Why AI hallucinations are inevitable and how better data can fix them

"AI is moving at breakneck speed, but it all depends on data. If your data is trusted, governed, and secure, you can get amazing AI outputs. There’s no AI strategy without a data strategy," says Vijayant Rai, Managing Director – India, Snowflake, at TechSparks 2025, setting the tone on what really drives AI use and applicability.

Snowflake, a SaaS-based AI data cloud provider, has seen about 6,000 customers use AI, a big jump from last year. The company has seen huge AI adoption across the board, from backend automation to customer insights platforms.

Kshitij Khandelwal, Founder and CTO, Pixxel, echoed a similar sentiment about data being the backbone of AI applications. Pixxel, which operates six satellites capturing Earth observation data, has built downstream tools that simplify access for users who aren’t experts in satellite imaging. "Most people look at a satellite image and can’t interpret it, so we use machine learning to make it actionable," says Khandelwal, speaking at a panel discussion on the topic of "Intelligence in Motion: Building AI-Ready Businesses."

These AI models can predict crop yield, identify diseases, or detect changes caused by climate. They also have applications in pollution monitoring, forestry, and carbon biomass estimation.

Coming closer to Earth, Madhusudhan Rao, CTO, Swiggy, says, "Honestly, Swiggy wouldn’t exist without AI. The scale we operate at, delivering millions of orders in under 10 minutes, would be impossible otherwise."

As per the latest quarterly shareholder letter, Swiggy saw 12 million average monthly transacting users for its quick commerce arm, and it delivered almost 1,000 orders a day from each of its 1,102 dark stores.

The company credits AI for managing its operationally complex business, both in the quick commerce and food delivery space.

"In terms of demand forecasting on what to stock where, because we are a very hyperlocal business, what sells a lot in the HSR area is very different from what sells in the Kanpur area. So, having the right quantity of the right items at these places—all of this leverages AI. Without AI, as I said, it simply would not be feasible for us to advance," added Rao, speaking at the 16th edition of TechSparks.

As extensive as these machine learning models are, they are still open to bugs and failure. Plus, the rapid pace of change in technology means that every time there is an upgrade, businesses are left to ensure that new models are compatible with existing systems.

"Hallucinations are almost like bugs in software. They are going to happen, and you just need to set up the right observability to ensure that you're catching them on time and mitigating the risks," added Rao.

Building on Rao's views, Balaji Thiagarajan, CTO and Product Officer, Flipkart narrated Flipkart's first AI deployment in the customer support space and learnings on how any AI output is only as good as the data it was trained on, and this is where the quality of data plays a crucial role.

"The fact of the matter is that hallucinations will happen, whether you like it or not. At some level, they are bound to occur. What matters is having the right controls, especially around where you deploy these AI use cases, to make sure that you do not hurt the customer. You need safeguards to ensure that such hallucinations do not reach the customer, and if they do, you learn from them and correct them. It is a continuous process," says Thiagarajan.