From Prompting to Production
For the last couple of years, 'prompt engineering' was the hottest new skill on the block. It was seen as the key to unlocking the power of generative AI tools like ChatGPT. The logic was simple: the better your instructions, the better the AI's output.
While this remains a useful skill, the Indian tech industry is quickly realising it's just the starting line, not the finish line. Companies are moving beyond the experimental phase. According to a recent EY-CII report, 47% of Indian enterprises now have multiple generative AI use cases running in production, with only 23% still in the pilot stage. This shift from experimentation to production means businesses no longer need just prompt-writers; they need builders, integrators, and strategists who can embed AI directly into their core operations to drive measurable value.
What Are 'Applied AI' Skills?
Applied AI refers to the practical implementation of artificial intelligence to solve specific, real-world problems. It’s less about building a foundational model from scratch and more about using existing AI technologies to create tangible business solutions. In the Indian context, this is where the real demand lies. Job descriptions are increasingly asking for skills that demonstrate an ability to use AI within a specific domain. For instance, Retrieval-Augmented Generation (RAG) and vector database experience are now among the most sought-after technical skills. This reflects a need for engineers who can build AI systems that work with a company's own private data, a crucial step for creating customized and relevant applications. Other in-demand skills include MLOps for deploying and managing models, AI for cybersecurity, and using AI tools within data analytics and software development workflows.
The Industries Driving the Shift
This demand for applied AI is not uniform; certain sectors are leading the charge. India's massive IT and Business Process Management (BPM) industries are at the forefront, reinventing their service offerings by embedding AI into everything from software development to customer support. The financial services sector (BFSI) is another major adopter, using AI for fraud detection, credit risk modelling, and personalised customer service through virtual assistants. In manufacturing, companies like Tata Steel and Mahindra & Mahindra are using AI for predictive maintenance and automated quality control, reducing downtime and improving efficiency. Meanwhile, e-commerce giants like Flipkart leverage AI algorithms to deliver personalised product recommendations, enhancing customer engagement and driving sales. Even non-tech roles in marketing, finance, and HR increasingly require basic AI literacy.
Bridging the Talent Gap
Despite the clear demand, a significant talent gap remains. A NASSCOM report highlighted that while AI job demand in India is projected to cross one million by 2026, the current supply of skilled professionals falls far short. Hiring managers report that while many candidates have a surface-level familiarity with AI tools, few possess the practical, job-ready competencies required. This has put the focus squarely on upskilling. Companies and educational institutions are collaborating to create more relevant training programs that go beyond theory. For professionals, the message is clear: mastering a tool is not enough. The greater value lies in combining AI knowledge with deep domain expertise in a field like finance, healthcare, or logistics. The future belongs to those who can use AI to solve problems within these specific contexts.
