The AI revolution in telecom: Maximizing benefits while mitigating risks

As we saw in the intro, the importance of AI in the Telco industry is projected to be huge, but to better understand how it’s being utilized in the Telco industry, we will explain the most common use cases.

AI for Smart Optimization and Forecasting

Again, Telcos are usually very large companies that employ a large workforce spread across various business segments, from retail stores and call centers to field technicians who ensure that the services provided to customers remain seamless and high-quality.

The complexity of telecom operations necessitates the use of cutting-edge technology to help predict demand and workload. For example, AI can analyze historical data on customer traffic, seasonal trends, and periods of increased or decreased usage to optimize workforce management, customer outreach, network performance, and overall resource allocation.

By doing so, AI contributes to more accurate cost assessments, improved operational efficiency, and ultimately, the overall success of telecom companies.

AI for Enhancing Customer Experience

We’ve all encountered situations where we faced an issue with our network provider and needed to contact customer support, whether by visiting a retail store for an in-person resolution or reaching out via chat or a phone call.

In this area, as pointed out by McKinsey, telecom companies are increasingly leveraging AI to optimize customer interactions and enhance the overall user experience. Thanks to its ability to process vast amounts of data in a second, AI serves as an ideal solution for swiftly resolving technical issues, suggesting solutions, or seamlessly connecting users with a human agent when necessary.

This has now become standard practice. Visit any telecom website, and you’ll find that nearly all telecom companies have integrated AI chatbots into their customer support platforms. These chatbots are fast, communicate naturally, process large amounts of information instantly, and efficiently resolve issues that were once handled by call center agents.

AI for Network Optimization and Self-Healing

Among its many applications, AI is significantly transforming how telecommunications companies maintain their networks. Until recently, network issues were typically resolved only after customers reported them. Unfortunately, we have all witnessed this at least once in our lives. It was a reactive approach that negatively impacted the customer experience and caused unnecessary frustration. 

But now, think about the last time you had an issue with a network outage. Can’t remember? We agree, and the reason for that is explained below.

To address customers’ frustration with network issues, telecom providers are increasingly leveraging AI to monitor network health in real or near-real time, autonomously detecting and resolving issues before customers even notice them. With its advanced analytical capabilities, AI can identify anomalies, predict potential failures, and proactively fix them before they escalate into major problems.

This AI-driven approach greatly enhances operational efficiency, saves resources, and ultimately improves customer experience by ensuring a seamless, high-quality telecommunications service.

Other applications of AI in the Telco industry

These are the three most common applications of AI in the telecom industry, but of course, there are many other use cases, such as:

• Smart coaching for training and onboarding new employees,
• Marketing for creating personalized messages that enhance conversion rates,
• Fraud detections like SIM swap or call rerouting frauds,
• and many others.

However, while all of this sounds great, AI, like any other technology, is not perfect and has its vulnerabilities. This means that the risks it brings are ultimately borne by telecom companies.But before we move on to the risks, let’s briefly understand the type of AI that telecom companies integrate into their systems.

First, we must clarify what we mean by AI - a system that demonstrates the ability to adapt to new situations, learn independently, understand, and take action. However, within the AI ecosystem, we distinguish several key players, forming an AI supply chain that includes the following:

1. AI model providers – companies that develop AI models from scratch, train them on vast datasets, and sell their capabilities to AI developers. Examples of AI model providers include OpenAI, Anthropic, Mistral, ElevenLabs, AlephAlpha, and many others.
2. AI developers – companies that apply models from AI providers to enhance or develop their own AI-powered products. For example, an AI developer might use ChatGPT to create a product that assists in writing blog articles. These companies then sell their products to end users. Examples of AI developers include HeyGen, Jasper, and Writer.
3. End users – individuals or businesses that use ready-made AI products, such as consumers, financial institutions, and healthcare providers. They integrate AI products into their systems to achieve their goals, whether it is improving operational efficiency, saving resources, or something else.

Based on this classification, it’s clear that telecom companies usually fall into the category of end users. In other words, telecoms sign contracts with AI vendors and integrate existing AI solutions into their systems for various needs.However, this integration can introduce numerous risks, which we will explore in the next section.

The telecommunications industry is one of the most heavily regulated sectors in the world. Over the past two decades, it has frequently been scrutinized by regulators, customers, and competitors, with every move closely examined. Due to the high level of responsibility these large companies bear, they have little room for risk, as mentioned in the introduction.

However, they are taking risks by integrating AI into telecom systems. Why? Because telecom companies often assume that AI compliance is the responsibility of the AI vendor. However, if a third-party AI solution used by a telecom company fails to meet legal requirements, the responsibility does not lie solely with the AI vendor - it may also fall on the telecom company itself.

In the following section, we will explore how a non-compliant AI vendor can put a telecom company at risk.

Regulatory Fines and Legal Liability

AI compliance is no longer optional - it is becoming a legal obligation. With the introduction of the EU AI Act, the first legal framework regulating the development, deployment, and use of AI, along with GDPR and numerous other international regulations, responsibility may shift also to l AI users, including telecom companies, if customers are affected.

• Under the EU AI Act, AI providers that fail to ensure transparency, accuracy, and fairness could face fines of up to €35 million or 7% of global annual revenue—whichever is higher.
• The GDPR already imposes severe penalties for mishandling customer data, and AI solutions that process sensitive information fall directly under its remit.

Reputational Damage and Loss of Customer Trust

‍Switching telecom providers is not a frequent occurrence, and in many cases, the decision to choose a telecom operator is not driven by price but by service quality. In other words, relationships in the telecom industry are largely built on trust - if a telecom provider offers a high-quality service and treats customers fairly, they are unlikely to switch, as changing providers is a process.However, breaching this trust can lead to customer churn, making reputational risk a major concern for telecom companies partnering with a potentially non-compliant AI vendor. If AI systems make mistakes - whether in network optimization, billing, customer behavior tracking, or even simple chatbot interactions, this can result in public backlash and damage to the telecom provider’s reputation.Potential issues that can harm reputation and customer trust:

• Leakage of personal customer data
• Network unavailability due to wrong AI optimization
• Discrimination of customers based on various factors
• Biased or weird behavior of AI chatbots

Operational and Financial Disruptions

‍AI is most often integrated into critical telecom areas where companies see their goals. From network optimization to customer support, if an AI system is unreliable or poorly maintained, it can lead to the following:

• Network downtime or reduced service quality, resulting in operational difficulties and customer churn.
• Unexpected financial costs, if AI model drift over time leads to poor fraud detection, incorrect billing, suboptimal network performance, etc.
• System replacement, if an existing AI system is found to be non-compliant, replacing it with another system is highly difficult and resource-intensive.

We can conclude that these risks extend far beyond just financial expenses. Besides immediate financial losses, the risks include legal battles, public backlash from customers, loss of trust, less time to focus on development, leading to falling behind the competition, redirecting budgets to crisis management instead of technological process, etc.In the next section, you can find the answers on how to select a good AI vendor.

As we have seen, when choosing an AI vendor, there are many risks that can seriously harm a telecom company. There is little room for error, so it is important to choose an AI vendor wisely.

You can check AI vendors manually, ask key questions to understand if they are compliant and if they pose a risk to your company. However, very few telecom companies have the internal capacity, tools, and resources to thoroughly assess AI vendors.

This is where TrustPath comes in to help telcos.

TrustPath specializes in AI risk assessment for telecom companies, ensuring that their AI vendors meet the highest standards in:

• Legal compliance (EU AI Act, GDPR, and any future regulations)
• AI model transparency
• Security and data protection
• AI model auditing

Do not ignore the benefits that AI can bring to your telecom, such as maximizing operational efficiency, reducing costs, improving processes, and boosting business results, but do it wisely. Contact us to help you assess your AI vendors and protect your business from these risks.