AI is quietly revolutionising talent management—making hiring smarter, learning sharper, and biases harder to hide

AI, say industry leaders, is not here to take away jobs; it’s here to help companies and candidates navigate the process more efficiently, and perhaps more humanely.

Take Schneider Electric, for instance. A few years ago, the company launched its Open Talent Market (OTM), an AI-enabled internal platform that connects employees to relevant roles across

the organisation globally. “We were realising that people were moving out of the organisation for opportunities to grow, and on the other hand, we were hiring people of the same profile in

some other part of the business,” said Sabih Kidwai, Director - head of Digital, Learning & Talent at Schneider Electric at HR and L&D Conclave, organised by Great Lakes Institute of

Management, Gurgaon.

With over 90% of employees on the platform, AI now maps skills from their profiles—broken down like LinkedIn does—and recommends matches across functions, not just within one’s current

domain. “So, someone in HR could find an opportunity in marketing because they’ve built some skill of strategy or wherever,” Kidwai said. But the tech also stirred difficult questions.

“There were a lot of emotions... ‘I’ve just hired someone and now he’s found an opportunity elsewhere.’ We insisted this is an open talent market. There are no restrictions, no locking

period.”

At Adani Enterprises, a similar internal initiative is underway. The group has profiled 45,000 employees through an internal system called Sarcham, designed to empower individuals to own

their careers. But beyond matching current roles, the team is working on capturing employee aspirations. “If you are into marketing and want to move into corporate communication or another

line of business, we’ve started capturing those aspirations too,” said Sudhir Sharma, head talent acquisition, Adani Group.

Externally, Adani is building its own AI tools tailored for Indian hiring challenges, where global systems often fall short. “All this stuff built in California was not for India… they never

envisaged that this kind of scale can happen here,” Sharma said. From scanning resumes to understanding why candidates drop offers, the focus is not just on fitment but also long-term

success. “Somebody came, worked with us, and left in two years. Were they a cultural misfit? If I can identify those profiles at the interview stage, I’ll weed them out.”

That level of precision also involves interviewing analytics. “The algorithm talks to the hiring manager saying, ‘These are the areas you’ve evaluated, and here are some you haven’t touched

from the JD.’ The system then goes back, checks the feedback and recommends if the candidate should move forward,” he said.

The need to embed AI deeply—and responsibly—isn’t limited to conglomerates. Consulting major KPMG has also deployed generative AI to make early hiring more holistic. Traditional campus

hiring relied heavily on cutoff-based tests. “But I always knew the test isn’t the right indicator,” said Misal, director & head talent acquisition, KPMG India. “So now we use gen-AI that

evaluates the individual in a 360-degree view—scores around stability, right fit, aptitude, technical skills.”

Once onboarded, the system then helps personalise learning. “The day somebody walks in, we enable a 360-degree learning programme based on the persona AI created for them,” Misal added. Even

resource deployment has become more fluid. “AI has helped us fungible-y use resources across consulting domains—it’s really helped us up our game.”

Still, not every organisation is rushing to adopt AI at scale. In healthcare, UnitedHealth Group is proceeding with caution. “There is a sense of threat towards personal spaces or jobs,”

said Pilith Pericho, campus university relations and diversity recruiting lead- India. “We’re still trying to build that awareness within our team before taking a big step into bringing that

scale of talent through AI.”

The ethical questions around AI-driven hiring are also far from resolved. What if AI models are trained on biased data? And what if hiring managers themselves are reinforcing outdated

filters?

Interestingly, Sharma from Adani is more worried about human bias than algorithmic error. “AI will learn and do it much better. The biases built into the panel’s head—it’s very difficult to

break them,” he said. But he hopes AI will help challenge those blind spots. “You need to also look at elements you haven’t been looking at. It will enable us to do the job faster, better,

and with higher quality.”

The key, said Bayer’s head HR, Rohit Sharma, is to avoid the hype. “Just because everyone’s doing it doesn’t mean we have to. We recently had to hire 300 medical reps in three months. We

used AI and LLMs for the first round, then compared it with what hiring managers said. When the correlation was high, it gave us confidence.”

Misal from KPMG echoed this view. “Every system—human or tech—has biases. The question is how deep do we want AI to go?” For now, they’re keeping it contained. “We don’t scan social media or

go too far. There has to be a boundary.”

That pragmatism extends to long-term planning too. AI may help identify internal skill gaps, but it must be paired with external insights. Sharma said, “Talent management has been reactive.

We’re covering everything, but not necessarily being better. Learning must align with the human experience. And I think there’s still a lag between how careers are evolving and how learning

is happening.”

Ultimately, as Kidwai summed up, the transformation is not just digital—it’s behavioural. “The algorithm eased it out, but there was a lot of human psychology and emotion we had to

manage—and we’re still managing. That process is on.”