Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

Over the past two decades, I have built a career at the convergence of technology strategy, network transformation, and business innovation within the telecommunications sector.

My professional journey began in the era of 3G, where I focused on RAN optimization and large-scale network rollouts. As the industry transitioned to LTE, I played an instrumental role in network design and operations, progressively advancing into leadership roles in 5G strategy, private network solutions, and digital transformation initiatives. I have worked across the full telecom value chain—engaging with Tier-1 operators, equipment manufacturers, and hyperscalers—delivering greenfield and brownfield transformation programs with measurable impact.

My core expertise spans several strategic domains. In 5G and Private Networks, I have architected and deployed dedicated networks for industrial sectors such as manufacturing, aerospace, and critical infrastructure, focusing on spectrum allocation, edge computing integration, and SLA-driven network slicing.

In the area of Open RAN and Telco Cloud, I have led vendor evaluations, solution blueprinting, and field pilots, collaborating with ecosystem stakeholders to realize disaggregated, containerized, and cloud-native architectures. I have also driven OSS/BSS modernization by transitioning legacy systems into agile, microservices-based, API-first platforms aligned with TM Forum’s Open Digital Architecture (ODA). I have shaped enterprise go-to-market strategies on the commercial front, enabling operators to bundle connectivity, edge compute, cybersecurity, and data analytics into tailored offerings for verticals such as healthcare, logistics, and utilities.

In the field of AI and automation, I have designed frameworks and delivered projects across RAN optimization, fault prediction, customer journey analytics, and predictive maintenance. Additionally, I have engaged in public-private partnerships, working closely with regulators and national digital initiatives to align telecom infrastructure rollouts with policy goals around digital inclusion, spectrum liberalization, and domestic industry development.
This multidisciplinary experience—combining deep technical knowledge, commercial insight, and ecosystem collaboration—positions me to provide comprehensive, end-to-end guidance for telecom operators as they evolve into agile, cloud-first, digital service providers.

 

Q2. What advancements are being made in AI-RAN architectures?

AI-RAN (Artificial Intelligence for Radio Access Networks) is emerging as a core enabler for intelligent, self-optimizing, and highly efficient mobile networks. Several key advancements are reshaping the way radio networks are deployed and operated:

RAN Intelligent Controllers (RIC) – Near-Real-Time and Non-Real-Time

Non-RT RIC (in SMO layer) - Enables policy-driven training, inference, and analytics.

Near-RT RIC - Controls real-time behavior of RAN elements via xApps (e.g., traffic steering, beamforming, load balancing).

Use of rApps/xApps marketplace - Allows CSPs to plug-and-play AI models from third parties.

 

Predictive & Proactive Network Behavior

AI models predict cell congestion, interference, and user demand trends

Dynamic spectrum sharing and resource scheduling are being handled proactively using reinforcement learning algorithms

Energy Efficiency Optimization

AI is used to switch off or throttle network elements during off-peak hours while maintaining SLAs

Vodafone and Ericsson’s AI energy-saving trials showed up to 18% energy savings in dense urban sites

Massive MIMO and Beam Management

Deep learning models assist in optimizing beamforming, especially in mmWave and mid-band deployments where line-of-sight conditions are variable

Federated and Edge AI

Distributed AI architectures support data locality, reduce latency, and preserve user privacy—essential for industrial IoT and private 5G

Vendors like Nokia and Samsung are integrating federated AI into RAN orchestration

Open RAN Ecosystem Innovation

O-RAN Alliance’s standardization of AI/ML workflows, model management, and interfaces is enabling vendor-neutral innovation and deployment
 

Q3. What strategic partnerships are shaping the OSS/BSS ecosystem?

The OSS/BSS space is undergoing a tectonic shift from monolithic, siloed systems to cloud-native, modular, and interoperable platforms, with several strategic partnerships driving this transformation:

Hyperscaler + OSS/BSS Vendor Alliances

Amdocs + Microsoft Azure: Cloud-native BSS and monetization as-a-service offerings; key deployments with Telefónica and AT&T.

Netcracker + AWS: Cloud-hosted BSS stacks including digital engagement, orchestration, and revenue management.

Oracle + Telecom Italia: Migration of legacy OSS to Oracle’s cloud-native OSS suite.

TM Forum’s Open Digital Architecture (ODA)

• Over 60 operators and 30 vendors have pledged to align with ODA for reusable APIs, Service-Based Architecture (SBA), and AI-driven service management
• Notable collaborations include Orange, Vodafone, and BT in building composable OSS frameworks with TMF Open APIs

Platform-Based Ecosystems

• Vendors are launching BSS/OSS marketplaces for agile onboarding of partners and services (e.g., Ericsson’s Service Continuum, Salesforce Telco Cloud)
• Use of GraphQL, Kafka, and Kubernetes for OSS/BSS integration is becoming a standard

Zero-Touch Service Assurance and Orchestration

ServiceNow + Accenture: AI-based service assurance models integrated into OSS workflows.

Rakuten Symphony is pioneering cloud-native OSS with full observability and API-driven lifecycle management.

These partnerships aim to shorten time-to-market, enable dynamic service composition, and future-proof OSS/BSS investments for digital services, slicing, and edge monetization.

 

Q4. What factors are propelling the adoption of private 5G networks? And how is government policy influencing private 5G deployment?

Industrial Automation & Smart Manufacturing

High reliability, deterministic latency, and massive device density enable use cases like AGVs, machine vision, and predictive maintenance.

Edge Computing & Data Sovereignty

Enterprises seek on-premises compute and localized breakout to meet latency and compliance requirements.

Wi-Fi Limitations

Private 5G overcomes interference, mobility, and security limitations of Wi-Fi in mission-critical environments.

Vertical-Specific Ecosystems

Vendor solutions tailored for ports, airports, hospitals, and energy utilities (e.g., Nokia MX Industrial Edge, Ericsson Industry Connect).

Growing Device Ecosystem

Ruggedized, certified 5G routers, sensors, and MEC platforms now support industrial applications out-of-the-box.

Government Policy Impact

Localized Spectrum Licensing

• Germany’s BNetzA allocated 3.7–3.8 GHz for industrial use
• UK’s Ofcom and Japan’s MIC have similar enterprise-friendly frameworks

Public Grants & Testbeds

EU’s Horizon Europe and the US’s NTIA support trials in logistics, defense, and smart cities

Smart Nation Strategies

Countries like Singapore, UAE, and South Korea are promoting 5G zones and private network corridors.

Regulatory Sandboxes

Policies allowing innovation without regulatory burdens for trials (e.g., India’s sandbox for enterprise 5G).

 

Q5. How are telecom operators leveraging public cloud services for network operations?

Telecom operators are increasingly adopting public cloud infrastructure and services to modernize their operations, with a focus on agility, cost optimization, and innovation

Network Function Virtualization (NFV) and CNFs:

5G Core (UPF, AMF, SMF) is now being deployed in Kubernetes clusters hosted by AWS, Azure, or Google Cloud

Operators like AT&T, Dish, and Swisscom run production-grade workloads on hyperscaler infrastructure

Telco-as-a-Service (TaaS) Models

CSPs offer packaged services (e.g., MEC + Private 5G + SD-WAN) via public cloud to enterprise customers

Examples: Verizon 5G Edge (with AWS Wavelength), NTT DoCoMo MEC Platform

DevOps & Continuous Delivery

• Public cloud enables automated CI/CD pipelines, GitOps workflows, and blue-green deployment of network functions
• Improves network evolution cycles from months to days

Big Data and AI Services

Operators leverage cloud-native data lakes and AI/ML tools (e.g., AWS SageMaker, Google Vertex AI) for real-time analytics, churn prediction, and anomaly detection.

Operational Flexibility & Disaster Recovery

Elastic resources allow scale-up during high demand (e.g., special events, emergencies)

Cloud-native disaster recovery frameworks are cheaper and more scalable than traditional NOC/DR infra

Key Challenge: Data sovereignty, latency sensitivity, and integration complexity continue to shape the degree and model of cloud adoption.

 
Q6. How are enterprises customizing SASE solutions for specific applications?

Enterprises are moving toward granular, application-aware SASE (Secure Access Service Edge) solutions that blend security and network performance into policy-driven frameworks:

Identity-Centric Access Controls

Role-based, device-based, and context-aware policies ensure the right access level for each application.

QoS-Driven Routing

SASE integrates with SD-WAN to route high-priority applications (like ERP, VoIP, video) via low-latency, high-bandwidth paths

Lower-priority traffic (e.g., backup, social apps) is routed through internet-based links

Vertical-Specific Customizations

Healthcare: Enhanced data loss prevention (DLP), HIPAA compliance, and endpoint detection.

Retail: Real-time fraud detection and payment card industry (PCI-DSS) alignment.

Finance: Low-latency trading applications with ZTNA and AI-based threat detection.

Integration with Cloud-Native Workloads

Secure direct access to SaaS, IaaS, and multi-cloud environments with uniform policies.

Unified Threat Intelligence

Real-time threat detection engines, often powered by AI/ML, are tuned to specific traffic patterns and application behaviors

SASE adoption is being led by enterprises pursuing hybrid workforce enablement, cloud migration, and tighter cybersecurity mandates
 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

How is your company positioned to create differentiated value in a disaggregated, cloud-native, and AI-driven telecom ecosystem?

My perspective/rational

In today’s telecom landscape, value lies not just in product features but also in a company's adaptability, interoperableness, and ecosystem readiness.