Author: Alistair Cox, Chief Executive, Hays plc
By 2020, it’s predicted that businesses across the world will spend a combined $47bn on artificial intelligence (AI), and it appears that every aspect of our lives – from shopping and leisure to work and personal finance – will be transformed as machines leverage data to provide us with tailored, personalised services at scale.
With such a wide-ranging impact, it’s only natural to expect that AI will also revolutionise the way that organisations search for and acquire talent. Automated technology can analyse the mountains of data across organisations and the wider job market, translating it into easily digestible formats which will ultimately help humans make better decisions and spend their time on higher value, higher impact tasks.
So what effect will this have on the way we source talent in the future? I see at least three areas of evolution:
AI will bring more efficient and fairer candidate screening
One of AI’s main benefits is that it allows processes to be completed at a rate and scale that is simply unachievable by humans. Therefore, I expect two particular subsets of AI to become widely adopted over the next few years:
- Natural Language Processing (NLP) – transforming text into structured, easily digestible data – it effectively lets a computer read language
- Natural Language Generation (NLG) – the reverse of NLP, transforming structured data into text – letting a computer write language
Both NLP and NLG have enormous potential in talent acquisition. The digital age has brought huge benefit to our industry. But it has also brought massive quantities of data that currently is handled largely manually. A simple job ad for example can elicit tens of thousands of responses, many of which may be wholly inappropriate applications, yet all must be screened in order to find the real stars. Straightforward yet often time-consuming tasks such as CV screening, drafting job descriptions and communication with candidates could take a matter of seconds using this new technology. That should free up the human experts to spend more time on the valuable role of working with the very best candidates on a personal basis – in effect putting the relationship back into the role, as that’s the crucial element required for success.
Here at Hays, we’re already utilising NLG AI in the candidate screening process with an external expert organisation in this area, and the early signs are that it works. This platform has certainly accelerated the shortlisting process for us, and it also enables our recruitment consultants to concentrate on assessing the individual candidates outlined by the technology to be the best fit for the role in hand, rather than pouring over a wider pool of hundreds of thousands who may not be suitable. Our consultants are freed up to concentrate on building relationships with their clients and candidates – something that definitely can’t be done via AI.
There’s a lot of emphasis today on eliminating bias in the recruitment process. I welcome that, but it is scientifically proven that unconscious bias can still exist even when great efforts are made to eliminate the more visible routes to bias. It’s interesting to see how, as well as increasing efficiency, automating sections of the screening phase can also lead to a decrease in subconscious hiring bias. After all, if the AI system is instructed to compile a shortlist by focusing solely on data around a candidate’s role suitability, it will by definition ignore demographic information such as age, race, and sex. There are pitfalls to beware of though, and it would be wrong to assume AI is inherently fairer. It’s equally possible for machine learning to automate an existing bias, and the “black box” nature of many AI techniques could mean you’re unaware of this even happening. For example, if the historical data used to train an AI screening algorithm had an inherent age bias in it, removing age data alone from the input files might not fix the problem as there are cases where AI can infer age based on first names and their popularity over time. As with any new technology, there’s a lot more to it once you start to get involved and develop it and beware the unintended consequences. Never forget that AI is only as good as the data you feed it, and to compile your ideal shortlist of candidates, you’ll need to provide comprehensive criteria on which qualifications, previous experience and specific skills would appeal to you.
AI will ensure a better candidate fit
AI could also enable businesses to emphasise candidate fit like never before, which should ultimately result in more successful, long-lasting hires. After all, we see that the number one cause for an unsatisfactory hire is a lack of cultural fit between employee and organisation.
This is already happening to a certain extent with skills – online job boards increasingly use algorithms to match their community of candidates to available roles. For example, a LinkedIn job posting will rank candidates by matching the information listed on their profiles to those in the job description. However, as AI (and the data collected by businesses) becomes more sophisticated, we can expect to see these algorithms become more complex and take preferences and fit into account, not just technical capability to fill a role.
Individual’s attitudes to benefits, company culture and salary preferences amongst other aspects can be assessed through survey metrics. Machines can scour the jobs market and process answers via an algorithm to provide businesses with a shortlist of candidates that match their organisation’s persona. Candidates themselves will also feel the effects, and you can expect vastly more accurate job recommendations and a more tailored outreach from prospective employers, honed even more closely to your preferences.
However, the human element will still be required, probably even more so, since it remains incredibly difficult for any machine to analyse the soft skills that remain so crucial to modern business. I’m yet to see an algorithm that can read things like humour, temperament or enthusiasm as effectively as a person can. And let’s not forget that ultimately human oversight is still required to compile criteria – I certainly wouldn’t want a machine deciding the persona of my business, and I don’t think it would do a particularly good job yet.
AI will help safeguard future talent pipelines
Aside from helping businesses hire the right candidates today, I believe AI will play a significant role in enabling organisations to retain and develop talent for the future.
We’ve seen the retail sector harness AI to prompt and nudge consumers with more personalised and interactive shopping experiences. I expect that in the coming years employers will follow suit, keeping staff engaged on a more specific, one-to-one basis. Again, the overall use for AI here is to supplement, not supplant, human management - an automated system could prompt a manager to catch up one-to-one with an employee who values frequent mini-reviews, or remind them that there is one member of staff who hasn’t yet been included in an internal reward programme. These are very basic use cases. As algorithms get more sophisticated, employers may for example find the machines telling them when and where they are likely to lose valuable people so that humans can intervene before it’s too late.
One exciting prospect is to utilise AI to supplement proactive human planning. An organisation’s talent flow is essentially another data spread that a computer can analyse to spot upcoming trends, either assessing when future revenue growth will require additional staff, or analysing calendar patterns to identify which time of year employees are most likely to depart, for example. I believe that this will become integral for larger businesses, who will then work with hiring managers and talent acquisition leaders to plan proactive hiring initiatives, rather than spending so much time on reactive, ‘fire fighting’ hiring.
As AI’s business case becomes more widely recognised, I expect that talent acquisition will begin to adapt very quickly indeed. Not only should this result in a far more efficient recruitment process, it will also provide talent managers with more time to focus on higher value tasks - and opportunities to leverage ever-important human nuance, which I believe can never be successfully emulated by AI. However, expect a deluge of new businesses and models claiming that they can overnight transform your fortunes as that is usually the bandwagon that starts rolling once a new idea starts to gain traction. It will not be easy to sift the real jewels from the rest of the noise and recruiters could well find themselves overwhelmed with choice and uncertainty on which route to take. Certainly in my own business we spend a lot of time looking for the ideas that will really make a difference for our clients and candidates, and that’s probably less than 10% of all the proposals that come across our desk.
However I also firmly believe that people, not machines, will continue to play the dominant role in hiring and staff engagement. We will need to set the criteria, we’ll need to bring that magic of human nuance to the screening and interview phase, and we need to build the person-to-person relationship which is all that ultimately matters when a candidate holds their pen over a contract. Today people do business with people and I hope that never changes. Despite the excitement and fears around the rise of AI, talent management largely remains a contact sport, where gut feeling, grounded in thousands of tiny facets of human experience which are never captured as data, plays just as strong a role as hard data.
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