Are Artificial Intelligence and Machine Learning reshaping Remote Work?

Artificial Intelligence, Machine Learning, and Remote Work: A Preface

Did you know that according to Morning Consult, about 85% of tech workers identified as working fully remote or following a hybrid model?

This has stirred an opinion that tech companies are spearheading the adoption of the remote work culture and this is not far from the truth.

Companies like Turing, Twitter, Spotify, Twitter, Apple, and Airbnb have fully adopted a fully remote and hybrid system for their workers. Of course, remote work is gradually becoming an allure for most top companies after the Covid-19 pandemic. Although most companies have been slow in adopting this work trend, there has been an increase in the consideration of the WFH (Work-From-Home) culture in most industries and companies. Interestingly, most Silicon Valley companies are now trying to adopt this culture as it has experimented during the Covid-19 pandemic, and it was seen to spur rapid growth and enable companies to hire from a global talent pool. As Turing’s CEO, Jonathan Siddharth puts it, ‘Silicon Valley has moved to the Cloud, Remote-first is the new way to build unicorns’.

You might think that only top companies are adopting this method because of its economic benefits. However, this paradigm shift is also being adopted by most startups because it is economically tough to compete with top Silicon Valley companies for top talents in the Bay Area. Now, most startups are forced to ask themselves, ‘Why don't we source talents globally and work remotely?’

For this purpose, Jonathan Siddharth remarked:

“Silicon Valley may still be the best place to start a company, but if you’re a founder, it’s now financially reckless to scale your company in the Bay Area. ‘Boundaryless’ companies are now the new normal — and this transformation calls for a new way to build companies with a globally distributed workforce,”

But how can companies effectively hire remotely and sustain a remote workforce churning out the same production numbers as an onsite workforce would? That is where Machine Learning and Artificial Intelligence come in.

Machine Learning is emerging as a strategy that helps employers efficiently source talents and conduct recruitment. This has become a trend due to the gradual increase in the labor force. The United States Bureau of Statistics (BLS), estimates that from 2016 to 2026, there will be an annual growth rate in the labor force of 0.7%, causing an 11.5 million increase by the end of the decade. To that end, Machine Learning is gradually becoming a tool that allays the recruitment challenges faced by employers both for on-site and remote working.

On the other hand, generic Artificial Intelligence is becoming an affiliate term with remote work; revolutionizing the way efficiency is promoted using remote work. Employers can now manage Work-From-Home employees, using AI-powered solutions that enable association of analytics and that indicate low engagement of employees.

How is Machine Learning reshaping Remote Recruitment?

It is pertinent to note that Machine being a subset of Artificial Intelligence, is mostly used for talent sourcing and recruitment in this domain. Machine learning is a suitable technology for recruitment because it possesses the ability to learn present situations, predict future events, and improve on predicted instances, all by leveraging available data. Machine learning favors remote recruitment in the following ways:

• Predictive analytics: Since employee turnover is a big challenge for global remote companies, employee engagement and retention is an increasingly important facet of human resource management. Machine learning employs predictive analytics in situations like this, to recommend candidates that have a higher chance of fitting into the company’s workforce as fast as possible and consequently, stay longer with the company.

• Applicant tracking and assessment: This is probably the most popular application of Machine Learning in talent sourcing and recruitment. It is particularly useful because most remote companies tend to receive high volumes of applications across roles. In this regard, Machine Learning helps to track applicants’ applications, calculate applicants’ fit scores, track interview journeys, and speed up the process of obtaining streamlined feedback from applicants.

• Eliminating hiring bias: The current status quo in hiring is heavily flawed and influenced due to reasons such as the biased nature of traditional hiring tools, neglect of a large pool of applications, and the unconscious human bias towards minorities. Machine Learning improves the hiring process by eliminating unconscious human bias and assessment of a large pipeline of candidates without bias or human error due to stress. However, many still argue that Machine Learning will not improve the hiring process in this regard because it inherently learns human behaviors and follows them. Factually, this can be avoided. Machine learning does not only learn human patterns but can be trained to improve on them. OpenAI and the Future of AI institute are implementing design principles that will make Machine Learning in AI ethical and fair.

• Attracting remote talent: This is one of the most interesting applications of Machine Learning. Companies like LinkedIn, Seek, Indeed, and Glassdoor employ Machine Learning in this regard to recommend top talents to fitting jobs using algorithms built with users’ search history, map data, connection, posts, and clicks.

Ways AI is reshaping Remote Work

The generic Artificial Intelligence is of utmost importance for remote work because of its useful applications that have helped scale most remote companies with a large number of remote workers. Among its numerous applications are:

• Time tracking and streamlining scheduling:Flexibility in working schedules is one of the biggest flexes of remote work. Artificial Intelligence improves flexibility through AI-based scheduling apps. These apps plan a worker’s day using various variables that are but are not limited to meetings, deadlines, or estimated time to finish a task. They also help users to find free time on their plates for other things that may or may not be work-related.
  
• Enhancing cybersecurity: It has become more challenging for companies with remote workers to keep their company infrastructure secure because team members who work from home are less likely to follow cybersecurity guidelines. This is why most companies employ cloud securities that could help vet access to their workplaces based on current characteristics. These cloud securities use Machine learning and AI to build and manage policies that decide a user’s access to a zero-trust network.

• Real-time support: For customer-centric businesses, AI helps remote workers to resolve customers’ issues in real-time. AI can help with issues like prompt allocation of customer complaints to the appropriate department for quick resolution. This is useful when entries are very large and human sorting can prove to be tedious and error-prone.

• Aiding communication through Email management and improvement of video conferencing: Email communication is one of the essential ways of remote team communication. It may seem unexceptional but AI enhances email communication in many amazing ways for remote workers. AI features in email can help remote workers group email by priority, apply the do-not-disturb feature or snooze, and Smart reply/Smart Compose features. Moreso, an AI tool called Flowrite produces email content by asking users for a bulleted list of content they would like to include in the email. This helps improve productivity, save time, enhance grammar, and can help those with dyslexia.

  In addition, communication in video conferences can also be improved in remote team meetings by using AI tools that feature analytics to track meeting engagement levels, and meeting lengths, and also make suggestions on how to improve future gatherings.

• Remote employee assessments: It can be quite difficult for team managers to track team members’ performance in a remote work setting. But AI can help track employees’ performance through employee data and by comparing key indicators in an instance. This implementation is always accompanied by human supervision.

Conclusion

Machine Learning improves remote talent acquisition in each of its three processes: Talent recruitment, Talent Sourcing, and Candidate Screening and Engagement. Artificial intelligence improves remote work in employee-centric and customer-centric conditions. This goes a long way to show how far AI and ML are being implemented to reshape the modus operandi of remote work, and probably the future of work. However, AI and ML are not without their flaws, but as growing technologies they have shown bright light and hope for the nearest future that may be hinged on remote work.