…real-world use cases for deep learning and automation in recruitment.

Last year Andreea Wade wrote a post about What This AI Recruitment Tech Company Sees and since then she has continued to share what she sees – through keynotes and various industry talks but also through direct engagement with our industry.

Andreea will be speaking at the upcoming AI conference in Mayo on November 20, 2018

Andreea | Opening.io

To register for the Conference – check out the website

Andreea:

This quick post includes additional insights into the applicability of deep learning in HR and Recruitment, insights extracted from our own experiences and interactions with the talent industry as well as from what we see validated in the market indirectly (talent strategies, competitor landscape, M&As etc).

Recruitment agencies are automating.

We see agencies upgrading their technology and platform stack, moving from one ATS and/or CRM to another, choosing API enabled entities with rich partner marketplaces and integration capabilities.

We see agencies working to move away from reliance on highly trained human resources to technology and platform stacks that can do most of the pre-screening work, enabling sourcing and recruiting teams to be presented with highly accurate long and shortlists within seconds from need to search to outreach.

We see agencies growing their sourcing networks internationally while enabling interconnectivity and efficiency through automation.

Some of the intelligent, deep learning technologies agencies employ to solve their real-world problems: search and match technologies (candidate to candidate match – search with a CV rather than a job description, job to candidate match, candidate to job match – reverse match and candidate rankings).

We have also seen large recruitment/staffing entities build their own candidate match scores (that link to various internal KPIs).

Clean data – both in preparation for GDPR and beyond. We have seen large (and medium sized) entities focus on database cleanup and management – including here tools that can instantly recognize if an external piece of data found on various online platforms is also found inside their own databases (from candidate names or candidate social media user names to niche technologies etc).

Also of interest here are skill recommendation technologies (skills mapped against millions of job description data points) enabling recruiters to write better job descriptions but also to surface skills that can better pinpoint a meaningful fit.

In-house recruiters are looking in.

Various levels of automation in this space (some mentioned above) with talent rediscovery becoming a key point. Two approaches here: own existing database and existing employee pools.

We see data extraction technology combined with search and match tech and engagement tech, chatbot technology: surface past applicant, find fit for current opening, contact the candidate and assess interest.

Deduplication and data enrichment are two key areas of interest and usage here.

Predictive technologies are being used more and more with technology being able to predict the probability of a (passive) candidate moving jobs.

HR Departments are looking for the bad fit.

I’ll explain.

While sourcers and recruiters are using technology like ours to get to the shortlist faster, HR departments are interested in who falls under the desired match threshold. The skills gap. Mapping and surfacing transferable skills is also something we hear more and more of: understand what’s missing, provide training, create new opportunities and retain and/or drill into existing employee data and surface own passive candidates from across sectors and departments.

We see more and more technologists being brought in for change management. From our own perspective – a science-first company building knowledge around recruitment data – we find that fewer points are up for debate and there is a firmer understanding of deep learning technologies, use cases and overall its transformational capabilities.

Job Platforms are looking at candidates differently.

New, emerging, fast growing talent platforms have it figured out: it is all about the candidate – better UI/UX, faster, better application process, all linked to more meaningful insights into data.

We see a need for parsing technologies that understand and extract contextual data, one-click application processes and better search and match technologies.

We also see a focus on candidate data/candidate profiles: from beautiful design to third party data extraction, automated profile building and profile enhancement (either general or specific, linked to an actual job).

Instant match. From resume upload to profile creation and job match fully automated and instant.

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We increasingly see the usage of chat bots (from RPOs to employers) – again, in an effort to engage with every single candidate, maintain a conversation, a two-way bridge aimed at understanding the needs and wants of the candidate at all levels of the recruitment funnel (from initial engagement to post-offer acceptance and onboarding).

We see a lot of change. From market adoption rates of new technology to the continuous improvement of said technologies. We see competition but also partnership and collaboration.

We’re a recruitment tech company doing a lot of work in NLP and NLU and we love the recruitment space, mainly because… it’s about people. I’m sure I omitted a lot of use cases (some purposely, tied to current R&D work we are doing) – but yes, we see change happening and happening so fast that it’s easy to miss what’s behind you or already ahead of you. I sat down an hour ago to write something quick but insightful (for those interested in the space), now interested in your voice.

What do you see from where you’re standing?

Andreea | Opening.io

 

To register for the Conference – check out the website

Acronyms explained:
RPO – Recruitment Process Outsourcing 
 
NLP – Natural Language Processing 
 
NLU – Natural Language Understanding 

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