Explore the ideas, technologies, and insights from global C-suite leaders driving digital transformation exclusively on ExtraMile by KnowledgeNile.
Welcome to the newest session, as we bring you a conversation with Steve Ancheta, Founder and CEO of Zig.ai. The firm is an AI-powered sales intelligence and execution platform, helping revenue teams automate workflows, eliminate admin tasks, and accelerate growth.
With more than 16 years of experience in sales and sales leadership, Steve built Zig.ai to fix a problem he experienced firsthand: reps spending too much time on admin tasks that prevent them from building relationships and closing deals.
In this insightful conversation, Steve shares what inspired him to launch Zig.ai, how the platform identifies rep strengths and gaps through pattern recognition, and early customer success stories.
He also discusses the role of AI in GTM strategy, what sets Zig.ai's AI-powered sales intelligence and execution platform apart, and his long-term vision for making Zig a category-defining layer in revenue infrastructure.
Get ready to unlock insights from a true sales veteran on building smarter, faster, and more resilient revenue teams!
Steve, thank you for joining us on ExtraMile! We're excited to dive into this conversation; welcome!
You’ve spent 16 years carrying quota, leading sales teams, and building systems to close the execution gap that drains revenue and leads to sales rep burnout. What challenges did you observe during your sales career that ultimately inspired you to launch Zig.ai?
Steve. I think the biggest thing that I noticed over my 16-year sales career was that sales reps are constantly having to do multiple jobs that are outside of sales, but are only held to the metrics that directly involve sales.
The problem is that most of their time is being spent outside of doing what they were hired to do. Essentially, it doesn't look great on them when they're not hitting numbers, but there has never really been any sort of fix for that. The answer has always been to throw more technology at the problem or throw more headcount at the problem, but never fix the problem at its root.
In 2023, I spent about $150,000 on tools that all solved pieces of problems individually and watched our numbers barely increase because of those purchases. We ended up abandoning a lot of those tools very quickly after implementation because they either weren't being used, changed the way my salespeople had to work, changed their psychology, or simply didn't help at all.
How is Zig.ai helping revenue teams scale at a faster rate with AI employees that can be managed from anywhere, and what aspects does it aim to fulfill?
Steve. The simple answer is that it allows teams to delegate everything that isn't what they were hired to do, which is build relationships and close business.
Everything else can be delegated to employees that don't get tired and never have too much on their plate.
What it aims to fulfill is giving sales reps back the time they need to successfully do the job they are measured against—how many customers they can get in front of, how they can grow deal sizes, and how they can focus on the right type of customers instead of just any customer willing to have a conversation.
It controls the chaos around them and allows them to operate in a much more efficient manner.
Zig is a single operating system that can be accessed from anywhere reps are comfortable working. Inside that system, it replaces what used to take multiple pieces of legacy software with teams of agents that perform the same tasks much faster.
It completely takes away the idea of every micro-problem being solved in its own silo without overall intelligence. It closes that loop completely.
What makes Zig.ai’s Sales Intelligence and Execution Platform unique from other sales engagement and revenue intelligence platforms in the market?
Steve. Most tools focus on one of two things.
Sales engagement tools focus heavily on outreach and lead generation. They play a percentages and volume game—the idea being that if I reach out to more customers, I'm naturally going to talk to more customers.
Revenue intelligence platforms are mostly built for coaching and management. They provide insights into what reps are doing so managers can coach them better.
The problem is that those tools don't really show immediate value for the rep. They show oversight value for the organization or management, but they don't provide a way to correct behavior. Even when they do, it's usually a one-size-fits-all approach.
The difference with Zig is that Zig is built for the reps themselves. It's with them side-by-side, shoulder-to-shoulder while they're going through their motions. It's figuring out where their strengths are and where their gaps are.
Instead of simply surfacing those gaps, it actually does those things for them.
The biggest difference is proactivity versus reactivity.
Can you share a success story wherein early customers have seen standout results after embedding Zig.ai into their teams' day-to-day workflows?
Steve. We have a customer, a B2B SaaS company out of San Francisco.
One of the biggest issues they had was speed-to-lead. They receive inbound interest from procurement teams at large organizations, but those opportunities are also going to competitors. The first company to respond and begin a conversation usually has the upper hand because they get to control the narrative.
With Zig, they were able to bridge that gap to almost instant response times. They've come back and told us that they're now the first company to get in front of those customers and because of that, they get the controlling stake in the conversation.
As a sponsor of the Impact Summit hosted by Winning by Design, how does Zig.ai see AI evolving from a standalone tool into a core operating layer for building resilient and compounding revenue growth systems?
Steve. It's infrastructure.
Zig, at its foundation, is infrastructure. It uses past data, current data, and pattern recognition to understand where a company wins, what customers they win with, and what language created those wins.
It also understands where they lose and why they lose, what competitors are doing, and how company research directly correlates to what they sell.
All of that becomes layered into a revenue intelligence layer that allows AI to make the proper decisions at the right moments going forward.
It anchors itself in that data and uses the pattern recognition that AI is so good at to make sure it's always operating in a winning motion.
How does Zig.ai identify sales rep strengths and areas for improvement? What strategies do you incorporate in this regard?
Steve. As mentioned before, it comes down to pattern recognition and being proactive rather than reactive.
If a rep has a meeting coming up, Zig looks at whether there are similar meetings based on industry, company size, stage of company, and other similarities.
It analyzes large sample sizes to determine what the common characteristics were among conversations that won, what the common characteristics were among conversations that lost, how to avoid the pitfalls, and how to double down on strengths.
We plug into any place where historical and ongoing data exists—email, documentation, CRM data—and use that to create a contextual graph or mind map of patterns and information.
That allows us to surface what winning looks like and the motions required to continue or increase success.
What role does AI play in modern GTM strategies, and where is it delivering the most value?
Steve. AI is becoming extremely important in modern go-to-market strategies.
It has already proven its value many times over in engineering, and now it's beginning to surface in sales.
Salespeople are using AI for things like writing emails, conducting company and customer research, conversational AI, listening to calls, creating transcriptions, and surfacing the right data from those conversations.
What we're not seeing yet—and what Zig aims to do—is close that loop.
How does one thing lead to another? Instead of forcing a human to make that connection, an AI agent should be able to recognize it and do the work automatically.
Right now, AI delivers the most value in areas that are highly data- or context-heavy, such as research, preparation, content writing, and follow-up.
Where AI is still not delivering tremendous value is in actual outreach—getting in front of the right customers instead of relying on sending 10,000 emails to get a handful of replies.
The phase we're moving into now, and what Zig aims to lead, is creating accuracy around the right customer at the right time, understanding why they're the right customer, and recognizing the patterns that indicate they're ready to engage.
Being a true sales veteran, how do you plan to redefine how revenue teams operate at scale? What’s your long-term vision for Zig.ai?
Steve. It's simple.
Let humans sell. Let humans build relationships. Let humans close business.
Let AI do everything else because it can.
Let AI handle everything surrounding the sale so the human becomes more successful and can multiply themselves without adding headcount.
In the future, when organizations have a quota gap, the answer shouldn't automatically be to hire more people. The question should be: what else can we implement inside our operating system and intelligence layer that will move the needle another 10%, 15%, or 20%?
That's why we're constantly releasing new functions and features. Every time we solve one problem, we uncover another gap and build for that next gap.
The long-term vision for Zig is for revenue teams of all sizes to use Zig every day.
We want Zig to become category-defining in revenue infrastructure and sales intelligence—an operating system that doesn't just surface information after the fact, but figures things out beforehand and executes on them before the human even knows they need to act.
It should already be there, proactively working on their behalf.
Tune Into Our Other Informative Interviews:
