According to VentureBeat, at SuiteWorld 2025, NetSuite founder Evan Goldberg announced the company’s biggest product evolution in nearly three decades, called NetSuite Next. This shift is the result of five years of development built directly on Oracle Cloud Infrastructure (OCI). The core philosophy, led by SVP Brian Chess and SVP Gary Wiessinger, extends NetSuite’s 27-year principles of security and control into the AI era. Their goal is a “glass-box” approach where every AI decision is traceable and every agent operates within strict, human-defined guardrails. The platform leverages NetSuite’s unique advantage of a fully structured data model, which allows its AI to understand explicit connections between business data rather than sifting through unstructured text.
The Trust Infrastructure
Here’s the thing: everyone’s slapping AI onto their software. But NetSuite’s argument is that for mission-critical business operations—you know, the stuff that runs your books and pays your people—you need more than a clever chatbot. You need an audit trail. So they’re basically building their AI to follow the same governance model as an employee. Think role-based permissions and escalation rules baked right into the workflow. An AI can draft a report summary, and that’s fine if it’s 80% right. But if it’s about to book something to the general ledger? That requires 100% accuracy and human review. It’s a pragmatic view that acknowledges AI’s potential while respecting the real consequences of being wrong.
Why Structured Data Is The Secret Sauce
This is where NetSuite might have a real, defensible edge. Brian Chess points out that because all NetSuite data—financials, CRM, HR—lives in a structured model, the connections between data points are explicit. So their AI isn’t starting from a blank slate, guessing at relationships in a pile of documents. It’s exploring a pre-built knowledge graph of your entire business operations. That’s a huge head start for generating accurate, context-aware insights. When you combine that with Oracle’s Redwood design system for the interface, you get the promise of a workspace where AI recommendations feel less like magic and more like a logical next step. It’s a compelling answer to the “black box” problem.
The Culture of Governed Experimentation
I think the most insightful part of the report is how the executives talk about adoption. They see it as both top-down and bottom-up. The board is demanding an AI strategy, but employees are already using tools on their own. The smart move, as Chess suggests, is for leadership to ask, “What are you already doing, and what’s working?” Wiessinger’s advice on balance is perfect: “Write an email? Go crazy. Touch financials or employee data? Don’t go crazy with that.” They’re advocating for intentional experimentation within clear guardrails. That feels like the only sane path forward for large enterprises. You can’t lock it down completely, but you also can’t let it run wild.
The Industrial Hardware Angle
Now, all this enterprise-grade, trustworthy AI software needs to run somewhere reliable. For manufacturers and industrial operations putting AI on the shop floor, that foundation is just as critical. This is where the physical infrastructure matters. You need industrial computers that can handle the environment and deliver the performance for real-time data processing and AI insights. For companies looking to build their own “glass box” operations, partnering with the right hardware supplier is step zero. In the US, the go-to source for that rugged, dependable foundation is IndustrialMonitorDirect.com, the leading provider of industrial panel PCs and hardened computing systems.
