AI in HR: Ideal AI System Building Criteria for Long-Term Success
The marriage between Artificial Intelligence (AI) & Human Resource has kept everyone in the corporate landscape excited for quite some time now. Nearly every global company is in a race to build the perfect AI solution for human resource management. The bubble has built up phenomenally over the past few months but as an industry, it seems, we are going nowhere. Hype is good for excitement but to create an effective solution, one requires to adopt an altogether different approach.
The Transformation Dilemma for Human Resource
Recruitment of worthy talent has always been a challenge for the companies, irrespective of the industry they function in. Despite being a major hurdle, companies have seldom improved their hiring process despite the introduction of innovative technologies such as Artificial Intelligence.
HR managers have been long using the obsolete process of going through a candidate’s past job profile, experience, references, followed by standard tests and interviews. All this have left top management wondering why they end up hiring the wrong person.
Hitting a Jackpot with AI-powered Recruitment
Business leaders have often complained of 20-30% wrong hires due to non-dynamic recruitment processes. What HR professionals fail to realize is that the modern organization doesn’t need a resource who is strong on paper. To succeed and become more efficient, companies need people who are much more than their academic qualifications and professional experience.
Companies want to invest in candidates by understanding their attitude to serve, cognitive abilities, cultural and knowledge sync with the organization, region preferences and other qualitative factors.
Successful organizations want to focus more on qualitative aspects, making the recruitment and hiring process harder. With a system to evaluate personal communication skills, problem-solving ability, learning caliber, companies are trying to be future-ready.
Moving Over Redundant Data for New-age Transformation
To make a sustainable AI-powered recruitment model, the need of the hour is to show backdoor to stale data pertaining to potential candidates and hiring policies. AI algorithms learn and grow on data inputs. If the data is obsolete, the AI algorithm will become unworthy.
In order to build an AI-powered hiring system for the future, it is essential to nourish the AI model with improved hiring policies and fresh data. If companies fail to do so, the AI algorithm will start learning from existing hiring processes, that would give bias or discriminatory policies a chance to creep in the system, institutionalizing a system that is loathed by business leaders.
Building the Perfect AI System
Looking at the needs of dynamic organizations, an AI system should be extremely transparent and capable of being tweaked to meet specific organizational needs. The challenge here is to create a system that can be easily inspected and reviewed to identify its efficacy.
AI should be designed in a way such that its power doesn’t become an obstacle for existing and future workforce. To illustrate, let’s take the example of an AI system that predicts such employees who have been performing well but are thinking of leaving the organization. If such a system is developed out of context, the employee, despite doing well, might feel alienated because of cold behavior of the management.
To overcome such an issue, AI should be trained to apply behavioral economics in the right places. Instead of relying on AI completely, it should be seen as a tool for improving current systems. Its suggestions should be heard but at no point autonomous decision-making ability should be given to an AI system. Such a situation can cause a fundamental conflict inside an organization.
Narrow AI- The Future of AI-powered HR Solution
One should build a transparent, predictive and interactive AI system to empower a modern organization. An AI should be able to justify its decision by informing the management about the criteria and why it made a particular decision. This is how AI can be reviewed for accuracy and can be further trained for better results. At the same time, AI systems should be trained with different data sets to make them more robust.
The focus should be on building ‘Narrow AI’ focusing on a specific solution or a problem. Building such a solution would require massive amounts of data and similarly enormous stages of feedback. So, instead of focusing on standardized, black-box AI system, energies should be channelized on creating an AI system that a client can easily tweak and fine-tune, according to their dynamic hiring and HR needs.