Applicant tracking systems (ATSs) and recruiting management systems (RMSs) are the two AI-infused approaches to modern talent management that have gaps and limits. However, while AI-based platforms are critical for innovative outlooks to talent management, their implementation is fraught with difficulty. For example, a recent study by Harvard Business School (HBS) and Accenture discovered that due to the way the tracking systems are connected, over 10 million workers are eliminated from consideration. Similarly, according to a recent Wall Street Journal article, current talent management systems are operating as intended, screening millions of resumes for keywords and phrases that match job descriptions while excluding many qualified candidates.
Employers are, however, increasingly implementing AI-based platforms for talent management in addition to traditional ATSs and RMSs. Recently, Eightfold.ai gave a realistic, pragmatic look at how AI is changing the nature of employment and what needs to be improved. The goal is contextual intelligence, with AI assisting HR teams’ experience, insights, and intuition. This “intuitive” knowledge allows AI systems to provide more detailed, relevant, and correct outputs by assessing words and attitudes and cultural and environmental settings. In addition, it enables AI systems such as chatbots and virtual assistants to comprehend language, audio, video, and images in the real world, allowing them to behave more like humans than traditional computers.
Role of CAI
Contextual AI (CAI) is an AI that allows algorithms to perceive information in the same way that humans do. CAI is an excellent notion when a more comprehensive understanding of human situations might improve the user experience. Self-driving automobiles, facial recognition, and quality control are among the most typical cases. CAI can do its best job in voice-based assistants and conversational bots. Users can train their own unique AI models utilising business-specific data sets with contextual AI. CAI is not only a necessary aspect of the current digital landscape, but it is also expected to improve and evolve swiftly to provide marketers with more relevancy and attention.
Context is a crucial component of machine learning (ML), and by utilising CAI, one can empower the system to:
- New knowledge:
To obtain a deeper grasp of any event, a CAI system can pick up patterns and characteristics in the data and deduce context hints from a few supervised learning cases. This enables your AI system to learn unsupervised, figuring out new scenarios on a case-by-case basis, just like a person.
- Knowledge transfer :
AI systems can use what it’s learnt in one environment to a different one to improve performance on a similar task. A CAI in charge of transcribing a company meeting, for example, may instantly recognise and correlate a project name stated in another meeting.
- Infer context:
AI systems grow better at examining all aspects of a situation to discern what the end-user genuinely needs at that moment as it learns from each interaction. For example, a self-driving automobile may detect environmental cues such as wet roads and approaching pedestrians and adjust its speed accordingly.
Talent mobility is becoming the most reliable technique for reducing the talent gap and staffing roles critical to many businesses’ growth. Organisations can factor in what’s best for a candidate in terms of skills and competencies while still meeting their talent management needs by employing AI and machine learning. According to HBS and Accenture, AI-based systems are critical for optimising every area of people management, beginning with recruiting. There is a need for greater research in this area. In the near future, there will be a lot more. The work of Indian researchers will be critical to these advancements.
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Nivash has a doctorate in Information Technology. He has worked as a Research Associate at a University and as a Development Engineer in the IT Industry. He is passionate about data science and machine learning.