While headlines focus on sovereign GPU clouds, AI-RAN, or LLMs optimized for telecom networks, two critical dimensions often remain in the background of telco AI strategies: data governance and regulation, and the building of vertical AI ecosystems. These two pillars are essential to turn AI into a sustainable competitive advantage.
Data Governance, Regulation & “AI Trust”
In telecom, every data packet carries sensitive information — location, identity, usage patterns — making trust the true currency. The arrival of generative AI and autonomous models multiplies the value of this data… but also the risks.
For operators, data governance now goes far beyond basic GDPR compliance:
- Full lifecycle management: collection, classification, anonymization, storage, deletion.
- Explainability and auditability: the ability to justify any AI-driven decision to a regulator or customer.
- Resilience against misuse: proactive bias detection, anomaly monitoring, and model manipulation prevention.
📌 Operator use cases
- Orange: deploying EU-compliant data platforms to deliver secure AI-powered B2B services in healthcare and finance.
- NTT Docomo: using AI for real-time fraud detection on 5G networks, combining instant analysis with secure historical storage, approved by Japanese regulators.
- Telefonica: integrating “Explainable AI” mechanisms into its network APIs, enabling customers to understand how and why QoS is adjusted.
By 2025, the EU AI Act will require human oversight, technical documentation, and certification for certain high-risk AI applications. Telcos can turn this into a commercial advantage by positioning themselves as “trusted AI operators” that guarantee compliance and data sovereignty.
Vertical AI Ecosystems: monetizing beyond connectivity
The other often underestimated challenge is embedding into industry value chains. AI, combined with the network’s unique strengths (low latency, massive coverage, edge computing), allows operators to become co-creators of vertical solutions.
📌 Operator use cases
- SK Telecom: partnering with Hyundai for in-vehicle AI, combining traffic prediction and energy optimization through 5G edge computing.
- Verizon: predictive maintenance AI for ports and warehouses, using 5G-connected drones and real-time video processing at the edge.
- Vodafone: “AI for Agriculture” in Africa, analyzing weather data, IoT sensors, and satellite imagery to optimize irrigation and yields.
- Etisalat: AI-powered smart stadiums in the UAE, processing video feeds and sensor data to enhance safety and spectator experience.
The winning approach:
- Identify industries where the combination of network + AI + edge creates a strong barrier to entry.
- Co-develop solutions rapidly with industrial partners.
- Leverage sector-specific data to continuously improve models and lock in competitive advantage.
What i think
From my perspective, telcos are standing at a crossroads. Investing in AI infrastructure alone is not enough — it’s the minimum ticket to play. The real competitive edge will come from building trust as a core brand asset and embedding deeply into industry-specific AI ecosystems where their network capabilities become indispensable.
Those who master these two pillars won’t just survive the AI wave — they’ll shape it, defining new revenue models and positioning themselves at the center of tomorrow’s digital economy. The rest risk remaining passive carriers, watching others capture the value they’ve helped enable.