Customers almost never ask ”can you build this” anymore. What they actually want to know is ”can I trust it.”

In this episode of The AI Frontier Playbook, I sit down with Sapna Grover, Senior Director at Microsoft leading the Cloud, Data & AI Center of Excellence. Before this, she spent close to three decades in software, as a scientist at India's Defense Research and Development Organization, in leadership roles at Pegasystems, and in engineering leadership at Microsoft across Windows and Search. Today she's helping the world's largest companies actually put AI into production.

Sapna breaks down why AI flips the traditional software path: you start with the business outcome and the data, not the code, and you have to define what a ”good” answer even looks like since AI rarely gives the same response twice. Using a live airline customer-support example, she walks through what AI evaluation really takes: a golden data set, an LLM as judge, and tracing every decision so AI stops being a black box.

We also get into why the most dangerous AI failures are silent, quality drifting while every dashboard stays green, and the single most expensive mistake she's seen companies make: falling for the technology before the problem. She closes by sharing the AI agents she's built for her own life, including one that drafts her Friday team update and another that tracks her protein intake better than any app could.

You'll learn:

• Why the fundamentals of building great products haven't changed, even though AI has changed the engineering problems underneath them
• How AI flips the traditional software path, starting with business outcome and data instead of code
• Why ”did it produce the expected answer” is the wrong question for AI, and what to ask instead
• How to build a golden data set and use an LLM as a judge to evaluate AI at scale
• Why tracing turns AI from a black box into a glass box you can actually debug
• Why the most dangerous AI failures are silent, with quality and trust eroding while every system metric stays green
• The most expensive mistake companies make with AI, and why it usually isn't a technical one
• Why AI needs the same financial discipline as cloud infrastructure, and how uncontrolled cost quietly kills ROI

Whether you're a product leader shipping your first AI feature or a technology leader responsible for what happens after launch, this episode gives you a practical framework for building AI your organization can actually trust.

Resources & Links

Guest LinkedIn:linkedin.com/in/sapna-grover-9170032

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