Introduction
Generative AI uses advanced machine learning techniques to analyze large datasets and generate new content based on the context, style, structure, and tone of the original data. When creating content, the AI model draws from patterns in the data to create outputs that are often indistinguishable from human-created material, whether it’s text, images, code, or even music.
How Gen AI work
- Training of model: It serves as the foundation for the Gen AI application and tool which consists of existing data like text, images or audios.The neural network’s architecture is designed to identify patterns and relationships in the dataset. For example, an image generation model might be trained on billions of pictures to learn the patterns of color, shape, and composition.The most common foundation models today are large language models (LLMs), created for text generation applications.
- Tuning: Most of the foundational models are generalist in nature. Its knowledge base is very wide but it can’t generate specific types of output with desired accuracy or fidelity. For that, the model must be tuned to a specific content generation task. It involves feeding the model labeled data specific to the content generation application questions or prompts the application is likely to receive, and corresponding correct answers in the desired format. It also involves human feedback for greater accuracy or relevance.
- Generation, evaluation and re-tuning: When a user provides a prompt (e.g., “Shimla in snowfall”), the model processes this input. For text-based models, this involves breaking down the prompt into “tokens” (words or parts of words). Through token processing, the model can better understand relationships between words and generate more nuanced outputs, enhancing the model’s ability to create coherent sentences and maintain context over longer passages, ultimately improving its performance in tasks like text generation and conversation.
Measure of Gen AI tools’ performance
The performance of Gen AI tools is how fast it can process input and generate output? It is based on:
- Latency(response time): Time taken between giving input and receiving output.
- Throughput: How many outputs the model can generate in a given time. It is often measured in tokens per second(TPS)
- Inference time: The total time the model takes to “think” and produce output for one prompt. It is measured in trillion of operations per second(TOPS).
Applications of Gen AI
- Software code generation : Software developers use generative AI to write, update and maintain code, automate, debugging and assist with app testing during app development.
- Customer Support : Improve customer interactions through enhanced chat and search experiences
- Fraud detection and risk management : By spotting suspicious behavior, detecting fake documents or messages and real time alerts with continuous improvement in filtration.
- Assist with repetitive tasks like replying to various queries, checking compliances and translating text into different languages.
Generative AI in action
- The Digital India BHASHINI initiative, or “BHASHa INterface for India,” is the Indian government’s generative AI-led language translation platform to to break language barriers and ensure digital inclusion for India’s diverse population
- AI-powered virtual try-ons: Online fashion platforms like Myntra are using generative AI to create virtual try-ons. A user can input their specifications for a wedding outfit, and a ChatGPT-powered feature provides outfit choices, saving them from multiple manual searches.
- Intelligent chatbots: E-commerce platforms such as Zomato and Blinkit are implementing AI-driven chatbots to manage customer queries more efficiently.
- Customer support: Major Indian banks like HDFC, ICICI, and SBI have deployed AI-powered chatbots like HDFC’s “EVA” to handle millions of customer inquiries instantly, reducing the need for human agents.
- Enhanced teaching tools: AI assistants help teachers automate administrative tasks like creating lesson plans, quizzes, and assignments. Startups like upGrad and Simplilearn also leverage generative AI to augment human tutors.
- Virtual health assistants: AI is integrated into apps from players like Tata Health and Practo to offer instant responses to patient inquiries.
Where does India stand in this revolution
According to a report published by Elastic, 81 per cent of Indian organizations have adopted (GenAI), positioning India as a leader in this technological domain. And more than 40 per cent of organizations have fully integrated Gen AI tools in their workflows.
India’s Gen AI market was valued at US $1 billion in 2024 and it is projected to grow over US $8 billion by 2030 with a CAGR of 38 per cent.
The Government of India is also promoting Gen AI to harness its potential by launching the India AI Mission with a budget of over US $ 1 billion.
Indian businesses are emerging as flag bearers in the Generative AI domain. Reliance Industries, in its 2025 AGM, announced several Gen AI initiatives under the Jio ecosystem, in collaboration with Meta and Google. Leading IT firms like TCS and HCLTech are also leveraging Gen AI tools to scale their operations and drive innovation. Additionally, numerous consumer-facing companies and content creation platforms are using Gen AI to deliver personalized and tailored search experiences for Indian consumers.
But there is also another side of the coin in this revolution for India. India lacked a skilled workforce for the Gen AI domain. TeamLease Digital (a specialised staffing firm) have found that there is only one qualified engineer available for every 10 open Gen AI roles in our country. This indicates a severe shortage of talent in this fast moving era of Gen AI. Forecasts show that by 2027, there could be 2.3 million AI-related job openings, but only 1.2 million professionals to fill them—leaving a gap of over 1 million roles. NASSCOM data highlights that only about 16% of IT professionals currently possess AI skills, making India’s workforce today ill-prepared for widespread AI adoption. This extreme mismatch is reshaping India’s job market. Salaries are soaring, non-metro cities are emerging as hiring destinations, and Global Capability Centres are creating new career pathways. But without urgent upskilling, the report warns, the gap will widen further.
Strategic imperatives for India
- Upskilling and reskilling of over 1 million workers to bridge the gap
- Shift from theoretical learning to hands-on learning like prompt engineering, analytics and fine tuning LLMs
- Public Private training collaboration and expand training and infrastructure to tier 2 and tier 3 cities, democratizing access and diversifying talent pools.
India’s Generative AI talent shortage is one of the biggest hurdles on its journey to becoming a global AI leader. This gap is creating both a national challenge and a massive opportunity. Whether you’re a student looking for an edge, a working professional aiming to stay relevant, or a university leader shaping curriculum—embracing AI education today means leading the future tomorrow. By investing in AI courses, certifications, and hands-on learning, individuals can future-proof their careers while institutions can become breeding grounds for the next wave of AI innovators.
