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Artificial Intelligence has finally gone mainstream. But AI is more than just a shiny buzzword that can entice customers. The recruiting and sourcing AI paradox is real, and while many are struggling to understand how exactly to connect the data dots and put it all into practice in a scalable way, we’ve been working on how to make a better and more effective use of our AI-powered system.

After such change brought on this last year and a half, pretty much everyone seems to agree that the top priority in the recruiting world is agility. One of the main pain points for recruiters is limited search functionality when candidate sourcing, which means it’s easier to start an external search from scratch rather than reconsidering compatible candidates that might already be within their database.

This ineffective time waster means they can’t take advantage of previous search efforts, slowing down the process of finding the perfect match for the role they’re trying to fill. So what can we do from our side to work on saving recruiters’ time and get closer to achieving their agility goal?

Well, when it comes to sourcing AI and machine learning, the possibilities are – you guessed it – endless. For now, we’ve decided to add two key features that will strengthen and enhance certain processes: suggestions and candidate recommendations. Let’s dive into what these features are and how they work.

Semantic Suggestions

When candidate sourcing in many systems, recruiters have had to search for specific keywords and use their expertise to create complex boolean strings in order to find anything other than the most obvious profiles.

These searches are often very literal and make it hard to find hidden gems in their talent pools that might be a perfect fit for the role but have different experience or skills. Without any assistance from these systems, searching for top talent within an existing database proves to be an obstacle that we knew we could help overcome.

Our latest addition in Advanced Search goes beyond word-for-word search and helps recruiters find ideal candidates in no time by leveraging machine learning to expand search criteria.

Let’s say there’s an open position for a Java Developer. When you go ahead and search the talent pool for candidates that fit the “Java Developer ” criteria, Avature Semantic Suggestions will not only pull that specific search term but also provide you with a selection of other relevant suggestions that may fit what you’re looking for.

You’ll be able to find hidden talent thanks to a curated and optimized in-house ontology that’ll expand your search criteria.

Avature's advanced search feature, displaying semantic suggestions as the user types keywords and showing candidate results.

With this new search experience, you’ll be able to turn non-experts into experts, fast-tracking the learning curve and finding hidden gems as effectively as a seasoned recruiter. And not only does it save time and allow for an agile sourcing process, it also favors transparency by giving visibility to the recruiters regarding how certain results were obtained.

Our white-box approach enhances the recruiter’s decision-making process as well, providing suggestions and letting the recruiter choose accordingly instead of making the decision for them.

Candidate Recommendations

The next exciting new feature to hit our sourcing AI and transform recruiters’ and sourcers’ lives is Candidate Recommendations. This is an AI-based mechanism that can suggest potential candidates for a specific job requisition.

The Avature platform, with an automatic intelligence generated list of best matching candidates to fill a developer role.

What does this mean for recruiters? Well, this feature goes a step further, so they don’t have to run a search from scratch. When they open a job requisition record within the Avature platform, they’ll see a new section called “Recommendations”, which consists of a list of candidates which the system will order according to the compatibility with the role.

This is shown by a percentage score, which will depend on similarities between the job requisition and the candidate profile. You’ll have complete control over the decision-making process that the platform follows and the elements it takes into account when making candidate recommendations.

Furthermore, this sourcing AI feature doesn’t make any decisions for your recruiters but rather draws their attention to a set of candidates that might be a great fit for the opportunity in question. It’s quick and easy to review them and decide if they’d like to shortlist them for the role or exclude them from this specific search.

Some lucky customers are already giving our new functionality a go as part of beta testing, but rest assured that we’ll let you know when it hits the platform on a broader scale.

If you’d like to know more about this or other features, please reach out to your Avature representative.

To Conclude…

These are just two of the newest enhancements to our sourcing AI and the platform itself. We’re constantly working on new features and sharing releases every fortnight and AI is certainly no exception.

Our NLP Team is continuously working on improving existing capabilities and delivering new functionality that will further improve the day-to-day lives of recruiters and other stakeholders. So stay tuned for more exciting updates!

If you’d like to learn more about how to better implement sourcing AI into your tech stack and HR talent practices, make sure to check out our recent roundtable with Fosway, where our resident AI expert, Rabih Zbib, shares powerful insights about the present and future of AI.


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