Africa’s ICT landscape is shifting toward intelligent infrastructure, where artificial intelligence (AI) and automation enable networks to move from reactive fixes to predictive, data-driven operations. As the industry edges closer to autonomous networks, balancing machine intelligence with local demands in unique environments has emerged as a challenge.
In an exclusive interview with Telecom Review Africa, Charles Kuppusamy, Chief Operating Officer, CMC Networks, explained how the company is combining on-the-ground, local expertise with artificial intelligence for IT operations (AIOps) to deliver intelligent infrastructure that provides a ubiquitous customer experience (CX) across Africa.
What does “intelligent infrastructure” mean in the context of African networking?
Firstly, I want to say that Africa is not playing catch up in AIOps; it is leading it. The challenges we see in Africa make AI essential for enabling customers to reduce risk, increase control, and simplify growth.
Intelligent infrastructure is about more than just technology upgrades; it represents a fundamental shift in how networks are managed, and services are delivered. This infrastructure adapts, responds, and evolves in real time, driven by data, AI, and automation. We’re talking about platforms that use telemetry, machine learning (ML) and contextual awareness to optimize performance, predict issues before they happen, and deliver seamless customer experiences at scale.
We’re seeing a shift from reactive operations to predictive, AI-driven service management. What’s driving that shift, and how urgent is it?
It’s not just a trend; it’s a necessity. Legacy systems based on human intervention and reactive troubleshooting can no longer keep up with today’s networking demands. This is especially true with the rise of hyperscalers and AI workloads. Providers must transition to predictive, automated service models to meet the performance demands of enterprise software-as-a-service (SaaS), cloud-native environments, and customer expectations.
The urgency is real and there’s a competitive gap between the carriers that have started and the ones that haven’t. CMC Networks already has a growing bank of data that we’re putting to work to identify patterns, predict potential disruptions, and optimize how we deliver services across Africa.
How is automation and AI changing the way your teams manage service delivery, maintenance, and performance at scale?
We’ve seen a complete transformation. Instead of drowning in millions of events and alerts, our systems now use AI to filter, correlate, and surface only what matters. A good example is how we’ve reduced 65 million telemetry events to just 10,000 relevant tickets. This eliminates “noise” and allows teams to act faster.
AI helps us identify emerging issues in real time, reroute traffic during outages, and even simulate network changes before deployment. We aren’t replacing people; we’re amplifying their impact.
Can you share a concrete example of how AIOps has improved customer experience or service reliability?
Utilizing AI-powered event correlation and ticket clustering, CMC Networks has reduced mean time to repair (MTTR) by up to 38%. That translates to fewer disruptions, faster resolution, and greater customer satisfaction.
When a subsea cable undergoes maintenance, our systems automatically reroute traffic, notify impacted customers, and ensure uninterrupted service without manual intervention. That level of proactive response builds trust and keeps us ahead of customer expectations.
What impact is AI having on the roles and responsibilities of network operations teams?
It’s redefining them. AI is taking over repetitive, time-consuming tasks and freeing engineers to focus on complex problem solving and strategic improvements. Natural language processing (NLP) and large language models (LLMs) are making it easier for non-technical teams to interact with network data.
Even roles in the network operations center (NOC) are changing. Chatbots now handle routine latency or performance queries, and no-code tools let teams build automation workflows without needing to code themselves. But this also means upskilling is needed. We’re blending traditional engineering with data science, scripting, and AI model tuning.
How do tools like no-code automation or natural language interfaces change who can access and use network insights?
They’re democratizing access to network intelligence. Sales, support, and even field teams can now query real-time network data using simple, conversational interfaces. This breaks down silos, improves cross-team collaboration, and enhances the overall customer experience.
With so much focus on AI, how do you balance machine-driven decisions with local knowledge, especially in complex regions?
That’s a critical point. In markets like Africa and the Middle East, local context, regulatory realities, and infrastructure challenges mean you can’t rely on automation alone. We combine advanced AIOps with on-the-ground teams who know the nuances of each environment. This hybrid model reduces risk, enhances accuracy, and ensures that automated decisions align with real-world conditions.
What’s your vision for the next phase of intelligent infrastructure? Are we close to fully autonomous networks?
That’s where we’re headed. We’re already seeing systems that detect and resolve issues without human input. The next phase will integrate AI into the entire service lifecycle, from provisioning and optimization to customer communication and planning.
Digital twins will help us simulate changes before deployment, and intelligent assistants will support both internal teams and customers in real time. Ultimately, intelligent infrastructure will not just improve efficiency; it will optimize customer experiences and accelerate the growth of Africa’s digital economies.
