Tech Talks Daily
00:03:02 · Neil C. Hughes, Brian Chess
[00:03:02] S0: And there is so much going on here at the moment, and I think it can be overwhelming for the average business leader wherever they are in the world. So what technical changes are needed when enterprise software moves from just AI suggestions to agents that complete work? [00:03:16] S1: Well, enterprises face a whole range of challenges. They've, in many cases, had humans in the loop because the loop was all humans because that's all there was was humans. And now they've gotta figure out how do we have a loop that maybe involves a lot of AI and, relatively speaking, fewer humans. The nice thing is for businesses that are running on NetSuite, the sis... The data is already organized. The processes are already in place. The roles and permission system already exists. We've got a nice setup to make that transition easy on our customers, and we're really proud of what they can now do. And before joining me today, I had a quick look [00:03:59] S0: at your stuff online. You've created so much great content out there, I saw a clip of you saying users connecting AI through NetSuite Next are exploring work that they have never attempted before and feel empowered doing it. Do you have any examples just to to bring that to life?
00:04:14 · Brian Chess
[00:04:14] S1: One of the things we see is AI acting as a teacher. Yeah. So someone says, is my account configured, for... Oh, let's name something. Who's logged in recently? NetSuite knows we have that information, and we've also made it accessible to people if they know where to look, but they haven't always felt like that was in their power to do it. Now they can just ask ask Oracle, and it will not only tell them the answer, it will tell them how they... How it got the answer. And now they can do it whenever they want because it taught them how that part of the system works. So AI is a fantastic enabler because it's a great teacher, and so it can take people into doing things. I'll give you another example. An administrator frequently might not be a developer. But they can ask, how does this script in my account work? And now they're starting to learn a little bit about the code, and they can talk to Ask Oracle about, well, why... What if I wanted to do this? What would that look like? And it can coach them through it, and we see that over and over again. We had an example today with our customer MCNC where all of a sudden the payroll administrator was out. Oops. Everybody still wants to get paid, but we don't have payroll administrator. So now the boss of the payroll administrator, the controller, needs to step in. Well, they weren't trained on the system. Yeah. How's everybody gonna get paid? Ask Oracle walked her through the process, and that's immensely powerful.
00:05:55 · Neil C. Hughes, Brian Chess
[00:05:55] S0: It really is. And I'm curious, again, people listening and watching, which NetSuite workflows are maybe best suited for AI agents today, and where is automation probably still too risky just to rely on? [00:06:09] S1: Well, we are really happy with what we've done around financial close. Yeah. And this is a process that every company has. Largely, they've had to manage it on spreadsheets. Yeah. And we looked at that, and we said, you know, we can automate a whole bunch of this now. But they've got their spreadsheet, and they want that close to happen the way they want it to work. Why is it on a spreadsheet? Because it's being run by the finance team. Yeah. That's that's their weapon of choice. Yeah. So how about we just ask them for their spreadsheet? Show us your spreadsheet. We'll create a model of that, and now you can do exactly what you used to do inside NetSuite and incrementally automate that process. That's really important because in order to trust the system, you don't wanna take a huge leap all at once. You wanna go a step at a time. And so we've enabled that. So financial close is a great example, but you look at supply chain management. A lot of people doing things there, trying to understand what's going on with with their vendors. If you look at workforce management, that's another one where I I gave you the example of, well, like, who's who's logging in? Who's using this thing? There's a lot that's happening there too. So it's really all over the suite. And in a former life, I'm an IT guy,
00:07:37 · Neil C. Hughes, Brian Chess
[00:07:37] S0: and I was helping. I was the resource for month end, year end period, and it's the scariest time. There's so much at stake there, isn't there? There's a lot of stress involved, so what kind of feedback have you had from departments that go through that stress, month after month, year after year? Well, it is [00:07:56] S1: a holy grail of sorts to make that a a a not stressful period Yeah. To a lot of people. I think I'd include myself in this in this camp. I've been working with generative AI since generative AI came around. It still feels a little magical to [00:08:13] S0: me. Yes. [00:08:14] S1: Some of these things where, like, you have a major problem, and then it just poof disappears feels like magic. [00:08:20] S0: Yeah. Really does. And how does the platform give an AI system enough business context without exposing data or permissions it shouldn't have? I suppose that is one of
00:08:31 · Brian Chess
[00:08:31] S1: the first questions you're asked as well. So we allow Ask Oracle, the conversational interface, the same permissions that the user has. So Ask Oracle under the covers cannot do things that that user can't do. And so that's where we start. Well, if you're building an agent, then you need to choose those permissions. The way we did that is by modeling them the same way you would model an employee. So you give the agent a role, and then it can only access those things. A pattern that we've seen from software development is software developers start skeptical. They say, I'm not sure what this thing is gonna do, so maybe you start read only. You can analyze for me, but you're not going to push any buttons for me. And then pretty soon that gets old because, oh, look. It analyzed and says I have to take action. It analyzed and says I have to take action over and over and over again. And pretty soon you're like, could you just take care of that for me? Okay. Now we'll maybe let you write a little bit. We'll let you make some of those modifications, but still very limited. And then over time, trust it more and more. Now part of your question there also was about, well, what about the context? Yeah. It's a really nice part of NetSuite is that we have the context because we're a suite. We're not just managing the financials. We're not just managing the warehouse. You can see all of it. That's really, really good context for AI. The other part, though, is people have always learned how to navigate these systems in particular ways, and they wanna be able to impart their knowledge to the AI. That's where skills come in.
00:10:13 · Neil C. Hughes, Brian Chess
[00:10:13] S0: Yep. [00:10:14] S1: So being able to write down what is the procedure, how do we use the system to get the business outcome that we want is really, really important. After somebody writes it down, then they can also share it. So now instead of just having Joe be the one everybody goes to to get a particular thing done [00:10:34] S0: Yeah. [00:10:34] S1: Joe can say, hey. Here's how you do it for yourself, which Joe has been looking forward to for a very long time in in many cases. [00:10:42] S0: Yeah. Everyone's got a go to person in their organization. And another big topic over the last twelve, eighteen months is hallucinations, model drift. So what controls help prevent inaccurate outputs from becoming incorrect financial or even operational actions? The