Large Language Models (LLMs) have transformed how we search for information, generate content, and make decisions. Factual information still remains an integral part of the new wave of AI. Aravind Srinivas, CEO of Perplexity, posted yesterday on X, "The inaccuracy in financial queries on Perplexity Deep Research is being addressed. Examples of current failures include using old bitcoin prices or old market caps of the companies for a task like make me an investment case for Palantir or Bitcoin. We’re fixing this. For finance specifically, data accuracy is a must and high stakes. We’re also working on bringing a more trustworthy data source to this. Appreciate the folks who flagged these bugs.”
This is just one example that inaccuracy remains a persistent challenge—especially in high-stakes domains like finance, manufacturing, life sciences, and consumer packaged goods (CPG), where relying on outdated financial metrics, inaccurate production data in manufacturing, obsolete clinical trial results in life sciences, or incorrect consumer demand forecasts in CPG can lead to costly mistakes.
More broadly, in the enterprise world, many critical facts don’t come from open-ended text generation but from structured data—databases, logs, telemetry, transactions, and other well-defined sources. Enterprises depend on this data to power operations, ensure compliance, and make informed decisions.
As AI adoption accelerates, organizations need to think critically about how LLMs interact with structured data. Broadly, we see three categories of use cases:
- Structured-Only – Where accuracy is non-negotiable, structured data remains the single source of truth.
- Structured + LLMs – LLMs enhance structured data by providing explanations, summarization, or discovery capabilities. For example, AI-based customer support systems can analyze structured interaction logs to provide contextual responses, while predictive maintenance in manufacturing uses LLMs to interpret sensor data trends.
- LLM-Only – When creativity or synthesis is of high importance, but factual accuracy is less critical.
Here’s a breakdown of where each approach fits:
Use Cases for Structured Data, LLMs, or Both?
As the above table shows, 5 out of 6 representative use cases rely on structured data to complete the experience. We anticipate this trend will remain as even more innovative uses of LLMs are unearthed - facts and structured data will always be important.
The Future: Data Observability for Trustworthy AI
As enterprises integrate LLMs into decision-making, the need for data observability is greater than ever. Structured data must be monitored for accuracy, completeness, and timeliness. When LLMs are involved, their interaction with structured data must be tracked to prevent misleading outputs.
At Acceldata, we believe that enterprise AI must be built on a strong foundation of trustworthy data. Without reliable structured data, even the most powerful LLMs risk becoming nothing more than eloquent but unreliable storytellers.
Enterprises that successfully navigate this balance—leveraging structured data for precision, LLMs for augmentation, and observability for trust—will lead the next wave of AI-driven transformation.
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