TLDR: Sandeep Sahai, CEO of Clearwater Analytics, asserts that high-quality and accessible data, rather than just large language models, will be the crucial differentiator for enterprises aiming to succeed in the generative AI era, emphasizing the need for accurate data to prevent AI “hallucinations” in business applications.
In the rapidly evolving landscape of generative artificial intelligence, the true differentiator for enterprises will not solely be the sophistication of their large language models (LLMs), but rather the quality and accessibility of their underlying data. This was the key message from Sandeep Sahai, Chief Executive of Clearwater Analytics, a NYSE-listed investment accounting solution provider, as reported by The Economic Times on August 21, 2025.
Sahai highlighted a critical oversight in the current generative AI discourse, stating that “people are not thinking enough about where the data is and getting access to the data, as the focus is more on the large language models (LLM).” He underscored that while LLMs are powerful, their effectiveness in business applications hinges on the data they are trained on. For enterprises, where a high degree of accuracy is paramount, having the right data is essential to ensure that AI models perform reliably and “doesn’t hallucinate.”
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The CEO’s remarks come at a time when generative AI is fundamentally reshaping how businesses operate, from automating creative processes to enhancing decision-making. As companies increasingly integrate AI into their core functions, the ability to leverage clean, relevant, and comprehensive datasets will be the deciding factor in achieving tangible business outcomes and gaining a competitive edge. Clearwater Analytics, with its focus on investment accounting solutions, likely deals with vast amounts of precise financial data, giving Sahai a unique perspective on the practical challenges and requirements for successful AI implementation in data-intensive fields. The emphasis on data quality and governance suggests a move towards more robust and trustworthy AI deployments in the enterprise sector.
