Elio Narciso sits down with the operators and builders putting agents into production, to dig into what actually works: the data underneath, the governance, and the stack that makes agents reliable.
Real conversations with people who have run these systems and seen exactly where they crack.
Agent Stack Masters, by Scalestack.
Everyone can build with AI now, but in many cases, that's become the problem. Maggie Lam of Mazarin Consulting sits down to talk RevOps governance, GTM systems, clean data foundations, tool sprawl, build vs. buy, and why companies that invest in RevOps scale faster.
Everyone can build with AI now, but that is often the root of the problem. Maggie Lam explains why tool sprawl is killing your GTM efficiency.
🎙️ Maggie Lam of Mazarin Consulting joins the podcast to break down why the build vs. buy debate has shifted in the era of artificial intelligence. Instead of rushing to launch new custom solutions, she outlines the importance of establishing RevOps governance first. When you prioritize clean data foundations before adding more tech, you create a system that actually scales rather than one that creates technical debt.
We discuss the hidden costs of fragmented infrastructure and how to determine if your company is truly ready to build a proprietary system or if buying a standardized tool is the smarter path. By focusing on the right GTM systems, you can ensure your team spends time on revenue generation instead of maintenance.
Subscribe for weekly breakdowns on the systems that power revenue growth, and let us know in the comments: are you currently building or buying your next revenue tool?
0:00 Introduction & Background
0:32 The Shift to Remote Work and Global Teams
2:08 Consulting and the Role of Senior Expertise
3:12 The Evolution of Service Companies in the AI Era
6:35 Finding and Developing Talent in Consulting
9:30 The Future of CRM and Customization
12:26 Trends in Data Management and Tool Utilization
15:21 Aligning Processes for Success
18:24 Identifying Successful Patterns in Revenue Operations
21:16 RevOps: The Underwater Struggle
24:52 Building a Strong RevOps Foundation
32:10 Build vs. Buy: Strategic Decisions in RevOps
41:06 Governance in the Age of AI
28 Sept 2026
The CRO Is a System Builder, Not a Closer — Brian Krause
A Chief Revenue Officer's real job is building the system that makes deals repeatable, where sales, marketing, and RevOps move as one integrated motion instead of three disconnected teams. That's the thesis Brian Krause, CRO at Aware, brings from a career built across AnyVision, Veriff, and Incode, where he more than doubled North America's ARR. Brian believes that revenue growth follows structure, not heroics, and that structure is what separates a CRO from a VP of Sales with a bigger title.
Throughout his years of experience one thing keeps resonating wherever he goes: financial fluency is the foundation underneath that structure. Reading a P&L the way most people read a deck lets a CRO build ROI models tied to what a customer's business actually needs.
Which is why he challenges himself and his team every day to reach their potential. Borrowing a line from coach Nick Saban, he knows that average is a human condition, but he’s convinced that great leaders push their teams to be extraordinary, showing them they're capable of more, then stay shoulder to shoulder while they prove it.
In sales, information is the real currency, and AI has become the tool that shortens the distance between a rep and the right data necessary to take action and boost productivity. He knows it can be tempting for RevOps teams to spin up different tools and systems while handling sensitive information, so it’s important for teams to prioritize security and the CISO get involved early enough to keep customer data out of the wrong model.
8 Jul 2026
"If Your Data Is Wrong, Your Agent Is Wrong" — Yoni Tserruya
Data is commoditizing. More providers, more alternatives, more coverage. On paper, that should make the data layer less interesting. But Yoni Tserruya, CEO and co-founder at Lusha thinks the opposite is true.
When data was consumed by a human, a bad record meant a wasted call. When data is consumed by an AI agent making autonomous decisions at scale, a bad record compounds. The agent routes wrong, scores wrong, prioritizes wrong. And it does it a thousand times before anyone notices. The problem isn't scale. It's that agents don't catch their own mistakes.
That's what pushed Lusha to make a deliberate call: move away from the all-in-one platform, go deeper into the data layer. API-first. Open by design. Usage-based pricing. No seats required. Because the next consumer of their data isn't a salesperson opening a browser tab. It's an agent calling an API at scale, with no patience for stale records.
Lusha turns ten this year. It started as a side project, a bootstrapped tool for pulling contact data mostly for recruiters, until Google called because it wanted to buy licenses. That was a decade ago. And the way Yoni tells it, the next decade doesn't look anything like the last one. Because the consumer of data is completely changing.
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YouTube views
19,259
Score snapshot 15 Sept 2025
Apple Podcasts (US)
5.0 / 5
1 ratings
Spotify
5.0 / 5
3 ratings
Podcast Authority Score: 43 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
34
Social presence
0
YouTube
72
Engagement
60
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Elio Narciso host the show. Published by Scalestack.ai.
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