Section 3:

Network Readiness: A Foundational Component for AI Success

Section 3

Network Readiness:

A Foundational Component for AI Success

As businesses continue to modernize their operations and become more reliant on AI, network downtime poses an increasing threat.

Data disruption: Many AI deployments require a continuous flow of data. Downtime can interrupt data streams, leading to gaps in model accuracy, training delays, and incorrect insights.
Automation breakdown: A growing number of companies are using AI and ML to power processes like chatbots and monitoring systems. Downtime can lead to delays and bottlenecks.
Loss of trust: End-users and customers may lose confidence when AI produces incorrect inputs or stops working. This can delay internal adoption and threaten AI investments.
Model drift: Inconsistent data flow during an outage can produce an issue called model drift, where AI systems make decisions using outdated and incomplete information. This can impact prediction quality and system performance.

The bottom line is that having a modern and reliable network is no longer optional in the AI era. Businesses should be using services like SD-WAN, SASE, and CASB to optimize connectivity, reduce downtime, and protect cloud data flows.

Section 3: Network Readiness: A Foundational Component for AI Success

Key Network Considerations for AI

Bandwidth and data throughput:

AI demands high-speed data transfers to support intensive workloads.
Consider the volume, frequency, and size of data being moved— especially before using large training models.
Bandwidth limitations can lead to processing delays and performance bottlenecks.

Latency

Real-time applications (autonomous systems, interactive agents) need ultra-low latency.
Reducing latency improves responsiveness and overall user experience.

Reliability and redundancy

High availability is critical for AI-driven services.
Implement robust failover mechanisms, including power and connectivity backups.
Continuously monitor traffic to identify and eliminate bottlenecks.

Scalability and future proofing

Ensure the network infrastructure can scale with evolving AI demands.
Prepare for emerging AI use cases, such as expanding from text-based to video-based chatbots.
Plan for seamless integration with next-generation services like 5G and 6G integration.

Section 3: Network Readiness: A Foundational Component for AI Success

Using AIOps to Enhance Network Performance

Modern networks are highly distributed, complex, and data-heavy. Traditional performance monitoring tools are often unable to provide the deep visibility and critical insights that businesses require.

This is where AIOps can make a big difference. Sitting at the intersection of AI, ML, and big data, AIOps is designed to automate and enhance IT operations—enabling more efficient networks, infrastructure, and applications. By ingesting and analyzing vast volumes of data generated by modern IT environments, AIOps can instantly detect issues, uncover insights, and take corrective action in real-time.

Core capabilities include:

Data ingestion at scale
Correlation and pattern recognition
Anomaly detection
Predictive insights
Automation and remediation

A Chicken and Egg Scenario:

AIOps aims to improve network performance and reliability. But to operate effectively, AIOps also needs a stable, data-rich network to learn from and operate on.

Before implementing AIOps or any AI deployment, it’s necessary to conduct a network assessment and ensure that the right components are in place to support the new technology. You can always start with a relatively stable part of the network and gradually expand as the platform and network mature.