Presented by Oracle NetSuite
When a company tells you this is their biggest product release in nearly three decades, it’s worth listening. When the person saying this founded the world’s first cloud computing company, it’s time to pay attention.
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 broader approach lies a quiet shift – one that focuses on how AI behaves, not just what it can do.
“Every company is experimenting with AI,” says Brian Chase, SVP of technology and AI at NetSuite. “Some ideas succeed and some don’t, but each one teaches us something. That’s how innovation works.”
For Gary Wissinger, SVP of application development at ChessBase and NetSuite, the challenge is in making AI operate responsibly. Rather than redesign its systems, NetSuite is extending into the AI age the same principles that have guided its strategy for 27 years – security, control, and auditability. The goal is to make AI activities traceable, permissions enforceable, and outcomes auditable.
This philosophy underpins what Chess calls a “glass-box” approach to enterprise AI, where decisions are visible and each agent works 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), which is trusted 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 great foundation at OCI,” Chess says. “That infrastructure provides much more than compute power.”
Built on the same OCI foundation that powers NetSuite today, NetSuite Next gives customers the performance, scalability, and security of OCI’s enterprise-grade platform, as well as access to Oracle’s latest AI innovations.
Wissinger emphasizes the team’s approach, “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 better solve those needs.”
This philosophy extends to Oracle’s ecosystem. He says 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 – an AI system that is open to innovation and based on Oracle’s security and scale.
data structure benefits
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 comes in and it’s structured, the relationships between the data are clear,” explains Chess. “This means that AI can start exploring the knowledge graph that the company is building.”
Where typical 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.
“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,” Wissinger says.
That scope, combined with the underlying business logic and metadata, allows NetSuite to generate recommendations and insights that are accurate and explainable.
Oracle’s Redwood Design System provides the visualization layer for this data intelligence, which Goldberg described "Modern, clean and intuitive" Workspaces where AI and humans naturally collaborate.
designing for responsiveness
One downside of enterprise AI is that many systems still function as black boxes – they produce results but provide little visibility into how they got to them. NetSuite is different. It is designing its systems based on transparency, making visibility a defining feature.
“When users can see how the AI arrived at a decision – finding a path from A to B – they don’t just confirm accuracy,” Chess says. “They learn how the AI knew how to do this.”
That visibility turns AI into a learning engine. As Chess says, transparency becomes a “fantastic teacher,” helping organizations understand, improve, and trust automation over time.
But Chess cautions against blind trust: “The disturbing thing is when someone presents something to me and says, ‘Look what the AI gave me,’ as if that makes it official. People need to ask, ‘What is its basis? Why is this right?’,
NetSuite’s answer is traceability. When someone asks, “Where did this number come from?” The system can show them the complete logic 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 built directly into workflows helps ensure agents act only within authorized boundaries.
Wissinger puts it bluntly: “If the AI produces a concise summary of a report and it’s 80% of what the user wrote, that’s fine. We’ll learn from their feedback and make it even better. But booking in general ledger is different. It has to be 100% accurate and that’s where control and human review really matters.”
Auditing Algorithms
Auditing has always been part of the DNA of ERP, 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 can use to trace what humans did. Code is auditable. When an LLM creates code and something happens to the system, we can trace it back.”
That traceability transforms AI from a black box to a glass box. When an algorithm accelerates a payment or flags an anomaly, teams can see what inputs and logic led to the decision – an essential protection for regulated industries and finance teams.
secure extensibility
The second part of trust is freedom – the ability to expand AI without risking data exposure.
The NetSuite AI Connector service and SuiteCloud platform make this possible. Through standards such as Model Context Protocol (MCP), customers can connect external language models while keeping sensitive data secure inside the Oracle environment.
“Businesses are hungry for AI,” Chess says. “They want to start putting it to work. But they also want to know that those experiments can’t be derailed. The NetSuite AI Connector service and governance model gives partners the freedom to innovate while maintaining the same audit and permissions logic that controls native features.”
Culture, experimentation and railing
Governance structures work only when people use them wisely. Both executives see AI adoption as a top-down and bottom-up process.
“Boards are telling CEOs they need an AI strategy,” Chess says. “Meanwhile, employees are already using AI. If I were the CEO, I’d start by asking: What are you already doing, and what’s working?”
Wissinger agrees that balance is important: “Some companies work on a centralized AI team while others let everyone experiment independently. No one works on their own. You need structure for major initiatives and freedom for grassroots innovation.”
He offers a simple example: “Write an email? Go crazy. Touch financial or employee data? Don’t go crazy with it.”
Both insist that experimentation is essential. “Nobody should wait for us or anyone else,” says Wissinger. “Start testing, learn quickly, and be intentional about making it work for your business.”
Why does transparent AI win?
As AI moves deeper into enterprise operations, governance will define innovation as well as competitive advantage. NetSuite’s approach—extending its legacy of ERP control into the age of autonomous systems built on Oracle’s secure cloud infrastructure and structured-data foundation—makes it a leader in both.
In a world of opaque models and risky promises, the companies that win won’t just build smart AI. They will build AI you can trust.
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