Adapting to the New Normal: How AI is Disrupting and Enhancing Network Engineering

by Shoaib Qureshi

We’re sure you’ve been hearing more and more about the power and potential of Artificial Intelligence over the past few months. However, AI and machine learning have been part of the conversation for businesses looking to improve their IT capabilities for a while. Back in 2018 Gartner predicted that 70% of companies would be using at least one form of AI by 2030. Thanks to new deep learning models this figure doesn’t feel too far off the mark. AI is a powerful tool helping many IT leaders meet the challenges facing their businesses.

When looking for ways AI can be implemented one of the most promising fields is network engineering. It can be incorporated into a vast number of network tasks and operations, ranging from predictive maintenance, intelligent troubleshooting, and network optimization to security monitoring, anomaly detection, and performance analysis.

AI has the potential to revolutionise the field of network engineering, unlocking new opportunities for efficiency, reliability, and innovation in managing and optimizing complex networks. As a result, network engineers are in high demand. To fully realise the power of AI businesses and IT leaders who are looking to hire network engineering consultants need to be aware of AI’s full capabilities to ensure they choose the right person for their goals.

AI-Driven Enhancements in Network Engineering for Businesses

Using AI to support network engineers brings new opportunities and benefits to businesses. AI and machine learning is enabling network engineers to be more efficient, effective, and innovative as well as providing businesses with:

1. Enhanced security

AI can help you be more proactive when it comes to threat detection and prevention thanks to its ability to analyse network traffic patterns in real time and detect anomalies that may indicate potential security threats, such as intrusions or malware attacks.

2. Predictive maintenance

By collecting data from across your network AI can accurately predict when equipment or systems are likely to fail, enabling proactive maintenance interventions. This can result in reduced downtime, increased asset lifespan, and cost savings.

3. Intelligent troubleshooting

AI can quickly analyse vast amounts of data and learn from past troubleshooting cases to identify the root causes of issues and suggest effective solutions. These accurate and timely recommendations reduces the time and effort spent on manual troubleshooting and leads to faster resolution of issues, improved system reliability, and enhanced customer satisfaction.

4. Optimised network performance

AI tools can automatically configure network devices reducing the need for manual configuration and algorithms are able to collect, filter and analyse network data to optimise task such as routing, traffic prioritisation, and resource allocation. Resulting in improved network efficiency and user experience.

5. Data-driven decision making

AI is enabling network engineers to gain deeper insights from data and make data-driven decisions allowing them to work more efficiently. Automation of routine tasks can allow network engineers to use their time more effectively and innovate more widely.

AI-Driven Disruptions in Network Engineering for Businesses

Alongside these benefits AI is having a disruptive effect on traditional network engineering practices that can lead to challenges and potential drawbacks including:

1. Potential automation errors

AI algorithms can analyse vast amounts of network data in real-time to optimize network performance however this makes them reliant on accurate data to do so. Potential errors in automated configurations can result in network disruptions or security vulnerabilities that can impact network operations.

2. Evolving cybersecurity threats

Just as businesses leverage new technologies to enhance their operations so do cybercriminals. Increased use of AI in network engineering will lead to evolving cybersecurity threats. Networks will require continuous updates and improvements to keep up with the changing threat landscape.

3. Ethical concerns

The use of AI in network engineering raises ethical concerns. AI algorithms may not be free from bias and there can be privacy concerns surrounding data collection and analysis, and the responsible use of AI-powered tools.

Businesses need to work to ensuring that AI algorithms are transparent, explainable, and free from bias. There is also likely to be increasing compliance regulations related to data privacy, consent, and security in AI-powered network engineering processes that businesses should be aware of.

4. Job displacement and the need for upskilling

With so many tasks able to be performed by AI there are almost certainly engineers who will find themselves experiencing job displacement. Businesses may also find it hard to attract engineers with right skills in AI and machine learning to undertake transformation projects or oversee their networks. To lessen the impact on the workforce and access AI expertise more cost-effectively business might consider upskilling their existing engineers. In fact 88% of companies that see positive ROI from AI train non-specialists in leveraging the technology.

Strategies for Businesses Hiring Network Engineering Consultants

It is import for businesses to adapt to the changing landscape of network engineering in the era of AI. If you are considering implementing AI and machine learning into your network or if you want to stay ahead of the technological curve, accessing these skills will be crucial.

Additionally, if you choose to leverage a third-party solution, engaging a consultant with AI expertise can help you navigate the complex landscape of large-scale technology vendors and guarantee a successful implementation of AI technology into your business.

Ensure you are engaging a network engineering consultant with the right skills in AI networking for your needs by:

  • Understanding AI capabilities
    Knowing how AI and machine learning can support and enhance your network, its capabilities and limitations means you will be better placed to determine what skills you require from a network engineer.
  • Aligning your business objectives with AI-driven network engineering goals
    This step is crucial before hiring a consultant as it ensures that the consultant's expertise and recommendations are in line with the strategic direction of the business. Maximizing the potential benefits of AI in network engineering and resulting in a more successful implementation of AI-driven solutions.
  • Vetting consultants with AI expertise
    Review relevant work experience and check references and credentials to determine that your chosen consultants have a solid understanding of network engineering concepts, protocols, and technologies. Consider their ability to apply AI and machine learning techniques specifically in the context of network engineering challenges and requirements.
  • Utilising technical interviews and practical tests
    Evaluate technical skills in areas such as machine learning algorithms, data analysis, data modelling, and programming languages commonly used in AI applications (such as Python or R). Consider proficiency in relevant AI frameworks, tools, and libraries used in network engineering, such as TensorFlow, PyTorch, or Scikit-Learn.

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