Telecom operators across the Middle East are moving ahead with AI-driven network implementations at a faster pace than many global counterparts, according to industry discussions at Future Net MENA 2025 in Dubai last month.
With initiative like the Kingdom of Saudi Arabia’s recent announcements of 6G research partnerships, part of the country’s Vision 2030 technology roadmap, this highlights the region’s approach to network modernisation. The kingdom has committed significant resources to next-generation network infrastructure, with AI and automation playing central roles in these initiatives.
At the same time, Middle Eastern operators are facing unique challenges serving both major cities and remote desert regions, making AI-driven networks essential rather than optional. As Gulf states pursue economic diversification, network reliability has become a critical infrastructure priority.
Beyond incremental revenue and monetisation possibilities, these advanced technologies can dramatically enhance early fraud and security threat detection, which opens the door to important cost savings and reputational protection.
The AI journey for communications service providers – from pilots to production
For communications service providers (CSPs), deciding what to do first in this rapidly transforming environment is the big question. The first step in the AI journey begins with AIOps (AI-driven IT operations) teams examining how this technology can work with existing tools to streamline activities and utilise data from all systems. In the past, network operations teams with deep domain knowledge across multiple disciplines would be tasked with manually sorting through hundreds of key performance indicators (KPIs) and metrics to address trouble tickets and resolve an issue. Given the growing complexity of today’s networks and the vast quantities of data spun off by every network event, business service interaction, and subscriber device, this manual approach is becoming unsustainable..
Automated workflows based on AI and ML offer provide actionable insights from the avalanche of data being generated, reducing the need for manual intervention. That said, effective workflow automation still depends on combining customer experience issues with telecommunications network and domain knowledge expertise. The ability of CSPs to strike the right balance will be essential in delivering faster results, reducing weeks of troubleshooting down to a matter of minutes and achieving significant cost savings. This also frees engineers to focus on complex issues not suited to automation.
At LEAP 2025 in Riyadh, operators signed new MoUs to explore self-optimising networks that adjust in real time and maintain strong service quality. In Qatar, modernisation plans include 5G-Advanced rollouts, predictive maintenance, and AI-powered customer support, all aligned with the country’s Digital Agenda 2030 and the wider push toward digital transformation.
Real-time data – the accelerator
The history of telco tool development has shown steady progress toward the objective of reducing mean time to resolve (MTTR). AI and ML hold the promise of taking this journey to the next level, but CSPs shouldn’t feel as if this is an all-or-nothing proposition. Instead, taking an incremental approach to the adoption allows CSPs to be flexible and shift their strategy if something isn’t working as expected.
Key to an effective strategy is solutions that scale efficiently, reduce opex, reshape services and customer experience, and support more predictable, experience-led networks.
It’s important to keep in mind that training AI to understand an organisation’s network—teaching it how it should behave—takes time. Starting small has its advantages: it allows AIOps teams to properly train AI data models, going through a learning curve like a human. But it is critically important that the output of the AI be trusted.
Practical deployments are already proving what is possible throughout the region. In the UAE, autonomous drones equipped with AI now inspect telecom towers, detecting faults early, improving safety, and speeding up maintenance across the country’s digital infrastructure.
And in Bahrain’s manufacturing sector, a recent private 5G rollout is helping teams apply AI-powered analytics directly on site. By combining real-time monitoring with high-speed connectivity, operators are improving decision-making, cutting downtime, and enhancing safety, all in the flow of daily operations.
The path forward
The critical first step to gaining predictive insights is to deploy architecture to collect and process the data at source in order to deliver consistent real-time visibility across the entire network infrastructure. Real-time analysis is far more effective than reviewing large volumes of archived data dumped offline.
We’re also seeing major advances in regional infrastructure, particularly in Egypt, where investments in subsea cables, cross-border capacity, and AI-ready data centres are making Egypt a key digital hub for cross-continental exchange.
And across the wider Gulf, momentum is building. Strategic agreements around 6G Innovation and autonomous network design are paving the way for systems that can optimise themselves, powered by data and capable of adapting with little to no human input.
Telecom providers are currently experiencing a significant shift towards AI-driven transformations. Central to these transformations are advanced analytics and machine learning, which enable providers to harness high-quality, real-time data to enhance network performance. Such AI-driven insights are integral in supporting both internal network operations and interoperability with widely-used external platforms.
These capabilities facilitate proactive identification of potential network issues, enable more precise problem diagnosis, accelerate troubleshooting, and enhance network optimisation. As a result, telecom operators can achieve measurable improvements in customer experience, employee productivity, and overall operational efficiency. However, transitioning effectively to AI and AIOps typically demands a careful, incremental approach. Adopting a gradual strategy often proves the most successful path forward, ensuring sustained improvements and realistic, achievable outcomes.
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