India faces a critical challenge in shaping its technology workforce as artificial intelligence (AI) becomes deeply integrated into the industry. The National Association of Software and Service Companies (Nasscom) has highlighted the urgent need to maintain strong engineering fundamentals while adapting to AI-driven work environments. Without this balance, India risks developing a workforce that relies on AI tools rather than truly mastering AI technologies.
With over 90% of early-career tech professionals in India already using AI tools, Nasscom cautions that simply adopting AI is not the same as being AI-native. The organization stresses that the decline in routine coding tasks should not lead to a weakening of core technical skills. India has a unique opportunity to become a global leader in AI-native talent, but this requires a concerted effort from both academia and industry to rethink education and workforce development.
Why Preserving Engineering Expertise Matters
As AI tools automate many traditional programming tasks, there is a risk that new professionals may lose touch with foundational engineering principles. Nasscom warns that this could create a technology workforce dependent on AI assistance rather than capable of innovating independently. Being AI-native means integrating AI knowledge deeply into problem-solving and development processes, not just using AI as a crutch.
To achieve this, educational institutions must go beyond teaching basic coding. Strengthening engineering judgment and domain-specific knowledge is essential. This involves redesigning curricula and assessments to reflect the realities of AI-augmented work, encouraging students to think critically and solve complex problems with AI as a tool rather than a replacement.
Key Steps Nasscom Recommends for Industry and Academia
- Academic institutions should update teaching methods to emphasize engineering fundamentals alongside AI concepts.
- Assessment systems must evolve to measure AI-native capabilities instead of just coding skills.
- Companies need to redesign talent development strategies, focusing on foundational skills, mentorship, and independent problem-solving.
- Integrate AI verification processes into daily workflows to ensure quality and understanding.
- Continuous upskilling programs should be implemented for early-career professionals to keep pace with AI advancements.
- Hiring practices must shift from evaluating basic coding knowledge to assessing comprehensive AI-native competencies.
How This Shift Will Shape India’s Tech Future
The transition to an AI-native workforce will require Indian IT companies to rethink long-standing operating models. The traditional focus on coding proficiency alone will no longer suffice. Instead, firms must cultivate a culture that blends AI-augmented learning with hands-on experience and mentorship. This approach aims to produce professionals capable of leveraging AI tools intelligently while maintaining strong engineering insight.
By fostering these capabilities, India can position itself as a global hub for advanced AI talent. This will not only enhance the country’s competitiveness in the technology sector but also drive innovation across industries that increasingly rely on AI. Failure to adapt could see India fall behind as other nations develop more sophisticated AI-native workforces.
Frequently Asked Questions
Q: What does it mean to be AI-native?
A: Being AI-native means having deep expertise in AI technologies and integrating AI into problem-solving and development processes, rather than just using AI tools superficially.
Q: Why is preserving core engineering skills important?
A: Core engineering skills provide the foundation for innovation and independent problem-solving. Without them, professionals may become overly reliant on AI tools and lose the ability to create new technologies.
Q: How should hiring practices change to support an AI-native workforce?
A: Hiring should focus on evaluating comprehensive AI capabilities, including understanding AI concepts, problem-solving with AI, and foundational engineering knowledge, rather than just testing basic coding skills.
