Editor’s note: This article is based on insights from a podcast series. The views expressed in the podcast reflect the speakers’ perspectives and do not necessarily represent those of this publication. Readers are encouraged to explore the full podcast for additional context.
Artificial intelligence is evolving at an extraordinary pace, with much of the global conversation centered on large language models and breakthroughs in generative AI. However, a recent conversation on the “CAIO Connect” podcast with Alexy Thomas, who leads the data practice at EY Technology Consulting, highlights a critical but often overlooked truth: the real foundation of AI success lies in data.
With more than 25 years of experience in the data and analytics space, Thomas emphasized that while AI models may capture headlines, it is the quality, governance, and connectivity of data that will ultimately determine how effective AI systems become. His insights provide a timely perspective for India, a country that possesses vast amounts of data but still faces challenges in unlocking its full potential for AI innovation.
“India is data rich. However, one of the problems that we need to tackle fairly quickly is our data is within silos,” Thomas said. According to him, India has data with valuable datasets spread across enterprises, government systems, and publicly available sources on the internet. However, these datasets often exist in isolation, limiting their usefulness for AI applications.
The solution, he argues, is not to move all information into a single massive repository such as a data lake or warehouse. Instead, the focus should be on creating connected data ecosystems where information is organized using clear taxonomies and shared standards. When data is structured and labeled consistently, organizations can exchange and interpret it more easily. This interoperability would allow startups, data scientists, and enterprises to access and use data seamlessly, accelerating innovation and unlocking the true value of India’s data resources.
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Another key theme Thomas emphasized is the importance of AI-ready data. Not all data is suitable for training or powering AI systems, and organizations must ensure that their datasets meet several critical standards.
First, data must be trustworthy, meaning its origin, provenance, and lineage are clearly documented. Second, the quality of data is essential; AI systems perform only as well as the information they are trained on. Third, strong data governance must be in place, ensuring that ownership and accountability are clearly defined if issues arise. Fourth, AI should increasingly rely on structured enterprise data, which often contains high-value insights for business applications. Finally, Thomas highlighted the need to minimize bias in datasets, since biased data can lead to flawed AI decisions and reinforce existing inequalities.
Building a successful AI ecosystem also requires collaboration among multiple stakeholders. Thomas mentioned, “You don’t want AI to make decisions on wrong data. You don’t want AI to make decisions on biased data. So enterprises have a huge role to play.” Governments, meanwhile, can play a vital role by reducing data silos and creating frameworks that make high-quality data accessible for innovation while maintaining privacy protections.
Civil society also has a role to play by demanding transparency and accountability in how organizations use personal information. India has already taken steps in this direction, “The DPDP (Digital Personal Data Protection) Act that we’ve created is actually very forward-looking so that our data, yours and my data, is not used without our consent.”
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While much of the global AI race currently focuses on building bigger and more powerful models, Thomas believes the real transformation will come from widespread AI adoption. He draws parallels with the Y2K era, when India’s technology talent stepped up to solve a global challenge.
Today, the country’s deep talent pool, entrepreneurial ecosystem, and growing digital infrastructure position it to play a similar role in accelerating AI adoption across industries. The real opportunity lies in embedding AI into everyday applications across enterprises, consumer services, and public infrastructure rather than limiting innovation to a handful of advanced models.
Looking ahead, the next 18 to 24 months are likely to focus heavily on practical AI deployment within organizations, “we’re using AI to make the lives of employees, enterprises, consumers, customers easy.”
Businesses are already experimenting with ways to use AI to improve productivity, enhance customer experiences, and streamline operations. Over the longer term, advancements in autonomous agents, robotics, and physical AI could further transform daily life, handling tasks that range from routine office work to household activities.
Ultimately, Thomas reinforces a powerful message: while AI models may define the technological frontier, it is connected, trustworthy, and well-governed data that will determine how far that frontier can truly extend, especially for a data-rich nation like India.