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)

I’ve seen this pattern repeatedly with teams building analytics and AI products: the issue usually isn’t the quality of the models or the sophistication of the data. The technology often works just fine. The real breakdown happens earlier—when teams begin with the data they already have and try to figure out what to build, instead of starting with the decisions their customers need to make.

That approach often produces polished dashboards and compelling features that generate interest, but fail to drive real action. The missing piece is context. Decisions in the real world depend on incentives, habits, risk tolerance, and uncertainty—not just clean data. If your product doesn’t reflect that reality, it won’t meaningfully change behavior.

Another common trap is assuming all available data is *evidence* worth surfacing. This “more is better” mindset leads to cluttered analytics tools that offload interpretation onto users. Even conversational AI interfaces can fall into this, encouraging open-ended exploration without helping users reach decisions.

The analytics and AI products that succeed take a different approach. They’re designed around decision-making to reduce uncertainty, fit into real workflows, and guide users toward clear actions. In doing so, they bridge the gap between analytical capability and real-world value, making the product’s intelligence tangible, usable, and worth paying for.

• The core mistake I see people making during the discovery process of building an insights product (2:07)
• Improve your product strategy by working ‘backwards” and understanding what decisions customers are trying to make (6:06)
• Insights don’t equal decisions in the real world (7:39)
• Designing with a goal of improving the lives of users in mind (11:17)
• Prototypes as a means of discovery (vs. product/solution validation) (13:48)
• The bias of data availability (20:39)
• Using AI and LLMs for discovery and product UX (24:17)
• Why AI-assisted analytics products should shape UX around making structured decisions (31:03)
• Overcoming the Invisible Intelligence Gap (34:57)
• Final thoughts (37:21)

• CED: My UX Framework for Designing Analytics Tools That Drive Decision Making https://designingforanalytics.com/ced
• Need my help finding the right use cases for your analytics or AI product? Book a complimentary 1x1 discovery call with me: https://designingforanalytics.com/contact/

I’ve worked with a lot of teams building analytics and insights products and decision-support systems. The pattern I keep seeing isn’t that the math is wrong or the ML / AI models are weak. Much of the time, the technology is fine.

The challenge is that all that [not always artificial!] intelligence is not surfacing as value to your customer. Dashboards look impressive. AI features demo well. Pilots get strong reactions. And then… usage stalls. Sales cycles drag. Teams quietly revert to spreadsheets. Buyers, or rather, prospective buyers, say they “like the vision,” but deals don’t move into the “closed” stage.

If your gut tells you the primary blocker is not your sales process, pricing/packaging, procurement, data quality, or risk/compliance, then you may be suffering from what I call the Invisible Intelligence Gap.

Your product’s intelligence simply isn’t visible to them. Three forces tend to amplify this gap. First, the value translation gap, which is when buyers and users can’t easily connect insights to their own goals. Second is the workflow alignment gap resulting from the product not fitting how work actually gets done. Third, the trust and control gap involves users lacking confidence in how the system reaches conclusions. My frameworks like CED, FOWA, and MIRRR are designed to close these gaps by making value obvious, workflows smoother, and AI more trustworthy.

Highlights/ Skip to:

• The challenge of insights not providing value to buyers, end-users, and stakeholders (3:20)

• How the invisible intelligence gap manifests itself (6:42)
• Common symptoms of the invisible intelligence gap (8:10)
• Examples of how changes in human behavior cause the gap (10:00)
  • The (3) amplifiers of the invisible intelligence gap (11:47)
• The CED framework for addressing the intelligence gap problem (18:28)
• Addressing the invisible intelligence gap with FOWA (20:14)
• Using MIRRR to solve the invisible intelligence gap (21:25)