AI in Telecom: Hype with Benefits

Artificial intelligence, or AI, in telecom is just like AI in every other industry today — it’s dominating the discussions so heavily you’d think it wrote them. (And in some cases it did.) But there is meat behind that hype. So much so that our PortaOne CEO, Andriy Zhylenko, dedicated his keynote talk to the potential and the pitfalls of AI in telecom at our recent PortaOne African Meetup in Johannesburg. With the right human hands at the wheel, AI can level the playing field between large and small telcos by giving old-fashioned market savvy an edge over infrastructure resources and big marketing budgets. Use it carelessly, however, and you won’t get the results — a fact evidenced by the sea of mediocre content we’re all swimming in today.

At PortaOne, we’re excited 🤖 to see where AI goes, and we’ve been working hard at uncovering where the real benefits lie — and to map out the pitfalls so our customers can avoid them. So, today we are dedicating this blog to laying out those findings, and sorting the hype from the value when it comes to AI in telecom. How can we change our approach to telecom services to be successful in the era of AI? Where do people fit in? And how can we improve what we’re putting into AI so we can actually profit from what comes out? Read on, fellow humans, because we’re about to dive in. 

“But I’m Kind of Worried That AI Will Take Over!”

Before we kick off, let’s address this common fear. Yes, like all new and heavy-impact technologies, AI can be intimidating. But it’s not as new as you might think. As Neil deGrasse Tyson once explained in an interview with Piers Morgan, we’ve already been using artificial intelligence in some form or other practically since the birth of digital technology. In fact, he noted, it has long been integrated into our lives in ways we don’t even think about. (And hey, you could look at the evolution of AI as going back even further than that: in the 1800s, mathematician Ada Lovelace was calling Charles Babbage’s proto-computer “a thinking machine” that could go beyond mere calculation.)

And it’s also not just about to replace the human mind. Not in the telecom industry, and not anywhere else. To paraphrase Tyson’s view, AI is a tool, and like any tool, it needs a sentient person to wield it. And to further that tool analogy: all the better if that sentient person is skilled at using it.

To get what you need out of AI, in telecom and everywhere, you have to direct it. That’s what makes it the perfect medium for the output of our human minds – with all our skill, creativity, innovation, and competitive spirit.

The Role of AI in Transforming the Telecom Industry: Why It Matters

The market for AI in telecom is projected to reach $14.99 billion by 2027, driven by a compound annual growth rate (CAGR) of 42.6% from 2021. In other words… 💥kapow. But why has the telecom industry in particular embraced AI with such wide open arms? It all stems from a few very specific industry challenges that AI is very well suited to tackle:

1. Complex Network Management

As global traffic and network infrastructure expand, telecoms are finding that traditional management methods aren’t cutting it. By automating routine tasks, AI enables operators to optimize their resources so they can handle those complex systems without having to hire 💸 half the population of their country. It’s also helping operators offer the same resource-streamlining benefits to their customers. Take AI-driven call centers, for example: telecoms can equip businesses with the tools to offer virtual assistants that can expedite the resolution of many of their customers’ problems without the need for them to wait on hold. (Or for the businesses to expand their own call center staff.) 

2. The Constant Chase for Revenues

AI can facilitate the development of new products and services, opening new revenue streams for telecoms. Deutsche Telekom, as one example, anticipates that AI will have contributed €1.5 billion in revenue from new offerings by 2027. 

3. Customer Expectations

In a highly competitive market, customers will walk away if you deliver anything less than a seamless experience. AI helps telecoms personalize and accelerate services to improve that sense of customer satisfaction and prevent excessive churn. Approximately 62% of telecom providers are now using generative AI to enhance their customer experience. By 2027, that figure is expected to reach 90%. 

4. Fraud

When you have fraud, you have revenue leakage. AI systems can detect and prevent fraudulent activities by analyzing patterns and anomalies in real time. 

5. Putting Data to Its Best Use

This is the key to getting the most out of AI in telecom. You’ve heard the saying “garbage in, garbage out.” Getting good outputs from AI depends on having good inputs. As it happens, telecoms tend to collect vast amounts of data from their users. If you — the human — can lean into analyzing what data you have, what data you can get, and how you can use it to equip customers with information they can really use, you can create some very compelling services just by learning how to ask AI the right questions. Think: Dynamic pricing. Predictive analytics to give an early warning about service outages. Automated, personalized offers that exactly match a given customer’s usage needs. Sentiment analysis to uncover where your customer pain points are. Or: insert your amazing idea here.

So when you look at all these areas of potential, it’s really no wonder that telecommunications is expected to remain among the industries with the fastest-growing uptake of AI. And when you think about it, since almost all industries rely on telecom and our advancements, we’re bringing everyone else along with us.

What Are Some Examples of AI Implementation in Telecom?

Okay, that all sounds good, right? But, you might be asking, how are telecoms using AI today, in the real world? So, let’s look at a few use cases for AI in telecom that are actually active right now. Here’s where the potential is hitting the pavement.

1. Network Optimization: AI in Telecom Turns the Complexity into Simplicity

Telecom networks are like living organisms, constantly shifting under the weight of billions of simultaneous connections. With the rollout of 5G and an explosion of IoT devices — forecasted to surpass 25 billion by 2030 — networks are facing unprecedented complexity. Traditional static management methods no longer suffice. The answer? Say hello to self-optimizing networks (SONs), powered by AI.

What are SONs?
In simple terms, self-optimizing networks use machine learning algorithms to monitor network traffic in real time, predict congestion points, and dynamically adjust network parameters. These systems enable telecom providers to optimize resources, improve reliability, and reduce downtime.

Why It Matters
Ericsson estimates that AI-driven network optimization improves operational efficiency by 15-20%. These systems also detect and resolve faults up to 50% faster, reducing service disruptions and protecting customer satisfaction.

AI in Telecom Case Study: Nokia’s MantaRay Cognitive SON
In June 2024, Nokia announced it had deployed a new cognitive SON for client STC Group to help the company boost its network efficiency. The company says its MantaRay SON improved STC’s network performance by up to 30% while reducing both energy consumption and the need for manual work.

2. Predictive Maintenance: AI in Telecom Keeps Companies Ahead of Failure

Telecom networks rely on extensive physical infrastructure, including cell towers, fiber-optic cables, and data centers. Maintaining this infrastructure is essential, but very, very costly. Worse, traditional reactive maintenance often results in downtimes. (Meaning: costly just got costlier.) Predictive maintenance uses AI to flip that script.

How It Works
AI systems monitor infrastructure in real time, analyzing performance data to detect anomalies. By identifying subtle changes that precede equipment failures, these systems allow operators to intervene before disruptions occur.

The Numbers
According to Deloitte, predictive maintenance can:

• Reduce downtime by 5-15%
• Lower maintenance costs by 20-25%
• Prevent up to 70% of unexpected failures

AI in Telecom Case Study: AI Protecting European Infrastructure
Vodafone and other European telecom operators have successfully used AI to monitor environmental stress on cell towers, such as extreme weather or wear and tear. These systems ensure uninterrupted service, particularly in remote or harsh conditions.

3. Fraud Detection and Cybersecurity: AI in Telecom Helps Defend the Frontlines

Telecom fraud is a growing menace, costing the industry over $45 billion annually. Scams like international revenue share fraud (IRSF), fake profiles, and identity theft are becoming more sophisticated, requiring equally advanced countermeasures. AI in telecom has some solutions.

How AI Is Helping to Prevent Fraud

AI excels at identifying anomalies in real-time network traffic. Machine learning models analyze behavioral patterns to flag suspicious activities, while automated systems can neutralize those threats immediately.

How It Might Hurt
While AI can help prevent fraud, it also introduces new challenges. Generative AI, capable of creating deepfakes and voice clones, is being weaponized by fraudsters. In response, telecom companies are racing to invest in AI systems that are capable of detecting synthetic content.

AI in Telecom Case Study: SK Telecom
SK Telecom deployed AI-driven cybersecurity tools that reduced cyber incidents by 25%. These systems continuously monitor network activity, predicting vulnerabilities and reinforcing security protocols before a breach occurs.

4. Customer Experience: AI in Telecom Unlocks Hyperpersonalization

As users get accustomed to having instant resolutions, personalized offers, and 24/7 support, AI is becoming a key pillar of corporate customer engagement strategies. And we’re not just talking about those ubiquitous AI chatbots – the technology now goes much deeper. Virtual assistants powered by natural language processing, or NLP, can understand complex queries, retain context across conversations, and provide tailored solutions. AI systems can also analyze customer data to identify cross-selling and upselling opportunities. For example, operators can recommend higher-tier plans to heavy data users or offer IoT bundles to enterprise clients.

Predicting Churn
AI doesn’t just respond to customers. It can also anticipate their needs. By analyzing usage patterns and sentiment data, AI can flag at-risk customers and suggest interventions like tailored discounts or enhanced services that can help prevent them from hitting that “cancel” link. 

A New Corporate Acronym: The CDP

One of the systems behind AI-driven hyperpersonalization is the CDP, or customer data platform. These platforms take data from multiple sources (transactional, demographic, geographic, etc.), unify them, and share them across systems to create insights and drive those churn-preventing decisions.  

AI in Telecom Case Study: Vodafone’s TOBi
TOBi, Vodafone’s digital assistant, has transformed how the company interacts with its customers. TOBi resolves queries, facilitates plan upgrades, and even processes transactions. Since its implementation, TOBi has reduced checkout times by 47%, boosted conversion rates, and increased overall customer satisfaction for Vodafone.

5. Operational Efficiency: AI in Telecom Streamlines Businesses

In a sector where margins are constantly under pressure, AI provides operators with the tools to help companies – and themselves – trim costs and liberate employees from mundane tasks. 

How Does AI Increase Operational Efficiency?
Let’s focus here on just one concept that operators are using to scale their organization’s operations. Robotic Process Automation, or RPA, automates repetitive tasks such as billing, order management, and customer data updates, significantly reducing human errors and processing times. RPA-driven customer support systems can swiftly resolve basic queries, leaving human agents to handle more complex issues.

The Numbers 

Research by NVIDIA shows that:

• 67% of telecom companies report revenue growth linked to AI.
• 19% attribute over 10% of their annual revenue increases to AI initiatives.

AI in Telecom Case Study: RPA at AT&T

AT&T has been using various forms of Robotic Process Automation since 2015 to automate busywork processes. Now, they’re adding in AI to create RPA bots that also work across applications to complete tasks. One specific RPA bot prevents overage charges for businesses. It “validates invoice data against contract terms, does basic calculations and transfers data into the accounts payable system, triggering the payment process… then automatically routes invoices for approvals and follows up with deadline reminders.” The bot even takes into account regional compliance regulation and specific company rules. AT&T says the bot has reduced processing times “from minutes to seconds” and saves thousands of staff hours each year.

Enhancing 5G Efficiency Through AI

5G is officially here! And those 5G networks are continuing their triumphal march around the world. Artificial intelligence promises to amplify this revolution even further. By 2030, 5G is expected to carry 80% of global mobile traffic, serving 5 billion+ subscribers. However, the complexity of managing these networks has increased significantly compared to previous generations. Unlike 4G, where networks had relatively predictable traffic, 5G must handle dynamic demands, high volumes of data, and diverse applications like autonomous vehicles, remote surgeries, and smart factories.

By analyzing large amounts of real-time network data, AI can identify patterns and adjust the network to detect traffic spikes, allocate bandwidth, reroute traffic to prevent congestion and more. And af course there is the automation of repetitive tasks, like detecting and fixing network faults, allowing engineers to focus on more strategic improvements.

AI is also enhancing 5G’s capability to manage multiple network slices — virtual segments of a network dedicated to specific applications, such as gaming or industrial automation. By allocating resources dynamically to these slices, AI ensures that each application gets the performance it needs without wasting capacity.

What Are the Key Benefits of AI and 5G Integration?

This happy marriage has only just begun, and we can expect to see many innovations come from the nexus of AI and 5G over the next few years. But here are a few that are happening right now.

1. Superior Network Performance

With AI at the helm, 5G networks can optimize ⚡themselves in real time. AI dynamically adjusts resources, such as bandwidth or processing power, to meet changing demands. This prevents bottlenecks and reduces frustrations like dropped calls, buffering during streaming, or slow internet speeds.

2. Seamless IoT Management

The rise of IoT is bringing literally billions of devices online. Smart home sensors. Smart cars. Or smart medical devices and industrial machines. Smart herd monitors. Heck, smart everything! Managing that scale, however, is a significant challenge. AI automates IoT device provisioning, ensuring devices are connected and functioning without manual intervention. It also monitors these devices for anomalies, predicts maintenance needs, and schedules repairs proactively. That all reduces the risk of failures and lowers operational costs.

3. Ultra-Low Latency for Critical Applications

Many applications that rely on 5G need extremely low latency to function. (Think: autonomous vehicles, remote surgeries, or augmented reality.) AI in telecom enhances this by processing data closer to the user at the edge of the network, minimizing the time it takes for data to travel. For instance, in autonomous vehicles, AI ensures real-time communication between cars and traffic systems, improving safety.

4. Energy Efficiency and Sustainability

Operating 5G networks consumes significant energy, but AI reduces this impact 🌱. AI can analyze network usage patterns, and switch off unused resources during low-traffic periods to optimize power usage in real time. Some telecom operators, like China Mobile, have already reduced energy consumption by 20% using AI. The upshot: operators can align with environmental goals and enjoy cost savings.

5. Support for Advanced Applications

5G’s capabilities open doors for technologies like smart cities, industrial IoT, and immersive AR/VR. However, these applications demand consistent and reliable network performance. AI ensures that networks can meet these demands by managing the complexity of these systems. For example, in smart cities, AI analyzes traffic and environmental data in real time, optimizing traffic signals and energy usage, while 5G ensures fast and reliable data transmission.

Challenges of AI in Telecommunications

So that’s a deep look at the potential. But of course, this analysis wouldn’t be complete without a close look at some of the mountains still to climb in the world of AI in telecom. A few of them are significant! There are infrastructure, regulation, workforce readiness, and ethical considerations. Addressing these hurdles will be critical if telecom operators want to fully leverage the benefits of AI.

Integration with Legacy Systems

Telecom companies often rely on decades-old infrastructure, which lacks the flexibility and scalability needed for AI integration. These legacy systems, built for simpler networks, are incompatible with the advanced processing and data requirements of AI-driven operations. For instance, automating network optimization or deploying AI for real-time predictive maintenance often requires systems capable of handling enormous amounts of data in milliseconds.

Upgrading or replacing such systems is not only technically challenging, it’s also expensive. Especially for large-scale operators managing infrastructure across multiple regions. Transitioning to modern systems involves risks like operational downtime and potential disruptions to customer services.

To get past this challenge, many operators are adopting phased integration strategies. Most kick off with pilot projects that can help them identify ROI before they scale AI across their networks. 

Workforce Development

Among the respondents to a 2023 survey of how telecom companies plan to use AI, 34% named a lack of skilled personnel as their second-biggest challenge to AI adoption. (Yes, you have it right… staffing problems seem to be a barrier to a solution that promises relief for staffing problems.)

AI is making work easier in many ways, but it also represents a major shift in paradigm. That means people need to learn new ways of working, and businesses need to learn new ways of strategizing how to use their resources. Training programs that teach employees about AI tools, data analysis, and model management are becoming standard in the industry. Partnerships with universities and AI research centers have also become a common strategy to attract talent and foster innovation. For example, AT&T’s large-scale upskilling initiatives have helped the company transition smoothly into AI-driven operations.

Regulatory Compliance and Ethical Considerations

The implementation of AI in telecom must adhere to strict data privacy regulations. In Europe, there’s the General Data Protection Regulation or GDPR. In California, it’s the Consumer Privacy Act (CCPA). Over in Canada, it’s the Personal Information Protection and Electronic Documents Act (PIPEDA), along with other regulations. These frameworks require transparency in how customer data is collected, processed, and stored. For telecom operators, compliance is challenging because of the vast amounts of sensitive customer data they manage daily.

Additionally, there are ethical complications, such as AI bias and decision-making transparency. Algorithms trained on biased datasets can lead to discriminatory outcomes, affecting customer trust and regulatory compliance. Similarly, over-reliance on AI for decision-making without human oversight can raise accountability concerns. 

To address these challenges, telecom operators are investing in explainable AI models, so-called XAI, that make AI processes more transparent, while developing inclusive datasets to reduce bias. Telefónica, for instance, has introduced an AI ethics board to oversee its deployments, ensuring ethical and regulatory adherence while maintaining public trust.

ROI Uncertainty

While the long-term potential for cost savings and revenue growth is substantial, many operators struggle to quantify the immediate ROI of AI in telecom. The benefits of AI may take months or years to materialize. Furthermore, poorly executed implementations can lead to inefficiencies and financial losses.

To address ROI concerns, operators are focusing on use cases with clear financial benefits. By targeting these high-impact areas, telecom companies can demonstrate short-term gains while building a foundation for broader AI adoption.

The Future of AI in Telecom: Generative, Predictive, Collaborative – and Human

None of these challenges are close to big enough to slow down the momentum of AI in telecommunications. Only last year, 43% of telecom respondents reported investing in generative AI, with applications ranging from personalized marketing to automated customer support. And then there is the adoption of AI-powered predictive analytics, which is enabling operators to anticipate and address potential operational and customer service challenges proactively.

On our side, we’re thrilled to see the rise of open-source AI platforms, which are fostering collaboration and innovation across the telecom sector. These platforms democratize access to advanced AI technologies, empowering smaller telecom operators to compete with industry giants. This shift could drive industry-wide advancements, leveling the playing field and accelerating innovation.

Most importantly, we believe that the evolution of AI will remain in human hands. Like all things, what we get out of AI depends on what we put into it. Human insight, human oversight, and human queries are the three most important ingredients to implementing AI in telecom in the most beneficial way.

Building the Future of Telecom with PortaOne

Yes, AI has the power to revolutionize telecommunications. But if you want success, you can’t just blindly adopt new, cutting-edge tools as they come along. It takes strategic (read: human!) planning, and long-term strategy. You need to overcome challenges like legacy system integration and skill shortages. You need to ensure you are maximizing ROI. And you need a skilled, experienced partner who understands how to navigate both the benefits and risks. 

For almost 25 years, PortaOne has been helping telecom operators unlock the benefits of emerging technologies. And that includes AI, 5G, and IoT. Whether it’s through AI-driven service automation, billing and provisioning optimization, or new ways to enhance your cloud PBX offerings, we offer practical solutions tailored to your needs. And we can help you make a plan to adopt those solutions in the most effective possible way.

Let’s build a connected future together. Share your challenges or projects with us, and we’ll help you transform them into opportunities for innovation and growth. Reach out today to start the conversation!

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