Four years on and it seems organisations are still trying to get to grips with that balance between work from home and employee office attendance. News this month one of the Big Four firms has been utilising data to track when employees come to and from the office has caused much discussion and debate when it comes to managing a hybrid workforce.
It is clear there is continued turbulence facing businesses as the nature of work arrangement continues to evolve. On a recent webinar we discussed the role technology is playing when it comes to helping with the recruitment and retention of employees. In the same way technology can enhance the customer journey it can also be adopted to rethink the work experience to boost productivity and engagement for a hybrid workforce.
Virtual Recruiting
Technology is being used by HR departments and agencies to host video interviews, virtual events, surveys, and assessments to evaluate applicants remotely. It allows easier collaboration with hiring teams to evaluate applicants, gather fair and consistent feedback, check for unconscious bias, and decide who is the best fit.
Using AI businesses can also create virtual environments that simulate real-life scenarios, allowing candidates to showcase their skills and abilities in a more realistic and immersive way. A consistent theme was “do not lose the human touch” as there is still a need for meeting face to face and for potential candidates to come into the work environment to see and feel firsthand the culture. Technology helps with screening, but it cannot replace human judgement.
Onboarding
Technology is automating many of the manual tasks associated with onboarding, such as collecting paperwork, scheduling training sessions, and sending out welcome emails and is freeing up HR professionals to focus on more strategic tasks whilst ensuring that all new hires are processed in a timely and efficient manner. Many are providing new recruits with online resources accessible 24/7 such as training materials, company policies and other helpful information and allowing personalisation based on their role and experience rather than a one-size fits all approach.
Some organisations are creating outstanding employee experiences with their own virtual assistant and in one case providing staff pulse checks to understand how employees are feeling daily. It also sets automatic triggers for a 30-60-90-120-day plan for the smooth transition into a new role, giving direction to what can be a confusing time, and allowing the employee and managers to set expectations and monitor progress.
Retention
With attrition an increasingly challenging issue, AI-powered algorithms are efficiently processing vast amounts of data from various sources, providing a deep understanding of employee sentiments and turnover reasons. Leveraging AI brings multiple advantages to employee retention efforts, including uncovering hidden correlations and predictive indicators of turnover.
With AI platforms, large volumes of data are being used to gain insights into factors contributing to employee attrition, such as job satisfaction, work-life balance, and career growth opportunities. This data is being utilised to identify and address at-risk employees’ concerns proactively. For example, an employee who’s remained in one position for a long time may have unrealised potential to succeed on another team in the company. Leaders can capitalise on AI-driven insights to spot that opportunity and present it to the employee, offering a new challenge and possibly keeping them from looking elsewhere.
Qualification bias
Qualifications have been a convenient filter for assessing a candidate’s intelligence, perseverance, and acquired knowledge, and it was acknowledged that this approach often overlooks practical skills and real-world experience. With all the new technology available for screening and evaluating the actual skills of candidates many are rethinking this, which I believe, can only be good news.
It depends on your business model, of course, but consider how much is biased and how much is necessary. In today’s AI world, do remember that the large language models were trained on Internet data, with a strong qualification bias built in.
