In this final part of my three-episode series on accelerating sales and adoption in B2B analytics and AI products, I unpack a growing challenge in the age of generative AI: what to do when your product automates a major chunk of a user’s workflow only to reveal an entirely new problem right behind it.

Building on Part I and Part II, I look at how AI often collapses the “front half” of a process, pushing the more complex, value-heavy work directly to users. This raises critical questions about product scope, market readiness, competitive risks, and whether you should expand your solution to tackle these newly surfaced problems or stay focused and validate what buyers will actually pay for.

I also discuss why achieving customer delight—not mere satisfaction—is essential for earning trust, reducing churn, and creating the conditions where customers become engaged design partners. Finally, I highlight the common pitfalls of DIY product design and why intentional, validated UX work is so important, especially when AI is changing how work gets done faster than ever.

Highlights/ Skip to:

Finishing the journey: staying focused, delighting users, and intentional UX (00:35)
AI solves problems—and can create new ones for your customers—now what? (2:17)
Do AI products have to solve your customers’ downstream “tomorrow” problems too before they’ll pay? (6:24)
Questions that reveal whether buyers will pay for expanded scope (6:45)
UX outcomes: moving customers from satisfied to delighted before tackling new problems (8:11)
How obtaining “delight” status in the customer’s mind creates trust, lock-in, and permission to build the next solution (9:54)
Designing experiences with intention (not hope) as AI changes workflows (10:40)
My “Ten Risks of DIY Product Design…” — why DIY UX often causes self-inflicted friction (11:46)

Links

Listen to part I: Episode 182 and part two: Episode 183
Read: “Ten Risks of DIY Product Design On Sales And Adoption Of B2B Data Products”
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