Transforming Workforce Management With AI

AI has emerged as a powerful tool with the potential to revolutionise the workplace. Most common challenges in workforce management can be solved using technologies like natural language processing, machine learning or predictive analytics. In fact, people analytics is fast becoming a prominent catalyst for enterprises to navigate the digital and hybrid workforce.

Here’s how AI can create an impact in workforce management

Artificial intelligence can transform every phase of the employee’s journey starting from talent acquisition to performance management, learning and development to workforce analytics.

Implementing AI at the talent acquisition stage itself can help businesses select the right candidates for the job by eliminating human biases. Similarly, AI algorithms can be leveraged to impartially assess the impact of an employee’s effort in an organisation. By unifying data across various silos in an organisation – for instance past performance of an employee and the certifications s/he has under their belt – HR managers can also help craft meaningful career paths for them.

AI can also optimise workforce scheduling and resource allocation. For example, traffic data in a city can be leveraged to learn if certain employees living in heavy traffic neighbourhoods would be more productive if they worked from home for a part of the day.  Or if a recurring personal appointment in an employee’s calendar could help automatically set an alert to anyone seeking their time at the particular hour.

So, what are the challenges in implementing AI solutions?

While AI promises to potentially transform the workplace, several challenges ranging from data quality to ethical considerations, and of course the big one, change management, can come in the way of a successful AI implementation. Needless to say, successfully integrating AI tools into existing systems and processes requires careful consideration of these challenges.

Data quality and availability

An AI is only as good as its data and therefore, ensuring a regular flow of high-quality data is crucial. To achieve this, organisations must endeavor to implement rigorous data governance practices, ensure data integration, and champion data quality improvement efforts. Once businesses are able to establish robust data management frameworks, accurate and relevant data can be leveraged to derive meaningful insights and predictions.

Ethical Considerations

The basis for people analytics is people data and that gives rise to ethical concerns. It is one thing gathering data from machines and quite another gathering it from people. For example, should a company have access to an employee’s personal medical data? Having it can help a company equip their infirmary better or design healthier menus in the pantry. On the other hand, it could also be used to dismiss medical insurance claims when the employee falls ill.

It is crucial, therefore, that businesses set strict guardrails and prioritise fairness, accountability, and transparency while deploying AI algorithms. This doesn’t just help prevent bias in decision-making but also ensure responsible use of AI technologies. 

Change management

Change management remains one of the biggest challenges when implementing any new process, software or AI algorithms. With AI, it gets even tricker since the popular narrative is that it will take away jobs.

Conventional wisdom suggests that effective communication and involvement will help mitigate employee resistance to change. While this is true, it is important for businesses to highlight what’s in it for the employees. Change management becomes easier when an employee sees the benefits. Articulating what will change and how this change will add value to their jobs and reduce anxiety around AI implementation, and employees will embrace change more openly instead of having any trepidation or desperation.

Upskilling the workforce

Communication can be effective only if it is backed with serious intent and real action. AI implementation will automate several tasks. It is crucial for businesses to first ensure that those performing the said tasks are identified and upskilled. This will ensure that when time comes, they will be able to make the transition from their existing jobs to the new ones smoothly. 

AI-powered technologies won’t just help enhance operational efficiency and save costs, but also help employees be prepared for the future around the corner. By leveraging AI, businesses can streamline talent acquisition, optimise performance management, improve training and development programs, and enhance employee engagement. 

Surely, challenges exist but they can be overcome by focusing on data quality, ethical considerations, employee involvement, and upskilling efforts.