Demand for AI talent in India is estimated to rise from 600,000–650,000 to more than 1,250,000 between 2022 and 2027, according to a joint report by Deloitte India and NASSCOM. With the AI market expected to grow at 25%–35% annually, this points to a widening demand–supply gap and an urgent need for upskilling.
This widening gap is hitting the Indian CIOs hard. Budgets are approved and pilots are running, but scaling remains constrained by talent. It’s not only that there’s a dearth of AI “experts” but there is also a lack of people who know how to link the models to business outcomes, manage the risks of deployment, and maintain them at scale in production.
It’s also a problem particularly amplified in industries not typically characterized by rapid innovation, such as manufacturing and real estate, as compared to technology companies and start-ups, which are far better equipped to lure experienced talent.
In response to this challenge, technology leaders are deploying innovative strategies to bridge the gap.
Tap Fresh Graduates
Badar Afaq, CIO at KCT Group, close to a 100-year-old diversified conglomerate, advocates that CIOs should look at fresh graduates who are often more comfortable with newer tools and quicker to experiment.
“CIOs should build teams that include young, fast-learning talent, especially fresh graduates who are often more comfortable with newer tools and quicker to experiment. These professionals can adapt rapidly and help bring fresh thinking into the organization,” he says.
His advice is not to wait for the “perfect” AI hire. Instead, use assignments and hands-on problem solving to identify the right people early. “In our case, we have already experimented with this approach, and it has worked well,” Afaq says.
According to him, AI cannot be a side responsibility for existing infrastructure or cybersecurity teams. “Organizations should create a dedicated AI capability rather than expecting existing infrastructure, cybersecurity, or system administration teams to handle it as a side responsibility. AI needs focused attention, basic data analysis skills, and familiarity with modern tools. Not every team member has to be deeply technical, but they do need practical awareness and a working understanding of how AI is applied,” he says.
Run Lean, Partner Smart
Not every organization needs a large, permanent AI team. Chander Khanduja, Group CIO at Baxi Group, a diversified company, advocates a leaner model, especially in manufacturing and other traditional industries.
“It’s not necessarily always about employing the full-time resource; you may only need that expertise for a day or two a week and use somebody from the ‘big boys’ such as a firm of consultants,” Khanduja says. “It really comes down to how mature your organization is and how important that role is to the company. For highly regulated sectors, you may need a much more permanent setup, but in manufacturing, a leaner model can work well.”
He acknowledges that AI is a young discipline and that top talent is often young, highly mobile, and looking for excitement, something not every traditional business can offer. “In such cases, working with startups and/or a third-party vendor can work.”
CIOs, therefore, have to be realistic about what they can build in-house and where external partners can fill gaps. A hybrid model, a small core team plus selective use of consultants, startups, and vendors, can be more effective than trying to staff everything permanently.
Adopt a Hybrid Model
To tide over the situation, Puneesh Lamba, CIO at CMR Green Technologies, a non-ferrous metal recycling company, has adopted a hybrid model. First, he trained employees on how to use AI. Then he used partners for developing the first few applications. After that, he built five or six applications in-house. For larger applications, Lamba still works with partners.
“It depends on what we are trying to solve. If it can be delivered in less time internally, and it’s something that’s likely to remain in constant flux, we will want to own it internally. If there’s a gap of skill or the scale is just too massive, we will use a partner.” That way, we get speed without losing control,” he says.
Every month, CMR Green Technologies conducts two AI training sessions across the organization. One of those sessions is personally led by Lamba.
“This reflects how seriously we take upskilling. We pick one AI skill and train all employees on it. Then we record the session so employees can come back to it and new members can also benefit from it. We have six to 12 months down the line, and then we go back and check in with employees. We say ‘So you learned this stuff, how are you going about implementing what you learned’? There has to be accountability. Training cannot just be a box-ticking exercise,” he says.
Collaborate with Academia
Even with the right hiring and partnering strategy, talent gaps will remain. Tejasvi Addagada, Head of AI Governance at HDFC Bank, sees collaboration as essential. “Personally, I get into conversations with IITs and research institutions — research scholars, people from R&D — and we exchange views on what a control framework or risk framework for AI should look like, how we can get to a better set of models, and how we can cut short on inference time or the number of tokens we use. Collaboration is the only way, a fluid exchange of thoughts between organizations to come up with something innovative,” he says.
For CIOs in sectors where hiring and retaining AI talent is tough, this is a powerful lever. “IITs come with rich research publications and research know-how, and that can be transferred to institutions. Not only IITs, but any research institutions, even external ones. Internal teams can take months to do research, but the best way is to collaborate with those who have already produced research and work with them to see what part of it can be leveraged,” he says.
