Generative AI - The Indian Standpoint
Holding a bachelor’s degree in Computer Science from University of Madras and having completed the Leadership Development Program in Digital Strategy from Indian School of Business, Prakash is a seasoned industry professional with over 27 years of experience in Digital Transformation, Customer Experience Transformation, Omni-Channel Retail, Supply Chain Management, and Store Operations areas. Prior to join Ascendion in 2022, he has successfully handled key leadership and managerial roles across Collabera, Cognizant and TCS.
What is your perspective on the current Generative AI landscape in India?
Currently, India is at the forefront of Generative AI adoption, wherein the country’s Gen AI market which stood at $1.1 billion in 2023 is expected to grow by 17x and be worth over $17 billion by 2030. The Indian startup, along with the SME ecosystem being highly focused on Gen AI technology has been instrumental in driving this accelerated growth. What’s interesting to note is the ability of Indian tech companies to be agile while innovating. The tech space in India is always curious and constantly working on adopting technologies and experimenting with them. The government understands the need for AI research and supports the cause with the National AI Mission and the NITI Aayog’s AI for Development program. These initiatives are helping to create a conducive environment for Gen AI innovation in India.
Throw some light on the latest technologies that businesses can leverage to enhance the effectiveness of Gen AI.
Gen AI is powered by language models and datasets that process information much quicker than basic AI-powered models. To maximize the effectiveness of Gen AI, enterprises must look at solidifying their Large Language Models (LLMs), strengthen their Generative Adversarial Networks (GANs) and build robust data management. LLMs are large datasets trained on massive amounts of data that helps the machine think more like a human and work toward producing creative content. However, this requires qualified data and Gen AI engineers who can build and manage LLMs and GANs. On the other hand, it is a known fact that data is the ultimate raw material that influences how Gen AI works. So in order to get maximum impact from one’s dataset, it is important to have agile data management practices such as data quality, diversity, ensuring data fairness, managing data privacy transparency and being accountable.
Briefly explain about a few moral and ethical factors organizations must consider while implementing Gen AI.
Prior to implementing Gen AI into their existing process, it is paramount for businesses to establish a robust governance plan that will help manage ethical and moral challenges that might arise during the implementation more effi-ciently. Also, organizations must strictly adhere to data privacy regulations by verifying the origin and usage of data that is being used to train Gen AI models. Additionally, they should establish clear guidelines Gen AI usage in terms of security, privacy, and compliance, while simultaneously also ensuring to democratize Gen AI in a way that business users, the tech and governance process are all in place. Furthermore, businesses must continuously monitor the impact of Gen AI implementations, identify areas for improvement, adapt accordingly, and track progress towards achieving specific goals such as cost savings or efficiency improvements.
How can businesses ensure that their workforce is properly skilled to reap maximum benefits from Gen AI?
As the need for Gen AI grows exponentially, it is difficult for talent demand to keep-up with talent supply in this space. Thus, it is important for busi-nesses to work with Gen AI experts and market innovators who can train and guide their talent. The first step in this regard is to upskill their existing talent pool. Additionally, companies must work on a train versus hire checklist to identify areas where employees need support and where new talent will help bridge the gap. Lastly, they must onboard skill development enterprises who can offer training across all stages of adoption including ideation, design, test, implementation, innovation, and monitoring.
In your opinion, what does the future hold for Gen AI in India?
Since India is today positioned as a solid partner and a leader in the Gen AI space, there is no doubt that we will soon witness Indian engineers to emerge as experts in the field and leading the world in Gen AI adoption. Also, India will create the biggest Gen AI talent pool; meaning that young engineers of our nation will create the next big thing in the Indian software engineering space. To summarize, in order to experience the transformative impact of Gen AI, organizations must embrace its early adoption, formulate efficient strategies, and have a well defined roadmap to realize its full potential.
What is your perspective on the current Generative AI landscape in India?
Currently, India is at the forefront of Generative AI adoption, wherein the country’s Gen AI market which stood at $1.1 billion in 2023 is expected to grow by 17x and be worth over $17 billion by 2030. The Indian startup, along with the SME ecosystem being highly focused on Gen AI technology has been instrumental in driving this accelerated growth. What’s interesting to note is the ability of Indian tech companies to be agile while innovating. The tech space in India is always curious and constantly working on adopting technologies and experimenting with them. The government understands the need for AI research and supports the cause with the National AI Mission and the NITI Aayog’s AI for Development program. These initiatives are helping to create a conducive environment for Gen AI innovation in India.
Throw some light on the latest technologies that businesses can leverage to enhance the effectiveness of Gen AI.
Gen AI is powered by language models and datasets that process information much quicker than basic AI-powered models. To maximize the effectiveness of Gen AI, enterprises must look at solidifying their Large Language Models (LLMs), strengthen their Generative Adversarial Networks (GANs) and build robust data management. LLMs are large datasets trained on massive amounts of data that helps the machine think more like a human and work toward producing creative content. However, this requires qualified data and Gen AI engineers who can build and manage LLMs and GANs. On the other hand, it is a known fact that data is the ultimate raw material that influences how Gen AI works. So in order to get maximum impact from one’s dataset, it is important to have agile data management practices such as data quality, diversity, ensuring data fairness, managing data privacy transparency and being accountable.
Briefly explain about a few moral and ethical factors organizations must consider while implementing Gen AI.
Prior to implementing Gen AI into their existing process, it is paramount for businesses to establish a robust governance plan that will help manage ethical and moral challenges that might arise during the implementation more effi-ciently. Also, organizations must strictly adhere to data privacy regulations by verifying the origin and usage of data that is being used to train Gen AI models. Additionally, they should establish clear guidelines Gen AI usage in terms of security, privacy, and compliance, while simultaneously also ensuring to democratize Gen AI in a way that business users, the tech and governance process are all in place. Furthermore, businesses must continuously monitor the impact of Gen AI implementations, identify areas for improvement, adapt accordingly, and track progress towards achieving specific goals such as cost savings or efficiency improvements.
How can businesses ensure that their workforce is properly skilled to reap maximum benefits from Gen AI?
As the need for Gen AI grows exponentially, it is difficult for talent demand to keep-up with talent supply in this space. Thus, it is important for busi-nesses to work with Gen AI experts and market innovators who can train and guide their talent. The first step in this regard is to upskill their existing talent pool. Additionally, companies must work on a train versus hire checklist to identify areas where employees need support and where new talent will help bridge the gap. Lastly, they must onboard skill development enterprises who can offer training across all stages of adoption including ideation, design, test, implementation, innovation, and monitoring.
In your opinion, what does the future hold for Gen AI in India?
Since India is today positioned as a solid partner and a leader in the Gen AI space, there is no doubt that we will soon witness Indian engineers to emerge as experts in the field and leading the world in Gen AI adoption. Also, India will create the biggest Gen AI talent pool; meaning that young engineers of our nation will create the next big thing in the Indian software engineering space. To summarize, in order to experience the transformative impact of Gen AI, organizations must embrace its early adoption, formulate efficient strategies, and have a well defined roadmap to realize its full potential.
