The Future of Work and Skilling in the AI Era: What India’s Summit Really Revealed
There’s a question echoing through boardrooms, HR meetings, startup hubs, and government offices alike:
What is AI actually doing to jobs—and what are we going to do about it?
At the India AI Impact Summit 2026 in Delhi, that question wasn’t avoided or softened. It was confronted head-on. An entire day focused on the future of work and skilling, bringing together labor economists, CHROs, vocational training leaders, and policymakers.
And while opinions differed on tactics, there was one point of agreement:
AI isn’t wiping out jobs at scale.
The real crisis is that the skills gap is widening faster than we’re closing it.
Here’s what stood out—and why it matters right now for businesses, workers, and policymakers.
The Skilling Crisis Is Already Here
The AI-era skilling gap is the growing mismatch between what AI-enabled workplaces now demand—critical thinking, AI tool fluency, data interpretation, human–AI collaboration—and what most education and training systems are still producing.
By 2030, this gap could affect 60–90 million Indian workers.
A joint brief from NASSCOM and McKinsey & Company shared at the summit put hard numbers behind the concern:
- 69% of Indian enterprises struggle to hire workers with sufficient AI fluency
- ₹2.4 lakh crore in annual productivity could be unlocked if the gap is closed
- Only 11% of India’s workforce has received formal AI or data-skills training
These aren’t distant forecasts. They describe today’s hiring reality.
Five Major Shifts That Defined the Skilling Debate
1. The Job Market Isn’t Shrinking—It’s Splitting in Two
Economists at the summit were clear: AI is not triggering mass unemployment. It’s creating a bifurcated workforce.
Workers who collaborate effectively with AI are commanding premium wages. Those who cannot are being edged out of roles that are losing long-term value.
Data shared by the World Economic Forum’s India chapter showed that while 85 million jobs are being transformed, 97 million new ones are emerging. The catch? These new roles require entirely different competency profiles.
For businesses: Career paths need redesign. High performers who don’t adapt to AI workflows will stall—hurting morale, productivity, and retention.
2. Technical Skills Are the Baseline. Human Skills Are the Edge.
A recurring theme throughout the sessions was simple:
AI handles the repeatable. Humans must own the irreplaceable.
Organizations that deployed AI expecting to reduce headcount discovered something surprising. Their most valuable employees—the ones with judgment, client relationships, ethical reasoning, and creative insight—became even more valuable.
Those who relied mainly on repetitive, template-based, or retrieval tasks became vulnerable.
Here’s how the emerging competency hierarchy looks:
Tier 1 (AI-protected):
Strategic judgment, relationship management, ethical decision-making, creative direction
Tier 2 (AI-augmented):
Data analysis, research, content creation, code review, project management
Tier 3 (AI-replaced):
Data entry, basic reporting, FAQ responses, template drafting, transcription
For businesses: Invest in Tier 1 leadership capability and ensure every knowledge worker reaches Tier 2 fluency.
3. Vernacular Skilling Is the Multiplier Most Companies Miss
India’s skilling challenge isn’t just technical—it’s linguistic.
Most AI training resources are English-only. But roughly 78% of India’s workforce primarily operates in another language.
This gap is enormous.
The summit highlighted the success of iGOT Karmayogi’s vernacular AI tutor model and several other initiatives:
- Skill India Digital Hub now offers AI-guided learning paths in 14 regional languages
- TCS iON launched an AI-powered assessment platform in 11 Indian languages
- Reliance Jio partnered with NASSCOM to deliver AI literacy modules via JioPhone in six Indic languages
For enterprises: English-only training programs aren’t neutral—they structurally limit adoption. Bilingual and vernacular delivery is not an accommodation. It’s performance infrastructure.
4. Degrees Are Slowing Down. Micro-Credentials Are Speeding Up.
Traditional degrees take years. AI skill cycles evolve in months.
That mismatch is accelerating the shift toward stackable, role-specific micro-credentials.
At the summit, major technology firms including Google, Microsoft, IBM, and Amazon Web Services collectively recognized a unified India AI Skills Passport framework.
The passport allows workers to stack portable credentials across:
- AI Fundamentals
- Prompt Engineering
- Domain-Specific AI (Healthcare, Finance, Agriculture, Legal)
- AI Ethics and Governance
- Agentic AI Workflow Design
For HR teams: Hiring purely on degree signals is becoming a weaker filter. Competency verification is moving toward modular, stackable proof of skill.
5. Gig Workers Face the Highest Risk—and the Least Support
India has approximately 77 million gig workers—the largest gig workforce globally.
Yet most AI skilling initiatives focus on formally employed knowledge workers.
That leaves logistics workers, delivery partners, retail contractors, and informal labor segments exposed.
At the summit, NITI Aayog announced a Gig Worker AI Readiness Initiative, targeting platform workers with mobile-first, offline-capable AI literacy modules.
For businesses dependent on gig labor: This isn’t just a social issue. It’s a supply chain risk. If displacement accelerates without reskilling support, service quality and operational continuity will suffer.
A Practical Framework for Organizations: SKILL-AI
Drawing from multiple enterprise case studies shared at the summit, a structured approach emerged:
S — Scan
Map each role against AI displacement or augmentation risk.
K — Know the Gap
Assess AI fluency across teams using standardized tools.
I — Invest Selectively
Prioritize Tier 1 and Tier 2 roles first.
L — Localize Delivery
Provide training in the languages your workforce actually uses.
L — Link to Career Paths
Tie AI skill acquisition to visible promotion and compensation outcomes.
AI — Adopt AI for Skilling
Use AI tutors, adaptive learning paths, and AI-driven assessments.
The core insight: Skilling must be strategic, not symbolic.
Case Study: Infosys Retrains at Scale
One of the most discussed examples came from Infosys.
To support rapid AI adoption, Infosys launched Wingspan 2.0, an AI-powered internal learning platform.
Key decisions included:
- AI-based baseline competency assessments
- Personalized learning paths by role
- Courses offered in English, Hindi, Tamil, and Telugu
- Direct linkage between course completion, project allocation, and promotion eligibility
- Built-in peer learning cohorts
Results within 18 months:
- 52,000 employees completed AI modules
- 34% reduction in time to deploy AI-augmented project teams
- Employee L&D satisfaction rose from 61% to 84%
The takeaway: Personalization, language accessibility, and career alignment drive completion and impact.
What NodeSure Technologies Is Paying Attention To
For companies like NodeSure Technologies, the implications are immediate:
- Establish AI fluency baselines across delivery and client-facing teams
- Develop vernacular-capable training resources
- Recognize or participate in the India AI Skills Passport framework
- Redesign career architecture to visibly reward AI-augmented performance
The companies that move early won’t just build stronger teams—they’ll build defensible competitive advantages.
Conclusion: Skilling Is Now a Strategic Imperative
The future of work in the AI era is not just an HR concern. It’s a business continuity issue.
The India AI Impact Summit 2026 made that unmistakably clear.
Organizations that treat AI skilling as optional spending risk being unable to deploy AI effectively, retain capable talent, or compete with firms that invested earlier.
The national ecosystem is accelerating—through the India AI Skills Passport, gig worker initiatives, and vernacular learning infrastructure.
The only question left for businesses is simple:
Will your workforce strategy keep pace?