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Welcome back to UXChange, I’m Jeremy — and today we’re talking about something I’ve been wrestling with personally: when should we actually use AI?

We all love what it can do — speed, automation, magic. But at what point does using AI stop helping us think, and start replacing the thinking itself?

In this episode, I share my personal heuristics for using AI at work — not as a researcher, but as a knowledge worker trying to stay effective, curious, and human in an AI-powered world. I unpack questions like:

• When is AI genuinely adding value, and when is it just noise?
• How much control are you willing to give away for efficiency?
• Where’s the line between delegation and disconnection?

I’ll talk about why some tasks must stay human — especially those that shape your understanding, your craft, and your credibility — and how I’ve learned to decide where AI fits in my creative and research process.

🎧 Listen now for a raw, reflective episode about the mindset, trade-offs, and self-trust we all need in this new era of AI-driven productivity.

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Welcome back to UXChange — this is Part 2 of our series on Measuring the User Experience of AI.

In the first episode, we explored vanity vs. sanity metrics — the surface-level KPIs that look good on dashboards versus the deeper indicators that truly define trust, satisfaction, and control.

Today, we’re going a step further. I’ll break down why we need a new approach to measure AI experiences, and why our traditional UX frameworks simply don’t fit anymore.

If you’ve ever asked yourself “how do I even measure if my AI is working well?” — this episode will give you the foundation to start answering that question the right way.

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In this new series, we’ll dive deep into what it really means to measure the success of AI-enabled user experiences. Beyond surface-level KPIs and “vanity metrics,” we’ll talk about the sanity metrics that actually reveal whether your product is usable, reliable, trustworthy, and meaningful for real people.

I’ll break down complex ideas like trust, perceived control, satisfaction, and holistic measurement in a way that’s practical for designers, researchers, and product teams.

If you’ve ever wondered how to move from counting clicks to understanding confidence, or how to measure what truly matters in AI, this series is for you.

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In this episode, I explore how much of user experience happens beneath the surface of awareness — and why tapping into the unconscious is essential for better product design. 

• Why users often say one thing but do another
• The limits of relying only on interviews or only on observation
• How unconscious motivations, needs, and fears shape technology use
• The power of co-creation and generative workshops to reveal hidden dimensions
• Practical ways to integrate unconscious insights into product decisions

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In this episode I explore how product teams can better anticipate and measure user experience in a world where interaction patterns are shifting—from clicks and forms to voice and chat. 

• Why AI is transforming the way users interact with technology
• The importance of treating design choices like “ingredients” in a recipe
• How to build internal benchmarks to predict user experience outcomes
• Lessons from AI tools like ChatGPT and the rise of “Generative Engine Optimization”
• Practical ways to align intended vs. actual user experience through experiment design

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I break down the difference between vanity metrics (what looks good) and sanity metrics (what actually matters), plus the new measurement challenges when users collaborate with AI instead of just using a product. Essential for anyone designing or evaluating AI features.

Topics:

• Why traditional UX metrics fail with AI systems
• The dangerous difference between vanity and sanity metrics
• How collaboration changes everything we measure
• Essential metrics you're probably missing

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📝 Episode Description

• Why AI breaks traditional UX rules
• The costly mistake of treating AI like automation
• How that 5% error rate could lose you big deals
• Essential if you're adding AI to your product

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In today's episode, I shared my thoughts on AI from my dual perspective as a UX professional and admittedly picky user. Here's what we covered:

🧠 My UX Philosophy

• I'm that friend who gets frustrated when interfaces don't work as expected (yes, I might scream at my phone occasionally 😅)
• This pickiness led me naturally to UX 
• My low patience = high empathy for users!

💡 AI vs. LLMs - Let's Get Clear

• AI is a broad field, but I'm concerned when we call LLMs "artificial intelligence"
• LLMs predict the next word in a sequence - fascinating but limited!
• Setting proper expectations is crucial ⚠️

🎨 My AI UX Guidelines

1. Be honest about limitations
   • Tell users when AI might be wrong
   • Careful with the mysterious "magic" sparkles ✨
2. Offer control & feedback
   • Always include options to give feedback
   • Provide manual backup options
   • Show clear examples of what to ask
3. Respect privacy
   • Be transparent about how user data is used
   • Remember the Figma incident? Yikes! 😬

🛠️ Tools Doing It Right

• Perplexity: Shows its reasoning process - love this!
• Contextual AI in everyday apps > standalone chat interfaces

🔮 Coming Up Next

I'll be tackling trust in AI, the job-replacement debate, and more practical UX patterns!

🤗 Stay Connected!

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Until next time!

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Check out the Youtube version for my slides: 

https://youtu.be/v3gBHYA1O_E

Explore the critical first question in UX research: Should research even be done in the first place? In this first episode of Sizing UX Research Efforts, we walk through a structured approach to validate research needs.
This episode covers:

• Key questions to ask before starting any research
• Impact framework for evaluating research value
• Prioritization criteria for research needs

Perfect for UX researchers, product managers, and designers who want to ensure their research is strategic, impactful, and necessary.

✅ Subscribe for future episodes on sizing your UX research efforts!

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- This episode dives into the essentials of setting goals and measuring success in UX. 
- We’ll explore the HEART framework by Google and discuss how to make UX automatic for everyone. 
- Learn how to define your vision & mission to drive meaningful change in user experiences. 

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