Addressing the complex relationship between generative AI and network connectivity to stop business leaders scratching their heads

The rise of artificial intelligence has gripped the world like no other technology since the dawn of the internet. In the past three months alone, we have seen British prime minister Rishi Sunak and Elon Musk attend an AI safety summit to discuss how it will transform our workplace. Tech giants Google, Amazon, and Microsoft then announced a combined investment of more than £25 billion to bulk-up their cloud capacity for the expansion of generative AI – a sum that is expected to accelerate in 2024. Finally, we ended the year with the Collins Dictionary awarding AI the title of ‘word of the year’.

As businesses confront the awesome potential of how to deploy generative AI tools across their operations, it is becoming all too apparent that many companies have been forced to rethink their network management systems.

The reason for this is simple. Generative AI requires vast amounts of computing power and data-crunching to perform effectively, and it demands fast and flexible network solutions that can keep up with the rapid advancements of its technological capabilities.

Taking advantage of generative AI

Several industries have already gained a significant advantage by switching up their network management systems to accommodate the generative AI revolution. And the results have been profound.

One example is the banking sector, where generative AI has been successfully deployed to create not just responsive chatbots but intelligent virtual assistants. These have streamlined customer-facing operations by providing seamless interactions with personalised and conversational responses. Additionally, these virtual assistants pour through masses of banking data on an hourly basis and then execute automated tasks such as fund transfers, monthly payments, financial history tracking, and even new account onboarding – all without human assistance.

Similarly, in the e-commerce sector, large online retail firms have experienced a radical shift in the management of their product description generation whereby the exhaustive process of manual inputting has been replaced by dynamic automation. Their generative AI tools can now process enormous quantities of customer data in real time to create informative content, individualised recommendations, and predictive analysis into future buying habits based on user preferences and search behaviour patterns.

Likewise, the global stock market has been turned on its head by leveraging generative AI tools in the guise of predictive trading algorithms which ingest gigantic volumes of data and then forecast nuanced investment decisions, allowing portfolio managers to anticipate shifts in the market before they happen.

Examining your current network

However, for all the wondrous benefits that generative AI offers, they will remain beyond reach if your business network infrastructure is unable to store, process, and retrieve the massive datasets which this burgeoning technology feeds on.   

So, before you consider which generative AI tool or platform to use, you should first consider a thorough analysis of your current network ecosystem and determine whether it has the capabilities to ensure seamless and augmented workflows. For instance, does your network have the edge computing capabilities to process IoT data and deliver real-time quality insights? Can you watch and audit how your generative AI tools are interacting with your network?

Another essential factor is making sure your network is secure. Most froward-thinking businesses are now operating in a multi-cloud environment where they are pulling in data from a variety of public and private clouds. This certainly improves efficiency, but it can also lead to disaster if the underlying network is insecure. Your data integrity could easily be comprised without a private, safe, and dedicated connection between your various data pools and the generative AI models processing that data.

A third factor is the rapid progression of generative AI technology. Your network needs to be quick and agile to support future AI advancements, which means having the flexibility to scale or upgrade on demand to support not only your business growth, but also prevent the onslaught of cyberattacks that will inevitably follow as generative AI evolves.

Then there’s the human factor. Introducing generative AI models into your business operations requires training your IT team to make sure they can harness its full potential and deploy it effectively across your network. Not only that, but with generative AI being so dynamic, your team will need regular upskilling to ensure your network capabilities remain equally adaptive and dynamic.

Simplifying the solution

It's no surprise that many business leaders are looking for an ‘easy button’ that helps them understand the complex, shape-shifting nature of generative AI and the demands it places on their network systems. Fortunately, this easy button already exists – and it’s called Network as a Service.

Effectively outsourcing the operating, maintenance, and upgrading of your entire network to a trusted service provider, a NaaS (for short) allows businesses to create a network infrastructure specific to their connectivity needs and pay for it with a subscription-based or flexible consumption model.

For example, an online global retailer will likely require different network connectivity requirements than a manufacturer which needs to connect between facilities and its headquarters. However, while these connectivity requirements differ from business to business, it’s important to choose a NaaS provider that has the power and agility to adjust to generative AI workloads.

Firstly, if you are going to be accessing different data sources, then your NaaS platform must offer fast and efficient private network connectivity on a global scale, one that is interconnected with all the major hyperscale cloud providers and can also extend your reach to hundreds of cloud on-ramps worldwide. Better still, if the platform owns the underlying network infrastructure, then it will deliver an assured quality of service.

Secondly, your NaaS provider must meet the stringent security and data compliance regulations that we have already seen implemented across geographies in 2023, such as the EU’s AI Act, the UAE’s Data Protection Law, and India’s Digital Personal Data Protection Act. Similar policies and further regulations will surely follow in 2024.

Thirdly, and crucially, your NaaS provider must have the flex bandwidth and agility to deliver fully automated switching and routing on demand. By doing so, it gives you the ability to access unlimited data pools and then seamlessly plug them into their generative AI models to create high-performance data processing. In cost terms, this flexibility also means you only pay for what you use.

But where the easy button really comes into play is in the management and maintenance of your customised network. Basically, you should choose a NaaS provider that takes care of everything through a one-stop portal and managed 24/7 by a team of engineers who can fix all your network niggles such as transit delay, packet loss, jitter, and lagging. This leaves you to concentrate on what matters most – deploying your generative AI models safely across your infrastructure and growing your business on your own terms.