On Thursday, March 21st our Workday® team hosted an event in Los Angeles to celebrate the launch of Workday® 32. This event provided a platform for HRIS and Workday® professionals to mingle, share experiences, tips and tricks of the trade.
As machine learning and artificial intelligence (AI) are increasingly becoming a hot topic, GQR CEO, Steven Talbot joined event-goers to share his thoughts on these emerging technologies and their impact on the talent acquisition and HR communities in a Q&A session between himself and GQR SVP, Chris Hurley.
Continue reading to learn how Steven envisions AI fitting into HR and talent acquisition functions.
“Steven, how do you see AI changing HR and the Talent Acquisition landscape in the next three to five years?”
“In short, I see it evolving these functions in every way possible. Internally, we’re looking at the application of AI-based matching engines to surface the highest quality, qualified candidates for relevant positions.
As of April 2019, GQR has been around for ten years. So, we’ve spent the best part of a decade developing a massive pool of candidates and vacancies. Now, we’re at the point where it’s not possible, let alone efficient, to access that pool of data – the only viable way for us to do that is through machines.
Regarding those machines, we’ve got two different choices – automated Boolean searches or (going for the real deal) artificial intelligence. The way we see it, AI is much closer to replicating how talent professionals assess candidates and how they look at resumes. It’s not just about understanding keywords, but also pulling out the context and deeper layers of meaning.
The next element we’re looking at is trying to quantify motivation and personality data to create robust benchmarks. That will put us at a point where we can combine multiple layers of quantitative data that can be analyzed to identify different attributes in organizations. Again, looking at what we’ve done internally, the things we’ve learned about our organization in the last 12 months, since trialing some of our motivation and personality profiling tools, has completely altered my opinions of things as the information we’re uncovering is rooted in data rather than superficial, emotional responses.
So, those are a couple of ways I see AI and machine learning evolving these functions – by creating layers of automation and information that will enable people to thrive at what they’re best at. High-value elements such as assessing all of the human components these roles entail, understanding motivations, aspirations, desires, the determinations and the goals people, and candidates have.
Technical skillsets and current or past experiences, all of those items can be quantified and mapped into vector space, living in clusters as archetypes. Machines are now capable of doing that – particularly with all the latest advances in AI.”
“To gather all of this qualitative information, what will that process look like?”
“The qualitative information gathering still comes down to people. Some people have a lot of experience and natural intuition when it comes to understanding candidates and assessing what makes sense for them – particularly those who are successful in our space. That intuition is incredibly valuable, and I think AI is still a long way away from replicating that level of standard.
What we’re talking about here is more about the voluntary components – giving people the opportunity to come and discover more about their motivations. When talking to people about what they want, there are the classic superficial responses that mostly focus on external elements – getting paid more, working for more prestigious organizations, wanting more recognition and fancier titles. By in large these things aren’t what truly motivates people and, in our experience, they don’t motivate the best or highest performing individuals.
Part of building our motivational platform is developing a discovery process that equips our candidates, clients and teams with insight to better understand motivation from a quantitative basis. There are elements where we are great at intuitively understanding people, and there are other elements where we make assumptions rather than diving a layer deeper to understand people intrinsically.
The first iteration for us is to capture this information in a questionnaire, survey and a discovery platform. This allows people to go in and learn more about their personality, motivations, intelligence, curiosities, etc. In doing so, we can challenge many of the preconceptions people have regarding what they want, to not only help identify careers that deliver on their intellectual and technical capabilities but that also genuinely fulfill them and keep them motivated.
If candidates want to share this information and data with us so that it can better inform our searches and the organizations that we look at, that’s really the end game that we’re looking at.”
“In thinking about career mapping, how can this AI technology help build an automated roadmap to inform candidates of the skills they need to work on or develop to achieve their next, or longer term, aspirations?”
“If we’re looking at technical skills, within the AI platform we’ve built, candidates will only see roles they’re qualified for and ones they are nearly qualified for – it will allow them to apply for the positions they’re eligible for and serve up recommendations for the ones that are just out of reach.
This has many different applications, but candidate experience is one we’re excited about. A lot of applications come through from individuals who aren’t relevant or qualified. If these individuals know that from the outset, it has the possibility of creating a better experience by not creating false expectations.
It also educates candidates on the technical components that are missing from their skillset or background – recommending that an individual’s profile should be updated to include specific information or that particular skills or experiences need to be acquired to be eligible for specific roles.
We don’t believe in automating the entire life cycle. These tools really just tell us what questions to ask and inform us of possible correlations – it’s up to us to figure out why the correlations exist. This way, looking into the future, we could say, the best CFOs in a specific context tend to have these specific types of motivations and personality traits and this is how you develop them.”
“Where do you think skills gaps will emerge in this new landscape where AI and machine learning are playing a bigger role in organizations?”
“I think in this new landscape, becoming technically savvy is going to be really important. Then, as these things mature, they became more intuitive so that people who aren’t as technical can reap the same benefit. More obvious, but the adoption of AI and machine learning will drive demand for those core skill sets within data, data science, technology, etc.
I think the biggest challenge or skills gap that will be missing from organizations will be change management. I believe change will come about faster and fiercer than it ever has before. Having the best tools and technology is worthless if people aren’t using them – as people, we tend to respond with fear if we don’t understand something. So, having a robust change management strategy and people who are capable of managing change, I think, is probably the hidden part people don’t think about.”
“Do you foresee any issues or concerns around privacy or data protection?”
“We’re a global business, so we just had the fun opportunity of going through the whole GDPR process – data protection and privacy are very much fresh in our minds! The reality is that these things are essential, and people should retain and get more control and ownership over their data. Businesses are likely to respond conservatively to ensure compliance, and the potential downside of that could be a degradation of service for people because there’s less access – it’s possible we’ll see a rebalancing of this in the future.
The best opportunity to come out of the buzz around data protection and privacy that I can see is that people will have to stop relying on high-volume spamming and impersonalized services. This places a higher value on human interaction or intervention to understand other people. I think we can overcome the data compliance concerns, and this encourages more one-on-one conversations with people.”
“How has the Talent Acquisition landscape changed in the last ten years and what do you foresee (and are excited about) for the next five years?”
“When we formed GQR in 2009, there were hiring freezes everywhere. So, part of being a good recruiter required you to be efficient at producing massive volume. Opportunities were so rare then, which meant you had to address an enormous market to find a couple of gems. A decade ago, and definitely decades previously, the best recruiters were the ones who created opportunities because they behaved like machines. There were positive elements of that because it created volumes of opportunities – the downside being the quality of those opportunities.
Working in this way, meant there was less space for doing the enjoyable components of your role – aka speaking to people, having the capacity for creative-thinking, developing relationships, etc. AI platforms can do the work to reveal some of those hidden gems, allowing people to focus less on volume and more on the human element of the role. In the next five years, I hope that talent acquisition specialists can concentrate more on the human, creative items.”
“And the question on everyone’s mind, do you feel AI will replace Talent Acquisition and HR functions in the next ten years?”
“I don’t think it can. And I don’t think it should. It can replace a lot of the monotonous and repetitive work and cut a tremendous amount of time off of tasks like pulling data on retention and attrition – serving up these insights automatically. This means professionals in this space can spend less time researching and more time exerting influence and driving organizational change. I don’t see robots being the most effective managers – they can’t absorb the environment they’re in, or gather feedback, or generate relationships in the same way a human can. I can’t envision organizations that care about quality ever looking to robots to entirely replace their human labor force without simultaneously losing a massive amount of value.”
