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.

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.

How do you overcome the challenges of building useful services from good data on a security-focused platform that’s compatible with your existing infrastructure? And how do you find a platform that many IT professionals are already familiar with? That’s where Red Hat comes in.

Red Hat’s expertise, partner ecosystem, and foundational technology can help you create, deploy, and monitor AI models and applications using the right data, to build services your customers can trust. Using open source technologies, Red Hat unites data scientists, developers, and operations on a cohesive platform so you can gather insights and build intelligent applications. And it’s all built on the foundation of Red Hat® Enterprise Linux® and Red Hat OpenShift®—industry-standard environments and platforms that work with your existing systems.

The internal tools are combined with Red Hat OpenShift AI, which provides a common platform for teams to operationalize AI applications and ML models with transparency and control. OpenShift AI gives teams trusted, operationally consistent capabilities to experiment, serve models, and deliver innovative applications. Using foundational models—from partners like IBM, open source resources like Hugging Face, or developed internally by your organization—Red Hat OpenShift AI offers a single AI application platform to bring them all together.

Of course, AI workloads run in containers much like your other modern applications. Red Hat OpenShift offers a scalable application platform suitable for those AI workloads. It allows customers to use leading hardware accelerators such as those from Red Hat partners NVIDIA, Intel, and many others.

Red Hat Enterprise Linux AI integrates the IBM Granite family of open source-licensed LLMs and InstructLab, a community-led solution for enhancing LLM capabilities. It lets you develop, test, and run Granite family LLMs for enterprise applications. The lower profile of Red Hat Enterprise Linux AI makes it an ideal platform for breaking down the cost and resource barriers to experimenting with and building AI models. At the same time, it provides the tools, data, and concepts needed to fuel the next wave of intelligent workloads.

A good platform needs to connect to the best tools, both inside and outside of Red Hat. Red Hat's partner ecosystem connects you with solutions for creating, deploying, and managing models for AI-powered intelligent applications.

Red Hat is dedicated to advancing the modern telecommunications industry by offering telcos reliable, scalable platforms that simplify the development and deployment of AI and ML models. Strong partnerships and flexible solutions help Red Hat and its partners navigate AI complexity as it continues to grow and change, so we're prepared to help our customers.