NetSuite Unveils Trustworthy AI with Glass-Box Approach at SuiteWorld 2025

At SuiteWorld 2025, Evan Goldberg, the founder and Executive Vice President of Oracle NetSuite, made a significant announcement that has the potential to reshape the landscape of enterprise software. He declared the launch of NetSuite Next, which he described as the company’s most substantial product evolution in nearly three decades. This bold statement is not merely a marketing ploy; it reflects a fundamental shift in how businesses can leverage artificial intelligence (AI) within their operations. The focus is not just on what AI can do but on how it behaves, emphasizing trust, transparency, and accountability.

In an era where AI technologies are rapidly evolving, many companies are experimenting with various applications of AI. Brian Chess, Senior Vice President of Technology and AI at NetSuite, noted, “Every company is experimenting with AI. Some ideas hit the mark, and some don’t, but each one teaches us something. That’s how innovation works.” This sentiment captures the essence of the current technological landscape, where trial and error often lead to breakthroughs. However, the challenge lies in governing AI responsibly, ensuring that its deployment aligns with ethical standards and business objectives.

NetSuite’s approach to AI governance is rooted in principles that have guided the company for 27 years: security, control, and auditability. Rather than reinventing its existing systems, NetSuite is extending these foundational principles into the AI era. The goal is to create a framework where AI actions are traceable, permissions are enforceable, and outcomes are auditable. This philosophy is encapsulated in what Chess refers to as a “glass-box” approach to enterprise AI. Unlike traditional “black box” models, where decisions are opaque and difficult to interpret, NetSuite’s AI is designed to be transparent, allowing users to see how decisions are made and the rationale behind them.

The foundation of NetSuite Next is built on Oracle Cloud Infrastructure (OCI), a robust platform that supports many of the world’s leading AI model providers. This integration means that AI capabilities are embedded directly into the core of NetSuite Next, rather than being added as an afterthought. Chess emphasized the importance of OCI, stating, “We are building a fantastic foundation on OCI. That infrastructure provides more than compute power.” By leveraging OCI, NetSuite Next offers customers access to Oracle’s latest AI innovations while benefiting from the performance, scalability, and security that come with an enterprise-grade platform.

One of the standout features of NetSuite Next is its structured data model, which serves as a critical advantage over competitors relying on unstructured data. Chess explained, “One of the great things about NetSuite is, because the data comes in and it gets structured, the connections between the data are explicit.” This structured approach allows the AI to explore a knowledge graph that the company has been building over time. In contrast to general large language models (LLMs) that sift through unstructured text, NetSuite’s AI operates on structured data, identifying precise links between transactions, accounts, and workflows. This capability enables the delivery of context-aware insights that are both accurate and actionable.

Gary Wiessinger, Senior Vice President of Application Development at NetSuite, highlighted the breadth of data available within the platform. “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,” he stated. This comprehensive view allows NetSuite to generate recommendations and insights that are not only relevant but also explainable, enhancing the overall user experience.

Transparency is a cornerstone of NetSuite’s design philosophy. Many enterprise AI systems function as black boxes, producing results without offering visibility into how those results were achieved. NetSuite aims to change this paradigm by designing its systems around transparency. Chess articulated this vision, saying, “When users can see how AI reached a decision — tracing the path from A to B — they don’t just verify accuracy; they learn how the AI knew to do that.” This level of visibility transforms AI into a learning engine, fostering an environment where organizations can understand, improve, and ultimately trust automation over time.

However, Chess also cautioned against blind trust in AI systems. He remarked, “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?’” To address this concern, NetSuite emphasizes traceability. When users inquire about the origin of a particular number or decision, the system can provide a detailed explanation of the reasoning behind it, reinforcing the importance of accountability in AI-driven processes.

Governance is another critical aspect of NetSuite Next. AI agents within the system adhere to the same governance model as human employees, incorporating roles, permissions, and escalation rules. This role-based security is embedded directly into workflows, ensuring that AI agents operate within authorized boundaries. Wiessinger succinctly summarized this approach: “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 has always been a fundamental component of enterprise resource planning (ERP) systems, and NetSuite is extending this discipline to its AI functionalities. Every action taken by AI agents, every workflow adjustment, and every model-generated code snippet is recorded within the existing audit framework of the system. Chess explained, “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.” This level of traceability is crucial for organizations operating in regulated industries, where understanding the decision-making process is essential for compliance and risk management.

Another significant aspect of NetSuite’s approach to AI is the concept of safe extensibility. Businesses today are eager to harness the power of AI, but they also want to ensure that their experiments do not compromise data security. The NetSuite AI Connector Service and SuiteCloud Platform facilitate this by allowing customers to connect external language models while keeping sensitive data secure within Oracle’s environment. Chess noted, “Businesses are hungry for AI. They want to start putting it to work. But they also want to know those experiments can’t go off the rails.” The governance model established by NetSuite ensures that partners have the freedom to innovate while maintaining the same audit and permission logic that governs native features.

The culture surrounding AI adoption within organizations is equally important. Both Chess and Wiessinger view AI implementation as a top-down and bottom-up process. Chess pointed out, “The board is telling the CEO they need an AI strategy. 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?” This perspective highlights the necessity of balancing centralized AI initiatives with grassroots innovation. Wiessinger echoed this sentiment, stating, “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 provided a simple analogy to illustrate this balance: “Write an email? Go crazy. Touch financials or employee data? Don’t go crazy with that.” This approach encourages experimentation while ensuring that sensitive areas remain protected. Both executives emphasized the importance of learning quickly and being intentional about making AI work for individual businesses. “No one should wait for us or anyone else,” Wiessinger urged. “Start testing, learn quickly, and be intentional about making it work for your business.”

As AI continues to permeate enterprise operations, the governance of these systems will define competitive advantage as much as innovation itself. NetSuite’s strategy — which extends its legacy of ERP controls into the realm of autonomous systems, built on Oracle’s secure cloud infrastructure and structured-data foundation — positions it to lead in both areas. In a landscape filled with opaque models and unfulfilled promises, the companies that succeed will not only develop smarter AI but will also create AI systems that users can trust.

In conclusion, NetSuite Next represents a significant advancement in the integration of AI within enterprise resource planning. By prioritizing transparency, accountability, and governance, NetSuite is setting a new standard for how businesses can leverage AI technologies. As organizations navigate the complexities of AI adoption, the lessons learned from NetSuite’s approach may serve as a valuable guide for creating trustworthy and effective AI solutions. The future of enterprise AI is not just about harnessing the power of technology; it is about building systems that foster trust and empower users to make informed decisions.