Our world is changing rapidly with so many exciting possibilities and artificial intelligence (AI) is at the heart of this transformation. AI is reshaping how we live, work, and interact with technology from self-driving cars to healthcare diagnostics, and robot waiters to advanced virtual assistants, AI is here to stay.

According to Cisco’s AI readiness index, 84% of companies agree that AI will significantly impact their businesses soon and the race to implement AI is on. McKinsey estimates that AI could deliver up to $4.4 trillion annually to the global economy by 2030. This explosion is exciting but presents challenges for existing network infrastructure.

AI systems require large amounts of data and computational power to function efficiently, and with AI's increasing adoption, networks are under more pressure than ever before. For example, Gartner predicts that by 2027, more than 40% of current data centers deploying AI workloads will face limitations due to electrical power constraints. Businesses are quickly adopting AI, but this adoption is only as strong as the networks supporting these intelligent systems.

In this article, let's look at how network infrastructure is evolving to meet the growing demands of AI workloads, from the need for reduced latency and increased bandwidth to enhanced security features, and how AI is in turn helping network teams reach unchartered territories in network management and fault detection.

How AI Models Work Today

To get a better understanding of why AI is resource-intensive, we need to look at how AI models work at their core
1. Data Collection and Preprocessing:
Data is the foundation for training models to recognize patterns and make predictions. Preprocessing involves cleaning, formatting, and handling inconsistent information to ensure the usefulness of the data. Streams of images, texts, and audio files are all used in training AI models.
2. Model Training:
Models are trained using many iterations of them trying to find correlations and relationships between inputs and corresponding outputs.
3. Inference:
Once the model is trained, it enters the inference phase, where it can make predictions on new, unseen data. The model uses the learned patterns to process real-time input and generate outputs like classifications, translations, or decisions.
4. Continuous Learning:
The models are continuously updated with new data to adapt to changing trends and improve performance.

The development of AI models is extremely data intensive. OpenAI’s GTP-3 released in 2020, was reportedly trained on over 175 billion parameters of around 45TB of text and code. GPT-4 released in 2023 promising to be 10x more advanced, was trained with a staggering data set of over 1.76 trillion parameters.

AI Usage and Applications Today

The job certainly doesn’t stop at developing models, businesses and individuals are already using AI. Let’s look at a few applications of AI today;
1. AI in Business
AI-powered Business tools helping businesses collect, analyze, and visualize data more efficiently and effectively, providing improved decision-making, increased productivity, and reduced costs.
2. AI in Healthcare
AI-powered tools helping doctors and healthcare professionals diagnose diseases, develop new treatments, and provide personalized care to patients.
3. AI in Education
Personalized learning, improved student engagement, and automating administrative tasks for schools and other organizations are now possible with AI.
4. AI in Finance
AI helping financial services institutions in personalizing services and products for specific customers, managing risks and fraud through predictive analysis, and enforcing transparency and compliance.
5. AI in Manufacturing
In manufacturing, AI is Improving efficiency by automating tasks, such as assembly and inspection, increasing productivity by optimizing production processes, improving quality by detecting defects, and improving quality control and assurance.

The Growing Demands on Network Infrastructure

As artificial intelligence (AI) continues its development and adoption, the requirements from network infrastructure and performance are rising.
We have seen how massive amounts of data are constantly being processed, transferred, and analyzed in real time. This rise in data creates a strain on existing network systems and to meet these demands, network infrastructure must evolve in these key areas:

1. Security: ISACA reports a 75% increase in cyberattacks, with 85% of these attacks being powered by generative AI, making traditional security tools ineffective. Financial data and health records for example, can simply not fall into the wrong hands. This is why networks need enhanced encryption, intrusion detection systems, and advanced threat prevention techniques to protect data from potential cyberattacks.
2. Latency: It certainly won’t be a pleasant experience having to repeat voice commands to turn on the lights at home or get your robot assistant to change your music. With the unlimited promise of AI in robotics, autonomous driving, and even healthcare, the delay between data input and AI response must be minimized. Networks must grow to reduce lag and provide immediate responses to incoming requests.
3. Bandwidth: Video streaming, Online gaming, and Content creation are just the basics of data requirements in households today. Deloitte speculates that by 2025, households may require at least 100-300Mbps network speeds to meet their needs. AI adoption demands intensive data transfer, putting significant pressure on network bandwidth. Network capacity must expand for these workloads to be supported.

How Are Modern Networks Responding To AI Workloads?

From dial-up internet speeds of 56Kbps to 5G speeds of up to 20Gbps that let you stream games from remote cloud servers, networks have come a long way in development and advancements. However, for AI networks are still being re-engineered to deliver scalability, flexibility, and intelligence in the following;

1. Edge Computing: This is simply bringing your content closer to you reducing latency, and processing times, and easing the burden on centralized servers, an important requirement for systems requiring real-time analysis.
2. 6G Technology: You think 20Gbps is fast, how about 100Gbps? It's happening. Networks are getting ready to enter this next phase in development creating a more capable environment for AI workloads, especially in IoT and autonomous technologies.
3. Software-Defined Networking (SDN): SDN offers greater network flexibility by separating the control plane from the data plane. What this means for AI is that network administrators can now dynamically allocate resources, prioritizing which workloads receive the bandwidth and performance they need.
4. Network Function Virtualization (NFV): Think about servers and virtual machines(VM). With NFV, the need for dedicated hardware for each network function is eliminated, improving cost efficiency, flexibility in network management, and scalability.

So What's Next? Optimizing Networks Using AI

Interestingly, AI is not just dependent on networks—it’s also being used to improve them. In this section, let us look at how a few of the largest telecommunication players and network providers are leveraging the power of AI to predict traffic patterns, enhance their infrastructure, and provide better services.

1. Nokia: As a technology leader across mobile and cloud networks, Nokia has been focused on transforming telecommunications for the past three decades. Today, Nokia’s latest AI development could re-configure networks through human speech. With its Natural Language Processing advancement, simply saying ‘optimize the network at X location for Y service’ will get the job done. Amazing!
2. Cisco: This communication giant in collaboration with NVIDIA has developed the first multi-instance GPU that unifies data analytics, training, and inference for faster utilization of AI resources by their customers.
3. Verizon: Goodbye outages. The renowned mobile operator is utilizing AI/ML techniques to identify high-risk excavations and potentially eliminate several fiber cuts annually
4. AT&T: North America’s largest wireless network has developed its generative AI tool for employees - Ask AT&T an innovative tool for improved customer service delivery

Conclusion: A Symbiotic Evolution

The relationship between AI and network infrastructure is symbiotic—just as AI places new demands on networks, it also provides the tools to meet those demands. As businesses continue their race to the inevitable AI future, let’s catch the trends together. Subscribe, follow me on social media, and get the latest advancements in security, analytics, and all things technology right here.