Paul Gampe, Chief Technology Officer at Console Connect, explores the complex relationship between generative AI and network connectivity.
Artificial intelligence has become a game-changing technology, widely regarded as the most significant breakthrough since the advent of the internet. Over the past three months, a summit on AI safety was attended by the British Prime Minister, Rishi Sunak, and Elon Musk to discuss how this technology will transform our workplace.
A joint investment of over £25 billion was announced by tech giants Google, Amazon and Microsoft, to expand their cloud capacity for generative AI – a sum that is expected to accelerate in 2024. In 2023, Collins Dictionary chose AI as the ‘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 that 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.
Harnessing the capabilities of generative AI
Numerous industries have already capitalised greatly by adopting generative AI technology within their network management systems. And the results have been remarkable.
For instance, the banking sector has successfully implemented generative AI to create not just responsive chatbots but also 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shift in the management of their product description generation, whereby the exhaustive process of manual inputting has been replaced by dynamic automation.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 efficiently handle vast amounts of customer data in real-time. This not only facilitates the generation of informative content but also enables personalised recommendations and predictive analysis of future buying habits, leveraging user preferences and search behaviour patterns.
Generative AI tools have also been used as predictive trading algorithms in the global stock market. These algorithms can process massive amounts of data to forecast investment decisions with great precision. This allows portfolio managers to predict market shifts before they occur and make informed investment decisions accordingly.
Analysing your network infrastructure
Generative AI offers incredible benefits to businesses, benefits which will remain beyond reach if your business network infrastructure is unable to store, process, and retrieve the massive data sets which generative AI 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 forward-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 compromised 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.
Let’s not forget the human factor. Incorporating generative AI successfully involves training your IT teams to utilise its full potential and deploy it efficiently throughout your network. It is essential to provide your team with regular upskilling to help them keep up with the dynamic nature of generative AI. This will ensure that your network capabilities remain equally adaptable and dynamic over time.
A simple solution
It’s not uncommon for business leaders to seek a straightforward solution that simplifies their understanding of the intricate and ever-changing nature of generative AI and its impact on their network systems. Luckily, such a solution already exists – and it’s called Network as a Service (NaaS).
By outsourcing the operating, maintenance, and upgrading of an entire network to a trusted service provider, businesses can tailor 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. 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. As well as this you should be looking for 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 will deliver an assured quality of service if it owns the underlying network infrastructure.
Furthermore, your NaaS provider must meet the security and data compliance regulations that have been implemented across geographies in 2023. Examples of these include the EU’s AI Act, the UAE’s Data Protection Law, and India’s Digital Personal Data Protection Act. It is likely that similar policies and further regulations will surely follow in 2024.
In addition to this, your NaaS provider must have the flexibility and agility to deliver fully automated switching and routing on demand. This will allow you to access unlimited data pools and easily integrate them into their generative AI models for high-performance data processing. This flexibility ensures that you only pay for what you use, reducing unnecessary costs.
One of the major benefits of NaaS is the ability to provide easy network management and maintenance. By choosing a NaaS provider that offers a comprehensive portal for all your needs, which is managed 24/7 by a team of skilled engineers, you can be assured that network issues will be resolved quickly and effectively. Issues such as transit delay, packet loss, jitter and lagging will be easily resolved, allowing business owners to focus on deploying generative AI models safely across their infrastructure and grow their business in a hassle-free manner.
