17. Your AI Product is Failing — Microsoft's UXR Team Knows Why
episode
Product Impact Podcast | Secrets to unlocking the value of AI
42 min
4 speakers
7 chapters
transcribed 1 month ago
Transcript
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What problem do invisible AI failures cause and why do traditional metrics miss them?
Welcome to the Product Impact Podcast. Our guests today are three exceptional researchers from the Microsoft UXR team. Christopher Monnier, Chuck Kuang, and Wendy Wang lead AI-powered UX research on Microsoft Copilot. They're responsible for the Consumer App and now expanding into enterprise. Building a super app, one product designed to handle any use case for any kind of user, comes with challenges that most research teams never face. So understanding product market fit when your product is everything for everyone. And proving how to improve when scale makes traditional research methods unmanageable. Those challenges led this team to pioneer new approaches to participant recruiting and to develop what they call UX evals, a new method for scoring the quality of LLM outcomes that the broader research community has been paying some pretty close attention to.
In this episode, you will learn what usage data hides about whether your AI product is actually working and which metrics matter once adoption stops being enough. What UX evals are and how they differ from traditional evals and unit tests, and why they close gaps that automated testing just structurally cannot. We cover which research methods hold up at LLM scale and which ones break down under the weight of real users and how you research a product whose audience is literally everyone. Every industry, every archetype. Regulated environments, hundreds of millions of interactions. And where this team is taking UX research next, the new methods they're developing that the rest of the field will eventually follow.
This conversation is essential for anyone building with AI, because LLMs have made every business believe that it's possible to build an agent that can handle any use case. That belief becomes immensely difficult to act on the moment it meets real users. So to develop, scale, measure, and manage in a way that actually serves people rather than just impressing them in a demo. We're gonna have three different perspectives coming from Microsoft Copilot. And so let's jump into it. Copilot is the most used AI product in the world by Yeah. What is the hardest part of existing under the weight of so many different user expectations and restrictions and regulated environments?
Wendy, I feel like you should go. Cause you I feel like you and maybe Chuck, you guys have done the most recent stuff on uh on uh working on big company stuff.
It's definitely a uh huge responsibility to be the AI for these huge uh corporations and but we also want to be able to deliver the best experience for them. And oftentimes what we've learned on the consumer side is that users, it's hard for them to articulate what they really want until they see different concepts or different variations. So that's why, you know, Chuck, Chris, and myself have developed this methodology around side-by-side evals that have helped the consumer side really understand from the user's perspective what's important in having an interactive conversation with an AI. So part of what we are trying to do on the commercial side is also to bring that type of thinking and user-centeredness and how we think about how do we improve response quality.
Oftentimes it's easy to just look at the copilot response and think about, you know, what are the small tweaks we can make, but if we can bring that side-by-side experience and that eval experience with the end users themselves who have these very complex work context, work grounding, et cetera. and be able to deliver an experience that actually is truly measurably different, that's the type of value that we want to be able to bring to to our commercial users as well.
So we're gonna talk a little bit more about evolves in a a couple minutes. So I won't get into that much right now, but when you say side by side experience, can you define that for us?
Typically when we do our evaluations at Copilot, we've led sort of this uh framework where we asked uh users to come with their own prompts and they test out uh one model in isolation, they come up with the prompt, interact with the AI.
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Chapters
7 chapters
1
What problem do invisible AI failures cause and why do traditional metrics miss them?
0:01–7:17
2
How do Microsoft’s UX researchers define and run side‑by‑side evaluation studies?
7:17–15:35
3
Why did the team start with just 10 users and a comparative question to surface real‑world issues?
15:35–24:47
4
What are loss‑pattern taxonomies and how do they become a shared language across product teams?
24:47–33:22
5
Which key metrics (engagement, retention, sessions) move when loss patterns are addressed?
33:22–37:18
6
How can you diagnose user‑unarticulated dissatisfaction by comparing two model responses?
37:18–42:04
7
What research questions should teams ask to uncover trust vs. model problems in AI products?
42:04–42:52
Speakers
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