AMD India’s Jaya Jagadish Says AI Skilling Must Teach Where Technology Creates Real Value

India’s push to build an AI-ready workforce should not stop at teaching students how to use artificial intelligence tools. The bigger priority is helping them understand where AI can create meaningful value — and where using it may not be worth the cost, according to Jaya Jagadish, Country Head and SVP, Engineering at AMD India.

Speaking to ET Online, Jagadish said AI adoption would require more than technical familiarity. Strong domain knowledge, sound judgement and creativity will remain essential as businesses determine which tasks should be automated and which still require human expertise.

“You can’t just slam everything with AI because AI comes at a cost,” Jagadish said.

Her comments came as AMD and the University of Delhi announced a strategic collaboration to train up to 10,000 students in AI skills over the next year.

AMD and Delhi University Partner to Train 10,000 Students

The partnership, formalised through a memorandum of understanding (MoU), is designed to give students and faculty practical exposure to AI development rather than limiting training to theoretical concepts.

Participants will receive access to AMD’s open-source ROCm software platform, which is used to build and run artificial intelligence and high-performance computing workloads on AMD GPUs.

The programme will also provide learning resources, technical workshops and developer GPU cloud credits.

The objective is to allow students to move through the full development cycle — from experimenting with AI applications to building, testing and deploying solutions using industry-oriented tools and computing resources.

Jagadish said access to infrastructure is an important part of developing practical AI skills.

“You need to have access to the hardware and the platform where you can build AI,” she said.

AI Skills Alone May Not Be Enough

The growing interest in AI among students is encouraging, Jagadish said, but she believes the most valuable workers will be those who combine AI capabilities with expertise in a specific field.

She pointed to two complementary skill sets: understanding how AI is built and used, and knowing how a particular industry or profession works.

“AI awareness — how to build with AI and how to use AI — is, of course, very important. But along with that, your expertise in your domain is absolutely essential,” she said.

That combination could become increasingly important as AI moves into sectors where technical knowledge alone cannot determine whether a particular application makes sense.

For example, knowing how to operate an AI system does not necessarily mean knowing whether a business problem requires one in the first place.

‘Does This Really Need AI?’

Jagadish’s central argument is that AI adoption should be driven by value rather than technology for its own sake.

AI systems can improve productivity and automate repetitive processes, but their deployment can also involve computing, infrastructure and operational costs.

That means companies and workers need to evaluate whether introducing AI will actually improve an outcome.

“You need to know where to apply it,” Jagadish said.

She also urged users to exercise judgement when evaluating potential applications of the technology.

“You need to use your judgement and ask: Does this really need AI?” she said.

The approach suggests that AI literacy should include not only tool proficiency but also the ability to assess efficiency, cost, relevance and potential business impact.

AMD Wants Students to Move From Using AI to Building With It

The University of Delhi partnership is intended to address another gap in AI education: the difference between using existing AI applications and actually developing AI solutions.

Students may learn the fundamentals of artificial intelligence in a classroom, but practical development requires access to computing infrastructure, software frameworks and opportunities to experiment.

Through AMD’s ROCm ecosystem and cloud-based GPU resources, participants will be able to develop and test AI applications and explore how the technology can be incorporated into existing workflows.

Jagadish described the programme as a way to create an environment where students can experiment, build and learn collaboratively.

The broader ambition is to make AI development more accessible rather than concentrating advanced infrastructure and opportunities among a relatively small group of students and institutions.

“We don’t want this to become a technology that is available only to a premium class of people,” Jagadish said.

University of Delhi Sees AI Partnership as Talent Pipeline

University of Delhi Vice-Chancellor Prof Yogesh Singh said the collaboration would bring academic education together with access to contemporary AI computing platforms.

The partnership is expected to support the development of a stronger pipeline of AI developers and researchers by giving students exposure to technologies used for real-world AI and high-performance computing workloads.

Such industry-academia collaborations are becoming increasingly important as universities attempt to align curriculum and practical training with the rapidly changing requirements of the technology sector.

AI Could Change Jobs Rather Than Simply Eliminate Them

Jagadish also addressed one of the biggest concerns surrounding AI adoption: its potential impact on employment.

She does not expect AI to simply eliminate jobs across the board. Instead, she believes the nature of many roles will change as workers use AI to automate certain tasks and spend more time on complex or higher-value activities.

“You won’t be out of a job, but your requirements will be different. Your landscape will be different,” she said.

This shift could require workers to continuously update their skills as AI changes how tasks are distributed within organisations.

For employers, meanwhile, it could mean redesigning job descriptions and workflows around a combination of human expertise and AI-assisted productivity.

AI Reskilling Will Extend Beyond Computer Science

The demand for AI skills is also unlikely to remain confined to traditional technology roles.

Jagadish argued that artificial intelligence is becoming a broadly applicable technology with potential uses across industries ranging from agriculture to space.

As a result, future professionals may need to understand how AI intersects with their particular domain rather than becoming AI specialists in isolation.

An agriculture professional, for instance, may need to understand AI applications relevant to farming without becoming a machine-learning engineer. Similarly, specialists in manufacturing, healthcare, finance, research or other fields may increasingly need enough AI literacy to identify useful applications within their own areas of expertise.

The Future Workforce Will Need Domain Expertise and AI Literacy

The emerging model of AI skilling therefore goes beyond producing people who know how to operate generative AI tools.

Workers will increasingly need to understand what AI can do, where it should be applied, what it costs and where human judgement remains indispensable.

For students, that means technical AI exposure could be most valuable when paired with strong fundamentals in another discipline.

AMD’s partnership with the University of Delhi is aimed at providing one route to that practical experience by combining academic learning with access to AI software, GPU infrastructure and development resources.

As AI adoption expands across industries, Jagadish’s message is that the most valuable skill may not simply be knowing how to use the latest AI system. It may be knowing when to use it, why to use it and what problem it is actually solving.