In conversation with Techitup Middle East, Telecom AI Strategist – Junaid Ahmed shares insights on telecom industry transformation, use cases, and the roadmap to Level 4 autonomy
The telecom industry is rapidly shifting towards autonomous networks. How do you see this transformation unfolding, and what’s the role of AI in it?
The telecom industry is rapidly moving beyond traditional connectivity. We’re entering a phase where networks evolve autonomously, learning, adapting, and optimizing in real-time. AI is no longer an add-on; it is now foundational and is becoming the operational backbone. I see operators progressing from Level 1 to Level 4 autonomy, where systems can predict, act, and optimize without human intervention. This evolution isn’t just about efficiency; it redefines how networks deliver value in a digital-first era.
You often emphasize the importance of telecom operators owning their AI models. Why is that critical?
Operators manage mission-critical infrastructure and handle sensitive user data. Outsourcing AI models to hyperscalers may reduce initial friction but poses long-term risks to sovereignty and differentiation. In my view, telecom operators should build and manage their own AI pipelines including training, data governance, and model orchestration. This is vital for ensuring resilience, protecting data sovereignty, and shaping service logic aligned with local and strategic priorities.
Can you share real-world AI implementations you’ve led that delivered tangible value to telecom operators?
Certainly, I’ve led several high-impact implementations designed to solve real-world network challenges, including:
- Root Cause Analysis with Graph AI, which reduced fault isolation time by over 60%
- FWA Experience Optimization, where ML-driven analytics improved service availability by 35%
- Zero-Trust Security Frameworks, tailored for AI-managed 5G and industrial IoT environments
These projects were deployed at national scale and directly influenced network assurance, customer experience, and strategic planning models. They demonstrate how AI can be operationalized beyond pilot environments to deliver measurable impact at scale.
You’ve been actively involved in several industry platforms. How have these engagements amplified your influence?
These platforms allow practitioners like me to contribute not just opinions, but actual and practical frameworks. I’ve been involved in global initiatives like TM Forum Catalyst projects, including an award-winning use case on service-centric automation. Sharing field-tested and real-world models helps raise industry standards while also enabling cross-operator learning. It’s about turning innovation into blueprints others can replicate. These contributions have also been referenced by other industry professionals and cited in published telecom research.
What common misconceptions exist around AI in telecom, and how can leaders move forward with clarity?
The biggest misconception is treating AI purely as an optimization layer. In reality, it should be viewed as a value creation layer, enabling real-time assurance, energy-efficient networks, proactive customer engagement, and SLA-based monetization. Operators must transition from AI-as-a-tool to AI-as-a-core-strategy, embedded across architecture, operations, and commercial models.
My advice to network leaders is to prioritize use cases that deliver measurable value like predictive assurance, anomaly detection, or closed-loop energy savings. At the same time, invest in long-term AI capability with talent, data readiness, and model governance. And think ecosystem-wide, the future of telecom lies in programmable, autonomous, and service-aware infrastructure that can power everything from smart cities to industrial automation.
You’re authoring a book on autonomous networks. Can you tell us more about it?
The book, “Autonomous Networks Journey: Level 0 to Level 4,” aims to provide a practical blueprint targeted at telecom strategists, network architects, and policy stakeholders. It covers the full transformation lifecycle from strategy to implementation including case studies, governance models, and architecture frameworks. My goal is to help decision-makers navigate this shift with clarity and confidence.
