When the data analytics boom set about a decade ago, a three-month certification sufficed to land a job. Fresh graduates became data analysts overnight, and dashboards became the new spreadsheets.
But in the age of AI, the same formula seems broken. One may struggle to get hired even after completing an AI certification.
Why is that happening? Why isn’t AI creating jobs the way data analytics once did?
It’s a question that many aspirants, and even industry veterans, are asking as they watch the AI wave reshape the nature of work itself.
The Maturity Gap
In an exclusive interaction with AIM, Mayank Verma, head of AI at Xebia, said the gap lies not in enthusiasm, but in maturity, both of technology and of the companies adopting it. Xebia is an IT services and consulting firm headquartered at Atlanta, Georgia.
“Back in 2009-10, when data analytics started booming, anybody who did a certification could get a job,” Verma said.
He highlighted that the tools were immature and that one needed human brains to crunch numbers and make sense of the data.
That human dependency fuelled the job market. But with AI, the tools are far more capable.
Verma explained that the Large Language Models (LLMs) can analyse data and even generate insights. So companies need AI practitioners.
He highlighted that today’s AI landscape needs “a practitioner who has been there, done that — someone they can trust.” The demand for such experienced talent is immense, he added, but the market is mainly filled with aspirants from other tech backgrounds who take courses but are unable to meet enterprise expectations.
“This is a transitionary state,” Verma noted, describing the current skill gap between AI practitioners and learners. He believes the trend will mirror the analytics boom, where demand will soon accelerate. “The moment it crosses that small lower curve, it [will go] to the acceleration side,” he said.
In an earlier chat with AIM, Rohit Sharma, president of consumer business at EdTech platform upGrad, said India’s AI skilling ecosystem currently spans three levels. The foundation is AI literacy, which includes short courses teaching professionals to use AI tools in their daily work.
Programmes like upGrad’s “U&AI” certification with Microsoft have seen over one lakh sign-ups, signalling massive enthusiasm for entry-level upskilling focused on productivity rather than innovation.
Beyond basic literacy, job-linked reskilling courses tailor AI for domains like finance, healthcare, and manufacturing. However, few professionals reach the advanced stage, building and fine-tuning models.
“We’re somewhere between levels one and two right now,” Sharma noted. Experts believe that while India has no shortage of AI learners, it still lacks creators and architects.
This imbalance mirrors Verma’s view, that the market is full of aspirants, while companies urgently seek experienced practitioners who can translate AI promise into business impact.
The Operator Paradox
Rakesh Ravuri, CTO & SVP – engineering at Publicis Sapient, a digital business transformation company, offered a more pragmatic, and perhaps, bleaker view.
The analytics boom created “operator jobs”, roles where employees used BI tools like Tableau without needing deep statistical knowledge, Ravuri said.
“Someone who could produce charts or dashboards without understanding the fundamentals. You will see similar operator roles in AI, people prompting tools to do things.”
But those jobs, he warns, will not last long. “If something is easy to do, eventually an agent will do it,” he said. “Right now, we still need humans to operate AI tools. But soon, agents will become the operators.”
In other words, the very accessibility of AI may make entry-level roles redundant faster than ever before.
Between Aspiration and Reality
Both perspectives underline a central truth: AI isn’t killing jobs, it’s killing shortcuts. Unlike the data analytics era, where tool familiarity was enough, the AI era demands depth, understanding data, logic, ethics, and deployment.
The entry barrier may be higher because the technology itself is superior. While certifications may still hold value, they no longer guarantee employment. They mark the beginning of a journey, not a shortcut to win the race.
For now, AI seems to be creating more curiosity than careers. But, as Verma suggested, the story isn’t over. Once the ecosystem matures, the floodgates may open, except for those looking at easy achievements.
Because this time, AI-linked hiring seeks fundamentals and experience, not certificates.
The post Why AI Isn’t Creating Jobs the Way Data Analytics Did appeared first on Analytics India Magazine.
