Presented by Oracle NetSuite
When any company tells you this is its biggest product launch in nearly three decades, it’s worth listening. When the person saying this started the world’s first cloud computing company, it’s time to take notice.
Evan Goldberg, founder and vice president of Oracle NetSuite, did just that at SuiteWorld 2025, calling NetSuite Next the company’s biggest product evolution in nearly three decades. But behind this broad vision lies a quieter change – focused on how AI behaves, not just what it can do.
“Every company is experimenting with artificial intelligence,” says Brian Chess, vice president of technology and artificial intelligence at NetSuite. “Some ideas hit the mark, some don’t, but each one teaches us something. That’s how innovation works.”
For Chess and Gary Wiessinger, vice president of application development at NetSuite, the challenge is to manage AI responsibly. Instead of reinventing its system, NetSuite is extending the same principles to the AI era that have guided its strategy for 27 years – security, control and auditability. The goal is to enable tracking of AI activities, enforcing permissions and controlling results.
This philosophy underlies what Chess calls a “glass” approach to enterprise AI, in which decisions are evident and each agent operates within human-defined boundaries.
Built on the foundation of Oracle
NetSuite Next is the result of five years of development. It is powered by Oracle Cloud Infrastructure (OCI), which powers many of the world’s most essential AI model providers, and AI capabilities are integrated directly into its core rather than added as a separate layer.
“We are building a fantastic foundation on OCI,” says Chess. “This infrastructure provides more than just computing power.”
Built on the same OCI foundation that powers NetSuite today, NetSuite Next provides customers with access to Oracle’s latest AI innovations, as well as the performance, scalability and security of an enterprise-class OCI platform.
Wiessinger emphasizes the team’s approach of “needs first, technology second.”
“We don’t focus on technology,” he says. “We take a customer-first approach to customer needs and then figure out how to use the latest technology to better meet those needs.”
This philosophy extends throughout the Oracle ecosystem. NetSuite’s partnerships with Oracle’s AI Database, Fusion Applications, Analytics and Cloud Infrastructure teams lend a hand NetSuite deliver capabilities that third-party vendors can’t match – it says its AI system is both open to innovation and powered by Oracle’s security and scale.
Advantage of data structure
The heart of the platform is a structured data model, which is a key advantage.
“One of the biggest advantages of NetSuite is that data comes in and gets structured, and the connections between data are clear,” explains Chess. “This means that the AI can start mining the knowledge graph that the company has been building.”
Where general LLMs review unstructured text, NetSuite AI works on structured data, identifying precise connections between transactions, accounts and workflows to provide contextual insight.
Wiessinger adds: “Our data spans finance, CRM, commerce and HR. We can do more for customers because we see more information about their business in one place.”
Combined with built-in business logic and metadata, this scope enables NetSuite to generate recommendations and insights that are exact and understandable.
The Oracle Redwood design system provides a visual layer for this data intelligence, creating what Goldberg described as a “modern, clean and intuitive” workplace where AI and humans collaborate naturally.
Designing for responsibility
The downside to enterprise AI is that many systems still function like a black box – producing results but providing little insight into how they arrived at them. NetSuite is different. It designs its systems around transparency, making visibility a defining feature.
“When users can see how the AI made a decision – tracing the path from A to B – they’re not just verifying accuracy,” Chess says. “They find out how the AI knew how to do it.”
This visibility turns AI into a learning engine. As Chess puts it, transparency becomes a “fantastic teacher” in helping organizations understand, improve and, over time, trust automation.
However, Chess cautions against blind trust: “What’s disturbing is when someone presents something to me and says, ‘Look what the AI gave me,’ as if that makes the information credible. People need to ask themselves, ‘Oh, look what the AI gave me?’What justified it? Why is this true?”
NetSuite’s answer is traceability. When someone asks, “Where did this number come from?” the system can show them the full justification.
Management by project
AI agents in NetSuite Next follow the same management model as employees: roles, permissions and escalation rules. Role-based security built directly into workflows helps ensure that agents operate only within authorized boundaries.
Wiessinger puts it clearly: “If the AI generates a narrative summary of the report and it contains 80% of what the user would have written, that’s fine. We’ll learn from their feedback and make it even better. But general ledger posting is different. It has to be 100% correct, and that’s where human checks and verification really matter.”
Algorithm audit
Auditing has always been part of ERP’s DNA, and NetSuite now extends this discipline to artificial intelligence. Every agent action, workflow adjustment, and model-generated code snippet is logged as part of the existing system audit structure.
As Chess explains: “It’s the same audit trail you can utilize to find out what people did. The code can be audited. When LLM creates code and something happens in the system, we can trace its history.”
This traceability transforms AI from a black box to a glass box. When the algorithm expedites a payment or flags an anomaly, teams can see exactly which inputs and logic led to the decision – an important safeguard for regulated industries and finance teams.
Safe expansion
The other half of trust is freedom – the ability to extend AI without the risk of data exposure.
This is made possible by the NetSuite AI Connector and the SuiteCloud platform. With standards such as Model Context Protocol (MCP), customers can connect external language models while ensuring the security of sensitive data in the Oracle environment.
“Companies are hungry for AI,” Chess says. “They want to start implementing it. But they also want to make sure that these experiments can’t go to waste. The NetSuite AI Connector Service and governance model gives partners the freedom to innovate while maintaining the same audit logic and permissions that govern native features.”
Culture, experiments and guardrails
Governance frameworks only work if people use them wisely. Both executives see AI implementation as a top-down and bottom-up process.
“The board is telling the CEO that they need an AI strategy,” Chess says. “Meanwhile, employees are already using AI. If I were the CEO, I would 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 allow everyone to experiment freely. Neither works on its own. You need structure for huge initiatives and freedom for grassroots innovation.”
He gives a simple example: “Write an email? Go crazy. Touch finances or employee data? Don’t go crazy with it.”
Experimentation, as both emphasize, is necessary. “No one should wait for us or anyone else,” Wiessinger says. “Start testing, learn quickly, and make it work for your business.”
Why limpid AI wins
As AI moves deeper into enterprise operations, management will define competitive advantage as much as innovation. NetSuite’s approach – extending its ERP control heritage into the autonomous systems era, built on Oracle’s secure cloud infrastructure and structured data foundation – puts it at the forefront of both.
In a world of muddy models and risky promises, winning companies won’t just build smarter AI. They will build an AI you can trust.
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