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This is the time when AI has truly arrived and is making an impact on the global economy far and wide.
AI is not just limited to automating routine tasks, it is the transformative force redefining the legacy way of doing business across industries. And these redefined businesses inevitably lead to redefining job roles, be they in the form of reskilling the workforce to thrive in this evolving era of continuous learning or upskilling to build the future.
In this article, we will cover the skills one needs to make a successful career in today’s AI-driven economy.
The Shifting Work Dynamics
As an AI leader, I have seen different cycles of AI. From earlier enthusiastic phases to the current, more realistic one, AI has evolved from a niche technology to an essential pillar of strategic growth across varied sectors and industries.
This rising trend of AI has brought automation in manual and repetitive tasks. As such routine tasks are first to get impacted, everyone is wondering which jobs are safe then.
What is it that AI is not able to do, yet?
Note the emphasis on “yet,” because the skills I am going to mention shortly may soon come under the net of AI. With that timeline awareness in mind, and until we reach this outcome, the skills that are valuable for the foreseeable future require human creativity, emotional intelligence, and complex problem-solving.
Clearly, there is a shift in traditional job roles, which on one side makes some skills obsolete, while also creating new opportunities. According to the World Economic Forum (WEF), more than 85M jobs are at risk of being impacted by automation and AI by 2025.
This is the interesting part: it also suggests that another 97M new roles may emerge that are more adapted to the new ways of working. They are more suited to the future of work that involves the synergies between humans, machines, and algorithms.
Therefore, our focus must be on preparing the workforce with the skills that can help them transition into these new roles.
Core Skills for the AI-Driven Economy
Data Literacy
As part of creating awareness about AI, the opportunities that come with it, and its role in shaping our future, I speak at several global forums and conferences. This is the question I am frequently asked: How did you start your AI journey?
Unlike the “hidden secret” that most would expect, my response is fairly simple: data. I had worked with data long enough that transitioning to AI seemed like a natural transition.
Data is the core of AI, hence it is important to build data literacy first. It involves the ability to read, work with, analyze, and communicate data. In other words, interpreting data insights and using them to drive decision-making is an absolute must for everyone from junior employees to senior executives.
No matter what is your role within an organization, honing this skill will serve you well in this AI-driven economy.
Those who say that data is the new currency or the new oil are not entirely overstating its importance. Organizations that can leverage their data effectively are better positioned to foster innovation and optimize operations by making informed data-driven decisions. And this is the arbitrage that distinguishes leaders from laggards.
Human-Centric Skills
Data-centricity is just one side of the coin: the other one is human-centricity. Yes, AI can handle many technical tasks, but it still lacks the human connect. It ties back to the point we previously touched upon about the skills that AI is not yet good at.
Soft skills, such as empathy, ethical judgment, and creativity have become increasingly valuable and include several dimensions of human behavior, such as emotional intelligence: there is no easy way to codify empathy. Think of how AI would manage conflict or navigate tough situations.
Even if there are scenarios where AI is being out to use where it is supposed to understand and manage emotions positively to communicate effectively — it still appears to be “artificial.”
Another aspect that highlights the importance of developing a high emotional quotient is the increasing use of AI to handle more routine tasks. In such a society, human interactions become a key differentiator in customer service, leadership, and team dynamics.
Critical Thinking and Problem-Solving
This one is my favorite and I have been emphasizing it since time immemorial.
In a world where everyone is running towards building solutions — and, the fact, that standard models and algorithms are available off-the-shelf — the key differentiator is in finding the right problems.
But how does one find the right problems? By thinking critically and objectively evaluating information.
Furthermore, AI is becoming much better at providing data and insights, but you still need a human to act on those insights. This requires the ability to interpret these insights, make decisions, and develop strategies that align with organizational goals.
Collaboration and Continuous Learning
AI is a highly collaborative field. No one person can build a high-performing, robust AI; it requires seamless collaboration across diverse teams. With diverse skills and backgrounds, a strong AI profile must possess the ability to communicate the results, the process, and the algorithms.
If you want to ace a career in AI, be the person who can tailor the talk to the right audience and speak at the right altitude.
Now comes the part where the skills we are putting emphasis on today might become less relevant tomorrow.
So, what should one do in those cases? Be agile, be adaptable.
Yes, adaptability helps you learn new skills quickly and respond to changing circumstances. You can do so by embracing a mindset of continuous learning and staying up-to-date with the latest technological trends.
The future of work requires us to prepare our arsenal of skills. Focus on honing data as well as human centricity, and be a life-long learner.
Here is to building the world we envisage.
Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.