AI Not Being a Standalone Solution in Network Automation

[Cengiz Özemli]

## Why AI Alone Isn't the Solution for Network Automation

While AI is rapidly changing how we view network operations, it cannot replace strong data foundations and proven automation practices. In network automation, AI is most effective when used in conjunction with deterministic automation built upon a reliable Network Source of Truth (NSoT).

AI is being integrated into network operations in many areas, from natural language interfaces to auto-remediation and predictive analytics. However, experience shows that the reality is more complex than expectations suggest.

### Automation Relies on Data, Not AI

Successful network automation, even before AI, always started with consistently reliable data. This data is collected in a structured, authoritative system called a Network Source of Truth (NSoT). The NSoT represents how the network should be built, how it should behave, its inventory, topology, and relationships in a way that automation systems can confidently use.

Without a reliable NSoT, automation relies on guesswork; scripts hardcode assumptions, workflows deviate from reality, and engineers fix errors with manual checks. Adding AI in this situation only increases uncertainty.

### The Difference Between Deterministic and Probabilistic Automation

Traditional network automation is deterministic; it always produces the same output with the same inputs. This ensures that automation is repeatable, testable, and reliable.

AI-based systems, on the other hand, are probabilistic; they infer intent, interpret context, and generate results based on probability rather than certainty. This is the characteristic that defines AI's value, but applying it directly to infrastructure carries risks.

Telling an AI system to "make this change in the network" is very different from running a tested and versioned automation workflow. Currently, most organizations do not trust probabilistic automation to make changes directly in production networks without review.

### Areas Where AI Excels in Network Automation

AI provides strong support in information and decision support areas:

• Reviewing large amounts of network data
• Comparing the desired state of the network with its actual state
• Detecting anomalies, deviations, and risks
• Suggesting possible actions and solutions

In these areas, AI enables humans to see complex patterns in the network more quickly, detect problems earlier, and analyze complex systems more effectively. However, decision-making is limited to "what to do," while "how to do it" remains under the control of deterministic automation.

### Reliable Automation: The Execution Layer

For insights gained from AI to translate into real operational value, secure, repeatable, and well-defined automation triggers are required. This means:

• Reviewed and tested automation workflows
• Clear preconditions and safeguards
• Consistency across environmental differences
• Harmless re-execution of automation

Human-in-the-loop (HITL) is currently critical; engineers review proposed changes, validate them in non-production environments, and add them to the approved automation catalog.

### Automation is a Prerequisite for AI

While it's common to hear that AI will make automation more accessible, in practice, automation is a prerequisite for AI. Without structured data, AI has no reliable data to rely on, and without reliable automation, AI has no operations to execute confidently.

Organizations that will benefit most from AI are those that invest in data models, sources of truth, and deterministic automation pipelines. AI does not eliminate engineering discipline; rather, it rewards it.

### Realistic Expectations for 2026 and Beyond

It's not realistic to expect large enterprise networks to be fully self-managing by 2026. Human-in-the-loop processes are still necessary for most production environments. However, progress has not stopped. AI-powered automation pipelines are shortening review times, increasing test coverage, and boosting confidence in automation results.

Over time, some human intervention steps will decrease or disappear; however, this evolution will be built upon data and automation.

AI may not always be the best answer for network automation. But when combined with reliable data and deterministic automation, it becomes one of the most powerful tools the industry has ever had.

The future is not "all AI," but rather the implementation of AI-informed decisions through proven automation. This combination ensures that networks are more reliable, resilient, and ultimately invisible in supporting business objectives.