The network automation landscape has reached a critical juncture in 2026, where many organizations find themselves at a crossroads. While the industry narrative has focused on technical challenges and tool deficiencies, the real bottleneck to NetDevOps adoption is far more human-centric than computational. What’s fascinating is that we’ve reached a point where the technology has matured to the extent that Python, Ansible, and source-of-truth platforms are no longer the limiting factors. Instead, the primary obstacles to automation success are rooted in organizational culture, skill distribution, and change management. This fundamental misunderstanding of where the true barriers lie has caused many well-intentioned NetDevOps initiatives to stall not because of technical limitations, but because they’ve been solving the wrong problem. The network automation revolution isn’t being held back by code quality or tool selection—it’s being impeded by the people who need to adopt and operationalize these technologies.
The 2025 State of Network Automation Survey from the Network Automation Forum provides compelling evidence that shifts our understanding of NetDevOps barriers. Surveying 681 network professionals across 58 countries reveals a stark contrast between perceived and actual impediments to automation adoption. While conventional wisdom might suggest technical challenges dominate, the data shows that only 10% of respondents cited technical limitations as their primary hurdle. In contrast, 44% identified people-related obstacles including skill gaps, organizational dysfunction, cultural resistance, and interpersonal conflicts. These findings align with another concerning statistic: nearly half of organizations have no formal metrics to measure automation success, creating a vicious cycle where initiatives can’t be adequately funded or justified without demonstrable outcomes. This disconnect between technical capability and organizational readiness represents the fundamental challenge facing NetDevOps adoption today.
The persistent industry narrative surrounding NetDevOps often romanticizes the concept of the ‘unicorn’ engineer—a mythical individual who simultaneously possesses deep expertise in network engineering, software development, and DevOps practices. While such individuals do exist, building an automation strategy around finding them is fundamentally flawed and unsustainable. This approach creates unrealistic expectations, sets teams up for failure, and overlooks the more realistic path to NetDevOps success. The reality is that organizations shouldn’t be searching for perfect candidates; they should be creating environments where current staff can develop the necessary skills incrementally. The unicorn narrative also inadvertently diminishes the value of existing network engineers, suggesting they’re inadequate rather than capable of evolution with proper support and structure.
Research from organizations like theCUBE provides critical insights into the most effective approaches to building NetDevOps capabilities within existing teams. The data clearly shows that organizations prioritizing internal upskilling significantly outperform those relying exclusively on external hiring. This finding challenges conventional HR practices that often default to recruitment rather than development when facing skill gaps. The implications are profound: your team likely already possesses the foundational knowledge needed for successful NetDevOps implementation. What’s missing is the evolution process that allows network engineers to gradually develop automation competencies. This approach not only builds more sustainable capabilities but also creates a culture of continuous learning that’s essential for long-term success in an increasingly automated network environment.
Creating the right conditions for NetDevOps evolution requires a fundamental rethinking of how we approach skill development and professional growth within organizations. Traditional training methods—weekend certification cramming, classroom-based instruction, and theoretical learning—simply don’t prepare network engineers for the realities of automation implementation. The most effective approach is to allow people to learn by doing, solving actual problems with new tools in their daily work. This means accepting that initial automation projects will inevitably take longer than manual processes, creating a temporary productivity dip that’s actually an investment in future capabilities. Organizations that embrace this philosophy understand that the learning curve is not a barrier but a necessary part of the transformation process. They create safe spaces for experimentation and failure, recognizing that the first few attempts at automation will be imperfect but valuable learning experiences.
Knowledge transfer in NetDevOps environments must shift from documentation-driven approaches to collaborative, on-the-job learning models. When organizations identify team members with stronger automation skills, the instinct is often to designate them as ‘the automation expert’ and silo their knowledge. This approach creates dependency rather than capability building. The more effective strategy is to pair automation-savvy engineers with their less experienced colleagues on real projects, allowing skills to transfer through hands-on collaboration rather than formal training materials. This collaborative approach mimics the apprenticeship models that have successfully transmitted complex technical knowledge throughout history. It also builds organic communities of practice within organizations, where knowledge flows naturally between team members rather than being formally documented and potentially lost when individuals leave.
The journey to NetDevOps mastery is rarely a single dramatic transformation but rather an incremental evolution of skills and mindsets. Consider the author’s personal experience transitioning from principal network engineer to consultant software developer at Dell EMC—this wasn’t a planned career shift but an organic response to the growing necessity of automation. Each script written, each playbook developed, represented a small step in a larger professional evolution. This pattern holds true for most network engineers who successfully adopt NetDevOps practices. The survey data reinforces this, showing that 92% of organizations credit automation development to ‘network engineers with automation skills’ rather than specialized software developers or dedicated DevOps teams. This suggests that the most effective path to NetDevOps isn’t through radical restructuring but through the organic evolution of existing talent.
Traditional network operations models treat documentation as an afterthought—a chore completed after changes are made, if remembered at all. This reactive approach to documentation creates a perpetual cycle of information decay and manual tracking that undermines automation efforts. NetDevOps represents a fundamental paradigm shift where documentation becomes the primary mechanism for change rather than a secondary record of what happened. Under this model, network configurations become byproducts of data rather than the focus of operations themselves. This approach, as Intel’s Greg Botts notes, means starting with data and allowing everything else to flow naturally from that foundation. This represents not just a technical change but a philosophical one—shifting from a ‘do-then-document’ mindset to a ‘define-in-data-first’ approach that makes automation both possible and meaningful.
Establishing a reliable source of truth represents perhaps the most significant—and most underappreciated—step in the NetDevOps journey. According to industry experts like EMA’s McGillicuddy, organizations often spend 12-24 months just building their foundational network data layer. While this may seem excessive, it’s actually time well spent, as it involves consolidating information scattered across multiple silos, cleaning up years of accumulated configuration drift, and building the authoritative data layer that makes all subsequent automation possible. The return on this investment is substantial: organizations with clearly defined sources of truth are nearly three times more likely to achieve consistent automation outcomes. Yet despite this clear correlation, 56% of organizations still operate without a reliable source of truth, attempting to build sophisticated automation on foundations that are fundamentally unstable.
The intent-driven methodology represents a transformative approach to network operations that fundamentally changes how we think about data and automation. Without this approach, network data serves merely as documentation—a passive record of past actions updated retroactively (if at all). With intent-driven networking, data becomes operational—defining not just what the network looks like but what it should look like. This subtle but profound difference means that changes are made by updating the source of truth, with automation ensuring that the network conforms to this definition. This flips the entire operational paradigm on its head, making documentation an automatic byproduct of operations rather than a separate administrative burden. Organizations that successfully implement this approach see dramatic improvements not just in efficiency but in overall network reliability and consistency.
AI capabilities have reached a point where they can genuinely enhance network operations, but their effective implementation requires careful consideration of organizational maturity. Today’s AI tools can handle natural language queries against network data, analyze configurations for optimization opportunities, generate automation code, and provide predictive insights. However, the technology itself isn’t the limiting factor—organizational readiness is. Network engineers have learned through hard experience that untested changes can have catastrophic consequences, and they’re appropriately cautious about applying AI without proper guardrails. The industry is beginning to develop necessary frameworks like the NIST AI Risk Management Framework and ISO 42001 for AI management systems, but most organizations haven’t yet established the equivalent of code review, testing protocols, and CI/CD pipelines for AI outputs that they’ve built for traditional automation tools.
The path to effective AI in NetDevOps isn’t a shortcut but a continuation of the foundational work required for any successful automation initiative. Too many organizations try to implement AI without first establishing the data quality and automation maturity that makes AI meaningful. The solution is to view AI adoption as the final stage of a three-stage flywheel: first, build a solid data foundation with a reliable source of truth; second, implement automation that improves data quality through consistent change management; third, deploy AI tools that can leverage the now-reliable data and mature automation practices. This sequential approach ensures that AI has something substantial to work with rather than trying to make sense of chaos. As Intel’s Greg Botts suggests, AI will ultimately uplevel the workforce rather than replace it—but this benefit only materializes when organizations have done the foundational work to be ready.
As we look toward the future of network operations in 2026 and beyond, organizations must recognize that NetDevOps success isn’t about finding perfect tools or hiring mythical unicorns—it’s about evolving your existing team and fixing your data foundation. The path forward requires acknowledging that the primary barriers to automation adoption are human and organizational rather than technical. Start by creating an environment where network engineers can gradually develop automation skills through real-world projects, supported by collaborative learning rather than formal training. Invest heavily in establishing a reliable source of truth that becomes the foundation for all subsequent automation efforts. Resist the temptation to implement AI solutions before your data and automation practices are mature enough to support them. Finally, recognize that the people who will drive your NetDevOps transformation are likely already on your team—they just need the opportunity, support, and time to evolve into the automation professionals of tomorrow.