By Johan Steyn, 8 October 2021
Published by Finweek: https://www.scribd.com/article/530822788/Interviewing-With-Bots
Imagine that your next job interview is with an Artificial Intelligence (AI) recruiting platform. It is a virtual meeting and the computer-generated person on your screen looks as life-like as you could imagine. It displays all the emotions and facial expressions that you would expect from a real person. You feel comfortable as it expresses empathy and even laughs at your jokes. If it only knew that you crack jokes when you are very nervous. For all you know it already does.
During the interview, a vast number of data points are being analysed. Facial recognition technology scrutinises your micro-expressions to determine potential signs of deceit. The algorithms in the background determine a baseline on your behaviour - like with a polygraph - to figure out how much anxiety and tension is influencing your behaviour.
Even your voice is being examined for signs of stress or insincerity. Meanwhile, machine learning models are comparing your suitability, mapping your responses to the role definition and the personas of the potential clients you will be working with.
A few weeks later you join the organisation. Your onboarding experience is mostly virtual but very effective. Even your learning and development path is successfully mapped to your personality and individual training needs. This is the best job you have ever had. You are so satisfied with your experience that you could never imagine working for another firm.
Already a reality
Many businesses are utilising massive volumes of data to have a better understanding of the client lifecycle for marketing and sales purposes. Consumers leave a substantial digital footprint, which marketers can leverage to deliver more personalised experiences and increase engagement.
As with customers, there is an employee lifecycle that includes recruitment, onboarding, training, performance management, and remuneration. These generate significant volumes of data. Mapping this data across the employment lifecycle enables businesses to significantly improve employee engagement and experience.
Recruiting smarter with algorithms
In the near future, recruiting is going to be dependent on a recruiter's capacity to automate their workflow and utilising the intelligent technology tools at their disposal. Even if it's challenging to do so impartially and efficiently, conducting a resume screening remains the primary problem in recruitment. While talent acquisition managers feel recruiting is extremely challenging, they acknowledge that the most difficult aspect is figuring out which applicants are suitable.
With the technological advancement in the field and the increasing difficulty of the task, new techniques are fast becoming available. The humdrum task of reviewing resumes all day and hiring people based on subjectivity, impressions and emotions rather than reasoning is over.
There are multiple case studies already of human capital management (HCM) departments utilising AI and data analytics for talent acquisition. AI systems can use text analytics to locate profiles on LinkedIn or to determine the best resume match based on job prerequisites.
Rather than manually sifting through hundreds of resumes, they may quickly restrict the pool of prospects and interview only the most qualified candidates. This is a critical benefit of AI across the board: it enables HCM workers to work more effectively and concentrate on higher-level activities.
AI can automate the entire recruitment process. Candidates can be assessed more thoroughly with the help of recruiters who can collect data on each of them. Special algorithms are used to determine candidate talents and experience, utilising numerous AI models.
The recruiter can pick candidates based on their talents and identify a suitable position for applicants where their skills are most needed. A new breed of computational cognitive tools would not only help businesses but also empower potential employees to enhance their abilities.
Because of our personal prejudices, we cannot be truly objective. No matter what type of company, people making biased decisions is a prevalent problem, and AI can help decrease this. Businesses have the chance to hire only the top prospects, as all decisions are made based on data predictions and behavioural analytics.
The potential for this new breed of technological platforms becomes more apparent once the individual joins the organisation.
Enhancing the experience for employees
The digital footprint created by employees can help organisations set themselves apart and see where they are in their career trajectories, resulting in lower employee turnover and increasing staff engagement, contentment, and productivity.
Companies can individualise the work environment for each employee by using AI. It extends their career span by decreasing challenges faced, delivering positive employment experiences. Organisations could use data to individualise employee benefits and remuneration.
Predicting attrition and absenteeism
AI can be used to discover the objective elements that impact worker turnover, to identify the key causes of employee departure from an organization, and to predict the likelihood of a worker quitting. By mapping and analysing massive volumes of employee data and behaviour, HCM professionals can detect and forecast the employees who are at risk of attrition, empowering managers to address the issue proactively.
A huge red flag, absenteeism is a leading indicator of attrition. Businesses can identify which employees will leave by utilizing predictive analytics to recognize patterns of absenteeism, which may allow them to take planned measures to avoid turnover and lengthen the employee lifecycle.
To improve the health and wellness of employees, employers should identify employees who are likely to take time off due to illness and offer them treatment options to reduce or eliminate absenteeism.
Learning and development (L&D)
L&D departments throughout the world are shifting to agile learning models that support individual learning rather than delivering broad-based solutions for the entire enterprise. No one needs to be trained on the same material any longer. Learners can be served with material that matches their learning requirements and strengths. Training curricula can be recommended based on prior behaviour, and predictive models and algorithms can be used to generate fresh content.
Employees must be equipped for new jobs through adaptive learning that will involve different skill sets, such as analytical-, strategic- and critical thinking. Cultural awareness and emotional intelligence are considered to be increasingly important.
Breaking down silos
Many companies are increasingly discovering the potential and scope of AI applications that enhance the employee experience from the moment of recruitment to the moment of exit. A company's value offer can be enhanced through integrating AI into HCM by including a layer of computer cognition, making it easier for firms to meet the needs of and retain valuable employees.
Corporations should digitise personnel data to provide effective analysis. For many firms, the current data situation is complicated by a wealth of siloed information that must be connected with external information systems to form a comprehensive organisational understanding. Businesses can improve worker efficiency and responsiveness by implementing a data-driven and analytics-based strategy to human capital management.
Imagine the future
Business owners need to realise that the smart technology era is at our fingertips. Through cloud technology and consumption-based platforms, the ability for firms large and small to bring computer intelligence into the employee lifecycle is not only within reach, but more cost-effective than imagined.
Of importance are your workforce strategy and business planning. These platform decisions should always be a people-led decision and not an isolated technology strategy. We can imagine the future and the art of the possible. We should take our people on the journey with us and realise the technological benefits already within our reach.
• Steyn is the chair of the special interest group on artificial intelligence and robotics with the Institute of Information Technology Professionals of SA. He writes in his personal capacity.
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