AI: The Imperative of Preparation
The best way to predict the future is to create it.
I keep on preparing because I don’t know when I will need it.
- Abraham Lincoln
The way we teach and work must change. For this we require the following preparatory activities:
First, companies will have to re-imagine entry-level jobs. These must reflect the emerging division of labor – roles that teach adaptability, not repetition. This will include jobs that involve ambiguity, emotional nuance, or complex human judgment that are difficult to automate.
Second, engineering colleges must understand that there is an enduring gap between what one learns in school and what companies are looking for. According to New York’s Federal Reserve Bank, computer science ranks seventh in college majors whose worst unemployment rates come next to physics (7.8%) and anthropology (9.4%). For today’s computer science graduate, getting hired is not only about technical skills but flexibility, constant learning, and a willingness to acquire hands-on experience outside the classroom. Internships and agentic skills will matter in the emerging division of labor.
College curricula will have to get real-world experience through internships, open-source projects and real-world artificial intelligence projects. The aim is to build domain expertise, such as translating a business problem into technological terms and then deploying an agentic toolkit to solve it.
In this emerging division of labor, self-learning is crucial for students. In the emerging scenario, university education becomes the first step of start of a lifelong learning process. Students’ vitae must reflect mastery of digital tools, hands-on-internships, open-source contributions and a lively GitHub repo, in addition to mark sheets. This requires cultivation of human skills no algorithm can replicate a new form of collaboration.
Third, software engineers whose current role is highly repetition with little variation, must start to think of evolving their skills. That doesn’t mean abandoning their fields. It means identifying and strengthening the parts of their jobs that require judgment, creative problem-solving, communication, empathy and adaptability.
In the emerging division of labor, jobs are no longer purely technical, and software engineers must possess dual capabilities. For example, Apollo Tyres is using micro-language models (data from individual machines or groups fed into a “mini ChatGPT”) for any of its operators to use normal English words to get to the root causes quicker and solve them. The company plans to use this data to teach models to ensure that the problem does not recur. The new way of collaboration is to orchestrate human-machine teams.
In healthcare, clinicians must learn to interpret outputs from diagnostic AI tools; doctors to learn build on the initial diagnosis given by AI platforms, like the one in Saudi Arabia or the one developed by Microsoft. Sun Pharma has rolled out a solution that tells medical representatives which doctor to meet, leading to a 0.5-0.6% increase in revenue.
In education, teachers must learn to use adaptive learning software to tailor instruction and develop skills that add value to the customized learning provided by AI. In logistics, planners must leverage predictive analytics to anticipate disruptions before they hit the supply chain. Manufacturing must use the gargantuan data available (e.g. SCADA, sensors) by capturing this data and running intelligent algorithms. The time has come to apply what was being applied to small use cases to bring about large transformations.
For example, Amazon has deployed AI-powered robots that can independently unload trailers and retrieve inventory, along with generative AI tools that enhance delivery routing and mapping - innovations that depend on both advanced tech and human oversight. What these roles share is a partnership between human judgment and machine efficiency. These roles amplify human decision-making by pairing it with machine-driven insights.
If the future unfolds as described above, then potentially the middle-class could be replaced by automation and AI. This resulting decrease in many people’s purchasing power would lead to postponement of home buying, decline in car purchases, trimming spends on lifestyle products, reduced holidays, and restrained eating out. This will have an impact on the economy and society, as happened in the US when manufacturing moved out. The need to prepare for this is pressing.
There is no silver bullet to make your job AI-proof. In order to tackle the emerging division of labor, self-learning is imperative for all. Concretely, this means that learning is not a one-time event but a continuous process of acquiring new knowledge and skills. You actively choose what you want to learn, how you want to learn it, and when you want to learn it. It is about embracing a mindset of continuous growth and development, recognizing that learning is a lifelong journey, not just a phase of life.
