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When any company says this is their biggest product release in nearly 30 years, it’s worth listening. When the man who says so founded the world’s first cloud computing company, it’s time to pay attention.
At SuiteWorld 2025, Evan Goldberg, founder and vice president of Oracle NetSuite, did just that, calling NetSuite Next the company’s biggest product evolution in nearly 30 years. But behind that overarching vision is a quieter shift that focuses not just on what AI can do, but how it works.
“Every company is experimenting with AI,” says Brian Chess, senior vice president of technology and AI at NetSuite. “Some ideas hit the mark, some don’t, but each idea teaches us something. That’s how innovation works.”
For Chess and Gary Wiessinger, senior vice president of application development at NetSuite, the challenge lies in managing AI responsibly. Rather than reinventing systems, NetSuite is extending the same principles of security, control, and auditability that have guided its strategy for 27 years into the AI era. The goal is to make AI actions traceable, permissions enforceable, and results auditable.
This philosophy underpins what Chess calls a “glass box” approach to enterprise AI, where decisions are made visible and all agents operate within human-defined guardrails.
Built on the foundation of Oracle
NetSuite Next is the result of five years of development. It is built on Oracle Cloud Infrastructure (OCI), trusted by many of the world’s most important AI model providers, with AI capabilities integrated directly into its core rather than being added as a separate layer.
“We’re building a great foundation on OCI,” Chess says. “That infrastructure provides more than just computing power.”
NetSuite Next is built on the same OCI foundation that currently powers NetSuite, giving customers access to Oracle’s latest AI innovations with the performance, scalability, and security of OCI’s enterprise-grade platform.
Wiesinger emphasizes the team’s approach as “needs first, technology second.”
“We don’t have a technology-first approach,” he says. “We take a customer needs-first approach and find ways to better solve those needs using the latest technology.”
That philosophy extends throughout the Oracle ecosystem. NetSuite’s collaboration with Oracle’s AI database, fusion applications, analytics, and cloud infrastructure teams allows NetSuite to deliver capabilities that independent vendors cannot match: AI systems that are open to innovation and built on Oracle’s security and scale, he said.
Advantages of data structures
At the heart of the platform is a structured data model that serves as a key advantage.
“One of the great things about NetSuite is that the data is entered and structured so that the connections between the data are clear,” Chess explains. “This means AI can start exploring the knowledge graph that the company has been building.”
While typical LLMs sift through unstructured text, NetSuite’s AI works on structured data to identify the precise links between transactions, accounts, and workflows to provide context-aware insights.
Wiessinger added, “The data we have spans finance, CRM, commerce, and human resources. We can see more of our customers’ businesses in one place, so we can do more for them.”
Combining built-in business logic and metadata enables NetSuite to generate accurate and explainable recommendations and insights.
Oracle’s Redwood design system provides a visual layer of this data intelligence to create what Goldberg described. "Modern, clean and intuitive" A workspace where AI and humans can naturally collaborate.
Designed with accountability in mind
One of the drawbacks of enterprise AI is that many systems still function as black boxes. Results are produced, but there is little visibility into how those results were arrived at. NetSuite is different. The company has designed its systems around transparency and is characterized by visibility.
“When users can see how the AI followed the path from A to B and arrived at its decisions, they are not just verifying accuracy,” Chess says. “They learn how the AI knew to do it.”
That visibility turns AI into a learning engine. As Chess puts it, transparency is a “great teacher” that helps organizations understand, improve, and trust automation.
But Chess warns against blind trust: “What makes me nervous is when someone presents me with something and says, ‘Look what the AI gave me,’ as if it were authoritative. People need to ask.”What was the basis for this? Why is that correct?”
NetSuite’s answer is traceability. When someone asks, “Where did this number come from?” the system can show you the full reasoning behind it.
Governance by design
AI agents within NetSuite Next follow the same governance model (roles, permissions, and escalation rules) as employees. Role-based security built directly into workflows ensures that agents operate only within authorized boundaries.
Wiessinger says, “If the AI generates a descriptive summary of the report and it’s 80% of what the user would have written, that’s fine. We learn from the feedback and make it even better. But posting to the general ledger is different. It has to be 100% correct, and that’s where management and human review are really important.”
Algorithm audit
Auditing has always been part of ERP’s DNA, and NetSuite has extended that discipline to AI. All agent actions, workflow adjustments, and model generation code snippets are logged within the system’s existing auditing framework.
Chess explains: “This is the same audit trail that humans use to figure out what they’ve done. Code is auditable. If an LLM writes code and something happens in the system, it can be traced.”
This traceability transforms AI from a black box to a glass box. If an algorithm accelerates a payment or flags an anomaly, your team can see exactly what inputs and logic drove the decision. This is an essential safeguard for regulated industries and finance teams.
Secure scalability
The other half of trust is freedom, the ability to scale AI without risking a data breach.
NetSuite AI Connector Service and SuiteCloud Platform make that possible. Through standards such as Model Context Protocol (MCP), customers can connect to external language models while keeping sensitive data secure within the Oracle environment.
“Enterprises are hungry for AI,” Chess says. “They want to start putting it into action, but they also want to know when that experiment goes off the rails. The NetSuite AI Connector service and governance model gives partners the freedom to innovate while maintaining the same auditing and authorization logic that governs native functionality.”
Culture, experimentation, guardrails
Governance frameworks only work if people use them wisely. Both executives view AI adoption as a top-down and bottom-up process.
“Boards are telling CEOs they need an AI strategy,” Chess says. “On the other hand, employees are already leveraging AI. If I were a CEO, the first thing I would ask is: What are we already doing and what is working?”
Wiesinger agrees that balance is key, saying, “Some companies go all-in on a centralized AI team, while others give everyone the freedom to experiment. Neither works in isolation. We need structure for major efforts and freedom for grassroots innovation.”
He gives a simple example. “Writing emails? Getting carried away. Touching financial and employee data? Don’t get carried away with that.”
Both emphasize that experimentation is essential. “No one should wait for us or anyone else,” Wiesinger says. “Start testing, learn quickly, and be conscious of applying it to your business.”
Why transparent AI wins
As AI becomes more deeply embedded in business operations, governance, as well as innovation, will define competitive advantage. NetSuite’s approach positions us to lead in both areas by extending our ERP control tradition into the era of autonomous systems built on Oracle’s secure cloud infrastructure and structured data foundation.
In a world of opaque models and dangerous promises, winning companies will do more than just build smarter AI. They build trustworthy AI.
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