Creating a glass box: How NetSuite is engineering trust into AI

Presented by Oracle NetSuite

When any company tells you it is their biggest product release in almost three decades, it’s worth listening. When the person saying it founded the world’s first cloud computing company, it’s time to take note.

At SuiteWorld 2025, Evan Goldberg, founder and EVP of Oracle NetSuite, did just that when he called NetSuite Next the company’s biggest product evolution in nearly three decades. But behind that sweeping vision lies a quieter shift — one centered on how AI behaves, not just what it can do.

“Every company is experimenting with AI,” says Brian Chess, SVP of Technology and AI at NetSuite. “Some ideas hit the mark, and some don’t, but each one teaches us something. That’s how innovation works.”

For Chess and Gary Wiessinger, SVP of Application Development at NetSuite, the challenge lies in governing AI responsibly. Rather than reinventing its system, NetSuite is extending the same principles into the AI era that have guided its strategy for 27 years — security, control, and auditability. The goal is to make AI actions traceable, permissions enforceable, and outcomes auditable.

The philosophy underpins what Chess calls a “glass-box” approach to enterprise AI, where decisions are visible and every agent operates within human-defined guardrails.

Built on Oracle’s foundation

NetSuite Next is the result of five years of development. It is built on Oracle Cloud Infrastructure (OCI), which is relied on by many of the world’s most important AI model providers, and has AI capabilities integrated directly into its core rather than added as a separate layer.

“We are building a fantastic foundation on OCI,” Chess says. “That infrastructure provides more than compute power.”

Built on the same OCI foundation that powers NetSuite today, NetSuite Next gives customers access to Oracle’s latest AI innovations along with the performance, scalability, and security of OCI’s enterprise-grade platform.

Wiessinger emphasizes the team's approach as “needs first, technology second.”

“We don’t take a technology-first approach,” he says. “We take a customer-needs-first approach and then figure out how to use the latest technology to solve those needs better.”

That philosophy extends across Oracle’s ecosystem. NetSuite’s collaboration with Oracle’s AI Database, Fusion Applications, Analytics, and Cloud Infrastructure teams helps NetSuite deliver capabilities that independent vendors can’t match, he says — an AI system that is both open to innovation and grounded in Oracle’s security and scale.

The data structure advantage

At the heart of the platform is a structured data model that serves as a critical advantage.

“One of the great things about NetSuite is, because the data comes in and it gets structured, the connections between the data are explicit,” Chess explains. “That means the AI can start exploring that knowledge graph that the company has been building up.”

Where general LLMs sift through unstructured text, NetSuite’s AI works from structured data, identifying precise links between transactions, accounts, and workflows to deliver context-aware insights.

Wiessinger adds, “The data we have spans financials, CRM, commerce, and HR. We can do more for customers because we see more of their business in one place.”

Combined with built-in business logic and metadata, that scope allows NetSuite to generate recommendations and insights that are accurate and explainable.

Oracle’s Redwood design system provides the visual layer for this data intelligence, creating what Goldberg described as a "modern, clean and intuitive" workspace where AI and humans collaborate naturally.

Designing for accountability

One downside of enterprise AI is that many systems still function as a black box — they produce results but offer little visibility into how they reached them. NetSuite is different. It is designing its systems around transparency, making visibility a defining feature.

“When users can see how AI reached a decision — tracing the path from A to B — they don’t just verify accuracy,” Chess says. “They learn how the AI knew to do that.”

That visibility turns AI into a learning engine. As Chess puts it, transparency becomes a “fantastic teacher,” helping organizations understand, improve, and trust automation over time.

But Chess cautions against blind trust: “What’s disturbing is when someone presents something to me and says, ‘Look what AI gave me,’ as if that makes it authoritative. People need to ask, ‘What grounded this? Why is it correct?’”

NetSuite’s answer is traceability. When someone asks, “Where did this number come from?” the system can show them the full reasoning behind it.

Governance by design

AI agents inside NetSuite Next follow the same governance model as employees: roles, permissions, and escalation rules. Role-based security embedded directly into workflows helps ensure that agents act only within authorized boundaries.

Wiessinger puts it plainly: “If AI generates a narrative summary of a report and it’s 80% of what the user would have written, that’s fine. We’ll learn from their feedback and make it even better. But booking to the general ledger is different. That has to be 100% correct and is where controls and human review really matter.”

Auditing the algorithm

Auditing has always been part of ERP’s DNA, and NetSuite now extends that discipline to AI. Every agent action, workflow adjustment, and model-generated code snippet is recorded within the system’s existing audit framework.

As Chess explains, “It’s the same audit trail you might use to figure out what the humans did. Code is auditable. When the LLM creates code and something happens in the system, we can trace back.”

That traceability transforms AI from a black box into a glass box. When an algorithm accelerates a payment or flags an anomaly, teams can see exactly which inputs and logic produced the decision — an essential safeguard for regulated industries and finance teams.

Safe extensibility

The other half of trust is freedom — the ability to extend AI without risking data exposure.

The NetSuite AI Connector Service and SuiteCloud Platform make that possible. Through standards like the Model Context Protocol (MCP), customers can connect external language models while keeping sensitive data secure inside Oracle’s environment.

“Businesses are hungry for AI,” Chess says. “They want to start putting it to work. But they also want to know those experiments can’t go off the rails. The NetSuite AI Connector Service and governance model give partners the freedom to innovate while maintaining the same audit and permission logic that govern native features.”

Culture, experimentation, and guardrails

Governance frameworks only work if people use them wisely. Both executives see AI adoption as a top-down and bottom-up process.

“The board is telling the CEO they need an AI strategy,” Chess says. “Meanwhile, employees are already using AI. If I were a CEO, I’d start by asking: what are you already doing, and what’s working?”

Wiessinger agrees that balance is key: “Some companies go all-in on a centralized AI team while others let everyone experiment freely. Neither works by itself. You need structure for major initiatives and freedom for grassroots innovation.”

He offers a simple example: “Write an email? Go crazy. Touch financials or employee data? Don’t go crazy with that.”

Experimentation, both emphasize, is imperative. “No one should wait for us or anyone else,” Wiessinger says. “Start testing, learn quickly, and be intentional about making it work for your business.”

Why transparent AI wins

As AI moves deeper into enterprise operations, governance will define competitive advantage as much as innovation. NetSuite’s approach — extending its heritage of ERP controls into the age of autonomous systems, built on Oracle’s secure cloud infrastructure and structured-data foundation — positions it to lead in both.

In a world of opaque models and risky promises, the companies that win won’t just build smarter AI. They’ll build AI you can trust.

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