AI can now draft a report, build a financial model or summarise a case study in seconds. The output usually looks right. The harder problem is knowing when it isn't.
That problem is where higher education now earns its place.
In India, AI is already part of the working day. The India Skills Report 2026 found that more than 90% of employees use generative AI tools. An EY-Parthenon and FICCI survey of 30 leading institutions found that 60% now permit students to use AI tools, and 53% use generative AI to build learning material.
So, the question is no longer whether learners should use AI. It is what remains hard enough to be worth teaching. I think about it as a four-part framework: Depth, Judgment, Application and Renewal.
Depth comes first because everything else depends on it. You cannot catch a flawed assumption in an AI-built valuation if you don't understand discounted cash flow. The paradox: AI removes much of the practice that used to build depth. So programmes have to put that struggle back on purpose, through problems learners solve before they reach for a tool.
Judgment is depth in action. Is this answer correct? Is it relevant to my context? What is it leaving out? A marketing manager who accepts an AI-generated customer segment without questioning the data behind it has outsourced the one thing her employer pays her for.
{{/usCountry}}Judgment is depth in action. Is this answer correct? Is it relevant to my context? What is it leaving out? A marketing manager who accepts an AI-generated customer segment without questioning the data behind it has outsourced the one thing her employer pays her for.
{{/usCountry}}Application is where credentials are being tested hardest. The same India Skills Report shows MBA employability falling from 78% in 2025 to 72.76% in 2026. The report links this to demand for applied, cross-functional managers. I'd put it more simply: a degree alone no longer signals readiness.
That puts assessment at the centre. The EY-Parthenon survey found 38% of institutions already use AI for automated grading. If AI can write the assignment and AI can grade it, what has the learner proved? Teaching application means assessing it through real business problems, live case defences and faculty who question the reasoning, not just the output.
This is where online higher education and upskilling can prove their value, if they are built for it. A working professional can take a concept from Tuesday's session and test it on a live problem at work by Friday. The classroom and the workplace start to feed each other.
Renewal is the ability to learn again. The World Economic Forum estimates that 39% of workers' existing skills will change or become outdated by 2030, and that 59 out of every 100 workers will need training. A degree earned at 22 cannot carry a career to 60.
That changes what higher education is for. It can no longer be a phase before work. It has to continue through it.
AI will keep making answers cheaper. Our job is to make sure learners know which answers to trust, and what to do next.
(The views expressed are personal)
This article is authored by Ranjita Raman, CEO, Jaro Education.