The impact of cognitive computing in HR management will enable HR leaders to get useful information and improve decision-making for all HR operations.
Although often used interchangeably, AI and cognitive computing differ from each other. AI enables machines to simulate human intelligence by learning, reasoning, and self-improving. Cognitive computing, in addition to doing all that, also allows machines to emulate human intelligence by providing AI a more human, emotional touch. In simpler words, they can be distinguished by the approaches they take. AI takes a logical approach, and cognitive computing takes a more human-centric approach to assist people in decision-making.
The ability of cognitive computing to provide human-emotional intelligence makes it a perfect match for people-centric functions like marketing strategy development, facility management, and HR management. Cognitive computing in HR management will help HR leaders to make better decisions to meet all the requirements of employers as well as employees.
The impact of cognitive computing in HR
Cognitive computing has the potential to transform HR management by assisting HR personnel right from hiring to developing talent to maximize their productivity in an organization.
Recruiting best talent
Acquiring the best talent starts with sourcing them. Although HR professionals know they can find talent through job boards or social media, acquiring them can still be both challenging and time-consuming. Manually screening resumes still remains a major challenge for HR professionals in talent acquisition. And to aid the recruitment process, HR managers have implemented AI systems to automate the screening process.
This has helped HR managers save a lot of time and focus more on deciding whom to hire. AI takes the logical approach and hence can find the right talent based on their skills and work experience. For instance, if a candidate has worked as a sales agent, AI systems will suggest hiring that candidate for the same position.
AI can help hire the right candidate based on organizations’ requirements. But, what traditional AI cannot help with is hiring based on organizations’ culture. For example, some organizations require candidates who can work in a team. And, some others require candidates who can work independently and individually. Cognitive computing can analyze candidates’ social media activities to suggest the best cultural fit. For instance, if a candidate is socially active and loves to interact with new people, then he can be a better fit for team-work tasks. Thus, cognitive computing can suggest candidates not only based on their skills and abilities but also based on their behavior.
Developing hired talent
Acquiring the right talent is just the beginning of HR managers’ responsibilities. They also need to develop hired talent. Every organization follows different processes to achieve their end goal. And, HR leaders have to make sure that the newly hired candidate quickly adapts to these processes. Lack of development in hired talent can result in a skills gap. AI is already revolutionizing corporate learning by providing personalized learning paths, building content at scale, and measuring training effectiveness. AI systems can monitor how an employee performs a specific task to detect the problems he or she is facing while performing that task. Based on such considerations, AI systems can develop a personalized learning path for that candidate. But if candidates are not interested in their job functions, then developing personalized learning paths is not of much use. And, that’s where cognitive computing comes into the picture to enhance talent development.
Cognitive computing systems can take into consideration factors such as employees’ objectives, interests, and skills gaps to provide more personalized learning paths. They can analyze all such information to suggest which employee will be best suited to bridge a particular skill gap. And, cognitive systems can also create learning paths based on their learning abilities. Along with assisting HR leaders in developing talent, cognitive computing can provide career guidance to employees. For example, based on employees’ objectives and interests, cognitive systems can suggest appropriate positions available in an organization.
Measuring employee engagement
There are several reasons for HR personnel to measure employee engagement. Measuring employee engagement will help them to create a more satisfied and engaged workforce. Employees who are engaged are less likely to look for a job change. And they are more likely to refer friends for employment and be satisfied with their jobs. AI has been helping HR professionals to measure employee engagement with the help of real-time feedback gathering tools, chatbots, and behavioral mapping.
Cognitive computing can achieve all these, and it can also analyze employee sentiments to measure accurate engagement. For instance, using deep learning, AI systems can detect employee emotions based on patterns and training data. But expressions cannot always convey the feelings of people. For instance, a smile on a face does not always mean that the person is happy. Also, AI takes a logical approach and provides quantitative results. But emotions are hard to quantify and measure, which makes it more challenging for AI systems to find accurate measurements.
Unlike AI systems that generalize everyone and give results based solely on historical and training data, cognitive systems can individualize a person. For instance, cognitive computing systems can potentially detect when a generally straight-faced person is happy or sad. This is accomplished with the help of computer vision and natural language processing (NLP). Computer vision detects slight changes in facial expression, and NLP with voice detection can analyze the vocal tone of employees to accurately measure employee engagement.
Cognitive computing systems can store such data about individual employees for future references. HR managers, with the help of accurate measures, can enhance employee engagement at the workplace. This will enhance employees’ confidence, motivation level, comfort, and interactions, which will ultimately benefit organizations.
Assisting in administrative tasks
HR managers have to answer several queries from existing and potential employees on a daily basis. And cognitive computing-based tools can help answer these questions for HR leaders. With the help of voice recognition and NLP, cognitive systems can understand the queries and provide the most appropriate response. The biggest example of cognitive computing capabilities to answer queries is IBM Watson.
Cognitive computing systems can also analyze a massive amount of employee data to assist HR managers in workforce administration. For instance, cognitive computing can suggest the most appropriate time to hire a new candidate based on the workflow. It can also suggest transferring employees from one process to another based on the workflow and candidates’ interests. Even AI systems can help in such administrative tasks. But AI can only show which employees to transfer and to what processes. Cognitive computing, on the other hand, can give outputs like AI systems and provide information on why to transfer those employees.
AI and cognitive computing in today’s world are eliminating the need for humans in repetitive manual tasks. But, HR managers are safe from being replaced by the disruption of these technologies. That’s because HR management is a human-centric process that thrives on human-to-human interaction. Although businesses can implement machines for every HR process-related interaction, employees won’t prefer interacting with machines all the time. It will neither be healthy for employees nor sustainable for employers. Hence, cognitive computing in HR management will play an assistive role and not eliminate the need for HR managers.
