Over the past five years, we’ve seen the concepts of machine learning and artificial intelligence go from pie-in-the-sky science fiction plot devices to mundane and expected components of digital assistants and enterprise productivity software. Far from heralding a robot revolution that seeks to enslave human beings in perpetuity, the rise of machine learning has really just helped us turn on our lights without needing to stand up, or figure out our spend tracking through mobile banking. There are brilliantly futuristic applications for it, obviously, from medical diagnostics to disease treatment and prevention, to self-driving autonomous vehicles. But a lot of what machine learning excels at is in process automation for the purposes of helping humans be more productive and effective at decision-making.
To invoke a very basic definition, artificial intelligence is leveraging things like bots, intelligent assistants, natural language processing, machine learning, and predictive analytics to inform business decisions. It is the cornerstone of what we often refer to as “digital transformation,” in that it is cumulative. Because machine learning is a component of AI, and arguably one of the most readily seen in today’s recruiting software solutions, we sought to gauge recruiters’ opinions on it in our GRID research. That being said, it’s not the first time we’ve asked about it.
... Artificial intelligence is leveraging things like bots, intelligent assistants, natural language processing, machine learning, and predictive analytics to inform business decisions. It is the cornerstone of what we often refer to as 'digital transformation,' in that it is culminative.
According to the 2018 UK Recruitment Trends Report, last year 29 per cent of industry professionals ranked of industry professionals ranked AI in their top three challenges for 2018, yet only 16 per cent ranked automation as a top-three priority. Over all, close to half of respondents (48 per cent) weren’t using automation for screening, and 30 per cent weren’t taking advantage of such technology for selection, onboarding, or candidate nurturing.
Now the questions we asked last year revolved primarily around automation as the basis for AI chronologically, i.e. “first we automate, then we append intelligent decision-making onto the information processed through automation.” This year we decided to evaluate the comfort level recruiters feel with the world of machine learning and, more broadly, AI. As an industry, do we understand this technology? Or is it just too early? After all, we’re not (presumably) engineers.
To that end, we asked our global respondents: “how would you rate your understanding of machine learning and artificial intelligence within recruitment?” We didn’t define the terms for our survey takers or go into detail about the ramifications of digital transformation in that specific question, because we really just wanted to get a quick snapshot. Respondents could rate themselves on a scale from one to nine, nine being “very strong understanding” and one being “no understanding.” People tended to congregate at the medians, with the majority of respondents picking either “no understanding,” “some understanding,” or “very strong understanding.” For the purposes of making sense of the data, which truthfully was confusingly displayed, we grouped it together by three categories, “novice understanding,” “some understanding,” and “high understanding.”
Here's what we found:
There was no single category that saw a majority, but most respondents felt that they were somewhat well-versed in ML/AI. What’s really interesting is that 36 per cent of respondents consider themselves to have a strong understanding of machine learning and AI, which is exciting because it speaks to the industry’s embrace of digital transformation as an opportunity for growth and value.
This is the first year that we’ve asked this exact question, so the proof in the pudding will come next year to see if there’s a chronological delta. The role dimension is compelling here, so when we segmented it by C-suite leaders versus recruiters/sourcers/salespeople, excluding CTO and IT leaders (because that would skew the results, of course), we found the following:
We compared this to front-line practitioners (who are also, arguably, mostly members of a younger demographic, be it Millennials or post-Millennials).
The differences aren’t huge but they’re significant; practitioners consider themselves better versed in ML and AI than senior leaders. This could be related to age - it’s to be expected that digital natives would understand more about newer technologies than any other demographic. And thankfully when we looked at just CTOs/CIOs/Heads of IT, every single one of those respondents claimed to have at least some understanding of ML and AI, and 67 per cent of them have a strong understanding.
When we sliced it by firm size, 30 per cent of respondents from 1-5 person shops claimed to have a strong understanding of ML and AI, versus 53 per cent of respondents from 500-1,000+ person shops. The larger the recruiting firm, the higher the understanding of technology. This makes sense in that large firms make greater wide-scale investments in technology, and likely have stronger training programs in place to help employees make the most of this technology. But in some ways the finding is counterintuitive, as respondents from smaller firms usually have to be well-versed in all aspects of the business, including technology. What we see here is an opportunity to offer education and training in ML and AI to SMB firms, provided that there is a desire there, which we didn’t assess.
While the question we asked in our GRID survey wasn’t particularly in-depth, we’ll be excited to see how the findings change next year. The overall weighted average was 5.26 out of 9. We expect that next year we’ll see this increase to at least 6.00.