Speed is often confused with good product thinking. The idea is that if teams can ship prototypes, dashboards, and models faster, they will automatically learn faster. But execution speed alone doesn’t ensure a clearer understanding of what’s actually worth building.

Instead, teams often fall into a loop driven by demo feedback. They present working prototypes, and users respond to what they can see in the form of interface design, visualizations, or surface-level data behavior. While this feedback feels positive, it’s often misleading. Teams can end up reacting to presentation (UI) feedback only to find it does not change propensity to buy or increase user adoption.

The key idea today is that prototypes can either be used to clarify the problem space and user needs or to validate the solution presented. Where I see most teams fail is that every artifact or prototype is seen as a solution to validate, and they can miss the forest for the trees.

Another approach borrows from blue ocean thinking, which focuses on creating value by looking for overlooked opportunities in the empty space—beyond the known “problem space” your customer knowingly lives in now.

Because AI lets us move so fast with prototyping, I think there is an exciting possibility to explore the blue-ocean spaces where your product could evolve to produce value.

As always, we seek to go beyond building “technically right, effectively wrong”—which doesn’t make people buy, use, or refer your product. Today, we look at what AI can help us to do to see even farther beyond the immediate problem space.

• Where the idea for this episode came from (00:46)
• Why faster building of artifacts with AI doesn’t necessarily mean faster market validation (2:09)
• How understanding the problem space results in fewer prototypes being created (5:40)
• Using blue ocean strategy to arrive at new products worth paying for (08:23)
• Finding missed market opportunities blocked by cost, tech limits, or risk (12:39)
• How AI-assisted user research fits into blue ocean thinking (14:33)
• The big picture: winners will figure out what’s worth building before they build it (20:42)

• Contact Designing for Analytics

I’ve seen this challenge again and again with teams building analytics and AI products: nobody can define what quality to the end user means or how to measure. The answer? “Adoption.” The problem is that “amount of usage” tells you nothing useful about your customer’s experience with your product beyond “it’s not zero.” So what should you be measuring instead so your buyers don’t quickly abandon once the end users get their hands on the keyboard (or agent!)?

The answer is to understand through qualitative measures what users’ experiences are like now, so you have an objective baseline from which to compare future product investment. When you can define their current experience’s quality, it’s much easier to imagine their better future, and you also now have a change you can measure. Measurable outcomes are the foundation of high-value, sticky B2B analytics and intelligence products—and when your end users’ lives are improved, the sales close, and the renewals aren’t questioned. So today, I jump into “how do you measure UX?” so you aren’t surprised when the sale doesn’t close or that renewal doesn’t come through unexpectedly.

• Why I think product adoption (i.e. product usage analytics) are misleading as a means to define whether your solution is valuable to users (1:34)
• Getting a better baseline reading of user experience so you can improve their life and your sales/retention KPIs (4:56)
• How to measure, hypothesize, and observe if your product is working “well” (7:35)
• Discovering where your product is being appreciated (20:28)
• What about when AI is in the loop? (23:05)
• The risk of creating bigger messes with AI capabilities (28:20)
• How to gain useful insights from your customer exposure time (31:28)
• The quantitative metrics you can use to help measure UX outcomes (36:17)
• Why "ship it and see if it gets used" isn't a product strategy (40:52)

• More Resources
• Get 1x1 Help from me if you know your product’s value is opaque, or the user experience is hindering your sales or adoption goals

I'm talking with Steve Ancheta, CEO of Zig, a platform designed to free sales teams from repetitive, non-revenue-generating tasks. CRM and logistical tasks can consume up to 72% of the week of a sales team, but Zig’s AI agents handle them so reps can focus on closing deals. Unlike tools built for managers, Zig follows a rep-first design—simple, intuitive, and aligned with the motivation to sell more—while also creating an intelligence layer that preserves institutional knowledge and accelerates onboarding for new hires.

I wanted to chat with Steve about how he built a product that is both used—and worth paying for—with AI under the hood. Rather than relying on chat prompts, Zig surfaces prioritized tasks in panels and cards, integrates with CRMs and Slack, and builds confidence scores from user interactions.

Because Steve comes from the world of sales—and that’s the domain his product sits in—I wanted to explore his “problem clarity” and share that with you, since I often find data and technical founders to be more solution-oriented and lacking in this area. Steve was an open book with me, and I’m hoping other founders trying to turn analytical complexity into commercial clarity can see how Steve is using AI and agents to make data work for end users—and worth paying for.

Finally, I also challenge Steve to answer whether Zig.ai is a software company or a services company with a product behind the scenes—a question you might also ask yourself depending on your GTM model.

• What is Zig.ai? (00:48)
• When managers see the value of a product but end-users don’t—and how product leaders need to react (5:20)
• What Zig’s UX is like and how it was designed (9:45)
• The sales process and risks salespeople face when demoing Zig (16:12)
• How Zig addressed their time-to-value challenge during the product experience (20:14)
• How Zig found a problem people were willing to pay to solve (24:16)
• We discuss whether an AI product company might be a services company with technology or a traditional software company (24:16)
• The Invisible Intelligence Gap Steve has observed within Zig’s business space (AI and analytics-powered sales tooling) (27:57)
• Why Steve isn’t worried about the major CRMs from building internal solutions to circumvent third-party tools like Zig (35:37)
• Steve Ancheta’s advice for trying to bring sophisticated data products to market (39:26)