Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

Skill Development

A future where boring, unsafe work is done by machines. And fulfilling, high value work is done by humans.

There are many ways to define Artifical Intelligence (AI), but one simple explanation is “intelligence demonstrated by machines”. Typically, looking at vast amounts of data, extracting patterns, making predictions, and acting based on those predictions. 

At the outset, it may sound hunky dory but there are growing fears and murmurs among the workforce (and worker unions) that AI will displace them from their jobs. The looming enemies include AI, internet of things (IoT), industrial robotics, basically anything to do with digital transformation.

Introduction of labor saving technologies is nothing new. So how is the AI revolution any different? How will it affect the future of work? Will it render the workforce unemployable due to lack of relevant skills? Will mass unemployment be triggered in the process? Policy makers, corporate leaders, educators and individuals need to understand these trends better to ensure a smooth transition.

Automation v/s Augmentation

All modern day jobs can be categorized into 2 buckets – high risk and low risk – in terms of their vulnerability to AI.  

High risk jobs are the ones that involve a combination of mundane, repetitive, predictable tasks that can be replicated by an intelligent robot or algorithm. Or in other words can be automated. Real life examples include assembly line processes, delivering packages, burger flipping, flight ticket rescheduling upon customer instruction, verifying prescriptions at a pharmacy, creating custom travel packages, serving tables at a restaurant, housekeeping or even writing basic news briefs.

Low risk jobs are the ones requiring a higher order of human intelligence or intuition. Which are harder to be simulated by an algorithm. Here, technology has potential to augment humans – in other words enabling them to do better work in less time. So instead of replacing them they work side-by-side as coworkers. Case in point – copy improvement (readability, grammar, etc.) for writers, instant background removal or lighting touch-ups for design teams, facial recognition assessment for recruiters to determine culture fit, teaching assistants bots for human instructors or trainers, logo generator for brand teams, virtual executive assistants (EAs) for CXOs, robotic nursing assistants to support over-burdened doctors, predictive sales AI for lead conversion, machine learning for financial underwriters to better assess loan applications. 

Skills in Demand

According to McKinsey – by 2030, up to a quarter of the global workforce may need to transition into new professions or at least upgrade their skills radically. Gone are the days where people could spend their entire lifetime in one occupation. Going forward, an avg. person will need to change careers 5-7 times during their working life. 

In the past century, we’ve seen the demise or diminishment of titles like travel agent, switchboard operator, milkman, elevator operator and bowling alley pinsetter. Meanwhile, new titles like app developer, social media director, and data scientist have emerged in the 2010s. 

In the future, core human abilities—such as adaptability quotient (AQ), storytelling, imagination, creativity, and emotional intelligence, which can’t be easily recreated by technology—will be more in-demand. There continues to be a rise in computer-related professions and demand in science, data analysis, software, engineering, digital marketing, quantum computing, cybersecurity, blockchain and design. Secondly, jobs that involve little or no automation but that do require compassionate human interaction—such as health care, social service, coaching and certain teaching occupations. Third clear trend is around creating an environmental edge, in which case climate literacy and green skills become supercritical. 

Universal Basic Income

One of the biggest policy challenges in response, will be to smoothen the wage pressure caused by the above shifts. As per OECD, around 30% of working age youth are neither in employment, education or training (NEET) in 2022. Inequality between the top 1% of earners and the other 99% of the less secure is widening further especially post-COVID. 

When such a time comes, society needs to think of a social security system that ensures distribution of income among the idle population with no means for food, shelter or clothing. A universal basic income (UBI) could be the answer to a circular flow of wealth. Recipients of UBI could return to school, invest in upskilling, start small businesses – the possibilities are endless. 

The Way Forward

As nations prepare for the digital age, one of the pre-requisites is credible workforce data to make decision making more objective. This could mean making use of more analytical forecasting models to predict supply and demand in the labor market. And integrating those findings into country-level, state-level and industry-level workforce strategies. 

Educational system should look to revamp themselves to become more responsive to dynamic future needs of India inc. Industry-academia collaboration, apprenticeships, real time curriculum tweaks and career mentorship – are some critical interventions. 

Companies need to build infrastructure, platforms and partnerships around corporate upskilling and reskilling of their existing people. Companies that make these timely investments stand to gain a competitive edge over those that continue with traditional workforce practices.

About the writer

Uday Nanda is co-founder of UpSkilledd – a peer to peer workforce training platform. Prior to this, he worked closely with CXOs from diverse industries, as a strategy consultant with AT Kearney in the APAC region. He is an alum of IIT Bombay having completed his M. Tech and B. Tech in Materials Science Engineering. 

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members