Published on April 28, 2026
For years, businesses have integrated AI and large language models (LLMs) into their operations, relying on their ability to process vast amounts of information. These technologies aimed to enhance productivity, streamline workflows, and improve decision-making. However, many organizations find themselves grappling with unanticipated hurdles when deploying LLMs.
Recent discussions with Harsha Chintalapani, co-founder and CTO at Collate, reveal that the struggle often lies in the quality and structure of real-time production data. LLMs, designed to generate human-like text, are proving inadequate when faced with unstructured or inconsistent data sources. This inconsistency has been a major roadblock for companies seeking to implement these advanced systems effectively.
Chintalapani highlights that inadequately structured data results in lower accuracy and effectiveness from these AI systems. Many organizations have discovered that their LLMs fail to deliver the insights needed for actionable intelligence. Without proper data management practices in place, the full potential of these technologies remains untapped, leading to further disillusionment.
The consequences of these data issues are significant. Businesses face delays in project timelines and increased costs due to inefficiencies. As companies continue to navigate this evolving landscape, addressing foundational data challenges will be critical for the successful integration of AI technologies in the future.
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