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Author's profile photo Dmytro Spilka

Machine Learning and Predictive Analytics will Power Remote Work in the Age of the New Normal

The emergence of the Covid-19 pandemic has prompted a widespread shift towards remote work over the past year. Technological measures have been made for many enterprises to maintain their operations and to keep employees connected at all times. Now, as we look to the future of employment in the age of the ‘new normal,’ machine learning and predictive analytics have been identified as two technologies that will drive tomorrow’s work from home (WFH) workforces. 

The widespread digital transformation towards WFH practices for businesses around the world would’ve been largely impossible if the pandemic emerged just five or 10 years ago, such as the pace that cloud computing is evolving. 

As Covid-19 began to lead to lockdowns around the world, businesses were faced with the challenge of keeping networks secure while employees used their remote devices and home WiFi to stay productive – continuing to collaborate on tasks and managing progress were among the challenges that emerged during the health crisis. ML and predictive analytics soon emerged as a means of aiding effective collaboration. 

Data shows that in India, remote work job searches from prospective employees surged 377% during lockdown, while job postings increased by 168%. Significantly, 60% of HR managers believed that they would continue to offer remote work even after the pandemic subsides. 

The data also shows that, contrary to commonplace theory, 83% of employers believe that offering remote work can lead to a boost in productivity. This statistic may bode well for the 83% of job seekers who believe that remote work is an important factor in their job search since the beginning of the pandemic. 

Employee sentiment towards working from home has grown after many workers gained a taste for the convenience of abandoning commutes for remote work. This indicates that WFH will remain pertinent after the pandemic, but do we have the technological infrastructure to support remote workforces over more long-term periods? Let’s take a deeper look into how ML and predictive analytics can play a central role in powering remote work long into the future: 

Machine Learning to Fill in the Gaps

In the wake of the pandemic, more businesses are set to adapt their models to accommodate digital transformation and prepare for the future of WFH. Shifting to remote working for employees isn’t a simple challenge, and not every employer has been able to successfully implement the system during lockdowns. However, artificial intelligence and machine learning for WFH innovations can play a significant role in establishing more modular and better-managed workloads. 

One of the biggest drawbacks of remote work stems from managing the productivity of employees which can effectively be covered by AI and ML. Various programs built on these technologies can help to craft WFH modules that not only help to manage productivity but also to delegate tasks to employees efficiently and effectively. These new approaches can play a key role in the development of remote work whilst keeping opportunities alive for full-time WFH employees. 

Already, we’ve seen cloud-based project management tools like Monday utilise AI to automate routine elements of the management of staff in the form of aiding the tactical planning of managers and the scheduling of meetings. Traffic light systems have been implemented by the app to provide a swift but comprehensive overview of employee performance without them feeling micromanaged by constant requests for updates. 

In the future, these platforms will continue to utilise ML to understand the behaviour patterns of employees and to help guide employers on how their workers typically handle the tasks that they’re given, which tasks they trend faster and longer on, and provide recommendations on how to distribute teams to optimise performance. 

Solving the Challenges of Data Science

The age of WFH provides employers with the opportunity to democratise the capabilities of data science, and minimise the number of trade-offs between the insights that data can provide and the time it takes to mine and take action with such clarity. 

The development of tools or features that are better placed to apply predictive analytics techniques to business problems means that data scientists can gain more time to focus on more complex challenges. In this way, business leaders can enable more teams to make data-driven decisions whilst keeping up with the ever-evolving business landscape at the same time. 

In the future, predictive analytics platforms will support WFH by reducing the volume of data exploration and prep work needed to gain actionable insights, empowering analysts to deliver data science outputs at lower costs and increasing the chances of creating successful models with more powerful exploration use cases. 

The combination of machine learning and predictive analytics can pave the way for driving a workforce with greater planning and management. For instance, predictive analytics can help to analyse patterns for time management from email and calendar data – helping to manage a distributed network of employees. 

AI and ML tools can also analyse video interviews to determine employee interest and productivity. Technology developed to enable monitoring, feedback, personalisation and even gamification will continue to grow to support the WFH movement. In the future, HR decision-makers will become increasingly dependent on big data to step in to drive productive business outcomes – paving the way for a more competitive advantage in the age of the new normal.

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