AI and Talent Development

Easily 9 out of 10 data people I know come from non-computer science backgrounds. Is this the sign of a declining and failing educational system or is it just the natural evolution of things?

As AI takes over the world across industries, one of the big topics of discussion is: what would humans do then? In more recent history, educational institutions have been responsible for providing an answer to this question. You would go to school to become an accountant, or a lawyer, or a doctor etc, and then you become one. The dotted line between “what do you want to be when you grow up” and “what you really ended up doing” has been connected to each other via various stages of progressively advanced education. Now the abundance of the question “what would humans do” and its tangential variants tells me that there is a big gap in people’s heads between educational output and what is realistic for people to do going forward. 

This is, of course, not a complete surprise. Easily 9 out of 10 data people I know come from non-computer science backgrounds. While “people I know” might not be exactly a representative population, I doubt anyone can argue against the possibility and the occurrence of significant career movement to areas that are superficially unrelated to one’s educational background in the past decade or so. Is this the sign of a declining and failing educational system or is it just the natural evolution of things?

A Historical Lens

If we take a look at how “education” has evolved over time, the major shifts over the past few centuries closely align with the various industrial revolutions. In other words, every time the work context has changed drastically, a combination of market forces, business interests, and government incentives created a force towards aligning what people learned as they grew up to what kind of workforce was needed. 

In the 18th and 19th century when work changed from manual labor to mechanization and rise of factories, it transformed the agriculture focused societies into industrialized urban centers, and kickstarted a growing demand for a literate and numerate workforce. Factory jobs required workers who could read instructions, measure, and do simple calculations. The increasing demand for skilled workers led governments to invest in primary education systems.

During the late 19th and early 20th century electrification, telecommunications, and large-scale infrastructure like railroads created another tectonic shift. As industries grew more specialized, the need for vocational and technical education became apparent. Schools began offering specific training for careers in engineering, manufacturing, and other technical fields. Polytechnic institutions and trade schools emerged to provide practical skills to the working class. Governments began extending compulsory education beyond primary school to better prepare students for skilled labor in an industrial society, and even public universities started to emerge. 

Then post-WW-II and until very recently, we have been going through the digital revolution where everything has been slowly becoming computerized. Schools and universities began integrating computers and other digital tools into the classroom. The rise of personal computers in the 1980s and 1990s and the internet in the 1990s dramatically changed how information was accessed and shared. Computer literacy became essential for students. The internet made distance learning and online education possible, democratizing access to education, and then Covid made it the norm. The rapid pace of technological change meant that workers needed to continually update their skills. Education systems began placing a greater emphasis on lifelong learning and adult education programs to help workers adapt to new digital realities and corporates started designing and running reskilling and upskilling programs. 

Rise of Language Models

One of the major technological shifts in the past 5 years has been our ability to computationally analyze and generate natural and formal language. While language in itself is not a complete representation of intelligence, the entrance of the large language models (LLMs) into the public vocabulary has created the speculation of the upcoming “artificial general intelligence” (AGI) and the 4th industrial revolution. If (when?) that actually happens, then we will be one of the first generations that might experience more than one industrial revolution in their lifetime which has significant implications for how we live, work, think, learn, entertain ourselves, socialize, find love, and more. The speculations are also partly based on the progress we are making in other technologies like robotics, IoT, bio-tech, and quantum computing, and the hope that more powerful AI systems mean more major breakthroughs in these areas as we saw in the case of protein folding. 

Even before AGI is here, the rate at which LLMs are getting better at tasks beyond simple linguistic ones is remarkable and the expectation is that we will see significant progress towards automation in tasks that are more traditionally reserved for the human brain. Multi-modal language models and their cousins, especially when combined with more traditional software scaffolds, are expected to be able to do all sorts of tasks that require entry to mid level expertise relatively soon. This type of automation is displacing many tasks that have traditionally been entry points for junior workers after going through the educational system. Routine tasks like data wrangling, report generation, writing software tests, and basic analyses—key areas where people typically learn on the job—are being automated by AI tools. As a result, fewer opportunities for hands-on learning exist at the lower rungs of the professional ladder. The tasks left for humans often require advanced problem-solving, decision-making, and strategic thinking, which are typically handled by senior employees. This could lead to a bifurcated workforce, where junior talent lacks the experience needed to develop into senior positions, potentially leading to talent gaps in more senior, complex roles.

So, the question is, what do LLMs and their future iterations mean for the future of learning and talent development?

Emerging Trends in Talent Development

It is hard to predict where things will go given the pace of change and chaos created by lack of preparedness in the society, industry, and academia. But a few general patterns are most likely to happen:

• Interdisciplinary Skills: The future of work is increasingly interdisciplinary. As AI integrates with fields like healthcare, finance, logistics, and even the humanities, talent development will need to focus on cultivating skills that cross boundaries between disciplines (e.g., AI for biology, AI ethics, or AI in creative industries). This shift will encourage universities and industries to promote cross-disciplinary education where AI workers (human or machine) collaborate with domain experts.

• AI Automation and Tooling: There’s a growing trend toward automated workflow execution and no-code / low-code platforms which require less technical know-how and might operate with natural language as an interface. Talent development will likely focus on higher-order problem-solving skills rather than specific subject matters like manual coding, as more AI tools abstract away the implementation details. The emphasis will shift to developing business acumen and problem-definition capabilities—being able to frame business problems and align them with AI solutions will become critical.

• Fluid Academic Disciplines and Accreditation: The fluidity in learning and working, particularly in AI-driven environments where automation and interdisciplinary skills are critical, does raise important questions about the future of formal, distinct academic disciplines. Rather than seeing academic disciplines as isolated silos, they will be viewed as building blocks for more fluid, interdisciplinary research and industrial work. And instead of solely focusing on the subject matter of the discipline, education will focus on methods of inquiry and problem-solving that are crucial in interdisciplinary collaboration. For instance, the scientific method in the natural sciences, the design thinking process in engineering, and the interpretative methods in social sciences all become the direct learning objectives. Finally, education could shift to a more modular structure where students can specialize in certain disciplinary areas but take modules from multiple fields to create customized learning pathways. This would maintain the rigor of disciplinary knowledge while allowing flexibility for interdisciplinary applications.

• Experiential Learning and Research: Universities will balance traditional disciplinary learning with experiential and project-based learning that reflects the real-world challenges of AI and automation. By integrating distinct academic disciplines with applied, hands-on learning experiences, universities will prepare students to work across boundaries in industry while still being grounded in the depth of formal knowledge.

• Experts Mentors and AI Gyms for Juniors: With the automation of the grunt work, more senior employees will spend some of their time creating learning paths and challenges in AI-powered learning playgrounds where junior employees work with their AI learning buddies to fill the talent gap and become skilled in advanced problem solving, critical thinking, and strategic planning. 

These are likely trends for undergraduate level education and early career development, and the next question becomes the impact of the AI revolution on the postgraduate part of academia and research.

Blurring Academia / Industry Boundaries

Another important trend is that the translation gap, the need for change in an academic idea to become operational in industry, has been shrinking as well. With more research and development firms getting funded, more corporations starting their research labs, and more prominent scientists working for the tech giants or starting their own companies, the separation of research responsibilities between academic and industry is quickly vanishing. This intermingling has started to bridge the gap between the technical knowledge required for developing complex AI systems and the operational expertise necessary to manage them. 

• Bridging the Tech-Business Divide: With more co-pilots and agent systems deployed, business people are more empowered to interact with the traditionally academic artifacts like language models and provide feedback about how those can fit into their operational workflows better. The more applied scientists, on the other hand, spend more time with their industry counterparts learning about what is needed to move their models and pipelines from the abstract world of the lab to the messy, real world. 

• AI System Lifecycle Management: Academics focus more on the entire AI system lifecycle—covering design, deployment, monitoring, and governance—to ensure researchers and students understand both the technical intricacies of building AI systems and the operational aspects of maintaining and scaling them over time. This would even go deeper towards topics like governance, fairness, bias, and ethics built by design in AI systems. 

• Human-Machine Interaction: Research and development in human-AI interaction will become increasingly relevant in the gray area between academia and industry as AI systems are integrated into everyday life and workplaces. Researchers and their industry counterparts will focus on designing AI systems that complement human decision-making and create seamless interactions between AI tools and users.

Verdict

The future of work and education is increasingly intertwined with technological advancements, especially as the onset of the 4th industrial revolution reshapes industries. As AI and automation accelerate, the tasks traditionally performed by junior workers may become obsolete, requiring educational systems to evolve beyond rigid structures like the four-year degree. Interdisciplinary learning, lifelong upskilling, and adaptability will be critical as the distinction between academic disciplines blurs in response to workforce demands. The role of industry in driving innovation, as seen in Nobel Prize-winning research from both academic and industrial sectors, underscores the importance of collaboration. Universities and organizations must create sustainable talent pipelines that focus on practical problem-solving and continuous learning, ensuring workers remain equipped for complex roles.