The Hiring Reset: AI Has Raised The Skill Bar In India

India’s education and skilling ecosystem is at an inflexion point. As artificial intelligence (AI) becomes deeply embedded in how companies operate, the focus is shifting from digitising classrooms to building an AI-ready workforce.

According to an Inc42 survey of over 200 founders, 60% reported integrating AI into customer or product workflows. This signals that AI is quietly compressing hiring across support, operations, basic coding, and content roles. However, demand for AI operations specialists, data stewards, and domain-led experts who can work alongside intelligent systems is rising.

The traditional education-to-employment model is being tested, as skills now change faster than curricula can keep up. For edtech startups, employers, and policymakers alike, the question is no longer about access to education, but about continuous employability in an AI-driven job market.

Against this backdrop, let’s understand how AI is redefining the education-to-employment equation.

What ‘Education-To-Employment’ Means In An AI Job Market

According to Nipun Sharma, CEO of TeamLease (degree apprenticeship), in an AI-driven job market, what the education-to-employment model must do is to address the growing gap between academic timelines and fast-changing technology cycles.

“Most curricula refresh every 3 to 5 years, while AI tools, models, and workflows now evolve every 6 to 12 months. Employers expect nearly 39% of core skills to change by 2030, which makes a shift from periodic curriculum updates to continuous, on-the-job learning essential,” said Sharma.

This gap is also reflected in Inc42’s employer survey, which highlights gaps in applied capability rather than academic qualifications.

Education-to-employment must therefore move towards work-linked learning that builds foundational skills, adaptability, and real-world problem-solving, enabling talent to remain productive as tools and systems evolve rapidly.

“The new definition must be: continuous employability, not one-time certification. This means modular learning, stackable credentials, and constant reskilling embedded inside work itself. Learning must become a subscription layer over careers, not a phase that ends before employment begins,” said Jaspreet Bindra, the cofounder & CEO of AI&Beyond.

Understanding The Shift In AI-Led Job Market

India is looking at a massive shift in the job market with growing AI adoption. Therefore, training and skilling of employees will need to rapidly transform to getting the workforce ready for an AI-coworker environment.

According to Abhishek Prasad, managing partner at Cornerstone Ventures, “The upskilling problem is equally challenging for employers, who will need to completely change their operating protocols, project and task planning, talent sourcing and training, assessments and appraisals, as the day-to-day scope of work itself transforms.”

As a result, the employers are moving away from credential-based hiring toward measurable indicators such as time-to-productivity, role readiness, six- and twelve-month retention, and internal mobility.

Meanwhile, industry analysts believe that AI adoption is reshaping demand across roles rather than leading to job losses.

However, it is estimated that up to 30% of work hours across some economies could be automated by 2030. This means jobs that require limited domain understanding, involve high volumes of work, and have easily verifiable outputs are most at risk.

“Such jobs are at risk because they can be templatised, easily formulated and done by automation. Some examples are: basic customer service like FAQ answering, simple boilerplate programming, translation, summarisation, essentially anything, which starts with less understanding of the domain,” said Peeyush Ranjan, the CEO of edtech startup Fermi.ai.

On the other hand, new jobs will emerge which will be all about AI operations, orchestration, contextualisation, and enhanced inferencing.

The most important skill of the future will be learning how to best use AI capabilities to maximise intelligence and productivity. The more one understands and the more they take ownership of the outcome, the more AI becomes a tool for them to deliver.

What Comprises The Next Upskilling Cycle?

The next upskill cycle requires edtech/skilling startups to be AI-native from the outset. They must also play a consultative role, helping enterprises rethink their training and workforce strategies as jobs evolve. As roles become more intelligence-driven rather than execution-focussed, companies will need partners who can guide them in redesigning skills frameworks, not just deliver courses.

“[Skilling/edtech] Startups that will be the big winners in this rapidly changing opportunity will be the ones that are capable of creating bespoke solutions for enterprises,” Prasad of Cornerstone Ventures said.

In essence, three things matter now:

• Outcomes not content: Courses don’t matter. Job transitions do. If a platform cannot show salary lift, role change or productivity impact, it won’t survive.
• Employer integrations: Skilling companies must be deeply embedded inside companies with hiring teams, internal mobility programmes, and workforce planning. Standalone learning apps will struggle.
• Credential portability: Workers need AI-proof credentials that travel across companies and geographies more like professional licenses than course certificates.

The next phase of edtech is increasingly shifting toward skills as infrastructure rather than consumer-led learning. Enterprise learning budgets are moving away from discretionary courses toward embedded training aligned with onboarding, role transitions, and workforce planning.

“In the age of AI-given answers, employers will not hire certificates, they will hire proven capability. If you can’t prove a student can do the job better than ChatGPT, your course is just expensive content,” said Ranjan.

Will Companies Pay For AI Reskilling?

With nearly half of graduates still improving their employability and startups under pressure to reduce time-to-productivity, traditional hiring models are being re-evaluated.

According to Sharma, instead of paying premiums for fully job-ready talent, companies are increasingly open to funding short, role-specific reskilling programmes tied to measurable outcomes. Besides, reskilling is increasingly viewed as a cost-efficient and sustainable alternative to repeated external hiring.

Further, Bindra argues that hiring only a “talent-ready” workforce is a losing strategy, as supply will not match demand, skills will decay too quickly, and retention will weaken. The more durable model is to hire for learning agility rather than static skillsets and treat reskilling as a core operating cost.

“This shift reflects optimisation rather than contraction. Our workforce insights show that while entry-level hiring has become more selective, demand for higher-impact roles requiring judgment, coordination, and domain expertise continues to grow, especially in GCCs and digital-first firms,” Sharma said.

It’s a given that AI will shorten skill cycles and raise the bar for employability. As skills become perishable and learning inevitable, the real question is whether India’s workforce and enterprises evolve faster than the technology?

The post The Hiring Reset: AI Has Raised The Skill Bar In India appeared first on Inc42 Media.