This topic covers the AI platforms and data foundations telcos use to automate network operations, customer experience, and service delivery. By 2025, operators are layering AI automation platforms and AI data platforms onto traditional OSS/BSS and radio infrastructure to enable predictive maintenance, closed‑loop orchestration, real‑time troubleshooting, and autonomous CX agents. Nokia’s telco-focused software and NVIDIA‑enabled GPU stacks are frequently used where low‑latency inference and large‑scale model training meet carrier-grade reliability. Key platform types include AI automation platforms (agent frameworks, real‑time assistants, policy engines) and AI data platforms (telemetry lakes, feature stores, and unified search). Representative tools: Observe.AI and Yellow.ai for contact‑center and CX/EX automation; Lindy and LangChain for building and governing autonomous AI agents; Dashworks for unifying apps/data into an omni‑search assistant; Run:ai (NVIDIA Run:ai) and FlexAI for GPU orchestration and workload routing across on‑prem and cloud; GitHub Copilot for developer productivity and automation workflows. Trends driving adoption are edge and hybrid cloud model placement for latency‑sensitive workloads, hardware‑aware orchestration to maximize expensive GPU capacity, and combining telemetry‑centric data platforms with agentic automation to close the loop between detection and remediation. Telcos must balance performance, observability, and compliance when deploying these stacks. For practitioners, the practical choice is less about a single vendor and more about integrating GPU orchestration, trustworthy agent frameworks, and a robust AI data layer to operationalize telco‑specific AI use cases reliably and at scale.