad Buying – the only conference totally focused on the technology and applications of programmatic ad buying by both publishers and advertisers.

• September 26-27, 2018 Bourbon Street New Orleans, Louisiana USA: The TAtech Fall Congress & World Job Board Forum – the only conference that brings together the global thought and business leaders of the TA technology industry.

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