How India can close the AI skills gap as demand for talent surges

India’s artificial intelligence ambitions are facing a growing workforce challenge as demand for specialised AI skills rises faster than the availability of suitable talent.

According to a Quess Corp report, India recorded nearly 3.5 lakh openings for AI-related jobs over a three-month period. The country has around 9.2 lakh AI professionals, but only about 2.6 lakh work in core AI roles such as machine learning, large language models and agentic AI. Nearly two-thirds of current hiring demand is concentrated in these specialised skills.

The gap is not simply about numbers. Employers increasingly need people who can combine AI capabilities with domain knowledge, business understanding, data literacy and sound judgement.

The challenge therefore cannot be solved simply by hiring more specialists. Competition for experienced professionals is intense. A more sustainable response is to build capability within the existing workforce while being selective about specialised external hiring.

Building capability from within

AI literacy is increasingly moving beyond technology teams. Employees across finance, human resources, operations, sales and other functions will need to understand where AI can improve their work and when human intervention remains necessary.That does not mean every employee needs to become a data scientist or AI engineer. A supply-chain professional who understands how AI can improve forecasting, or an HR professional who can use it to identify workforce trends, brings something equally important: an understanding of the business problem.

Qualifications will remain relevant, but adaptability, curiosity and problem-solving are becoming increasingly important. As technologies evolve rapidly, the ability to keep learning can be as valuable as what an employee already knows.

Organisations will therefore need to identify employees with adjacent capabilities and create pathways for them to move into emerging roles. Internal mobility and reskilling can become important sources of AI talent.

We are already seeing organisations move beyond measuring AI training by participation or course completion. The more meaningful test is whether employees can apply AI in their day-to-day work, solve real business problems and exercise judgement around the quality of its output.

From training to application

Training alone will not close the skills gap. Employees need opportunities to apply what they learn.

Cross-functional initiatives such as AI hackathons can help, particularly when participation extends beyond technology teams. Putting real business problems in front of employees allows them to understand how the technology can be applied meaningfully.

Practical experience matters because employees need to know how to frame a problem, assess an output, identify errors and decide when human judgement is necessary.

Experimentation must also come with guardrails, including clear standards around data privacy, cybersecurity, intellectual property and responsible use.

Leaders need AI fluency too

Managers will increasingly have to evaluate work produced with AI. That means understanding whether an output is accurate, questioning its assumptions and knowing when further verification or human intervention is necessary.

AI fluency therefore cannot stop with junior employees or technology teams. Leaders need enough understanding to manage AI-enabled organisations effectively and identify where the technology can genuinely improve productivity.

Rethinking what skills matter

If AI increasingly performs repetitive analysis, drafting and processing, productivity cannot be measured only by the volume or speed of output. Greater value may come from the questions employees ask, the decisions they make and how effectively they apply technology to the right problems.

Judgement, creativity, communication and ethical decision-making could therefore become more important as routine work becomes easier to automate.

For younger professionals, learning to use AI tools will increasingly become a baseline capability rather than a differentiator. What will matter more is whether someone can take a real problem, understand its context and use AI thoughtfully to solve it.

Creating a stronger talent pipeline

Closing India’s AI skills gap will also require closer alignment between employers, educational institutions and skilling providers.

India will need not only more AI engineers, but also professionals in manufacturing, banking, healthcare, retail and logistics who understand their industries deeply and know how to apply AI within them. Industry can contribute by giving students greater exposure to real business challenges through internships, live projects and partnerships with academic institutions.

AI may automate parts of a job rather than eliminate the entire role, shifting employees towards judgement, collaboration and problem-solving.

For India, the ability to develop this talent at scale will be as important as investment in data centres, chips and AI models. Companies will need to treat workforce transformation as part of their AI strategy rather than as a separate HR initiative.

Ultimately, closing the AI skills gap will depend not only on how quickly India adopts AI, but on how effectively its workforce learns to work with it.

The author is the group Chief Human Resources Officer, Bahwan CyberTek Group.

Disclaimer: The views expressed are the author’s own and do not reflect the official policy or position of Financial Express.