Only 18% of network automation initiatives fully succeed. That’s not pessimism — it’s data from Enterprise Management Associates (EMA) surveying 354 IT professionals about their automation strategies. Another 54% report partial success, and 28% say their projects have stalled or failed outright. If you’re planning or executing a network automation initiative, understanding why most fail is the difference between joining the 18% or the 82%.
Key Takeaway: Network automation projects fail primarily because of underfunding, integration complexity, and lack of architectural planning — not because the tools don’t work. Engineers with CCIE Automation skills succeed because they architect the system, not just the scripts.
What Does the Data Actually Show About Automation Adoption?
Two major surveys give us the clearest picture of where network automation stands in 2026:
The EMA/Itential Research (2024-2026)
According to EMA research published by Itential and covered by Network World:
| Outcome | Percentage |
|---|---|
| Full success | 18% |
| Partial success | 54% |
| Stalled or failed | 28% |
The “partial success” category is the most revealing. These are organizations that automated some tasks but couldn’t scale beyond initial wins. They got backups working but couldn’t automate service provisioning. They wrote Ansible playbooks for one platform but couldn’t integrate with their ITSM system.
The NANOG 2025 State of Network Automation Survey
The NANOG 95 survey (October 2025), presented by Chris Grundemann, surveyed network operators across industries. The stack-ranked automation adoption by task:
| Task | Fully + Partially Automated | Automation Rate |
|---|---|---|
| Backups | 336 respondents | 88% |
| Device Deployment | 322 | 78% |
| Firmware Upgrades | 262 | 67% |
| Service Provisioning | 236 | 59% |
| Non-Provisioning Config | 210 | 54% |
| Troubleshooting | 192 | 44% |
| Firewall Rules | 180 | 53% |
| Capacity Planning | 153 | 39% |
| eBGP & Interconnection | 143 | 37% |
| DDoS Response | 120 | 31% |
The pattern is clear: simple, repetitive tasks with low risk are highly automated. Complex, judgment-heavy tasks remain manual. Backups are essentially solved. eBGP peering — which requires understanding business relationships, route policy, and traffic engineering — is still mostly done by hand.
Why Do 82% of Automation Projects Fail or Stall?
The Itential/EMA research identifies five top challenges:
1. Integration Difficulties (25%)
The #1 challenge. Network automation doesn’t exist in isolation — it needs to integrate with:
- ITSM systems (ServiceNow, Jira) for change management workflows
- Monitoring platforms (Prometheus, Datadog, ThousandEyes) for closed-loop remediation
- Source of truth (NetBox, Nautobot) for inventory and intended state
- CI/CD pipelines (GitLab, Jenkins) for testing and deployment
- AAA/RBAC systems for who can approve and execute changes
Most organizations pick a tool (Ansible, Terraform) and start writing playbooks — without designing the integration architecture first. The tool works in isolation but breaks when it needs to talk to everything else.
2. Network Complexity and Lack of Standards (24.9%)
Multi-vendor environments, inconsistent naming conventions, one-off configurations from 15 years of organic growth, and devices running 6 different firmware versions. You can’t automate what you can’t normalize.
According to Gartner (September 2024), “Automation is key to I&O delivering greater value” — but automation of a messy network just creates automated mess faster.
3. Legacy Infrastructure (24.3%)
Devices that only support CLI (no NETCONF, no RESTCONF, no API). Switches running IOS 12.x that can’t be upgraded because they support a critical application. Firewalls with undocumented rules that nobody wants to touch.
The NANOG survey confirms this: automation adoption drops sharply for tasks involving legacy infrastructure. You can’t use NETCONF on a Catalyst 3750 running IOS 12.2.
4. Tool Complexity (23.7%)
Ansible is “simple” — until you need to handle error recovery, conditional logic across multi-vendor environments, and rollback procedures. Terraform works for cloud infrastructure but gets complex with network resources. NSO is powerful but has a steep learning curve.
The tooling landscape is also fragmented. According to the Network Automation Forum at AutoCon 4 (2025), the community is still converging on best practices for tool selection and architecture patterns.
5. Data Quality (22.3%)
Automation is only as good as its input data. If your CMDB says a switch is a Nexus 9300 but it’s actually a 9500, your playbook generates the wrong config. If your IPAM has stale entries, your automated provisioning creates conflicts.
Source of truth tools (NetBox, Nautobot) solve this — but populating them accurately requires an upfront investment that many organizations skip.
What Separates the 18% That Succeed?
Funding Is the Single Biggest Predictor
According to the Itential/EMA research, the correlation between funding and success is stark:
| Funding Level | Success Rate |
|---|---|
| Fully funded | 80% |
| Adequately funded | ~55% |
| Underfunded | 29% |
“Fully funded” doesn’t mean unlimited budget. It means:
- Dedicated headcount — at least one full-time automation engineer per 500-1000 managed devices
- Training budget — Python, Ansible, NETCONF/RESTCONF training for the team
- Tool licensing — proper licenses for NSO, Terraform Cloud, CI/CD infrastructure
- Executive sponsorship — a VP or Director who protects the initiative from being deprioritized
The organizations in the 29% success rate typically have “one engineer doing automation on the side of their regular job.” That’s not an automation initiative — it’s a hobby.
Architecture Before Scripting
Successful automation projects start with architectural decisions, not playbook writing:
- Define the source of truth — where does intended network state live?
- Design the integration points — how do ITSM, monitoring, and automation tools communicate?
- Establish the workflow — change request → approval → testing → deployment → validation → rollback
- Choose the abstraction layer — raw API calls vs. Ansible vs. NSO service models vs. Terraform
- Build the testing framework — pyATS, Batfish, or custom validation scripts
Only then do you write the first playbook.
Start with High-Value, Low-Risk Tasks
The NANOG data shows a clear pattern: organizations that succeed automate in order of risk:
Phase 1 (Months 1-3): Backups, compliance checks, inventory collection — zero operational risk Phase 2 (Months 3-6): Firmware upgrades, standard device deployment — low risk with rollback Phase 3 (Months 6-12): Service provisioning, firewall rules — moderate risk, requires testing Phase 4 (Year 2+): Troubleshooting, eBGP changes, DDoS response — high risk, requires confidence
Jumping straight to Phase 3 or 4 without the foundation is the #1 pattern in stalled projects.
How Does CCIE Automation Help You Beat the Odds?
The 82% failure/partial-success rate exists partly because automation is led by engineers who can write scripts but can’t architect systems. As we discussed in our AI network automation career analysis, the gap between “I can run an Ansible playbook” and “I can design an automation framework that scales” is the gap between CCNP and CCIE.
What CCIE Automation Validates
| Skill | Why It Matters for Success |
|---|---|
| NETCONF/RESTCONF + YANG | Standardized API access eliminates vendor-specific scripting |
| CI/CD pipelines | Automated testing catches errors before production |
| Infrastructure as Code | Terraform/Ansible at scale with state management |
| NSO service models | Abstraction layer that handles multi-vendor complexity |
| Python + pyATS | Custom validation and testing frameworks |
The Architect Gap
According to the Network to Code analysis of Gartner’s 2025 Strategic Roadmap, enterprises need “AI, automation, and security” as “immediate priorities.” But the Gartner projection that 30% of enterprises will automate over half their network activities by 2026 (up from under 10% in 2023) only works if there are architects who can design the automation systems.
CCIE Automation holders are those architects. If you’re building toward this career path, our first CCIE Automation lab guide and network automation career roadmap are practical starting points.
What’s the Public Sector Reality?
One data point that often gets overlooked: according to multiple sources at AutoCon and NANOG, 95% of public sector network changes are still manual. Government agencies, military networks, and regulated industries lag significantly behind commercial enterprises in automation adoption.
This is both a problem and an opportunity. The problem: these networks are massive, complex, and critically important. The opportunity: the demand for automation architects in the public sector is about to explode as agencies face the same staffing pressures that drove commercial enterprises to automate.
Frequently Asked Questions
What percentage of network automation projects succeed?
According to Enterprise Management Associates (EMA) research surveying 354 IT professionals, only 18% rate their network automation strategies as a complete success. 54% report partial success, and 28% say their initiatives have stalled or failed. The single biggest predictor of success is adequate funding.
What network tasks are most commonly automated?
According to the 2025 NANOG State of Network Automation Survey, the most automated tasks are: backups (88% of respondents), device deployment (78%), firmware upgrades (67%), service provisioning (59%), and firewall rules (53%). eBGP/interconnection provisioning (37%) and DDoS response (31%) remain largely manual.
Why do network automation projects fail?
The top challenges according to Itential/EMA research are: integration difficulties (25%), network complexity and lack of standards (24.9%), legacy infrastructure limitations (24.3%), tool complexity (23.7%), and data quality issues (22.3%). Underfunding is the strongest predictor of failure.
How much should companies invest in network automation?
Research shows that fully funded automation projects succeed 80% of the time vs 29% for underfunded ones. “Fully funded” typically means dedicated headcount, training budget, tool licensing, and executive sponsorship — not just approving a single engineer to run Ansible scripts part-time.
Is CCIE Automation valuable for leading automation projects?
Yes. The 82% failure/partial-success rate exists partly because automation is led by engineers without architectural expertise. CCIE Automation validates the design, orchestration, and troubleshooting skills needed to architect automation frameworks that actually scale — not just write individual playbooks.
The data is clear: most network automation projects fail because of organizational and architectural problems, not technical ones. The tools work. The question is whether you have the right people designing the system. CCIE Automation doesn’t just validate your scripting — it validates your ability to architect automation that actually succeeds.
Ready to fast-track your CCIE journey? Contact us on Telegram @phil66xx for a free assessment.