The computer may only be able to understand ones and zeroes, but it is sequences of these symbols that allow even the simplest of machines to uncover the deeper meaning behind data. In order to teach a machine to better understand the connections and causalities between different pieces of information, we first need to dive into the traditional ways we performed these operations.
What is contextual recruitment in a traditional sense?
Contextual recruitment is a type of recruitment centered around the use of information to better understand and decipher a candidate’s application. By incorporating additional informational variables into the decision making processes, such as variations in values related to assets under management (AuM), net inflows, revenues over a period of time, and the market focus the candidate is most active in, institutions are able to identify talent with the greatest potential.
In a traditional recruitment process, candidates are assessed and judged based on their qualifications and level of experience. Contextual recruitment goes along the lines of traditional recruitment but incorporates previously disregarded variables into the selection process. Recruiting like this opens additional opportunities for employers:
- It encourages them to assess potentially great candidates who were ignored throughout the previous processes
- It strengthens the diversity goals of the organization
A good example of the contextual dilemma when recruiting could be the candidate selection based on the academic institution they graduated from. In traditional recruitment, theoretically, the same rules apply to the A-level banking graduates from two different schools. The system overlooks the additional contextual data such as what kind and level of an educational institution are we talking about, what was the enrolment criteria, etc. Hypothetically, a Geneva Business School graduate would be equally graded as students from lower-tiered schools. This should never be the case.
The main goal of recruiting contextually is to introduce all varieties of talent from all spheres of life, with an accent on the ones who have previously been invisible in the selection process. Professional recruitment services have followed suit and adopted the contextual recruitment over the past several years but it is yet to be widely adopted. Hardly-achievable selection criteria stood in the way of hiring diverse teams even in cases where the data was pointing to a high overall quality of the potential candidates.
There are two main reasons why a diverse workforce is crucial in an international organization:
- Diversity improves the overall performance of the collective by bringing different approaches to problem-solving
- Diversity in the workforce usually mirrors to organization’s ability to acquire and properly service clients of various backgrounds, sizes, and locations.
Contextual recruitment is the essence of AI recruiting
In any line of business, when an organization decides to shift, the application of a selected solution usually becomes a major challenge. The challenges are then further intensified when a system being replaced is a user-facing one. A new system, no matter how sophisticated, needs to maintain and improve things like ease of use and general candidate experience. On the other hand, the new system should provide further strides in maintaining the quality of selected talent and tackle bias.
Not an easy task, you’d agree!
The thing is: it can be. Humans tend to get lost when operating large quantities of data, especially when that data is quantifiable by the factor of two for every next trait of the assessed candidate. Unless transferred to computer processing, contextual recruitment will most likely eat the whole working time of a recruiter.
Context is the essence of how an augmented intelligence machine understands the world around itself. The strings of data it processes only make sense if they are recorded with clear information of where its derived from when they were recorded and what do they represent. Presented with the sufficient amount of these strings, the AI will shorten the selection from hours to seconds, while not affecting the user experience at all and removing the bias altogether.
While definitely not infallible, AI-driven recruitment does show promising initial results. The same rules apply here: just like a human recruiter is getting better with time, AI will improve with practice as well.
