How will AI reshape the fundamentals of recruiting?
I think AI will have the biggest impact on candidates, and recruiter experience. On the one hand, it will increase productivity enormously, which I believe is very necessary because I think it’s going to be harder and harder to recruit people. So, it’s a necessity to become more productive because there are less recruiters, there are more positions to fill, it is harder to fill them. AI is going to really help out increasing that productivity.
I also believe it will create a more transparent labor market through better data insights. And in the end, I believe that transparency will lead to more equal opportunity and equal pay.
How is AI reshaping the everyday life of HR leaders and recruiters?
Looking at the application process, like it was maybe, well, like it still is for many companies today, especially corporate companies, they have a job, they post it online, they wait until they get candidates. These candidates often go into a black box, don’t hear anything for a very long time, and maybe one person gets hired and then the others get rejected. So that’s the reality of today, which is totally unworkable.
So, a good example of AI and how that changes is the speed in which we can respond to candidates with a relevant answer. So our matching engines would immediately recognize, “Hey, this could be a good fit.” So why not have a chatbot reach out to that candidate? Start doing some vetting with the initial question, preparing the interviewee, and immediately, automatically book an interview with a recruiter.
Instead of a candidate having to wait for days, they can announce really quickly and the recruiter will more easily hire people with a better-prepared interview. So that’s an example.
Our technology can also help you automatically reach out, and identify good candidates in your talent pools or even in your employees for a particular position. And then you can automatically reach out to them. Because we all know recruiters are great in connecting with people, but they really don’t have the time to start doing 30 calls a day, right? So why not have a chatbot do that and make that a process that works? It will get you the candidates that you really want to hire, rather than the ones that want to work for you.
How do you help organizations overcome resistance or skepticism?
We typically see the following concerns: One concern around bias. There’s another concern, which is about legislation. Then there is a concern about the quality of the AI. And finally the question of how far do you want the AI to go before you actually interact with a human again?
The most important thing that we always say is like, “Look, the way we develop AI in a very responsible manner. We’ve got really good controls in place.” And when you talk about an AI model, you always talk about algorithms and data. So it’s never just an algorithm or just data. So we’ve got really good processes in place to make sure that our models are unbiased and we make sure that we are actually always compliant with not just the existing legislation, but also the upcoming legislation.
We also do blogs around this topic. So we’re really trying to lead the pack.
How do you help industry leaders drive progress in TA and recruiting?
Many of our customers are actually exhibitors on this trade show. What we basically solve for them are very difficult problems by delivering foundational technology or components that they can then build on top of.
What emerging trends do you see and how are you preparing to stay at the forefront of these developments?
We’ve recently released our first version of a CV parsing solution using large language models. And what we just started about half a year, three-quarters of a year ago, is something called Textkernel Labs. And in these Labs, what we’re doing is creating really quick prototypes that our customers can use. These prototypes are all large language model-based, and we’re focusing very much on the generative AI aspect of these large language models.
So the great thing is that our customers can really quickly test new functionality. If they like it, and if we see that it solves a big enough problem, we then productize that solution. So we’re starting to build this whole suite of AI-powered, problem solvers, if you like, you could call them agents, which our customers can use in co-pilots within their environments, or it can just solve very specific problems.
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Gerard joined Textkernel 16 years ago as a (late) founder and owner. Gerard initially defined the Go To Market Strategy and as part of the Executive Team extended responsibilities to a broad range of KPI’s critical for the success and growth of the company.