Think of a phone network that can watch itself in real time and automatically fix problems, route traffic more efficiently, and offer new smart services to customers—like an automated, self-driving highway for data that telecoms can charge more for.
Traditional telecom networks are expensive to run, slow to adapt, and monetized mostly as commodity bandwidth. AI networking promises to automate operations, improve reliability, and unlock new, higher-margin services (like on-demand quality of service, network slicing, and industry-specific connectivity solutions).
Large-scale proprietary network telemetry and customer usage data combined with nationwide infrastructure and existing enterprise relationships give incumbents like Verizon and AT&T a data and distribution advantage that is hard for new entrants to replicate.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time inference at carrier scale on high-volume network telemetry, while meeting strict latency and reliability SLAs.
Early Majority
The focus is on embedding AI directly into network management and service delivery (self-optimizing, self-healing networks and monetizable quality tiers), rather than just using AI for generic customer support or marketing analytics.