When Scalestack audits a new enterprise prospect, CRM data quality typically comes back at 30-40%. That's the starting point for most companies trying to run AI agents across their GTM motion, and it's why most of those initiatives quietly fail. Elio Narciso, who left AWS to build Scalestack, makes the case that the missing piece isn't the AI layer, it's the orchestration middleware that sits between your data sources and your activation layer, and that without it, AI doesn't produce bad outputs, it weaponizes your existing bad data at scale.

What makes this conversation worth your time is that Elio goes well beyond "clean your data." He gets into the mechanics: why deciding when NOT to use AI and to use simple automation instead is one of the most important cost and scale decisions a GTM team can make, why dropping structured CRM picklists in favor of unstructured data may be one of the most underappreciated shifts happening right now, and why the GTM engineer role as it's currently defined is already becoming outdated, with software development as the more honest blueprint for where revenue teams are headed.

Topics Discussed:

• Enterprise CRM data quality averaging 30-40% at the point of AI deployment

• The orchestration middleware layer and why it couldn't exist before modern AI

• How forward deployment engineering translates business logic into agent missions

• The build vs. buy inflection point: when to stop experimenting with Clay and Claude and standardize

• Confidence scoring and agent reasoning trails as a replacement for data trust

• Why structured CRM picklists are becoming a liability as AI-driven data search replaces manual filtering

• Automation vs. AI agents: the cost and scalability decision most teams are getting wrong

• Why the GTM engineer title is already passé, and what software development tells us about what comes next

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