'What n8n means for Enterprise' - n8n Business Lab (November 2025)

I'm Nick. I'm leading the product um at NNN. So if a feature is not built to your happiness, then blame me for it, I guess. Um Oh, yeah. So super happy to hear your feedback. Maybe before we kick off the session, um quick raise of hands. Who's using Naden today? Very cool. I would say 60 to 70% which is great to see. Um before I start talking about NN or enterprises for NN, I wanted to talk about AI a bit obviously. Um and I think all of us know that we're in the middle of a of an AI revolution a little bit, right? Like it's not tomorrow, it's already begun. Um but maybe some of you have also experienced that this revolution is harder than it seems in the beginning. And I think when it comes to AI adoption or AI transformation, there's probably three phases that you can look at um which is experimental, production ready and autonomous, right? And I think many of us have probably done something in the experimental phase and it feels very nice. It's very easy to come up with flashy wow demos that feel great and then you start to get to the production ready phase and it's just much harder to get the last 20% done right because AI is not deterministic. It can give different answers to the same prompt. Um and making that production ready sometimes really hard. Um, and then I would say autonomous is probably something where almost nobody is yet. Um, where we really say like everything's working completely hands off. So I think we're probably right now in production ready a lot of the production ready use cases are possible by today but sometimes just not so easy. So the question is if it's not so easy, if it's possible like how can we get to production readiness, right? That's I guess what we all want. uh those are the high value use cases that we want to automate. Um and I think one thing that we at Nadn think about AI is that by now you basically have three puzzle pieces to attempt every task that you want to complete. Um and of course like at first we had humans right [clears throat] then we had code on top of it in any form and now the third puzzle piece is AI and the art here is really how you combine these three puzzle pieces together to achieve the outcome that you want because I think there's different opinions probably out there in the market but we at NN believe that these are the three puzzle pieces that will always work together so always human code and AI and that we not have like some dystopian future where everything is AI and we just sit around and do nothing. Um, so why do I tell you this, right? Um, obviously because I think combining these three puzzle pieces is really what NN excels at. This is what NN is really good at. And I think in general, Netn helps companies to speed up the AI transformation um by having an AI orchestration layer that is built for flexibility. uh that enables technical users to build highly customizable um and I an agents AI agents and workflows um while still having a really pragmatic and visual representation of what you're doing which helps to also loop in the business and not just the technical teams right be it reviewing tweaking of small things um it's just much easier to kind of collaborate on these things. Um, and we focus on technical users. Um, but like I mentioned, it's really much easier to collaborate. We also see more and more that in most companies we have workflow builders and consumers very often. Um, whereas the workflow builders maybe tend to be a little bit more technical. Um, the consumers are often the less technical and maybe a few more. I will go a little bit deeper into that later on how [snorts] we how we hope to support that more in the future with some of the features that we're building right now. And then of course Netn is built to be production ready. We're not just a tool where you can p some flashy demo, right? The experimental phrase that I talked about, but we're built for production ready use cases for critical things. And it's already being used by many or I guess hundreds of different enterprises

today. um that run business critical use cases within and I guess we are quite successful with that at the moment which is of course good for my job. Um so we are one of the top 50 GitHub projects out there um beating legendary companies as MongoDB, React or Jinx. Um we talked about the community. We really feel the community is really special. We have a very great big community out there and we also have lots of enterprise uh customers today that use us. I think 25% um of the Fortune 500 as well. So when we speak about enterprise right like why do enterprise customers love NadN? And I think for the folks of you that used NetN already, a lot of this hopefully feels similar um to something that you experienced already, right? Like one thing that is really important is that Netn is model agnostic. So you never have a model lock in. You can always choose the right model for your use case, for your company um that really works. Um even up to self-hosted models if you want to, right? So you're never locked in there. Um then and then is really built for the full life cycle. So from just experimenting around maybe building the first demo to bringing something to production to then making sure that it works in production. Right? We're talking about code reviews here, uh, git integrations, different environments, um, that you use, um, evals that you need to build for your AI workflows. Uh, hope everyone knows that eval are really important for AI. Um, and much more. And ultimately, we already spoke about it, so I won't stress that too much. It's really collaborative building in NN. Um, I think adoption is much easier. It's also much easier to just talk about these things and actually talk to the people whose process you're trying to improve, right? Because often times it's like you're trying to improve someone else's process. It's really technical. There's a lot of misunderstanding in between within it and that's much better. Most of you know this so I won't talk too much about it. We have a lot of integrations which is great. Um definitely kickstarting especially for the less technical users. But we are always built for flexibility, right? So one of yeah kind of our most prominent node is the HTTP request node which allows you to connect to everything that has an API out there. So you're never not able to connect to anything. And speaking of flexibility, um we really built Netn for flexibility and power. Like I personally know there's nothing as painful as hitting the ceiling of a tool that you use. It's not because you don't understand it but just because the tool is not capable of doing something. And I think as a technical person there's nothing more frustrating than this. Um so we really have that in mind and build for flexibility and power of course while trying to have this visual nature. That means that in every step you can bring in code be it JavaScript, Python um if you want it. That can be full code blocks like you see on the left. Um but it's also just um for transformations if you just want to change your data slightly um while building things. And this flexibility of course doesn't stop at um just the functionality, right? like you can also host NN however you want um while being able to look at the code. So if you really want to make sure if we're doing a good job, you can look into our code and criticize us if that makes you happy. Um but we also have a lot of enterprise features that are really built for enterprise needs, right? Like I mentioned environment. So you have different instances for development versus production um needs where you can push and pull changes. Um we have SSO and LDAP. Of course we um also have a way to store all your credentials externally. Um of course if you store them in net end it's also very well encrypted but in case you really need to store them somewhere else that's also something that we have. Um, so I think maybe one thing that I forgot in the past, I think we're talking a lot about AI now, right? But Netn predates AI. I think we heard here that some people use us for more than three years already. Um, back then we were an automation company. Um, basically thinking of the puzzle pieces. We just had code and humans um, there which allowed us to build a lot of the foundations that are now needed when

interacting. um with AI because we just build it from an automation perspective for deterministic code things. Um and that for some good uh occurrences uh fits very well with the needs of AI. Um so the things that we built then now make a lot of sense and we built them for a long time already in startup years of course. Um, and of course since I'm from product, um, I also wanted to grab the chance a little bit not only about like talking about the things that we have today, but also what we're focusing on right now, right? Because of course we want to make end even better for enterprise use cases and for many other things as well. So I wanted to share some things that we are currently working on or focusing on. Um and I guess one big theme that we're seeing is permissions and access. Uh which is of course very very important. We already have roles, projects, all that stuff. Um but we really want to make sure that you have super granular permissions um in Netn um that is actually coming very soon the custom roles and provisioning. So that means you can create custom roles with whatever you want with the scope and permissions and also that there's provisioning automatically that new users that come to NMN get the role they need or that are stored in your system. Um another thing that we hear quite a lot um I mentioned that we have projects in NNN. You can imagine project projects a little bit as workspaces for different teams. And what we want to make sure there is that every project is kind of its isolated island a little bit if you want to. Of course, um we have many features like data tables, variables and other things um that currently are not perfectly isolated and especially if you have business units that should not know about what the other business units do then that's a little bit unfortunate. Um so we want to make sure that these projects are more isolated um so that teams can work with full confidence. And then the last thing um that we're working on is dynamic credentials. I talked a bit about workflow builders and workflow consumers. Um which basically means that a few people build AI workflows and a lot more in the company use these AI workflows, these agents. And of course when you build something let's say with Slack um or Microsoft Teams you don't want the consumer to run your Microsoft Teams credential right like they should not have access to the data that is yours. Um, so that is something that we're working on right now that others can run or consume workflows from the outside um, all while kind of having their own permissions and credentials um, that are basically not the builders. Um, and it automatically kind of resolves to the right credential when consuming a workflow. And then the next theme um, and I really don't know how I do on time. Um, is of course AI building. We talked about AI. Um, we believe there's much more to do in AI to make sure we we're ready for what is in the future where you probably build a lot more complex agents that go more into this autonomous um space more into the production ready space. So, one thing that we really want to work on is um making human in the loop a first class citizen in an end. So, imagine you have a tool that pays out money um to users and your agent decides when to use this. This is probably something where you at least want to have a quick review before it just sends out the money, right? So, in the future, we will probably have something here where you can just put a note in between and can say, "Okay, before you call any of these tools, ask me first. " Um, same with um kind of asking for feedback. So, we really want to make sure that you in the future work alongside the AI. It kind of asks you for help. It asks you for feedback and you decide when that feedback should come or when you want to trust the AI fully autonomously. Then I think many of you know that we have the AI workflow builder by now which basically enables you to build workflows by just English text. Um we just released that a while ago. Um we are going to continue to focus on this quite a bit. Uh we believe there's much more to do much to improve on. Um, and we also feel it's so powerful, especially for maybe the less technical users, that they can just build or change workflows by just describing what they want to do. So, that's definitely something that we going to keep investing in. And then the last thing, I think I've never mentioned that anywhere so far, um, is that we are working on something that we call internally the chat hub. um a little bit you can imagine it to be a little bit like chat GPT but linked to NN model agnostic um

so that especially the consumers of AI can use NNN maybe easily consume the agents um that you've built but also use it for everyday tasks that they do today with JT GPT or claude and I think in many enterprises you don't want them to just call jet GBT or something right you want to make sure that they call the right models for the right tasks Um so that is what this uh chatub is going to allow companies to do in the future. Um yeah so much to that happy to talk about all of this feedback questions whatever um later but I think we also have five minutes Q& A now if I'm not mistaken. Thank you.