Most technology used by businesses and government organizations exhibits a paradox: it is intended to drive innovation and efficiency and reduce costs, simplifying our jobs and lives. However, it is often the case that the further technology advances, the more complex it becomes to manage.
Many of the artificial intelligence breakthroughs and applications we hear about often revolve around areas that we all relate to and understand – medical and scientific discovery, communications, planning, search – so OpenAI’s Chat GPT, Microsoft’s Copilot and For example, Google Gemini is all the AI-enabled technologies that we are familiar with and can personally benefit from.
And while GenAI has captured most of our attention over the past year, there is a fast-growing, much less visible class of AI that impacts most of us every day: the use of artificial intelligence in IT networks.
According to the Gartner Hype Cycle for Enterprise Networking, AI networking is at the peak of its expectations. At the time of the report, this was only the case in the most advanced 1 percent of enterprise networks, meaning the vast majority of the growth is coming from Gartner analyst Jonathan Forest noted this “AI networks deliver operational management savings of up to 25% by reducing support calls, enabling faster incident response, improving network availability and optimizing the end-user experience.”
That’s why forward-thinking organizations are implementing artificial intelligence to automate complex IT operations. At the company where I work as Chief Technology Officer, this is used, among other things, with what we call intelligent process automation. Simply put, we use AI to identify, report and address network incidents and events. In our case, artificial intelligence is used to speed up the resolution processes and, if necessary, pass the incident to a technician to resolve it.
The results we have achieved include:
- Average time savings of 38 minutes in triage per incident
- 83% gain in human analyst efficiency with AI real-time triage
- 58% of all incidents resolved by the IPA engine
It’s plumbing in an IT context. It may not be as popular as GenAI, but there is no debate about whether it makes us more productive and effective. Before we write it off as hopelessly and boringly efficient, here’s this gem from the 2023 Gartner Magic Quadrant for Managed Network Services:
– “By 2026, generative artificial intelligence (GenAI) technology will account for 20% of initial network configuration, up from almost zero in 2023.”
However, as with any emerging technology, there are pros and cons to AI networks.
The role of artificial intelligence in IT networks has the potential to improve network uptime and user satisfaction, streamline network management and accelerate incident resolution. More advanced AI networking solutions can provide proactive monitoring and simple problem-solving advice. They increase network effectiveness beyond what manual efforts can achieve. Ultimately, the goal is to improve user experience and optimize network management for businesses. Other benefits of AI networks include:
- Improved decision making, freeing up staff to focus on higher value projects
- An improved customer experience using chatbots, personalized marketing and virtual assistants
- Increased efficiency and productivity by reducing human errors and lowering operational and labor costs
Of course there are also disadvantages. Despite significant progress, some challenges remain, including data quality, as AI models often use network data to learn. Your AI, like your analytics, is only as good as the quality of the data it feeds. Learning AI models with the right data can initially be expensive, time-consuming and labor-intensive
What to consider when setting up AI networks:
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Interoperability – Ensuring that disparate systems, devices and protocols can ‘talk’ to each other so that the AI works across the entire network -
Privacy and data security – Because AI systems need to access network data, it is critical that the latest security protocols are in place
The transformative potential of AI in networking lies in its ability to simplify network management, improve security, and optimize application infrastructures. As networks evolve with the integration of diverse environments such as data centers, multi-cloud adoption, colocation facilities and edge computing, AI is quickly emerging as a critical tool to address increasing complexity.
The potential of artificial intelligence to revolutionize network management and organizational capabilities cannot be underestimated. As the technology landscape continues to evolve, the smart integration of artificial intelligence into IT networks will undoubtedly unlock unprecedented levels of innovation and efficiency from which we will all benefit.
