Nasscom Index Rates India’s Early-Career Engineers AI-Proficient, Flags Judgment Gap

Nearly 70% of India’s early-career technology workers qualify as AI-proficient and about 23% as AI-native, according to a Nasscom study released July 14. The State of AI-Native Talent in India covers workers with up to three years of experience along with final-year computer science and related engineering students, and it places more than 90% of that group in the top two of four capability tiers.

The Nasscom AI-Native Talent Index scores individuals across eleven dimensions and sorts them into four archetypes: AI-Native, AI-Proficient, AI-Enabled, and AI-Aware. The dimensions reach past tool use into reliance, fluency, orchestration, and creation, and they add engineering judgment, cognitive independence, technical grounding, learning, foundational capability, AI-augmented productivity, and responsible AI use.

Sangeeta Gupta, Senior Vice President and Chief Strategy Officer at Nasscom, drew the line the index is built to test: “AI skills penetration is not the same as being AI-native.” Without a rigorous measurement framework followed by action, she said, “India risks scaling a workforce that is AI-reliant rather than AI-native.”

Why the Gap Forms and What the Report Prescribes

The concern rests on how engineering skill has historically formed. Routine coding, debugging, and repetitive build work gave junior engineers the repetitions that produced technical depth and independent judgment. As AI absorbs that routine work, those repetitions thin out, and the study argues that employers and universities will have to recreate the learning conditions deliberately rather than assume they still occur on the job. The headroom the index identifies concentrates in engineering judgment, AI orchestration, and technical depth.

The prescription splits responsibility between academia and industry. Universities are asked to move past coding instruction toward engineering judgment, domain learning, and reworked assessment methods that AI assistance can otherwise distort. Employers are asked to shift hiring from testing coding knowledge to evaluating AI-native capabilities, adopt simulation-based and AI-augmented foundational learning, run structured periods of working without AI, protect space for independent problem-solving, deepen mentorship, and embed AI verification into daily workflows. The study also points to clear AI-use policies and reorganized engineering teams as conditions for an AI-native operating model.

How Nasscom Frames the Talent Mandate

The findings sit inside a talent-development agenda that runs through Nasscom’s own priorities, which include skilling for future-ready talent and positioning India as a global hub for digital talent. The association represents a technology industry it values above $300bn across more than 3,500 member companies spanning startups, multinationals, Global Capability Centres, and engineering firms.

That membership base gives the index a direct audience, since the hiring and capability changes it recommends apply to the same Global Capability Centres and engineering firms Nasscom convenes. The organization’s current programming reinforces the theme, with events built around the shift from standalone AI tools toward orchestrated systems of intelligence, the same orchestration capability the index measures in individual engineers.

Where SAP Enters the Picture

Nasscom’s chairperson, Sindhu Gangadharan, leads SAP Labs India, which places a major enterprise software employer at the head of the body issuing the talent guidance. Gangadharan holds a global role at SAP as Head of Customer Innovation Services and became the first woman to lead SAP Labs India.

The connection ties the study’s recommendations to conditions SAP has described in the same market. At the India AI Impact Summit in February, SAP leadership argued that AI value comes from embedding intelligence into enterprise platforms rather than from standalone models, and cited a customer base moving onto AI roadmaps. SAP runs its second-largest engineering hub outside Germany in India, with more than 17,000 employees. An early-career workforce that scores high on AI proficiency yet shows measured gaps in judgment and orchestration bears directly on the engineering talent such platform work depends on, and it gives employers a shared vocabulary for defining where entry-level readiness still falls short.

What This Means for SAPinsiders

AI efficiency could hollow out the leadership pipeline. When junior work disappears, companies lose the repetitions that produce senior technical judgment. Firms optimizing only for immediate productivity may create a future shortage of engineers capable of supervising AI-generated systems.

Talent development may become an operating-model advantage. Companies that formalize mentorship, verification, and independent problem-solving can convert widespread proficiency into dependable engineering capability. That could differentiate employers competing for Global Capability Centre investment, complex platform work, and long-term product ownership.

The index could reshape entry-level labor signals. Degrees and coding tests reveal less when AI can assist both learning and assessment. A multidimensional capability benchmark may become more valuable for comparing graduates, universities, and employer training programs.