Sebastian Thrun said, “Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It is really an attempt to understand human intelligence and human cognition”.

The way many people work could be fundamentally changed by generative AI. Some people might be excited by this concept. What this entails for others may be a concern. In industries where automation is possible, there is no doubt that this technology has the potential to greatly boost productivity and save costs. There may be employment losses as a result of this or, at the very least, fewer new jobs may be produced in select areas. On the other hand, it’s important to take into account that the use of generative AI could result in lower prices, which might increase access to related goods and services for customers from all walks of life and in different industries, such as for education and healthcare, which could increase production volumes and ultimately result in more job opportunities. The way work is done will continue to evolve as a result of our creativity and imagination, many current jobs will flourish and new ones will be developed.

To understand if generative AI will disrupt the job market and how its impact has been till now and what it might be in future, we had a roundtable discussion along with the leaders in the industry to give a fresher perspective. The session was moderated by Anees Merchant, EVP – Global Growth and Client Success at Course5i along with panellists Karthik Ramesh, VP – Client Partner – US Provider & Lifesciences at Emids, Rahul Thota, Founder and CEO at Akaike Technologies, Rathnakumar Udayakumar, Product Lead at Netradyne, Deepika Kaushal, Deputy Vice President at Piramal Capital & Housing Finance and Lavi Nigam, Lead Data Scientist at Google Cloud AI Ecosystem. 

Are we ready?

Technology is growing. It’s getting advanced but the use cases in which you implement it in and in what situation is very different. Few companies are advanced but, for most of them, they are in a basic state. Some are in the very initial stages of just consuming AI. How do we really consume the basic stuff, get the basic stuff on and then get into good analytical stuff which will drive outcomes. Technology is moving fast and very few companies will be able to keep an eye with respect to the pace and also in terms of people who are ready to adopt. 

—Deepika Kaushal, Deputy Vice President at Piramal Capital & Housing Finance

Multimodal

It depends on whom we are selling the store. Educating the clients is something that we’ll have to constantly do. But finally, it depends on who we are speaking to and how we are packaging it. To close a new client takes less effort than closing a principal data scientist. Getting the right talent who have had experience working with different modalities of data, especially the latest technologies is not easy. For one good talent, we have to sift through a lot and it’s not a great experience. For our business, the rate limiting bottleneck is getting the right talent. Otherwise, in terms of the market readiness for solutions like these, customers are ready to take on solutions.

—Rahul Thota, Founder and CEO at Akaike Technologies

The employers perspective of skills

The perfect analogy to make is ten years ago, it was a parallel triangle. There are people that the companies are looking for and you can find those talents across the board. Now, there are different places where you can go and sort them out. Certainly in the last couple of years, the landscape has tilted in a way that many of us didn’t expect to work, for technology to leapfrog this fast. Suddenly, what has happened is, the skill set has become niche and the demand and supply is out of the equation now. What’s really happening is you will find exceptionally talented people but the abundance of opportunity for the ones who have the right skill set is even higher. So, being able to tie somebody to a startup ecosystem has become more and more difficult. At this point in time, any talent that we think has the right skill set is far and few and they have enough options at their doorsteps. 

—Rathnakumar Udayakumar, Product Lead at Netradyne

Can organisations keep up with skills?

This problem has scaled in terms of opportunity and availability of talent. If you asked me today to address this problem, you have a rapid growth of technology threatening to outperform human talent at scale and speed. We focus early on investments within the company. In terms of innovation, we focus on not just the next horizon technologies but we also work closely with academia. It’s not just about going to campuses and doing events but working very proactively with them to take on some of the latest problems in AI/ML. This will always be a niche field while the market and potential continues to grow, we’ll have to look at innovative ways of sourcing. 

—Karthik Ramesh, VP – Client Partner – US Provider & Lifesciences at Emids

Scale: A skill challenge

With respect to scaling, there are two aspects to it. One is people scaling and the other is the value of scaling. It could be coming from scaling certain tools or some IP. In the second aspect, there’s a lot of work going on with a lot of no code tools that are available. The productivity of the individual itself is scaling. For example: There is work happening towards automating prompt engineering, providing data and then through algorithms coming up with the best prompts, that give the kind of outputs that are needed. It may not be true as of today but, going ahead, it could also happen that people who are good at English are all that we want for it.

—Rahul Thota, Founder and CEO at Akaike Technologies

When you look at scaling, you’re looking at both vertical scaling and horizontal scaling. At this point in time, scaling towards the technology side rather than the human resource is the way to go. That is exactly how the technology is scaling as well. There are ways to do things much faster than most people can, with the help of a copilot. Even though the copilot is just an assistant, it can work with someone who is capable of doing things on their own to speed things up even more, without needing a big team.

—Rathnakumar Udayakumar, Product Lead at Netradyne

Employee Perspective

One of the biggest myths in data science, or AI journey, that is a missing piece is the engineering culture. What we are not doing, and I think it’s important, is we don’t inculcate engineering culture and data science. To be a better data scientist or machine learning engineer, we need to inculcate those basic foundational engineering practices because when you go into an industry and you work on these models, foundational models, deep learning, ultimately, it has to be part of a stack, which is already software engineering driven. It’s important that we also adapt the evolution of how we are teaching students so we teach less of them in the stack of notebooks and more in the engineering sense.

—Lavi Nigam, Lead Data Scientist at Google Cloud AI Ecosystem 

How to build a career today?

You need to constantly build innovative ways of learning and learning need not be from various online institute’s, learning can also be internal. You need to continuously build your skill set. Today, you have so many online ways of doing it, forget formal education and institutes of eminence which gives a certification alumni status, but so many ways to self learn things. Your AI talent problems are not going to get solved by just hiring more AI data scientists. Some of these technology trends, we talked about generative AI, prompt engineering, cryptocurrency, blockchain, they’re all there. But they are not going to replace jobs or skill sets. You look for an individual who comes with a mindset and a passion to learn and unlearn. It’s not just data science, statistics and machine learning but basically being adaptable.

—Karthik Ramesh, VP – Client Partner – US Provider & Lifesciences at Emids

Shu Ha Ri – stages of learning towards mastery

From an education industry perspective, I think we are only preparing for a job because we are doing MBA in the similar fashion, we are not really going anywhere. Ten years ago, when we used to create predictive models, it was all about hypothesis testing, null hypothesis creating those models predictive models. Now ML is a new buzzword. So, everybody started getting caught on to learning and now we see it’s a black box. But from a formal education perspective, it really needs to inculcate that learning things creates interest. The education system has to focus more on building its aptitude than problem solving. Keep learning in a tech environment because technology is evolving as we speak, and include a business component to it. 

—Deepika Kaushal, Deputy Vice President at Piramal Capital & Housing Finance

Since 2017, and especially over the past decade, inventive human intelligence has enhanced the state of generative AI. Human intelligence created novel deep learning architectures, text statistical analysis techniques, and training methodologies for AI models using the extensive online ‘literature’. The Lovelace effect thus noted that artificial intelligence cannot not include human creativity and imagination.