NetSuite Founder on the End of ERP and What Comes Next

What if your ERP system came to you through a conversation, instead of asking you to learn its menus before you could get anything done?

Recording at SuiteWorld in Las Vegas, I welcomed Evan Goldberg, founder and executive vice president of NetSuite at Oracle, back to the podcast. We last spoke two years earlier about the company's origins. This time, the discussion centres on his description of AI as the UI and what that could mean for the people using business software every day.

Evan describes an ERP experience in which users explain what they want in natural language. One example is adding a dashboard metric by describing the information it should show. If the result is not quite right, the user can explain what needs to change. For me, the useful question is whether that makes software easier to fit around a person's work and how reliably it understands the request.

We discuss the knowledge behind those answers. Evan describes SuiteSkills as a way to bring together information about NetSuite and the procedures businesses use within it. He connects that work with consistent answers through NetSuite Next and through external tools using the AI connector service. His ambition is to support the models and ways of working customers choose, while making the information returned from NetSuite dependable. It is a product direction he describes, rather than an independently tested guarantee that every model will behave identically.

The conversation then moves from answers to actions. Evan points to order approvals, accruals, and agents looking for exceptions that deserve attention. He argues for beginning with a process people already want to improve and deciding which parts can run with human approval or greater autonomy. The appropriate balance will depend on the company and the work involved. A conversational interface does not remove the need to decide where authority belongs.

Personalisation is another part of adoption. Evan's view is that people are more likely to keep using an AI feature when they can explain how they want it to work. The dashboard example makes that concrete, but it also raises a wider question about how companies maintain consistent procedures while allowing people to adapt their tools.

We also discuss smaller businesses and the opportunity to access capabilities without building a large specialist team. Evan describes a startup offering and his belief that the same AI models can help companies of different sizes. The practical considerations still include eligibility, the work a team needs to accomplish, and the controls around its business information.

For partners, he sees an opportunity to make industry expertise available through reusable skills. A specialist's knowledge of a particular niche could inform how Ask Oracle helps customers, while the partner spends time developing that expertise further. It is an interesting view of how advice and software configuration may change as AI becomes part of delivery.

I also ask about software pricing. If agents complete tasks that once required someone to log in, what happens to the traditional per-user model? Evan acknowledges the question but offers a counterpoint: easier access to information and personal agents could encourage people to remain users. He does not announce a pricing change, leaving us with a business-model question that is still developing.

We finish with an unexpected customer conversation at Disneyland, which Evan uses to illustrate interest from everyday business users. It is a memorable anecdote rather than a measure of adoption, but it brings the discussion back to the people who ultimately decide whether these features earn a place in their working day.

I would love to hear your thoughts, so please share your experiences. If AI becomes the interface to your business systems, what would you want it to understand before you let it act on your behalf?