Can India Strengthen Its AI Future by Retaining More Talent?

Artificial intelligence is becoming an important part of discussions about research, economic development and technological competitiveness. For countries seeking to build their own AI capabilities, access to skilled researchers and engineers is only one part of the challenge. They also need an environment in which talented people can conduct research, develop products and build companies. The topic of Rajat Khare AI is explored in Business Today’s article about India’s AI ambitions and brain drain, which highlights talent retention, research support and the potential value of multilingual AI.

Why Talent Matters in Artificial Intelligence

AI development requires a combination of skills. Researchers work on models and methods, engineers build the systems that make those models usable, and product teams connect technology with the needs of customers and communities.

A country may educate a large number of technically skilled people but still struggle to translate that talent into locally developed products if research opportunities, funding or commercial pathways are limited. Skilled professionals may choose to work elsewhere when they believe another environment offers better resources, career development or compensation.

Business Today’s article presents brain drain as a challenge for India’s AI ambitions. It discusses the need to create stronger opportunities for researchers and technical professionals so that more of their work can contribute to innovation within the country.

Retaining talent does not necessarily mean restricting international mobility. Researchers can benefit from global collaboration, overseas experience and connections with international institutions. The broader challenge is to create attractive opportunities at home while allowing professionals to participate in global research and technology networks.

Connecting Universities, Research and Industry

Research can generate new methods and technical discoveries, but turning them into useful products often requires collaboration beyond academic institutions.

Universities may provide specialist knowledge, research facilities and trained graduates. Businesses can identify practical problems, test potential applications and develop products that customers can use. When these groups work separately, useful discoveries may take longer to reach the market.

Stronger industry-academia collaboration can help connect these stages. Joint research projects, internships, shared laboratories and partnerships between researchers and startups are possible ways to bring technical expertise and practical needs together.

Such collaboration also benefits from clear objectives. A project should establish the problem it intends to address, the evidence needed to assess progress and the responsibilities of each participant. Not every research project will become a commercial product, but a clear process can help teams evaluate its potential and learn from the results.

For India’s AI ecosystem, the important question is how to make research and development opportunities accessible to skilled people across different institutions and regions.

Why Research Funding Is Important

AI research requires people, computing resources, data, time and appropriate facilities. Funding can help researchers investigate new methods, test ideas and develop systems that would otherwise be difficult to build.

However, funding alone does not guarantee meaningful results. Research programmes need well-defined goals, suitable expertise and a way to evaluate progress. Support should also reflect the different needs of exploratory research, applied development and commercial product building.

Business Today’s article discusses stronger research support and investment in deep-tech startups as parts of the broader effort to strengthen India's AI position. The article also points to the importance of making research careers and opportunities more attractive.

For entrepreneurs, research funding can help reduce the gap between a technical concept and a product that is ready for further testing. For researchers, sustained support can make it easier to pursue complex problems that require more than a short development cycle.

The Opportunity in Multilingual AI

One of the central themes in the Business Today article is India's linguistic diversity. AI systems that work well across Indian languages could potentially serve users whose needs are not fully met by tools designed primarily for other linguistic environments.

Language is more than translating individual words. Useful multilingual AI may need to handle differences in grammar, context, local expressions and the way people communicate in different situations.

This creates technical and practical challenges. Systems need appropriate training and evaluation data. They should be tested with speakers of the languages they are intended to support, including people who use different dialects or have different levels of digital familiarity.

A model that performs well in one language may not produce equally useful results in another. Evaluation should therefore consider the intended application, the quality of the output and the consequences of an error.

If developed responsibly, multilingual tools could support applications such as education, customer services, information access and business communication. These are potential use cases, not a guarantee that every multilingual AI product will be accurate or suitable for every task.

Building AI for Local Needs

AI development is more useful when it addresses problems that people and organisations actually experience. Local needs may differ by language, geography, industry and available infrastructure.

For example, an application intended for a multilingual public service may face different requirements from a tool designed for software developers. A system for a small business may need to operate within limited budgets and straightforward workflows. A solution intended for a specialised industry may require carefully selected technical data and expert oversight.

Founders should therefore begin with a clear use case rather than assuming that a large AI model is automatically the best solution. They should ask who will use the product, what task it will perform and how its performance will be measured.

This approach can help researchers and entrepreneurs identify practical applications for AI. It also helps investors assess whether a proposed product has a clearly defined market and a credible development plan.

Making It Easier for Researchers to Build in India

Talent retention depends on several connected factors. Researchers need access to suitable facilities, funding and opportunities to work on challenging problems. They also need career pathways that reward expertise and provide room for professional growth.

Universities, companies and policymakers can each influence these conditions. Research institutions can support collaborative projects and practical training. Businesses can create roles that allow specialists to work on technically ambitious products. Public programmes can help support research and entrepreneurship where commercial funding alone may be insufficient.

Compensation is another consideration, but it is not the only one. Access to mentors, quality of research infrastructure, freedom to investigate important problems and the possibility of seeing work used in practice can also influence career choices.

There is no single policy that will resolve every cause of brain drain. Different fields and career stages may require different approaches. A sustained effort is likely to involve several institutions working together rather than relying on one initiative.

The Role of Deep-Tech Startups

Deep-tech startups can create a pathway from research to commercial application. These businesses may work on complex problems in AI, robotics, healthcare, energy or other technology areas. They can provide opportunities for engineers and researchers to apply their skills to products and services.

Yet deep-tech companies often face long development cycles, uncertain demand and the need for specialised expertise. Their funding requirements may differ from those of businesses that can launch a simpler product quickly.

Investors considering AI startups should look at the problem being solved, the technical evidence, the team’s capabilities and the path to customers. Founders should be clear about what their technology can currently do and what remains to be demonstrated.

This makes the relationship between research, entrepreneurship and investment important to the broader AI ecosystem. Talent needs meaningful projects, and projects need a credible route from technical development to practical use.

Measuring Progress Beyond Headlines

A country’s AI ambitions should be assessed through more than broad statements about leadership. Useful indicators may include the quality and reach of research, opportunities for skilled professionals, the creation of sustainable technology businesses and the practical adoption of locally relevant AI tools.

The right measures depend on the objective. A research programme may be assessed through technical results and collaboration. A startup may be assessed through product performance, customer use and commercial sustainability. A public-service application may need to demonstrate reliability, accessibility and value for its intended users.

Clear measurement makes it easier to understand whether initiatives are achieving their goals. It also helps policymakers and businesses identify areas where more support or a different approach may be needed.

Conclusion

India's AI future will depend on more than the number of engineers and data scientists it produces. It will also depend on whether skilled people can find meaningful research opportunities, whether institutions collaborate effectively and whether startups can turn technical ideas into useful products.

Business Today’s article discussing Rajat Khare’s perspective highlights talent retention, research funding, industry-academia collaboration and multilingual AI as important themes. These issues are connected: people need opportunities to build, and those opportunities need a supportive research and business environment.

The practical goal is not simply to keep talent within national borders. It is to create conditions in which researchers, engineers and entrepreneurs can contribute to ambitious work, collaborate internationally and develop AI that responds to real needs. Progress will require sustained effort, credible evaluation and investment in the people who make innovation possible.

Source

Business Today — Rajat Khare Believes India Can Lead the World's AI Revolution by Just Stopping Brain Drain

This article is an informational discussion of the themes reported in the source. It does not claim that talent retention or any individual policy alone will guarantee AI leadership.