Continuing from part 1..
Given the shift from employer-driven market to candidate-driven market, it’s becoming increasingly difficult to find the right talent for the organization. Recruiters need to scout for and engage with passive candidates, need to close the positions within the stipulated timeline and within budget while providing best in class candidate experience. In many cases offer drop-out is also emerging as a key challenge for the recruiters. AI in TA can be leveraged effectively to solve for most of the above-listed challenges.
a) Candidate Sourcing & Screening: Advancements in AI have made it possible to upload the profile of a role/employee in the machine and get all the similar profiles available on various job sites or the internet in general. Also, it is possible to stack rank all those profiles in the sequence of relevance based on advance candidate screening algorithms. Belong.co is already providing these services to many of its clients in India and abroad. Candidate Engagement: Multiple chatbots are available in the market which can engage with the candidates to fix up interviews and provide details on the role and the organization.
b) Candidate Assessment: Automated video interviews is a very interesting use case to assess the candidates based on their visual inputs, voice inputs and content inputs. Competency profile of the candidate can be created based on the aforesaid inputs and can be compared with the role requirements to make the decisions. Aspiring Minds is an example of a startup that has created the above-mentioned video interview solution powered by AI & ML. Also, these technologies can be effectively used to draw the correlation between the hiring assessment and on-the-job assessment; correspondingly system can show recommendations on what to assess, how to assess, who should assess when to assess and so an.
c) Candidate Onboarding: Automated AI based onboarding system can help reduce the time to productivity of candidates by engaging with them at the time when they really require support. Many companies have started to experiment with onboarding chatbots as well. A startup named Talla continues to innovate in the way that it tackles knowledge management and uses chatbots to answer employee queries. This is a great case study of how rLoop has used Talla to onboard new employees, introduce them to new projects, and get them set up the new tools they need. When team members have a question, they simply ask Talla. If Talla doesn’t already have an answer in its knowledge base, then it’ll automatically kick off a workflow to find the answer, add that information to the knowledge base, and notify the person that originally asked the question.
Learning for the current workforce is primarily characterized by 3 things. First is Learning on Demand, which means that learning is most effective when it is provided at a time when it is required. Second is Bite-Sized Learning, which is providing learning in short modules of 10-20 minutes duration. The third is Personalized Learning, which means curating the learning content keeping in mind the specific learning requirements of the employees. AI in L&D can be leveraged effectively to solve for most of the above-listed expectations.
a) Training Needs Identification (TNI): Instead of doing TNI once or twice in a year, it is possible to determine the real-time learning needs of an employee based on the data available about him/her in the organization. Various events in the lifecycle of an employee such as promotion, performance evaluation, role change, supervisor change, location change etc. can trigger different learning requirements for the employee. AI powers machines can help employees and organizations in this regard.
b) Content Curation: Learning content can be personalized based on the specific learning needs of an employee. For example, employees in the same role, at the same level, might have different career aspirations; and thus may require different learning inputs. Content recommendation engines have been built based on the career path of an employee, his/her future aspirations, existing potential to learn and many other variables. Also, intelligent simulators are built to train employees on how to act in unforeseen situations. Edcast is a learning product which delivers personalized learning solutions using AI & ML algorithms.
c) Learning Delivery: Different people have different learning styles. Some like to learn by doing, while some like to learn by reflecting or reading. Also, different people will require learning at different times. For example, imagine a situation where a sales guy gets a learning trigger just before he/she is entering into sales negotiation. Many companies use virtual learning assistant who can guide the employee, poke her and suggest her the most relevant content and the best delivery channel.
d) Career Development: For most employees, the problem is they don’t realize what skills they’re missing in order to advance to higher-paying positions, Josh Bersin says, based on the research he recently conducted with LinkedIn. “AI absolutely will start helping with that,” he predicts. Two innovative startups, Fuel50 and Gloat, are developing promising AI in this area. Both use algorithms to analyze successful career paths within a company, determine where there’s demand for more skills and offer personalized recommendations to employees to advance their careers.