From Prompting to Practical Application
The initial wave of generative AI was all about the conversation. Learning how to ‘talk’ to models like ChatGPT to get the best output became a widely discussed skill. While important, this was just the entry point. Today, Indian companies are looking
past the novelty and asking a more critical question: How can AI solve real business problems? The answer lies not in simply prompting, but in applying AI tools to tangible workflows. A recent report from Japanese brokerage Nomura found that AI is creating more jobs in India than it is eliminating, specifically in roles that involve data, engineering, product, and support functions. This signals a maturing market where the focus is on integration, not just interaction.
What Are 'Applied AI' Skills?
Applied AI refers to the ability to use artificial intelligence to build solutions, automate processes, and derive insights within a specific business context. This is less about building foundational AI models and more about using existing ones to create value. According to a July 2026 NASSCOM report, there is a growing gap between being merely 'AI-proficient'—using AI tools—and being 'AI-native'—having the deep engineering judgment to apply them effectively. Key applied skills now in demand include using AI for predictive analytics in finance, automating customer support triage, optimising logistics routes, and embedding generative AI into software development. It’s the difference between asking an AI to write an email and building an automated system that uses AI to send personalised emails to thousands of customers based on their behaviour.
The Indian Context: A Widening Skills Gap
India is uniquely positioned to become a global hub for AI talent, ranking first in AI skill penetration. However, there is a significant challenge. While the workforce is rapidly adopting AI tools, employers are struggling to find talent with deep, applicable skills. NASSCOM has warned that India risks scaling a workforce that is 'AI-reliant' rather than truly 'AI-native'. This is because AI is automating routine tasks that junior engineers traditionally used to build foundational knowledge, creating a potential experience gap. The demand for professionals is projected to exceed one million by 2026, but a significant shortfall is anticipated if upskilling isn't accelerated. This gap is not about a lack of awareness, but a shortage of professionals who can connect AI capabilities to business outcomes in sectors like BFSI, healthcare, retail, and manufacturing.
Building the New AI-Ready Professional
For professionals aiming to stay relevant, the path forward involves moving beyond basic AI literacy. It means acquiring skills in areas like machine learning operations (MLOps), data engineering, cloud AI platforms, and developing with large language models (LLMs). Job roles such as Machine Learning Engineer, Generative AI Developer, and AI Product Manager are seeing the highest demand. Importantly, employers are shifting towards skills-first recruitment, prioritising candidates who can demonstrate practical ability over academic credentials alone. This involves not just completing courses, but building project portfolios that showcase an ability to solve real-world problems. The focus for individuals must be on developing independent judgment and system design skills, which are crucial for overseeing and debugging AI-driven solutions.
An Organisational Imperative
The responsibility for this skills shift doesn't just lie with individuals. Organisations must also evolve. Companies that succeed will be those that redesign job roles and systematically build AI fluency across their entire workforce, rather than siloing it within a specialist team. This involves creating new onboarding and mentorship programs that compensate for the decline in routine entry-level tasks. According to a NASSCOM report, leading IT service companies are already reimagining internal skilling programs to cover the full stack of AI skills, from foundational to advanced and domain-specific. The goal is to create a collaborative environment where human expertise and AI efficiency combine to drive innovation and productivity.
