Editorial illustration for Data Quality Challenges Stall AI Adoption for Nearly 50% of Indian Enterprises
Qlik: Nearly half of Indian firms see data quality, governance as AI bottlenecks
The race to adopt artificial intelligence is hitting a critical speed bump in India's corporate landscape. While businesses eagerly chase AI's major potential, a stark technological reality is emerging: data quality isn't just a technical challenge, it's becoming a strategic roadblock.
New research reveals a troubling trend for India's digital ambitions. Nearly half of the country's enterprises are finding themselves stuck at the starting line, unable to effectively build AI technologies due to fundamental data management issues.
The problem runs deeper than simple technical glitches. Companies are discovering that clean, well-governed data isn't a luxury, it's an absolute necessity for responsible AI deployment. With regulatory landscapes shifting and 2026 compliance deadlines approaching, businesses can't afford to ignore these foundational data challenges.
What separates forward-thinking organizations from those left behind? The ability to transform raw information into reliable, auditable insights. And right now, that capability remains frustratingly out of reach for many Indian firms.
Qlik research shows nearly half of Indian enterprises cite data quality and governance as their biggest AI bottlenecks. Governed analytics platforms, offering lineage, auditability and quality controls, are now critical to scaling AI responsibly and preparing for upcoming 2026 regulations. Sajith Nambiar, head of solutions at UST, said Responsible AI is embedded into their accelerators through metadata-driven validation of data quality, lineage and consent.
Explainability frameworks generate contextual narratives for every AI decision with human-in-the-loop oversight, ensuring ethical alignment. Their goal: systems that are "accurate, explainable, auditable and ethically governed." The Road Ahead India's next stage of AI maturity will be defined not by model speed but by the strength of governance structures that power it. The message across industries is clear: governance must be designed into AI from day zero.
Indian enterprises are hitting a critical roadblock in AI adoption. Data quality isn't just a technical challenge, it's becoming a fundamental barrier for nearly 50% of companies trying to scale artificial intelligence.
The research from Qlik highlights a stark reality: without strong data governance, AI initiatives remain stalled. Businesses now recognize that responsible buildation requires more than just advanced algorithms.
Metadata-driven validation and quality controls are emerging as key strategies. Companies like UST are already embedding these principles into their AI accelerators, setting a potential blueprint for others.
Regulatory pressures are adding urgency to this challenge. With upcoming 2026 regulations on the horizon, enterprises can't afford to treat data governance as an afterthought. Governed analytics platforms offering lineage and auditability are no longer optional, they're needed.
The path forward requires a systematic approach to data quality. Indian companies must invest in platforms that provide transparency, validate consent, and ensure data integrity. Only then can they truly unlock the potential of artificial intelligence.
Further Reading
- Data Quality Holds Back India's AI Dream: Over Half of Firms Struggle - Entrepreneur India
- Qlik adds trust score to aid data prep for AI development - TechTarget
- AI for good starts with quality data - ITU
- AI Is Core to Strategy for 86%—But Most Are Stuck in Data Complexity - Qlik
- Qlik Continues Investment in Data Sovereignty with Launch of Cloud Region in India - Qlik
Related Reading
Common Questions Answered
Why are nearly 50% of Indian enterprises struggling to adopt AI technologies?
Data quality and governance challenges are preventing Indian companies from effectively implementing AI solutions. The lack of robust data validation, lineage tracking, and quality controls are creating significant barriers to AI adoption across the corporate landscape.
What role do governed analytics platforms play in addressing AI adoption challenges?
Governed analytics platforms provide critical capabilities like data lineage, auditability, and quality controls that are essential for scaling AI responsibly. These platforms help enterprises prepare for upcoming 2026 regulations and ensure more reliable AI implementation.
How are companies like UST approaching responsible AI development?
UST is embedding responsible AI through metadata-driven validation of data quality, lineage, and consent. Their approach includes developing explainability frameworks to ensure transparency and reliability in AI technologies.