The talent is there. The screening is not
Walk into most India hiring teams and you will hear the same refrain: there is no AI talent. Pull the actual sourcing data and the picture is messier. Recruiters are searching for AI titles. The candidates with the strongest applied AI capability often do not have those titles. They are senior backend engineers who shipped AI features. They are operations leads who automated workflows. They are analysts who built evaluation pipelines. None of them show up in a keyword search.
The shortage is partly real. It is also partly self-inflicted by screening processes that were designed for a job market that no longer exists.
Where hidden AI capability lives
Look in finance. Analysts who automated reconciliation are AI operators with domain depth. Look in operations. Managers who built demand forecasting workflows have shipped models you would otherwise pay an ML hire to write. Look in marketing. Growth operators running AI-assisted experimentation are usually faster than fresh AI hires at production work.
These candidates pass real interviews. They fail keyword filters.
How to widen the funnel without lowering the bar
Replace keyword screening with structured AI interviews that test what the role actually needs. Add semantic candidate search so a backend engineer with payments and AI integration experience surfaces for an AI engineering role even when their resume does not say AI engineer.
The teams that solve their AI hiring problem in 2026 are not necessarily paying more. They are looking in places everyone else is filtering out.