Report: Telcos Missing Opportunity to Improve Networks Using AI

Why It Matters

Network‑centric AI promises higher revenue streams and performance gains, yet legacy constraints risk leaving operators behind as AI workloads surge.

Key Takeaways

• AI adoption centers on efficiency, not network optimization
• 41% expect AI‑driven network enhancements within two years
• Data‑center networks ill‑suited for AI workloads
• WBBA releases optical network whitepaper for AI era
• New IP NDI benchmarks regional readiness for Net5.5G/6G

Pulse Analysis

AI is reshaping telecoms, but most operators still treat it as a cost‑cutting tool rather than a catalyst for new services. The WBBA’s survey reveals that while customer‑service bots and security analytics dominate current deployments, the untapped potential lies in using AI to dynamically manage and optimize the network itself. This shift could unlock higher margins, faster service rollout, and differentiated offerings that go beyond traditional voice and data.

The transition to AI‑ready networks is hampered by legacy data‑center designs that lack the bandwidth, latency, and resiliency required for intensive machine‑learning workloads. Many operators inherited multi‑vendor, fragmented infrastructures that cannot scale to the demands of generative AI and real‑time analytics. Upgrading to optical, cloud‑native architectures not only resolves these bottlenecks but also positions telcos to support emerging use cases such as autonomous vehicles, immersive XR, and massive IoT deployments.

To guide this evolution, the WBBA introduced two strategic tools: a whitepaper on optical network architectures tailored for AI, and the IP Network Development Index (IP NDI) that benchmarks readiness across wide‑area, campus, data‑center, and autonomous segments. These frameworks help operators assess gaps, prioritize investments, and align with the Net5.5G/6G roadmap. By embracing network‑centric AI now, telcos can secure a competitive edge, drive new revenue streams, and meet the escalating performance expectations of AI‑driven digital transformation.