191 - Turning Agents into Software that Sells [Smarter!] with Zig.ai CEO Steve Ancheta

Apr 1, 2026

Steve Ancheta, CEO of Zig, builds AI agents that automate CRM and admin work to let sales reps sell more. He discusses rep-first UX, panels and cards instead of chat, preserving institutional knowledge, and the tradeoff between product and services-led models. Practical tales of demos, time-to-value, and prioritizing high-impact workflows come up.

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INSIGHT

Seventy Two Percent Of Sales Time Is Non Revenue

Sales teams spend an average of 72% of their week on non-revenue tasks, creating huge opportunity by returning that time to reps.
Zig targets that busy work with AI agents so reps can focus on revenue and managers can decide whether to hire more staff or free existing capacity.

INSIGHT

Meet In The Middle Between AI And Familiar UX

Design must meet in the middle: a familiar, simple UI for reps with AI-first execution under the hood.
Zig enforces three-click completion and surfaces work in panels/cards rather than chat to reduce friction.

ADVICE

Use Confidence Scores Before Autopilot

Track per-user feedback to build confidence scores before enabling autopilot and only allow autonomous actions after thresholds.
Expose confidence metrics and let reps choose when to enable autopilot for emails or outreach.

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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.

Highlights/ Skip to:

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)