Experiencing Data w/ Brian T. O’Neill (AI & data product management leadership—powered by UX design)

186 - Why Powerful AI & Analytics Products Feel Useless to Buyers

17 snips

Jan 20, 2026

The discussion tackles why advanced AI products can feel irrelevant to buyers. A key point is the divergence in needs between fiscal buyers seeking ROI and end users looking for ease in their tasks. The host emphasizes that while model quality may become a commodity, the challenge lies in effectively translating data into clear benefits. He advocates for narrowly focused solutions that don't force customers to find their own use cases, warning that incomplete workflows can damage trust and undermine long-term retention.

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INSIGHT

Capability Is Not The Same As Value

Model quality and raw AI capability are becoming commoditized and won't by themselves drive sales or adoption.
Teams must translate intelligence into clear, defensible decisions buyers and users can understand.

ADVICE

Design For Buyers And Users

Design products to solve both fiscal buyers' needs and end users' job-level pains simultaneously.
Map outcomes to each group's incentives so buyers can justify cost and users see personal benefit.

INSIGHT

Narrow Scope Beats Broad Capability

Broad AI capability doesn't map cleanly to narrow buyer pain and raises perceived risk.
Early-stage products should narrow scope and target specific workflows or industries.

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I’m back! After about 7 years (or more) of bi-weekly publishing, I gave myself a break (to have the flu, in part), but now it’s back to business! In 2026, I’ll be focusing the podcast more on the commercial side of data products. This means more founders, CEOs, and product leader guests at small and mid-sized B2B software companies who are building technically impressive B2B analytics and AI products. With all the focus on AI, I want to focus on things that don’t change: what do value and outcomes look like to buyers and users, and how do we recreate it with analytics and AI? What learnings and changes have leaders had to make on the product and UI/UX side to get buyers to buy and users to use?

So, that brings us to today’s episode. Today, I’ll explain why I think model quality, analytics data, and raw AI capability are quickly becoming commodities, shifting the real challenge to how effectively companies can translate their data and intelligence into value that buyers and users can clearly understand and defend.

I dig into a core tension in B2B products: fiscal buyers and end users want different things. Buyers need confidence, risk reduction, and defensible ROI, while users care about making their daily work easier and safer. When products try to appeal broadly or force customers to figure out how AI fits into their workflows, adoption breaks down. Instead, I make the case for tightly scoped, workflow-aware solutions that make value obvious, deliver fast time-to-value, and support real decisions and actions.

Highlights/ Skip to:

Refocusing the trajectory of the show for 2026 (00:31)
Turning your product’s intelligence into clear, actionable solutions so users can see the value without having to figure it out themselves (4:32)
You’re selling capability, but buyers are buying relief from a specific pain point (7:33)
Asking customers where AI fits into their workflow is poor design (16:57)
Buyers and users both require proof of value, but in different ways (20:05)
Why incomplete workflows kill trust (24:18)
The importance of translating technical capability into something a human is willing to own (30:09)