# @the.datascience.gal on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DYzrfn4uQyT
- **Gondola URL:** https://gondola.cc/posts/65936014-thedatasciencegal-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/85a84ce5e3.jpg
- **Posted:** 2026-05-26T15:51:13.000+00:00
- **Account Owner:** Aishwarya Srinivasan | Data & AI | LinkedIn Top Voice (@the.datascience.gal) — https://gondola.cc/the.datascience.gal

## Caption

Your AI system answered 10,000 employee questions this week.

How many of those answers were actually right?
This is one of the biggest gaps I see in enterprise AI conversations. Everyone wants to talk about how many questions the system answered, how many workflows it touched, or how much time it saved. Those numbers matter, but they only tell one part of the story.

The more important question is whether the system is being evaluated consistently enough for people to trust it.

One lesson that stayed with me from my years at IBM is that evaluation cannot be treated like an add-on. With generative AI, you are evaluating how the entire system behaves in the real world, across different users, workflows, edge cases, and business contexts.

That means looking at things like:
• Which answers were accurate
• Which ones needed escalation
• Where the system failed repeatedly
• Whether users trusted the response
• Whether the business outcome actually improved

IBM’s Client Zero is a strong example of this at production scale: 94% of HR inquiries resolved, 86% of IT queries resolved, and 250,000+ seller questions handled in 2025.
Those numbers are meaningful because they are measured, governed, and improved continuously.

Building AI is becoming easier. Building AI that your business can trust every single day is where the real work begins.

Link in bio to see how ibm powers smarter businesses.
#IBMPartner

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