Key takeaways

• AI will change tasks and responsibilities, while increasing the importance of human judgment and problem definition
• Education needs to move beyond episodic learning toward continuous workforce development
• Critical thinking, strategic reasoning and interdisciplinary skills will become more valuable as routine work is automated
• Employers, universities and technology partners need to work together to close emerging skills gaps
• Workforce transformation must develop alongside AI-led business and technology transformation

Artificial intelligence is changing how people work, but its longer-term impact will depend heavily on how people learn.

In the latest episode of HCLTech’s Powering the Next Era of TMT podcast series,

Dr. Saikat Chaudhuri, Professor at UC Berkeley, joined Priyadarshi Ashok Das (Pad), Corporate Vice President, Telecom, Media, Entertainment and Education at HCLTech, to discuss the future of education and workforce development in an AI-led economy.

Their conversation explored how roles will evolve, why established education models may need to change and how organizations can prepare people to work alongside increasingly capable AI systems.

For Pad, education remains the foundation for navigating any major period of industrial change.

“Education is very close to my heart. I believe that’s a foundation,” he said. “Learning is extremely important, but we will see some amount of radical transformation, starting from schools to colleges to enterprise level.”

AI will change work without removing the human role

Anxiety around AI often focuses on displacement. Pad took a more optimistic view, while acknowledging that the nature of work will change.

“It’s not here to displace human beings,” he said. “Will it change the roles? Yes. Will some tasks get eliminated? Yes. But it’s all going to be to the betterment of society and the experience of human beings in a comprehensive way.”

The distinction between tasks and roles is important. Activities that are repetitive, clearly defined or easily documented are increasingly suitable for automation. The value people provide will move toward areas that require judgment, context and the ability to frame the right problem.

Pad compared this shift with earlier changes in software engineering. Developers may have understood the same programming language, but the quality of their work depended on much more than correct syntax. Strong practitioners understood system design, performance and the hidden technical debt that could emerge later.

AI can now perform some tasks more consistently and quickly, but organizations still need people who can determine how the technology should be used and what outcome it should support.

“How do you take the technology to build a competency?” asked Pad. “That’s where I would say some of the roles and responsibilities would evolve.”

Chaudhuri agreed that AI can raise the baseline rather than reduce ambition. He described allowing students to use AI tools for a project while reducing the timeframe from six months to two. The expectation was that students would use AI to accelerate foundational work and then apply their time to more demanding analysis.

“Go ahead and use all the tools that you want, because that just becomes an average standard baseline,” he said. “We’ve raised the bar and now go and do things which are much more powerful with it.”

The skills that matter are beginning to shift

As AI takes on more routine work, skills such as problem definition, critical thinking and strategic judgment become more important.

Pad pointed to the long-standing value of professionals who can combine different perspectives. In technology services, for example, delivery leaders with a commercial mindset and sales leaders who understand delivery have traditionally brought greater value because they can connect business requirements with execution.

AI is creating demand for similar combinations.

“Are you able to define the problem that well? That’s what is giving rise to prompt engineering, that’s what is giving rise to AI governance, that’s what is giving rise to many roles which do not exist today, or where not that much competency is available,” said Pad.

The ability to connect domain knowledge, business priorities and technical capability will become a differentiator. People will need to understand what AI can do, but also where it should be applied, how its outputs should be evaluated and what risks need to be governed.

That requires education to focus on more than the transfer of current technical knowledge. Technology changes too quickly for static expertise to remain sufficient throughout a career.

Education needs to become continuous

The traditional model of learning is concentrated heavily at the beginning of a person’s career. Students spend several years acquiring knowledge and then enter the workforce, returning to formal education only periodically.

Both speakers questioned whether this model is suited to an economy where technology and required skills change continuously.

Pad said the duration and structure of degree programs may increasingly be open to debate.

“I’m not saying a five-year or four-year degree course is too long, but I think there is a way to optimize that,” he said. “The technology change is incredibly fast.”

Chaudhuri proposed a model in which universities maintain an ongoing relationship with learners. Students could establish the foundations of critical thinking and problem-solving early, then return for targeted learning as their roles and industries evolve.

“If we had on-demand learning, let’s say you do a few years up front to get the basics and then you have an ongoing relationship with the university to keep learning the bits and pieces that you need as you need them in that relevant stage,” he said.

The same principle applies inside enterprises. Workforce development can no longer be treated as an episodic training exercise or a certification completed once. It needs to become part of how the organization adapts continuously.

Pad described this as microlearning supported by partnerships across employers, universities and education providers.

He mentioned HCLTech is working with education partners and universities to rethink learning, while also mapping roles and tasks across industry value streams to help clients develop workforce transformation roadmaps.

Workforce transformation must follow business transformation

A successful AI strategy needs to consider how work itself will change.

Pad described an approach that examines an industry value stream, maps the tasks performed across it and identifies which activities should be automated, redesigned or potentially eliminated. Those changes can then be connected to the people currently responsible for that work and the skills they will need next.

“If the AI strategy and business transformation are not linked to your workforce transformation, it is not a continuous process,” he said. “It is not just attending a 10-week training program and getting yourself certified.”

This moves workforce planning beyond broad AI literacy. It gives organizations a way to identify which roles will change, what new competencies will be required and where learning needs to be embedded into daily work.

It also requires ecosystem collaboration. No company, university or technology provider can address the scale of the transition alone. Employers understand their operating models and industry needs. Universities provide foundational learning and critical inquiry. Technology and education partners can help translate fast-moving capabilities into accessible, practical development.

Human skills will remain central

The conversation also explored whether education should begin developing critical and strategic thinking much earlier.

Chaudhuri argued that quantitative and analytical skills need to be complemented by qualitative reasoning, communication and practical application. He expects the liberal arts to play an important role in preparing people for an AI-led future.

“I actually believe the liberal arts are going to experience a renaissance,” he said. “To be a complete person, I think it’s important to develop what I call quantitative analytical skills, but also the qualitative side.”

Subjects such as philosophy, literature, music and art can develop forms of reasoning that differ from those taught through mathematics and science. Communication, curiosity and the ability to consider different perspectives will matter as people take on work requiring judgment, creativity and collaboration.

Pad agreed that these combinations can create a multiplier effect.

“That convergence and that combination just brings out a multiplier more than anything else,” he said.

AI may increase the speed and accuracy with which many tasks are completed, but the ability to determine what matters, communicate ideas and apply technology responsibly will remain human responsibilities.

Learning will define the next era of work

AI is likely to shorten the shelf life of specific skills while increasing the importance of learning itself.

For organizations, this means connecting technology investment to workforce planning and embedding development into transformation programs from the beginning. For educators, it means preparing students to adapt rather than training them only for a fixed role. For individuals, it means viewing learning as a continuous part of working life.

Pad closed the discussion with a reflection that captures the scale of that shift.

“Retirement is not at any particular age. We retire when we stop learning,” he said. “So, let’s learn together, let’s learn from each other, let’s deliver value to this beautiful world that we live in.”