Understanding the Data Costs Behind Enterprise AI With Acceldata

What happens to your AI budget when one business question becomes a series of data queries across several systems?

In this episode of Tech Talks Daily, I speak with Rohit Choudhary, founder and CEO of Acceldata, about the work taking place behind an AI response. An agent may need to find information, call several tools, and repeat parts of that process before it can return an answer. Each step can consume resources, and the person asking the question may have little visibility into the infrastructure activity it creates.

Rohit uses the example of looking for jewelry without knowing which vault contains it. The analogy helps explain why an AI system can spend time and compute finding the right source before it does anything useful with the information. We discuss his view that repeated queries and tool calls are putting pressure on enterprise infrastructure budgets, particularly where data is spread across older systems and cloud platforms.

His banking example brings the problem into focus. Information can pass from mainframes through multiple processing stages before reaching the applications people use. Copies may represent different stages of transformation, and the data that is suitable for one business purpose may be unsuitable for another. An agent needs to use the appropriate source, while the organization needs to understand the reliability and cost of that decision. The banks are not named, and the example should be understood as Rohit's account of the patterns he sees.

We also discuss why data quality cannot be separated from governance. Different business teams have different requirements, but AI services can draw on information that crosses those boundaries. Rohit argues for controls that operate throughout the data pipeline, including access restrictions, quality checks, and reconciliation. The business question is whether a policy actually changes what a running system can do.

One of his clearest principles is that an agent acting for a person should follow that person's permissions when working with enterprise data. We talk about how common standards can coexist with decisions made by individual teams about where data should live and which infrastructure best serves a particular use case. The conversation does not offer a universal answer about who should own every cost, but it does make the case for consistent controls across separate budgets and responsibilities.

Rohit also describes Acceldata's X-Lake platform and its approach to bringing intelligence to distributed data. Alongside the architecture discussion, I ask how leaders can distinguish productive AI activity from repeated processing and experiments that never become useful services. He expects a period of experimentation followed by a closer assessment of which applications provide lasting value. Faster application development can widen access to information, but it also makes it easier to build something whose ongoing cost has not been fully considered.

Finally, we consider the consequences of using unreliable data. Rohit's credit and drug-trial examples are hypothetical illustrations of how errors can influence decisions. They reinforce a practical question for anyone expanding an AI service. Can you explain the information it used, the permissions it followed, and the resources it consumed?

I would love to hear how your organization is approaching this, so please share your thoughts. Are you measuring the full data and infrastructure cost of an AI answer, or mainly the model bill?