AI is either already into or almost there when it comes to many spheres of enterprise functions today. One of the key areas where it is making a significant difference is in the hiring function. There are many enterprises that are reaping the benefits of AI helping them with hiring. There is an unspoken and oft unexplored data angle to AI in hiring. Let us explore that today.
The classic hiring team peeve is about the mountain of data that sits before them at every stage in the hiring process and for most part, they are right. When you look at hiring from a data angle, you will get a real picture of how overwhelming this is and the kinds of data that people have to deal with. For example, hiring isn’t a flat thing anymore, you can’t just go by resume and references to make calls. There are other things that need to be factored in and weighted correctly to help an enterprise take the right call on a hire or not decision.
Let me explain what I am talking about. Let us take an opening that comes up. The hiring team then diligently pushes the requirement internally and externally on job boards and then the resumes and references flow in. These are then vetted and shortlisted. Now the actual mountain appears before the team and has to be climbed/crossed. They need to get to other sources of data about the candidates to gather more information in the form of metadata. The most popular and humongous source of this information is social media. This makes the collection tedious and very stressful. There are various social media platforms that give insights into various aspects of a candidate. So in a nutshell, there is tons of data but no common structure and the management expects this to be gathered and sifted and collated in a format that helps them make an informed decision. The challenge is the collation of structured and unstructured data to draw common inferences and the sheer amount of data that is available. These two reasons make the human side of this aspect very strenuous and extremely prone to fatigue and error.
Human capital is the fulcrum of enterprise success and an enterprise which manages its talent chain with dexterity will be the one to succeed. However, C-suite managers are often seen grappling with talent issues because at the core lies the grey area of managing an individual within an enterprise context. To make matters more complex, talent definition, evaluation and benchmarks continue to be descriptive, limiting the use of process automation which are currently used by HR functions.
So, is the situation tough and unsolvable, actually no. There exist a lot on AI based solutions that help here. Spire TalentSHIP is one of the best ones that are now available as an AI support to your hiring. Here is how it works
Spire TalentSHIP® is an enterprise’s passport to intelligent talent computing. It is a cloud based technology that complements existing HR systems, however less or more automated they might be, with ‘contextual’ decision support analytics. This is imperative because every enterprise is unique and all issues, especially fitment of talent are contextual to it and cannot be aggregated or applied elsewhere. Spire TalentSHIP® enables all that which recruiters and decision makers have always wanted to achieve but could not.
Spire TalentSHIP® is a complete modular suite of 5 talent transformation solutions, which can be individually utilized across the five parts of talent chain (talent planning, talent sourcing, talent acquisition, talent deployment, talent management) or in entirety for the greatest impact. With Spire’s talent computing solutions, in addition to achieving positive business outcomes in the form of increase in revenue and decrease in costs, organizations can also measure and audit these results.
HR data is mostly unstructured by nature. Resumes, job descriptions, company rules and management plans with respect to talent are dynamic – they keep changing all the time. Legacy automation systems (HRIS, HRMS, ATS, HCM, WFM) which exist in the market are limited in enhancing business value since they do only linear processing with minimal analytics.
- Spire technology is contextually intelligent. It has two unique capabilities which play the critical role in enabling positive business outcomes. The first is Unstructured Data Comprehension (90% talent data is descriptive & unstructured) and the second is Contextual Data Analysis (because organizational context is always unique)
- What do we mean by ‘contextual intelligence’?
‘Contextual intelligence’ is Spire’s proprietary technology. Our technology generates ‘context’ out of both structured and unstructured data of any type of format. It makes data comprehension and interpretation most ‘relevant’ and as ‘accurate’ as possible.
- While computing talent data, Spire uses a contextual corpus cloud which matches search results to search terms/keywords in a contextually intelligent manner. A simple example: ‘Skills’ are not just exact matches of search terms that are pulled out but instead ranked as per ‘skill proficiency’; and search parameters are answered by a horde of related tags and their relationships. This makes computing ‘richer,’ ‘in-depth’ and ‘pin-pointed’. This results in 95% accuracy in contextual search and 80% accuracy in demand-supply mapping which is by far way ahead of existing systems.
- Spire TalentSHIP® is based on an advanced ‘search, match, map’ capability which makes talent computing absolutely ‘relevant’ and ‘unique to an organization’s own rules’ with contextual intelligence.
- Spire cares more about an organization’s ‘own set of business rules’ and applies them to talent computing. Spire’s contextual technology enables organizations to test different rules and business inputs while computing until desired results are obtained. It contextually ‘comprehends’ and ‘interprets’ the way humans do.
- Existing HR automation systems which are available in the market do not talk to each other. Spire TalentSHIP® does! It could be used as a complementary platform to existing systems or used as standalone modules.
Given the above data, it is clear that AI in hiring is the only way to solve the data complexity angle to hiring and to add context based angle to the enterprise hiring decisions that are otherwise dicey and simply costly if done using the error prone classical methods.