Making AI the ERP Interface With NetSuite

When would you trust your ERP system to move from answering a question to carrying out the work?

Recording at SuiteWorld in Las Vegas, I spoke with Brian Chess, who leads AI product and technology for Oracle NetSuite, about what that progression looks like in practice. Brian was an early developer when the business was called NetLedger, later left to build a company, and returned with a different perspective on the needs of small and growing businesses. Our conversation focuses on how AI can help people understand their systems, complete unfamiliar tasks, and gradually delegate work without losing control of the process.

One of Brian's most useful ideas is AI acting as a teacher. He describes users asking Ask Oracle who has logged into their account, or asking it to explain a script they do not fully understand. The benefit is the answer and the explanation of how to find it. Someone who previously needed help from a colleague can begin to understand a part of the system for themselves.

He also shares an example involving NetSuite customer MCNC. When the payroll administrator was unavailable, Brian says a controller who had not been trained on the process used Ask Oracle for guidance. The outcome he describes is straightforward and meaningful: payroll ran on time. It is a customer account from the interview, rather than an independently assessed result, but it illustrates how useful AI assistance can be without requiring a dramatic productivity claim.

Financial close provides another practical example. Brian describes starting with the spreadsheet a finance team already uses, modelling that process inside NetSuite, and introducing automation in stages. I recognise the appeal of that approach from my own time supporting month-end and year-end work in IT. Teams need to understand how the process will run, especially when a mistake can affect reporting or the system of record.

We discuss how permissions apply as software gains the ability to act. Brian says Ask Oracle operates with the permissions of the user, while an agent receives an assigned role governing its access. He describes a progression from read-only analysis to limited changes as users gain confidence. Human approval remains available for transactions, particularly while teams refine instructions and account for cases they may have missed.

Business context and shared knowledge are part of the same discussion. Brian explains how written skills can capture procedures and the intended business outcome, making knowledge available beyond the colleague who usually handles the task. We also talk about accountability. He describes transaction records showing which agent acted and when, together with an activity log. Those records help establish what happened, although they should not be confused with a complete explanation of every internal model decision.

The conversation also covers whether to build AI capabilities or use those embedded in a business platform. Brian's answer depends on the role the capability plays in a company's competitive position, the expertise available, and the time it wants to invest. His view of ROI is similarly specific to the business. Comparing vendor contracts with billing records may reveal savings, but the same result does not necessarily transfer to a company with different suppliers and arrangements.

We finish with the opportunity Brian sees for smaller businesses that have fewer specialists available. AI assistance may help people undertake work they previously needed a consultant or experienced colleague to explain. The question is how to extend that access while retaining appropriate permissions, review, and responsibility.

I would love to hear your thoughts, so please share your experiences. Where would conversational ERP make the biggest difference in your business, and which actions would you still want a person to approve?