Understanding AI in telecommunications

Artificial intelligence (AI) encompasses processes and algorithms that simulate intelligence and problem solving. Machine learning (ML) and deep learning (DL) are subsets of AI that use algorithms to detect patterns and predict outcomes from data.

In recent years, advances in the applications of AI, ML, and DL—such as readily available large language models (LLMs)—have led to novel use cases in many industries, including personalized recommendations in retail and fraud detection in finance. In telecommunications, these innovations have become a part of business.

Many leading telecommunications service providers have been using predictive AI for years to make operations more efficient. Some also use generative AI (gen AI) to deliver better customer experiences and increase their competitiveness in the market. However, applying AI in your telco comes with obstacles, including initial capital expenditures, security, and challenges in processing large quantities of data. IT solutions can help you use AI tools efficiently and cost-effectively to generate new revenue while protecting customer information.

AI applications can help overcome several telecommunications business challenges:

• Rising costs. Telcos have spent substantial amounts of resources on upgrades to stay competitive. For example, they’ve spent a lot on infrastructure to transform their networks in order to deliver new services and applications promised with 5G and AI. Using AI to add efficiency to the network or lower maintenance costs has the potential to mitigate the effect of these cost increases.
• Competition. Competition is getting steeper as customer expectations rise alongside expanded competitor services. Offering new AI-enhanced services, such as service chatbots and more efficiently managed network traffic, can help you match or outpace your telecommunications competitors.
• Network management and complexity. As traffic increases, global network complexity grows, requiring more resources to manage it.
• Lack of data-processing power. Your customer pools produce a lot of useful data. However, many telcos lack resources to analyze that data to more efficiently and effectively serve customers.

How to apply AI at the enterprise 

You can apply AI/ML to address these challenges in the telecommunications industry. Here are a few use cases:

• Network optimization. AI can help analyze network traffic to predict congestion and reroute traffic to avoid slowdowns. This can provide a better customer experience and help avoid unnecessary costs.
• Network assurance and predictive maintenance. AI can analyze historical data to predict when areas of the network and network infrastructure are likely to fail. This gives you more time to proactively plan for maintenance, which can also reduce costs.
• Network efficiency. Applying predictive AI to high-quality voice and video leads to using less network traffic. For example, forward erasure correction (FEC) or using erasure correcting codes (ECC) can protect data from the effects of packet loss by creating repair packets in advance. These packets can be used to recreate lost data.
• Service chatbots. AI models can speed up customer-service requests by using chatbots to address common issues, freeing up humans to handle escalations or other issues.

Despite the fact that AI can help you overcome telecommunications business challenges, adopting AI technologies is often difficult. Barriers like customer hesitancy, privacy concerns, and high costs are real and widespread, affecting how quickly the industry can evolve.

Distrust of AI 
Customers may be hesitant to engage with AI solutions, preferring human interaction instead of a chatbot—especially in scenarios addressing service issues. Whether it’s the fear of something new or the comfort of familiar legacy systems, customer hesitation can prevent the full transition to AI.

Data quality
Data quality is crucial for the success of data-intensive AI applications, such as predictive maintenance and service automation. The effectiveness of these applications depends on the quality of data they process. For instance, if the data is low quality, the AI models may fail to accurately predict maintenance needs. Implementing a platform that helps you create and deliver AI-enabled applications at scale across hybrid cloud environments is essential to make sure the data that’s fed into models is accurate and adequate.

Compatibility with existing infrastructure
Telecommunications organizations must integrate AI services with 5G networks and legacy systems. Doing so requires a unified platform that supports both modern and traditional networks and that can handle AI workloads.

Privacy concerns
In AI modeling, protecting private customer data is vital. Telcos need an AI platform that integrates with an ecosystem of trusted AI tools, so operators know where data is being fed, what has access to it, and what data is vulnerable to exposure. This is possible through a consistent, dependable platform for AI workloads that has a holistic operations, observability, and security implementation regardless of cloud environment.

Costs
The cost of integrating AI into telecommunications is significant, given the scale and complexity of networks. You need to carefully evaluate the potential return on investment (ROI) for each AI use case to justify the initial expenditures.

Talent acquisition
Hiring skilled professionals is critical. Telecommunications is a specialized field and AI professionals must have data science skills and experience working with the complexities of large network systems. This dual expertise is essential for effectively implementing and managing AI technologies in the industry.

Successfully deploying your AI workloads at scale depends on how efficiently and effectively your moving pieces are working together. Specifically, inference servers that can support larger AI models (like LLMs) and their more complex inference capabilities are essential to scaling AI workloads for the enterprise.

These AI tools help engineers use resources more efficiently to help scale: 

• llm-d: LLM prompts can be complex and nonuniform. They typically require extensive computational resources and storage to process large amounts of data. An open source AI framework like llm-d allows developers to use techniques like distributed inference to support the increasing demands of sophisticated and larger resoning models like LLMs.
• Distributed inference: Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.”  
• vLLM: vLLM, which stands for virtual large language model, is a library of open source code maintained by the vLLM community. It helps large language models (LLMs) perform calculations more efficiently and at scale.

Explore Red Hat AI

Red Hat AI is a platform of products and services that can help your enterprise at any stage of the AI journey - whether you’re at the very beginning or ready to scale. It can support both generative and predictive AI efforts for your unique enterprise use cases.

With Red Hat AI, you have access to Red Hat® AI Inference Server to optimize model inference across the hybrid cloud for faster, cost-effective deployments. Powered by vLLM, the inference server maximizes GPU utilization and enables faster response times.

Learn more about Red Hat AI Inference Server 

Red Hat AI Inference Server includes the Red Hat AI repository, a collection of third-party validated and optimized models that allows model flexibility and encourages cross-team consistency. With access to the third-party model repository, enterprises can accelerate time to market and decrease financial barriers to AI success.

Learn more about validated models by Red Hat AI