Inside the 2025 Gartner® Hype Cycle™ for Enterprise Networking

Hello everyone, and welcome to today’s webinar. For those of you just joining, we’ll spend about a minute here while everyone comes in from the waiting room—it always takes a little bit. We do have a poll for you today, so if you could take a second to answer it, that would be really helpful for us. We’ll chat about the results as we move along. Since today’s talk is all about Gartner and the Hype Cycle, we’re curious to know which of these emerging technologies will be most important to your team over the next two years.

Go ahead and select your top three choices in the poll—we’d love to see what’s top of mind for everyone and what’s most relevant in your environments. We’ll hang out here for a few more seconds while people continue to join. For those of you just joining, it’s great to see so many of you here. As always, we love seeing large groups join the Network to Code webinars, so thank you for that. We’ve got a really interesting session today—different from our usual technical deep dives.

This one focuses on Gartner’s perspective and how the Hype Cycle for Enterprise Networking applies to what we do. Alright, looks like we’ve stabilized in the waiting room, so let’s go ahead and get started. Today, we’re talking about Gartner’s 2025 Hype Cycle for Enterprise Networking—what’s real and what’s coming next. Before we dive in, just a few quick introductions. My name is Tim Schreyack, and joining me today is Tim Fiola. So yes, we’ve got the two Tims here.

If you’ve ever seen our Automation Loop series, you’ve probably seen us interact there as well. A couple of quick housekeeping items before we get into the content. You’ll notice the chat is disabled, so please use the Q&A feature for any questions you have. That helps us track and answer them, and it lets us share those answers with everyone in the webinar. Also, feel free to use the reaction button near the bottom of your screen—it helps us see what’s resonating with you throughout the session.

And of course, don’t hesitate to send in your questions at any time. It’s often easier to drop them in as they come to mind, and we’ll get to them during the Q&A at the end. So, let’s dive in. Today we’re going to look at the most recent Hype Cycle report—the 2025 Hype Cycle for Enterprise Networks. But first, let’s take a moment to explain what the Hype Cycle actually is, what it means for leaders and teams like yours, and how you can use Gartner’s research to identify the emerging technologies that matter most to you. Why does this matter?

The Hype Cycle helps us spot emerging trends that are shaping enterprise networking. Gartner’s analysts are out there identifying what’s coming next and helping us stay informed about where the industry is going, as well as general market trends. It also helps us inform strategic investments. We can use it to align our long-term networking goals, make better decisions, and plan more effectively for the future.

Another important part of the Hype Cycle is separating hype from reality—understanding which technologies deliver real value versus those that are simply generating buzz. The Gartner Hype Cycle also helps support innovation roadmaps. As a leader, it helps map technology adoption to business priorities—what technologies are out there and what value they provide. It also guides risk management, helping anticipate the challenges of adopting early-stage technologies versus more mature ones.

On the flip side, there’s also the competitive advantage aspect. If I stay too focused on established technologies while my competitors are already experimenting with emerging ones, they may eventually outpace me. So I have to balance risk management with the need to innovate, and the Gartner report helps navigate that balance. Now, when you look at the Hype Cycle, it’s important to understand that it’s really addressing two different concepts: hype and long-term impact.

Hype represents the initial excitement around emerging technologies—the media buzz, the marketing, the pilot projects, and the enthusiasm from early adopters. During this period, trends can feel very urgent, and there’s often a sense of FOMO, or fear of missing out. But this phase tends to overpromise in the short term. Then you have the long-term impact, which shows which innovations truly deliver sustainable value. This is the lasting value that a technology delivers once it’s mature.

Not every hyped technology will go on to deliver long-term impact, but the ones that do can be transformative—they can change your business locally or even reshape the entire industry you’re competing in. When you look at the Hype Cycle, we’re not just seeing what’s trendy; we’re evaluating which technologies are likely to matter and provide real value back to your business over the long term. It’s also important to understand the stages of the technology lifecycle that the Gartner report maps.

First is the innovation trigger phase, where a breakthrough or new concept sparks interest. Early pilots begin, but practical implementations are typically very experimental. Marketing pushes the excitement to the peak of inflated expectations. Everyone’s talking about it, but few projects are living up to the hype. FOMO, or fear of missing out, is at its maximum during this phase because of all the marketing and publicity around the technology. That inevitably leads to the trough of disillusionment, where reality sets in.

Many initial implementations have failed or underdelivered, and interest often declines sharply. Some may even declare the technology dead at this stage. But work continues—organizations are learning how to leverage the technology effectively, understanding the use cases better, and tuning the technology to match those needs. Finally, the plateau of productivity represents technologies that have matured substantially. They scale well and deliver real business value over the long term.

Understanding which phase a technology is in can help leaders decide how to invest: do you invest carefully, invest fully, or wait until the technology is more mature? Now that we understand why this report matters and the difference between hype and long-term impact, let’s look at the 2025 Gartner Hype Cycle for Enterprise Networking. At the bottom of the graph, you can see several interesting details. The dots for each technology indicate when Gartner predicts it will reach the plateau of productivity.

This is really important as it shows how close a technology is to mainstream adoption. For example, when we think about network automation—tools that automate provisioning, configuration, troubleshooting, operations, maintenance, validation, and reporting—it’s worth noting where it sits in the lifecycle. Interestingly, when we first reviewed the report internally, we noticed that network automation appeared to have “left” the Hype Cycle. But upon closer inspection, we saw that NetDevOps was still in the innovation trigger phase.

Gartner is making a distinction between network automation and NetDevOps: network automation is the what, while NetDevOps is the how. It’s a nuanced but important difference. NetDevOps is about how development and operational practices are applied to network automation. You can implement network automation without shifting your organizational mindset, but adopting NetDevOps requires a cultural and procedural paradigm shift.

Network automation’s presence outside the Hype Cycle doesn’t mean it’s finished; it’s simply recognized as more mature and moving into mainstream adoption. Many enterprises are still on a journey. Leadership questions have shifted from “Why should I automate?” to “How should I automate effectively?” Understanding both the concept and the technologies that deliver these capabilities is key.

Next, let’s take a deep dive into the key innovations highlighted in the Hype Cycle: NetDevOps, Network Digital Twins, Network AI Assistants, and Agentic NetOps. Looking at the poll results, it’s interesting to see what resonates with participants. Conferences right now are heavily focused on AI and digital twins. From your responses, around 75% are interested in Agentic NetOps and Network AI Assistants, but only 37% are focused on digital twins. This highlights the gap between marketing hype and practical adoption.

NetDevOps received a surprisingly high rating at 63%. This is notable because NetDevOps is primarily a cultural change. For those who identified NetDevOps as a priority, it’s worth reflecting on what steps you’re taking to enable your teams to embrace this paradigm shift. Many organizations are not fully prepared for this change. Foundational elements—like a network source of truth and automation tools—need to be in place to build a successful NetDevOps implementation.

Out of the 30 technologies on the 2025 Hype Cycle, some stand out as particularly worth watching. At the foundation of modern network innovation is the network source of truth. This is the starting point for building automation, NetDevOps, and other advanced network practices. You cannot innovate on top of your network without an accurate, up-to-date source of truth.

This is where a tool like Nautobot comes in, providing that capability by centralizing your authoritative network data and offering a flexible automation framework to simplify the adoption of new technologies. As the industry evolves, emerging technologies are increasingly designed to integrate with such automation frameworks. Before we dive into a few of these technologies, it’s helpful to understand how such a platform applies in practice.

As we look at the orange badges across the lower left of the screen, these indicate where Nautobot and its capabilities align with the trends coming out of the Hype Cycle. We’ll reference those as we explore how Gartner’s insights translate into actual implementation. The first technology we’ll examine is NetDevOps, currently positioned at the innovation trigger phase of the Hype Cycle. Gartner predicts it will reach the plateau of productivity within two to five years, meaning it will hit mainstream adoption relatively soon.

This is particularly interesting compared to something like network digital twins, which is further out. Preparing to implement NetDevOps in your environment is crucial. NetDevOps applies DevOps principles—automation, CI/CD, and network programmability—to manual processes, creating a more agile and reliable operational model. This is a significant shift from traditional approaches and represents an important step for the future of network operations.

NetDevOps matters because it boosts network agility, improves the ability to adapt to network changes, reduces errors, and speeds application delivery. These outcomes align with strategic objectives for most enterprises, achieved by treating network infrastructure like code. Equally important is enabling better collaboration between network and software teams. NetDevOps provides a framework for network and development teams to work together more effectively.

This is not a product you can purchase; it’s a methodology that changes day-to-day operations across all levels of the organization. Management alignment is essential, as adopting NetDevOps often requires cultural change, which can be a significant barrier in larger enterprises. This is why it may take seven to ten years to reach the plateau of productivity, due to cultural inertia and resistance. Part of the challenge is that many organizations are not prepared for NetDevOps.

Gartner highlights that a highly accurate network source of truth is foundational. At Network to Code, our primary focus is providing a network source of truth. In conversations with hundreds of clients across multiple industries, we have yet to encounter an organization with an accurate, up-to-date network source of truth prior to working with us. This foundation is necessary before implementing NetDevOps effectively. Organizations need to have their network source of truth in place, used to drive automation, before embarking on this journey.

It’s one thing to see NetDevOps on a chart, but implementing it successfully requires preparation and a reliable foundation. Clean data is a strategic asset. It unlocks other technologies, but to leverage them fully, you need accurate, validated, and accessible data. Nautobot provides that clean data source. With Nautobot, data is validated and usable by automation, making technological innovations far more practical to implement.

While Gartner predicts a seven- to ten-year path to mainstream adoption, Network to Code has helped enterprises accelerate this timeline, often achieving it in two to three years with outside guidance. Internal agents of change are rarely sufficient in larger organizations, so outside help can be critical to success. NetDevOps may spend a significant amount of time in the trough of disillusionment. Organizations will attempt it, but without the necessary prerequisites and foundation, they may struggle and become discouraged.

Only after completing the pre-work—building a reliable source of truth and implementing automation—can organizations fully embrace NetDevOps. A key part of this is changing how data is treated. In most organizations, data exists primarily as documentation. Engineers often consider documentation tedious and secondary. At Network to Code, we treat data as the mechanism for instantiating change. To implement a network change, you modify the data first, and the change flows into the environment.

This approach shifts team thinking: engineers start thinking “data first,” fundamentally changing how they approach network operations and laying the groundwork for successful adoption of NetDevOps. Engineers start to see data as important. It’s the way they manage their network, the way they manage their environment, and that really shifts their mentality. It changes data from being just documentation to something actively used in day-to-day operations.

Engineers don’t act on things that aren’t practical to their work, so we need to make data directly relevant to their daily tasks for them to embrace it. We have to meet them where they are, not simply tell them they need to do something, because no matter how much you insist, it won’t happen. The key is making this practical—something engineers actually want to use and can consume. This is a fundamental element of Nautobot and Network to Code’s philosophy.

In this year’s report, Network to Code is recognized as a sample vendor in NetDevOps, reflecting our ability to help organizations reach the plateau of productivity faster. This is how we add value: by enabling enterprises to implement these concepts more effectively. That covers NetDevOps. Now, moving on to network digital twins, only 37% of you indicated interest. Gartner agrees that while network digital twins are in the innovation trigger stage, they’re five to ten years out from mainstream adoption.

For those unfamiliar, a network digital twin is a virtual model of your network that mirrors topology, configurations, and behavior, allowing you to simulate, test, and predict changes safely. At a high level, this is exciting, but practical implementation is challenging. Network digital twins fit within NetDevOps because they integrate with testing pipelines. Before making a change, you can spin up a digital twin, test the change, and validate it, giving confidence that the change will have the intended effect in the live network.

Nautobot plays a role here by providing the intended state data to spin up these digital twins. The challenge in mainstream adoption is modeling the complexity of large networks. Servers are relatively simple, but a typical network switch may have 48 ports, each requiring 10–20 unique configurations. Representing all of that externally is much harder, which explains why NetDevOps adoption and network digital twins are slower to reach productivity. Intended state is a loaded term because it depends on the context of the configuration.

Most organizations don’t maintain the intended state externally, which is core to the DevOps philosophy: the configuration of a device should be defined outside of the device itself. Nautobot solves this problem by providing a platform to define and maintain the intended state, but the complexity of networks makes this a long-term challenge. Network AI assistance was the most popular topic, with 74–75% of you indicating interest. Gartner predicts it will reach mainstream adoption within two years.

These solutions use AI and machine learning to provide conversational, actionable insights for network operations. Essentially, they function as a chatbot for your network, allowing you to query and interact in natural language. This matters because networks are highly complex and difficult to understand in real time. AI assistants provide a gateway to interact with the network in plain English, pulling information from Nautobot as the source of truth.

Even public AI tools like ChatGPT or Copilot can interact with Nautobot, but NautobotGPT, built by Network to Code, leverages the data model and APIs to generate detailed insights, reports, and even automation tasks. For example, you could ask NautobotGPT to create a job to clean up data. While it may not produce 100% of the code, it can get you 90% of the way there, significantly accelerating work that might otherwise take weeks. Managers or team members can also generate complex reports quickly without needing developers.

This drastically improves efficiency, turning network operations into a more agile, responsive process. These are really interesting use cases. Interacting with AI assistants can dramatically speed up understanding and action. When you can comprehend a situation faster, you can respond faster. In day-to-day operations, AI assistants accelerate understanding of the network, troubleshooting, and coding tasks—for example, creating Nautobot jobs through NautobotGPT.

Tasks that might take days or weeks can now be completed in hours, allowing teams to reach working automation far more quickly. It will be interesting to see how much time network AI assistance spends in the trough of disillusionment, but currently the technology appears capable and well-defined. Gartner’s prediction that it will reach the plateau of productivity in under two years seems accurate. The only potential speed bump is the inherent unpredictability of AI.

Outcomes aren’t always as expected, and that could cause temporary setbacks, especially if teams encounter failures in their environments. Guardrails are being developed around AI assistants to minimize unpredictability, but organizations may still experience nondeterministic behavior. Understanding how AI works under the hood is important, even if you aren’t an AI developer. It’s similar to understanding basic arithmetic—you need to grasp the principles, not just rely on a calculator.

Firms may also spend time in the trough of disillusionment while learning to implement proper guardrails to mitigate nondeterministic behavior. Having a clean, validated source of data, like Nautobot, reduces AI guessing and hallucinations, creating a safer and more predictable environment for AI use. The fourth and final innovation is Agentic NetOps, currently in the innovation trigger stage and five to ten years from mainstream adoption, according to Gartner.

Agentic NetOps refers to goal-driven autonomous AI agents equipped with memory, planning, and sensing capabilities to manage network tasks independently within defined organizational policies. From today’s perspective, that level of autonomy can feel intimidating. Nondeterministic behavior at scale is a significant concern, and trust must be established before widespread adoption. This is why Gartner projects a longer adoption timeline and anticipates a lengthy trough of disillusionment, as organizations learn to deploy autonomous AI safely.

Agentic NetOps builds on AI assistance. An AI assistant could interact with a specialized agent designed to gather information or perform tasks based on your request, effectively expanding the assistant’s capabilities. While this makes AI more powerful and versatile, it also amplifies the need for guardrails to prevent hallucinations or autonomous actions that deviate from intended outcomes. Success with these agents will require significant progress in AI reliability, monitoring, and governance.

We’ve seen AI make unexpected mistakes, from minor missteps to major incidents like databases being deleted in production. These examples highlight why trust in autonomous systems must be earned carefully. A key part of building that trust is feeding clean, accurate data into these tools. That’s where Nautobot comes in—helping teams maintain, validate, and manage their data effectively. Embracing AI-driven network operations, including Agentic NetOps, requires a cultural and operational shift.

Teams must start planning now, even though Gartner predicts five to ten years for mainstream adoption, because preparing your people and processes takes time. Transitioning from CLI- or UI-driven management to intent-based, data-driven operations is a foundational step for leveraging network automation, NetDevOps, and eventually Agentic NetOps. Clean data is a strategic asset.

From Nautobot, clean data enables network automation, NetDevOps, and AI capabilities, including both training AI and providing the context it needs to make accurate decisions. Whether it’s digital twins or other emerging network technologies, success depends on a reliable foundation of clean data. Think of it like building a house: the shiny new kitchen or bathroom doesn’t matter if the foundation is weak. Similarly, emerging network technologies require a solid data foundation to function effectively.

This brings us to Nautobot as your foundation. As a network source of truth, Nautobot centralizes and validates network data, allowing teams to always know what’s deployed and running. It enables automation of routine tasks, freeing teams to focus on higher-value projects, and supports the integration of new tools and technologies—whether AI-driven insights, digital twins, or advanced NetDevOps workflows. Nautobot doesn’t just help you keep up; it gives you the platform to lead and confidently leverage emerging technologies.

Within Nautobot, there are multiple offerings tailored to different organizational needs. Nautobot core with NTC support ensures teams have the guidance and tools needed to manage networks effectively. Nautobot Cloud offers a hands-off approach to infrastructure management, handling deployment, maintenance, updates, backups, and availability, so teams can focus on running the network rather than the application itself.

Finally, NautobotGPT builds on this ecosystem, providing AI-driven insights and automation capabilities through the Nautobot Cloud application. Whether you prefer full on-prem control, a managed cloud approach, or AI-augmented operations, Nautobot provides flexible options to fit your environment. With that foundation in place, we’re ready to take questions from the audience. Let’s move into the Q&A section. When discussing digital twins, a common question is how to handle the missing functionality of virtual network devices.

In the Nautobot world, the approach is to describe intent external to the device, using Nautobot and tools like Design Builder. Design Builder allows network designs to be structured, vendor-neutral, and template-based. When deploying to a digital twin, Nautobot translates the intended state into a configuration that accounts for the limitations of the virtual environment.

The goal is risk reduction rather than elimination—digital twins reduce the likelihood of errors in network changes but cannot perfectly emulate every feature of a physical device. Traffic pattern and load testing is handled by the digital twin tool itself, not Nautobot. Nautobot’s role is to provide clean, accurate configuration data so the twin mirrors the real network, eliminating the need to manually replicate configurations multiple times.

Regarding Nautobot’s free version versus the official version, the core platform is open-source under Apache 2 and identical to the code used in Nautobot Cloud. The distinction comes with additional applications layered on top in Nautobot Cloud. While the majority of apps remain open source, some, like the OS Upgrades app, provide vendor-neutral, single-pane-of-glass OS upgrades across devices and are included in the official Cloud offering.

Organizations often ask how frequently they should realign their strategy based on the Gartner Hype Cycle. The answer depends on risk strategy, capabilities, and organizational culture. NetDevOps requires broad cultural changes, whereas AI Assistance may have more localized but immediate impact. Planning and alignment should be based on both the maturity of the technology and the team’s readiness to adopt it effectively. A related question is the role of digital twins in relation to Nautobot.

Digital twins serve as the environment that powers a virtual copy of your network. They need to be multi-vendor capable to handle Cisco routers, Arista switches, Palo Alto firewalls, F5 load balancers, and more. Nautobot provides the configuration for these virtual devices, ensuring that the digital twin reflects the real network. The configuration does not happen automatically—it comes from Nautobot, enabling accurate simulations and testing.

Finally, for organizations interested in cloud applications for on-prem installations, Nautobot supports flexible deployment options. Even apps that are part of the Cloud ecosystem, like the OS Upgrades app, can be made available for on-prem deployments with support from the Nautobot team. In closing, the key takeaway is to consider how your organization will position itself for the next 12–24 months, leveraging Nautobot as the foundation to adopt and scale these innovations.

The webinar highlighted the Gartner Hype Cycle, key emerging technologies, and how Nautobot maps to them, emphasizing that clean, accurate network data is a strategic asset that underpins automation, NetDevOps, AI Assistance, and future advancements like Agentic NetOps. Access to a complimentary copy of the Gartner Hype Cycle report for enterprise networking is available for attendees, providing further insights to guide planning and adoption.