Creating a glass box: How NetSuite is engineering trust into AI
Presented by Oracle NetSuite
When a company tells you this 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 far-reaching vision lies a quieter shift – one that focuses on how AI behaves, not just what it can do.
“Every company is experimenting with AI,” said Brian Chess, SVP Technology and AI at NetSuite. “Some ideas caught on, some didn’t, but they all teach us something. That’s how innovation works.”
For Chess and Gary Wiessinger, SVP Application Development at NetSuite, the challenge lies in dealing with AI responsibly. Rather than reinventing its system, NetSuite is extending into the AI era the same principles that have defined its strategy for 27 years: security, control and auditability. The goal is to make AI actions traceable, permissions enforceable, and results auditable.
The philosophy underpins what Chess calls a “glass box” approach to business AI, where decisions are visible and each agent operates within human-defined guardrails.
Built on the foundation of Oracle
NetSuite Next is the result of five years of development. It’s built on Oracle Cloud Infrastructure (OCI), which many of the world’s leading AI model vendors rely on, and has AI capabilities integrated directly into the core rather than added as a separate layer.
“We are building a fantastic foundation for OCI,” says Chess. “That infrastructure provides more than just computing 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 put the customer’s needs first and then figure out how we can use the latest technology to better solve those needs.”
That philosophy extends across Oracle’s entire 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 open to innovation and built on Oracle’s security and scale.
The advantage of the data structure
At the core of the platform is a structured data model that serves as a crucial advantage.
“One of the great things about NetSuite is that, as the data comes in and is structured, the connections between the data are explicit,” Chess explains. “That means the AI can start exploring the knowledge graph that the company has built.”
While general LLMs search unstructured text, NetSuite’s AI works on structured data, identifying precise links between transactions, accounts and workflows to deliver context-aware insights.
Wiessinger adds, “The data we have spans finance, CRM, commerce and HR. We can do more for customers because we can see more of their activities in one place.”
Combined with built-in business logic and metadata, this scope ensures NetSuite can 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 work together naturally.
Designing for responsibility
One downside of business AI is that many systems still function as a black box: they produce results but provide little insight into how they achieved them. NetSuite is different. It designs its systems around transparency, making visibility a defining characteristic.
“When users can see how AI made a decision – by tracing the path from A to B – they’re not just verifying accuracy,” says Chess. “They learn how the AI managed to do that.”
This visibility makes AI a learning engine. As Chess puts it, transparency becomes a “fantastic teacher,” helping organizations understand, improve, and trust automation over time.
But Chess warns against blind faith: “What’s troubling 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 justified this? Why is it right?’”
NetSuite’s answer is traceability. When someone asks, “Where did this song come from?” the system can show them the full reasoning behind it.
Governance by design
AI agents within NetSuite Next follow the same governance model as employees: roles, permissions, and escalation rules. Role-based security embedded directly into workflows ensures agents act only within authorized boundaries.
Wiessinger puts it plainly: “If AI generates a narrative summary of a report and it is 80% of what the user would have written, that’s fine. We’ll learn from their feedback and make it even better. But ledger posting is different. That has to be 100% correct and that’s where checks and human review really matter.”
Check the algorithm
Auditing has always been part of the ERP DNA, and NetSuite is now expanding that discipline to AI. Every agent action, workflow adjustment, and model-generated code snippet is captured within the system’s existing audit framework.
As Chess explains, “It’s the same audit trail that you might use to find out what people did. Code is auditable. When the LLM creates code and something happens in the system, we can trace it.”
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 what input and logic led to the decision – an essential safeguard for regulated industries and finance teams.
Secure extensibility
The other half of trust is freedom: the ability to expand AI without risking data exposure.
The NetSuite AI Connector Service and SuiteCloud Platform make that possible. Standards such as the Model Context Protocol (MCP) allow customers to connect external language models while keeping sensitive data secure within the Oracle environment.
“Companies are hungry for AI,” says Chess. “They want to get started with it. But they also want to make sure those experiments don’t get derailed. The NetSuite AI Connector Service and governance model give partners the freedom to innovate, while retaining the same audit and consent logic that applies to native features.”
Culture, experiments and guardrails
Governance frameworks only work if people use them sensibly. Both executives see AI adoption as a top-down and bottom-up process.
“The board is telling the CEO that they need an AI strategy,” says Chess. “Employees are already using AI. If I were CEO, I would start with the question: what are you already doing and what works?”
Wiessinger agrees that balance is key: “Some companies go all in on a centralized AI team, while others let everyone experiment freely. Neither works in isolation. You need structure for big initiatives and freedom for innovation at the grassroots.”
He gives a simple example: “Write an email? Be crazy. Touch financial data or employee data? Don’t be crazy with that.”
Experimentation, both emphasize, is absolutely necessary. “No one should wait for us or anyone else,” says Wiessinger. “Start testing, learn quickly and make it work for your business.”
Why transparent AI wins
As AI penetrates deeper into business operations, governance will determine both competitive advantage and innovation. NetSuite’s approach – which extends the legacy of ERP controls into the era of autonomous systems, built on Oracle’s secure cloud infrastructure and structured data foundation – positions the company as a leader in both.
In a world of opaque models and risky promises, the companies that win won’t just build smarter AI. They will build AI you can trust.
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