Network Automation & Orchestration Maturity Model

Maturity Model Overview

Itential’s experience delivering innovative network automation and orchestration solutions to network teams has made clear the need to present a well-defined model for how teams and organizations evolve their automation journey.

The model is designed for you to:

• Understand what an automation journey looks like.
• Identify where you, your team, and your organization are on the journey.
• Determine your desired future state and automation goals.
• Identify what challenges you will have to face to get there.
• Understand what capabilities need to be developed and in what order.

The maturity model organizes the automation journey into five progressive levels of maturity. Each level represents the sophistication of the network automation and orchestration capabilities and the business value delivered.

The five levels are:

• Level 1 – Basic Infrastructure Automation: Basic automation of network infrastructure with limited scope and application
• Level 2 – Advanced Infrastructure Automation: Advanced automation of network infrastructure with expanded scope and deployment
• Level 3 – Service Orchestration: Orchestration of network services to provide service-aware automation
• Level 4 – Self-Serve Networking: Self-serve networking capabilities allowing end-users to manage some aspects of the network without IT intervention
• Level 5 – Autonomous Networks: Autonomous network operations with minimal human intervention

A Historical Perspective

To understand where network automation is heading, it helps to understand how it got here. The history of network operations can be divided into distinct eras—each characterized by specific tools and operational approaches used to manage and maintain networks.

The diagram below maps the five eras of network operations and how they correspond to the maturity levels. You’ll notice that each era is characterized by an increase in operational efficiency and abstraction.

The 5 Eras of Network Operations

This era of network operations was characterized by mostly hands-on and manual configuration of network devices and infrastructure. Everything from IP address assignments to routing configurations had to be done manually using telnet or SSH connections to individual devices. This era was characterized by:

• Manual configuration of network devices
• Limited network visibility and monitoring
• Inefficient use of infrastructure and resources
• Long provisioning times and high error rates
• Distributed knowledge and expertise

During this era, networks relied heavily on specialized technicians who had intimate knowledge of the network topology and device configurations.

Era 2 – Management Frameworks (2005-2010)

As networks grew more complex, management frameworks and tools (like SNMP and syslog) were introduced to provide centralized monitoring and management of network devices. This era was characterized by:

• Centralized monitoring and alerting
• Standardized management protocols (SNMP, Syslog)
• Introduction of configuration management tools
• Improved network visibility and observability
• Reduced manual configuration tasks

During this era, the industry began to see the benefits of standardization and centralized management.

Era 3 – Automation Frameworks (2010-2015)

The introduction of automation frameworks and tools (like Ansible, Puppet, and Chef) enabled network teams to move beyond manual configuration. This era was characterized by:

• Automated provisioning of network devices
• Version control and configuration management
• Infrastructure as code (IaC) practices
• Reduced manual errors and improved consistency
• Faster provisioning and deployment cycles

During this era, network teams began to adopt software engineering practices and methodologies.

Era 4 – Orchestration and APIs (2015-2020)

As network infrastructure became more distributed and cloud adoption accelerated, orchestration tools and API-driven approaches became essential for managing complex, multi-vendor environments. This era was characterized by:

• API-driven automation and integration
• Multi-vendor orchestration and management
• Service-based approaches to network operations
• Cloud and hybrid-cloud networking
• Network functions virtualization (NFV)

During this era, network teams began to adopt enterprise IT practices and methodologies like DevOps and CI/CD.

Era 5 – Autonomous and Self-Serve Networking (2020-Present)

As networks continue to evolve and become more dynamic, autonomous and self-serve networking capabilities are emerging to enable network teams to deliver faster, more efficient services while reducing manual intervention and error. This era is characterized by:

• Autonomous network operations with AI/ML
• Self-serve networking capabilities
• Predictive analytics and anomaly detection
• Closed-loop automation and remediation
• Intent-based networking

During this era, network teams are adopting advanced technologies like machine learning and artificial intelligence to further improve network operations and efficiency.

Mapping Historical Perspective to Maturity Levels

The diagram below shows how the historical eras of network operations map to the five maturity levels.

Each level represents a progression from basic infrastructure automation to fully autonomous networks.

Key Characteristics at Each Level

Level 1 – Basic Infrastructure Automation

At this level, organizations are just beginning their automation journey. They focus on automating routine, repetitive tasks like device provisioning, configuration management, and basic monitoring.

Key characteristics:

• Manual processes still dominate
• Limited scope of automation
• Single-vendor environments
• Basic API integration
• Minimal business impact

Level 2 – Advanced Infrastructure Automation

At this level, organizations have expanded their automation capabilities and are automating more complex tasks. They focus on multi-device orchestration, cross-vendor integration, and application-aware networking.

Key characteristics:

• More comprehensive automation coverage
• Multi-vendor environments
• Advanced API integration
• Application-aware networking
• Improved operational efficiency

Level 3 – Service Orchestration

At this level, organizations have transitioned from infrastructure-centric to service-centric automation. They focus on providing network services and capabilities rather than individual infrastructure components.

Key characteristics:

• Service-centric approach
• Service abstraction and templates
• Multi-service orchestration
• Improved time to market
• Enhanced customer experience

Level 4 – Self-Serve Networking

At this level, organizations have empowered end-users and business units to self-provision network services, reducing the burden on network teams and accelerating service delivery.

Key characteristics:

• Self-serve portals and interfaces
• User-facing abstraction
• Self-service provisioning
• Reduced manual requests
• Faster service delivery

Level 5 – Autonomous Networks

At this level, organizations have achieved autonomous network operations with minimal human intervention. They use advanced technologies like AI/ML for self-healing, self-optimizing, and predictive capabilities.

Key characteristics:

• Autonomous decision-making
• AI/ML-driven operations
• Self-healing capabilities
• Minimal manual intervention
• Predictive and proactive operations

Core Capabilities at Each Level

The following tables list the core capabilities at each maturity level:

Level 1 – Basic Infrastructure Automation

Level 2 – Advanced Infrastructure Automation

Level 3 – Service Orchestration

Level 4 – Self-Serve Networking

Level 5 – Autonomous Networks

Assessing Your Current Level

To assess your current level of maturity, consider the following questions:

• Automation Scope: What percentage of your network operations are automated? Do you have automation across your entire network or just specific domains (e.g., LAN, WAN, security)?
• Vendor Coverage: Are you operating in a single-vendor environment or a multi-vendor environment? How well are you able to automate across different vendors?
• Service Orientation: Is your automation focused on individual infrastructure components or on delivering network services?
• User Enablement: Do your users have self-serve capabilities to provision network services, or do they have to submit requests to the network team?
• Operational Efficiency: How much manual intervention is required for network operations? Are there opportunities to further reduce manual tasks and improve efficiency?
• Business Impact: What business value are you realizing from your automation investments? Are you seeing improvements in service delivery time, cost reduction, or risk mitigation?

Progression Path and Challenges

Moving from one maturity level to the next requires careful planning and execution. Each level presents its own set of challenges and requires different capabilities and investments.

Progression from Level 1 to Level 2

Challenges:

• Expanding automation beyond basic provisioning tasks
• Handling multi-vendor complexity and integration
• Scaling automation across the network
• Managing technical debt and legacy systems
• Building necessary skills and expertise

Required Capabilities:

• Advanced scripting and programming skills
• Multi-vendor API knowledge
• Orchestration platform capabilities
• Change management processes
• Network automation expertise

Progression from Level 2 to Level 3

Challenges:

• Shifting from infrastructure-centric to service-centric thinking
• Designing and templating network services
• Abstracting infrastructure complexity
• Managing service dependencies and lifecycle
• Establishing service assurance practices

Required Capabilities:

• Service design and architecture skills
• Service orchestration platform capabilities
• Service abstraction and templating
• Service assurance and monitoring
• Business and IT alignment

Progression from Level 3 to Level 4

Challenges:

• Empowering end-users with self-serve capabilities
• Designing user-friendly interfaces and portals
• Implementing governance and policies
• Managing service requests and approvals
• Chargeback and metering of services

Required Capabilities:

• Self-serve portal design and development
• User experience and interface design
• Service request management
• Policy and governance frameworks
• Chargeback and billing systems

Progression from Level 4 to Level 5

Challenges:

• Developing AI/ML models for network operations
• Implementing autonomous decision-making
• Ensuring network security in autonomous environments
• Handling exceptional cases and edge cases
• Maintaining human oversight and control

Required Capabilities:

• AI/ML expertise and platforms
• Advanced analytics and big data capabilities
• Autonomous operations frameworks
• Security and compliance in autonomous systems
• Change management for autonomous operations

Case Studies and Real-World Examples

Let’s look at some real-world examples of organizations at each maturity level:

Level 1 Example: Enterprise Bank

Scenario: A large enterprise bank with a traditional IT organization and limited automation experience.

Current State:

• Manual provisioning of network devices with custom scripts
• Basic SNMP and syslog monitoring
• Limited API integration
• Highly manual and error-prone processes

Challenges:

• Slow provisioning times affecting business agility
• High error rates in manual configurations
• Limited ability to respond to business demands

Path Forward:

• Implement a centralized orchestration platform
• Develop playbooks for common network tasks
• Establish baseline automation across the network
• Build internal expertise in network automation

Level 2 Example: SaaS Provider

Scenario: A mid-size SaaS provider with a cloud-first architecture and growing automation needs.

Current State:

• Multi-vendor infrastructure (cloud providers, on-premises equipment)
• Basic Infrastructure as Code (IaC) practices
• Limited orchestration across infrastructure domains
• High manual effort for cross-domain changes

Challenges:

• Difficulty managing multi-vendor environments
• Limited visibility across domains
• Complex dependency management

Path Forward:

• Expand orchestration platform capabilities
• Implement advanced API integration
• Develop cross-domain automation workflows
• Establish multi-vendor integration patterns

Level 3 Example: Telecommunications Provider

Scenario: A large telecommunications provider with extensive network infrastructure and a focus on service delivery.

Current State:

• Service-based provisioning with service templates
• Multi-service orchestration capabilities
• Service catalogs for internal customers
• Service-level monitoring and assurance

Challenges:

• End-users lack self-serve capabilities
• Manual service request and approval processes
• Limited agility in service delivery

Path Forward:

• Build self-serve portals for end-users
• Implement service request management systems
• Develop user-friendly service catalogs
• Establish governance policies for self-serve services

Level 4 Example: Global Enterprise

Scenario: A large global enterprise with distributed teams and diverse business units with self-serve networking needs.

Current State:

• Self-serve portals for network service provisioning
• Service catalogs with pre-defined offerings
• Policy-based controls and governance
• Reduced manual network requests

Challenges:

• Manual intervention still required for exceptional cases
• Limited predictive capabilities for capacity planning
• Opportunities for further optimization

Path Forward:

• Implement AI/ML for predictive analytics
• Develop autonomous remediation capabilities
• Establish self-healing network mechanisms
• Implement closed-loop automation workflows

Level 5 Example: Leading Cloud Provider

Scenario: A leading cloud provider operating global infrastructure with autonomous operations at scale.

Current State:

• Autonomous network operations with minimal human intervention
• AI/ML-driven decision-making for network optimization
• Self-healing networks with automated fault detection and remediation
• Predictive analytics for capacity planning and optimization

Capabilities:

• Continuous optimization without manual intervention
• Rapid response to network events and anomalies
• Self-adaptive networks based on real-time conditions
• Predictive maintenance and planning

Technology Stack Recommendations by Level

Different maturity levels benefit from different technology stacks. Here are some recommendations for each level:

Level 1 – Basic Infrastructure Automation

Recommended Technologies:

• Configuration Management: Ansible, Puppet, Chef
• APIs and SDKs: REST APIs, native device APIs
• Monitoring: Nagios, Zabbix, Prometheus
• Version Control: Git, GitLab, GitHub
• Scripting: Python, Bash, PowerShell

Level 2 – Advanced Infrastructure Automation

Recommended Technologies:

• Orchestration: Itential, Ansible Tower, Terraform
• Multi-Vendor Integration: API aggregation platforms
• Monitoring: Elastic Stack, Datadog, New Relic
• CI/CD: Jenkins, GitLab CI, GitHub Actions
• Infrastructure as Code: Terraform, CloudFormation, Pulumi

Level 3 – Service Orchestration

Recommended Technologies:

• Service Orchestration: Itential, Kubernetes, OpenStack
• Service Design: Custom platforms, open-source solutions
• Service Monitoring: Elastic Stack, Splunk, Datadog
• API Management: Kong, Apigee, AWS API Gateway
• Data Platform: Kafka, RabbitMQ, Apache NiFi

Level 4 – Self-Serve Networking

Recommended Technologies:

• Self-Serve Portals: Custom web applications, commercial platforms
• Service Request Management: ServiceNow, Jira Service Management
• Identity and Access Management: Okta, Azure AD, Keycloak
• Policy and Governance: Open Policy Agent (OPA), Sentinel
• Billing and Chargeback: CloudBilling, Apptio, Chargify

Level 5 – Autonomous Networks

Recommended Technologies:

• AI/ML Platforms: TensorFlow, PyTorch, Scikit-learn
• Data Analytics: Spark, Flink, Beam
• Autonomous Operations: Custom ML models, specialized platforms
• Time Series Databases: InfluxDB, TimescaleDB, Prometheus
• Event Streaming: Kafka, Pulsar, RabbitMQ

Successful Implementation Strategies

To successfully move through the maturity levels, organizations should follow these proven strategies:

1. Build a Strong Foundation

Start with the basics:

• Assess your current state and identify gaps
• Build internal expertise and skills
• Establish processes and governance frameworks
• Start with low-risk, high-impact automation opportunities

2. Secure Executive Sponsorship

Ensure support from leadership:

• Align automation goals with business objectives
• Demonstrate ROI and business value
• Secure necessary funding and resources
• Get executive backing for change management

3. Adopt an Agile Approach

Use iterative and incremental development:

• Plan in sprints and iterations
• Deliver value incrementally
• Adapt and iterate based on feedback
• Celebrate small wins and build momentum

4. Invest in People and Skills

Build internal capabilities:

• Invest in training and education
• Hire experienced talent in key areas
• Create a culture of learning and experimentation
• Establish communities of practice

5. Implement Governance and Controls

Establish necessary controls:

• Define policies and procedures
• Implement change management processes
• Establish quality and testing standards
• Implement audit and compliance controls

6. Focus on Change Management

Prepare the organization for change:

• Communicate the vision and benefits
• Engage stakeholders throughout the journey
• Address resistance and concerns
• Celebrate successes and share learnings

7. Embrace Continuous Improvement

Keep improving over time:

• Measure and track progress
• Regularly review and optimize processes
• Stay updated with technology trends
• Learn from industry best practices

Common Pitfalls and How to Avoid Them

As organizations progress through the maturity levels, they often encounter common pitfalls. Here’s how to avoid them:

Pitfall 1: Moving Too Fast Without a Foundation

Problem: Organizations try to skip levels or move too quickly without establishing a solid foundation.

Impact: Technical debt, poor quality automation, and project failures.

Solution:

• Establish a strong foundation at each level before moving forward
• Build necessary skills and expertise
• Implement proper processes and governance

Pitfall 2: Lack of Executive Support

Problem: Without executive backing, automation initiatives lack resources and visibility.

Impact: Slow progress, limited funding, and difficulty gaining organizational buy-in.

Solution:

• Clearly articulate the business case and ROI
• Secure executive sponsorship early
• Regularly report progress and business impact

Pitfall 3: Underestimating Change Management

Problem: Technical implementations fail because of inadequate change management and stakeholder engagement.

Impact: User resistance, adoption challenges, and limited value realization.

Solution:

• Invest heavily in change management
• Engage stakeholders early and often
• Provide training and support
• Create a compelling vision for the future

Pitfall 4: Ignoring Skills Development

Problem: Organizations invest in tools and platforms but not in developing the skills needed to use them effectively.

Impact: Poor tool utilization, missed opportunities, and talented staff leaving the organization.

Solution:

• Invest in training and skill development
• Create opportunities for hands-on learning
• Build a culture of continuous learning
• Hire experienced talent when needed

Pitfall 5: Poor Platform Selection

Problem: Selecting the wrong platform or tool for your organization’s needs and maturity level.

Impact: Tool sprawl, integration challenges, and limited value realization.

Solution:

• Assess your requirements and future needs
• Evaluate multiple options thoroughly
• Consider total cost of ownership
• Plan for future scalability and growth

Pitfall 6: Lack of Metrics and Measurement

Problem: Organizations don’t establish clear metrics for measuring progress and business impact.

Impact: Difficulty demonstrating ROI and value, which can lead to loss of support and resources.

Solution:

• Define clear KPIs at the start
• Establish baseline metrics
• Regularly measure and track progress
• Report results to stakeholders

Pitfall 7: Not Focusing on User Experience

Problem: At later maturity levels (especially Level 4), organizations build self-serve portals that are difficult to use.

Impact: Low adoption rates, poor user satisfaction, and limited value realization.

Solution:

• Invest in user experience and interface design
• Involve end-users in design and testing
• Iterate based on user feedback
• Provide excellent support and documentation

How Itential Helps You Evolve

Core to Itential is the belief that automation solutions should meet users where they are in their journey, helping to expand the scale and impact of their network automation efforts by providing the right tools to evolve. The Itential Automation Platform is built to provide automation value and support automation evolution no matter where along the Maturity Model a user, team, or organization starts. This is achieved primarily via Itential’s API-first integration approach and its low-code, modular automation building features.

Evolving from Task Automation to Process Orchestration At the earliest levels of automation maturity, Itential provides a unified platform and a no-code option for building automation workflows. Teams with disparate automation efforts, where single engineers write scripts ad hoc, can come together to build automations for all their routine tasks and share automation assets in one place without learning special skills.

Evolving from Process Orchestration to Self-Serve Networking Maturing teams can leverage Itential’s robust integration and orchestration capabilities to build end-to-end automations, orchestrating entire processes instead of automating single tasks. And for forward-looking organizations looking to solidify and evolve their automation strategies, Itential’s ability to expose its own API to northbound systems allows for self-service delivery of network automations to whichever platform or system your end users require. This cloud-like service delivery drives up automation consumption and accelerates processes across the entire organization.

AI-Powered Network Automation and Orchestration

“Teams are increasingly using AI infrastructure tools to automate and orchestrate networking workflows. Using AI for network management typically begins with simple tasks with a high degree of human supervision, but can quickly progress towards autonomous IT operations and more comprehensive uses of AI for full infrastructure automation. Safe and reliable use of AI to automate networks requires platforms such as Itential’s Flow AI, enabling teams to translate natural language intent into governed workflows.

Conclusion

The Itential Maturity Model provides a clear roadmap for organizations seeking to evolve their network operations from manual, labor-intensive processes to autonomous, self-serve networking capabilities. By understanding the five maturity levels, organizations can:

• Assess where they are today and understand their current capabilities and limitations
• Define where they want to go and set clear goals for their automation journey
• Plan their transformation with a clear understanding of the steps, capabilities, and investments needed at each level
• Execute effectively with proven strategies and a clear understanding of common pitfalls to avoid

The journey from manual operations to autonomous networks is not easy, but the business benefits are clear: improved operational efficiency, reduced costs, faster service delivery, and improved customer satisfaction. Organizations that successfully navigate this journey will be well-positioned for success in an increasingly digital and dynamic business environment.

Whether you’re just beginning your automation journey or looking to advance to the next level, the Itential Maturity Model provides a proven framework to guide your transformation. Use it to assess your current state, identify gaps, plan your future state, and execute your transformation with confidence.