Universities urged to embrace continuous learning and innovation in AI era

The speakers argue that universities cannot respond to AI’s exponential growth through occasional updates alone. They must keep learning, testing new approaches, and innovating. This matters because AI is changing management, research, teaching, and the skills employers expect.

The Paari School of Business offers a concrete example. It is integrating AI into its curriculum and departmental workflows, while running faculty development programmes for teaching and research. These steps build institutional ability rather than treating AI as a one-time technology purchase.

The symposium also urged developing countries, including India, to strengthen technological capabilities and research networks. Computing infrastructure is concentrated in a few wealthy economies, so universities must collaborate and develop adaptable systems. The goal is to participate effectively in global innovation while preparing students and researchers for changing industry requirements.

In this context, becoming AI-native means making AI part of a university’s normal academic and administrative life. It is more than buying tools or offering a single course. AI would shape curriculum design, research methods, teaching practices, and departmental workflows. The aim is to prepare students and staff for changing industry requirements.

PSB provides the article’s example. The school is integrating AI into its curriculum and departmental workflows. It is also conducting faculty development programmes to improve how teachers and researchers use AI. These connected actions help turn AI from an isolated experiment into an institutional capability.

The article does not define a formal checklist for becoming AI-native. It presents the idea as an ambition supported by ongoing integration and skills development. For PSB, the direction is clear: develop people, processes, and academic practices that can adapt to intelligent environments.

AI can reshape management by changing how organisations plan, coordinate, and make decisions. It can influence organisational strategy, everyday choices, and leadership work. The symposium linked these changes to globalisation, which now involves easier communication, systemic interdependence, and greater operational complexity.

Allan Bird and Michael Stevens discussed AI’s implications for organisational strategy and decision-making during a session on human capital, leadership, and transformation. Their central caution was important: managers and employees should remain cognitively engaged. They should question outputs and think independently instead of accepting automated recommendations automatically.

The article does not specify particular AI applications or measure their effects. It does show that management is entering a more intelligent and interconnected environment. Organisations will need strategies that combine useful automation with human judgment, especially as operational decisions become more complex and globally linked.

The article does not state how much of the world’s advanced computing infrastructure is concentrated in wealthy economies. It gives no percentage, total, country count, or other numerical estimate. The only available description is that the infrastructure remains concentrated in a few wealthy economies.

That wording identifies an uneven global distribution of computing capacity. It means some countries and institutions have far greater access than others. The article connects this imbalance to the ability of developing countries, such as India, to participate effectively in global innovation.

Because no figure is provided, the scale cannot be quantified from this article alone. Its main point is structural rather than numerical: access is concentrated, and that concentration creates a challenge for countries trying to strengthen AI capabilities, research networks, and global research standing.

Advanced AI research depends on access to capable computing infrastructure. When that infrastructure is concentrated elsewhere, universities and researchers in countries such as India may face greater difficulty developing, testing, and scaling ambitious projects. This can weaken their ability to contribute to global innovation.

The article links this challenge directly to India’s need to strengthen technological capabilities and research networks. Shared networks can help researchers collaborate, exchange knowledge, and build capacity across institutions. They do not remove the infrastructure gap, but they can make national research efforts more connected and effective.

The article does not describe specific shortages, costs, or projects affected. It does state that computing concentration is a barrier to effective participation in global innovation. Strengthening infrastructure, collaboration, and research capacity is therefore presented as important for India’s global research standing and technological future.

When universities cannot match major technology companies’ computing resources, they can focus on capability-building rather than scale alone. Useful alternatives include research partnerships, shared computing facilities, open tools, efficient models, and projects designed around clear local needs. These approaches can widen participation without requiring every institution to own the largest infrastructure.

The article directly supports some of this direction. It calls for stronger technological capabilities and research networks in developing countries. PSB is also integrating AI into its curriculum and workflows, while training faculty to use AI in teaching and research. These actions build skills and cooperation alongside infrastructure.

The article does not list all these alternatives or compare their costs. The broader implication is that universities should experiment, collaborate, and develop adaptable expertise. Such strategies can help them participate in global innovation even when computing resources remain concentrated in wealthy economies.

Artificial intelligence is the field of creating computer systems that perform tasks commonly associated with human intelligence, including recognising patterns, generating outputs, or supporting decisions. Modern AI systems depend on data because examples provide material for learning. They depend on algorithms because algorithms define how information is processed and how patterns are used.

Large-scale computing supplies the processing power needed to train and operate many advanced systems. More data alone is not enough; the system also needs suitable methods and hardware to handle that data. Computing infrastructure therefore becomes a practical foundation for research, experimentation, and deployment.

The article focuses on AI’s effects rather than giving a formal definition. It does stress AI’s rapid growth and the concentration of computing infrastructure in wealthy economies. This helps explain why technological capability and research networks matter for countries seeking a stronger role in global innovation.