Skills Shortage and Poor Data Standardisation Emerge as Key Barriers
Key Highlights
- Skilled talent shortages and poor data standardisation are emerging as major barriers to India’s AI ecosystem.
- About 47.2 per cent of respondents identified talent availability as a significant competitiveness challenge.
- Talent availability ranked jointly with customer adoption as the top concern.
- Around 63.2 per cent of AI developers said lack of data standardisation was their biggest data-related hurdle.
- Copyright concerns were cited by 44 per cent, while data scarcity was flagged by 42 per cent.
- Only 15 per cent of respondents felt government datasets were readily accessible.
- About 96.2 per cent of AI startups and developers rely on open-source models and tools to some extent.
- Around 62.3 per cent reported a high level of reliance on open-source AI.
- The findings suggest structural capability gaps may be more pressing than concerns over market concentration.
New Delhi, August 19: Shortages of skilled talent and weak data standardisation are emerging as the biggest constraints on India’s artificial intelligence ecosystem, outweighing concerns over market concentration or exclusionary conduct by large technology firms, according to a report by policy think tank The Dialogue.
The study, titled Competition, Innovation, and Market Structure in India’s AI Ecosystem, found that the sector remains in a rapid developmental and expansionary phase, but structural weaknesses in talent availability and data usability could limit its ability to scale.
The report surveyed 308 stakeholders, including AI startups, business users and consumers.
Talent availability emerges as top barrier
Around 47.2 per cent of respondents identified talent availability as a significant barrier to competitiveness.
The issue ranked jointly with customer adoption as the top challenge facing the ecosystem.
The report said AI development requires more than a large general technology workforce, as companies need specialised professionals capable of building, training, deploying and scaling advanced AI systems.
The shortage of these skills could become increasingly significant as adoption expands across industries.
Specialised AI skills remain limited
Bhoomika Agarwal, Senior Programme Manager at The Dialogue, said talent availability is emerging as a critical constraint on AI competitiveness.
While India has a large technology workforce, developing advanced AI products requires specialised capabilities across areas including model development, deployment, infrastructure and scaling.
Competition for such workers can make it more difficult for startups and smaller firms to build teams capable of competing with larger technology organisations.
Data usability bigger issue than scarcity
The findings also suggest that the quality and usability of available data are more significant problems than simple data availability.
Approximately 63.2 per cent of AI developers identified a lack of data standardisation as their most significant data-related challenge.
This was substantially higher than the 44 per cent who cited copyright concerns and the 42 per cent who pointed to data scarcity.
Inconsistent formats, quality and standards can make datasets difficult to integrate into AI development workflows even when large amounts of information are technically available.
Government datasets remain difficult to access
Only 15 per cent of respondents believed government-held datasets were readily accessible.
Improved access to high-quality public-sector data could potentially support development of AI systems in areas such as healthcare, agriculture, public services, finance and infrastructure.
However, accessibility alone may not be enough if datasets are not standardised, machine-readable or sufficiently structured for AI applications.
The findings underline the importance of both data availability and data quality in developing a competitive AI ecosystem.
Open-source AI deeply embedded
The study found that open-source technology has become a major foundation of AI development in India.
About 96.2 per cent of AI startups and developers surveyed reported relying on open-source models and tools to some degree.
More than 62 per cent described their reliance as high.
Open-source models can lower development costs, give smaller firms access to sophisticated AI capabilities and allow developers to customise systems for local and industry-specific applications.
Competition concerns less prominent
The report suggests that immediate competitive constraints in India’s AI sector are being driven more by foundational ecosystem issues than by market concentration.
While questions around the role of large technology companies remain relevant, the survey findings indicate that access to skilled professionals, customers and usable data are currently more pressing concerns for participants.
India’s AI ecosystem is still expanding rapidly, giving policymakers and businesses an opportunity to address these structural gaps before they become deeper barriers to growth.
Building stronger AI capabilities
Addressing the talent challenge will require expanding specialised AI education and training while improving pathways for engineers and researchers to develop practical experience in deploying real-world systems.
At the same time, greater data standardisation and improved access to usable datasets could reduce development costs and accelerate innovation.
The report’s findings indicate that strengthening these foundational capabilities may be central to improving India’s long-term competitiveness in artificial intelligence.
