“AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved”—a quote by Kai-Fu Lee, the author of ‘AI Superpowers: China, Silicon Valley and the New World Order’. 

The idea of a job market has been evolving since the first industrial revolution when machines made industries more efficient and high yielding. 

Today we live in the era of the fourth Industrial revolution where AI and technology has changed the way we live, work and also how the workforce in the future may be affected. The fear of technology taking up jobs has been evident, especially due to our increased dependency on computers. 

Keeping this in mind, the question arises—what might the job market look like during the age of intelligent automation and who would be responsible for it?

At the Round table conference, moderated by Lavi Nigam, Lead Data scientist at Google along with panellists Pooja Goyal, Co-founder and Chief operating officer of Avishkaar; Amit Gupta Director, Consumer at e&; Parikshit Nag, Head of Data and Analytics at Indus OS; Ashwin Swarup, Vice President–Data Science at Digite and Krishnaswamy Divakaran, Director Analytics, with the agenda being  role of AI leaders when it comes to the job market from an automation point of view and what role do they play in it. 

The Round table Conference was hosted by AIM leaders Council—an invitation-only forum of senior executives in the Data Science and Analytics industry. 

Let’s hear what our experts have to say.

Clarity on the future of work with evolving automation 

The future of work is primarily affected by technology along with other factors like longevity, gender diversity and remote work. Technology has been in place but the pandemic was a catalyst for remote work. Optimising the workspace is what the next step must be especially after the comfort of remote work. 

Pooja Goyal, Co- founder and Chief operating officer of Avishkaar

With automation, we are getting rid of redundant work and using human intelligence the way it should be. Machines are learning from historic patterns with the real time component. We need to upskill the people so that they understand the working mechanisms of algorithms and the reason why such recommendations are made. Digital transformation is not stagnant. AI understanding or interpretability, or AI is something which will also become a mandate at some point in time to understand how these systems work.

Parikshit Nag, Head of Data and Analytics at Indus OS

Human behaviour is essentially a mean reversion model. The data that is being generated is not a machine but it’s humans who are generating this data. It is important to humanise work for which collaboration is an important tool. Data science will transition from being rule based and the future will have exceptional handling where they try to figure out why the algorithm is failing. 

Ashwin Swarup, Vice President–Data Science at Digite

Dependency on AI and its effect on upskilling

Our education system works in silos. There needs to be an understanding of multiple disciplines and bringing it together as a project. The speed of change, which is the ability to learn about the disciplines is going to accelerate and we need to learn and unlearn along the way.  

Pooja Goyal, Co- founder and Chief operating officer of Avishkaar

Upskilling of people in data science organisation wide where we need new algorithms, new experiments and innovation. Education academies within the organisation are important to bring in AI awareness. Domain experts who understand AI to make a business impact that solve real life business problems.

Amit Gupta, Consumer at e&

The education system is not robust enough as it creates an understanding of Data science being simple, which it is not. We need to take the effort of growing as an individual which may be understanding technologies, nuances it comes with and understanding how to interpret machine learning models. 

Parikshit Nag, Head of Data and Analytics at Indus OS

The industry and education systems need to collaborate. The only way to solve it is internships. The industry should invest in interns because there’s no way to design courses for the domain but to be part of the domain by working on the ground. The tool, task and the learning need to be brought together.

Ashwin Swarup, Vice President–Data Science at Digite

Ethical use of algorithms 

Data science models help us make better decisions because they can form patterns, crunch information. But the challenge is that these are black boxes and the transparency is missing. This cannot be solved at an individual level but the push has to come from policies by organisations.

Pooja Goyal, Co- founder and Chief operating officer of Avishkaar

A human should not be in the position where the AI makes the decision.

Ashwin Swarup, Vice President – Data Science at Digite

Analytics is not mainstream and is a service provider in companies. Most organisations should have a seat for the chief analytics officer which brings in a level of leadership and accountability automatically becoming ethical. The structure of the data science teams must ensure that there are people to develop, deploy, feedback and recycle because only then the value of AI would be there but still giving the required product out within the realms of the society. 

Krishnaswamy Divakaran, Director Analytics

Over the last four years, the use of data has increased by 270 per cent. AI has the potential to create more innovations that will not only create new sectors like the Gig economy but also lead to the creation of more jobs. 

The fear that revolves around ‘Human or Computer’ can be altered to ‘Humans and Computers’. AI is being adapted into every industry and we need to be aware and embrace the new technology around us. As Kai- Fu Lee says, “AI will do the analytical thinking, while humans will wrap that analysis in warmth and compassion.”

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.