Telecommunications

The AI-native core: Highly resilient telco architecture using Google Kubernetes Engine

Wed, 04 Mar 2026 08:00:00 +0000

The telecommunications industry has reached a critical tipping point. Traditional, on-premises-heavy data center models are struggling under the weight of escalating infrastructure costs and an under utilization due to availability and compliance requirements. But the AI era demands exponential scale and beyond-nines reliability. The question for operators is no longer if they should modernize, but which architectural path will help them do that fastest.

Modernization isn't a "rip and replace" event; it’s a strategic choice. Today, we’re showcasing how Google Kubernetes Engine (GKE) can serve as a high-performance foundation for two versatile deployment strategies: cloud-centric evolution and strategic hybrid modernization.

The two paths to network modernization

Every operator has a unique appetite for risk, regulatory landscape, and investment base, with some prioritizing agility, and others emphasizing the need for local control. You can use GKE to support both approaches:

1. Cloud- centric modernization: Agility at scale

This path is for operators looking to fully harness the cloud's elasticity. Whether you’re migrating your own containerized network functions (CNFs) or building a cloud-native service like Ericsson-on-Demand, the goal is the same: move the heavy lifting to Google Cloud.

The benefit: By running mission-critical workloads like Voice Core or Policy Control Functions on Google's global fiber backbone, operators can scale instantly for peak events and move toward "zero-human-touch" operations.
The economics: Transition from heavy upfront CAPEX to a "pay-as-you-grow" model. You no longer need to over-provision hardware that sits idle; the cloud absorbs the bursts for you.
Time to market: Accelerate time to market for new services like fixed wireless access, IoT and private 5G.

2. Strategic hybrid modernization: Cloud agility, local control

For many telcos, a hybrid approach offers a better balance. Here, operators can selectively move agile control plane components and data analytics to the cloud while keeping latency-sensitive user-plane functions on premises or at the edge.

The benefit: Optimize for ultra-low latency and meet strict data sovereignty requirements by keeping data plane traffic local, while still gaining the AI-driven insights and orchestration power of the cloud.
The versatility: Using GKE, you can run your control plane workloads in the cloud and data plane services directly in your own data centers or at the network edge, enjoying a unified operational model across your environments.

Engineering the "telco-grade" foundation

Today, we are proud to showcase how GKE has evolved into the industry's most specialized platform for containerized network functions (CNFs), backed by massive momentum from operators and equipment vendor partners.

It’s achieved this thanks to a variety of capabilities.

Connectivity and isolation

Standard Kubernetes wasn't designed for the complex traffic separation that telcos require. GKE bridges this gap with:

Multi-networking API: A native Kubernetes way to manage multiple interfaces per Pod, bringing standard Network Policies to every interface.
Simulated L2 networking: A "migration superpower" that allows legacy applications to maintain their Layer-2 operational model while running on a modern cloud-native stack.
The telco CNI: Support for Multus, IPvlan, and Whereabouts on specialized Ubuntu images. This allows operators to isolate management, control, and user planes with surgical precision.

Persistent reachability

In a world of ephemeral containers, telco functions need stability. GKE enables this through:

GKE IP route: We’ve integrated equal-cost multi-path (ECMP)-like functionality directly into the GKE dataplane. If a workload fails, it is automatically and rapidly removed from the service path, providing high availability without complex external router configurations.
Persistent IP: GKE provides the static IP support that 5G core functions require for consistent reachability across their lifecycle without NAT that isn't available on standard Kubernetes.

Sub-second convergence

For telcos, every millisecond of downtime is a lost connection. GKE’s dataplane via HA Policy is optimized for near-zero downtime with ultra-fast failure detection and convergence, offering operators the choice between self-managed recovery or fully Google-managed failure detection.

Shifting from "saving" to "solving" with AI

For operators, the ultimate goal of modernization is to transition to an autonomous network. By running the core network functions on a platform adjacent to Google Cloud AI and data platforms such as Vertex AI and BigQuery, they can turn telemetry into actionable changes to optimize the network. Some use cases and benefits that modernization enables include:

Predictive AIOps: Use AI to identify performance degradation and trigger automated healing before a call ever drops. Use the cloud for on-demand burst capacity during sporting events or service launches. Or use the data from your GKE-hosted 5G core to fuel AI-powered automation that anticipates issues before they impact subscribers.
Intent-driven programmability: Shift from expensive, reactive operations and cut down new deployment setup times from several weeks to a couple of hours.
Monetize insights: Leverage AI on cloud-native data to identify and capture entirely new revenue opportunities in addition to rightsizing your networks.

Your journey, your terms

The future of telco is intelligent, resilient, and incredibly flexible. Whether you are taking your first step into a hybrid deployment or launching a fully cloud-hosted core, Google Cloud is your strategic partner.

Join us at MWC: Visit booth #2H40 in Hall 2 to see these solutions in action, including live demonstrations of mobile core running on GKE.

Fueling the autonomous network: Google Cloud and DigitalRoute simplify data readiness for AI

Wed, 04 Mar 2026 08:00:00 +0000

To achieve Level 4 and Level 5 autonomous network operations (ANO), the telecommunications sector is increasingly turning to AI to build self-healing, self-optimizing networks capable of predictive maintenance.

However there is a significant roadblock: AI is only as good as the data fueling it. While the industry fixates on AI agents, large language models (LLMs), and AIOps, the underlying data remains messy and fragmented. You can’t build a valid AI model on broken data.

Today, we’re announcing a new collaboration with data usage and monetization platform provider DigitalRoute to solve one of the industry’s biggest hurdles: data readiness. We’re launching reusable data pipelines that transform raw, chaotic network noise into high-quality, “AI-ready” data, enabling the next generation of autonomous agents.

Moving from "data swamps" to digital twins

Before an AI agent can resolve a network anomaly, it must first understand the environment. In a modern telecom network, this is a massive engineering challenge for four reasons:

Format disparity: Network data arrives in dozens of proprietary formats (e.g., ASN.1, XML, CSV, Binary) that can vary by vendor and domain. AI can't read these without a translator.
Volume and velocity: 5G networks generate billions of billing and event data records daily. Processing this in real time with ultra-low latency can crush legacy mediation systems.
Schema evolution: Every time a network function is upgraded or a new 5G feature is rolled out, the underlying data structure changes. This usually "breaks" traditional data systems.
Correlation complexity: A single customer session is often fragmented across multiple network elements. Without stitching these fragments together, AI lacks the context to understand the actual customer experience.

The result is a "data swamp" — massive amounts of unrefined information that AI models cannot use effectively.

The solution: A cloud-native architecture for data readiness

To address this, we’ve architected a solution with DigitalRoute that bridges legacy telecom protocols with cloud-native intelligence. By running DigitalRoute’s Usage Engine Private Edition on Google Kubernetes Engine (GKE), we’ve created a reusable pipeline that scales dynamically across edge and cloud locations.

This architecture provides three distinct advantages:

Normalization at the edge and core: We decode complex binary messages into a unified common data model and filter out irrelevant "noise" before it hits storage. This helps ensure downstream AI is decoupled from vendor-specific constraints while significantly reducing data egress costs.
The "dual-path" strategy: Once normalized, the data flows into two specialized environments:

For real-time operations: Data is pushed to Spanner to build a "network digital twin." This allows AI agents to traverse network topology over time to perform a quick root cause analysis.
For long-term analytics: Historical data flows into BigQuery. Because BigQuery is serverless and scales instantly, it allows operators to analyze petabytes of network traffic without managing infrastructure, feeding 'data products' directly into Vertex AI for rapid model training.

Contextualized subscriber traces: Through our Network and Subscriber Trace Analysis (NSTA) solution, we align messages from different nodes based on timestamps and identifiers. This builds a coherent, chronological session flow, giving AI agents the end-to-end context of the subscriber’s journey.

This architecture ensures that whether an AI agent needs to react to a fiber cut in near real-time with Spanner or predict a capacity crunch next month with BigQuery and Vertex AI, it is always consuming consistent, high-quality data.

From data to outcome: Enabling autonomous agents

This partnership is about enabling production-grade AI use cases. By establishing these reusable pipelines, we provide the foundation for:

Rapid anomaly detection: Agents can detect subtle deviations in network performance because the baseline data is consistent and noise-free.
Automated root cause analysis: When a failure occurs, agents can correlate radio access network (RAN) issues with core trace data instantly to pinpoint the fix.
Predictive maintenance: Using historical data in BigQuery, operators can train Graph Neural Networks (GNN) to predict capacity bottlenecks days in advance.

Accelerate your autonomous network mission

Data readiness is the difference between a "science project" and a production-grade autonomous network. By standardizing the ingestion layer today, telecoms can build the essential foundation for the AI-driven networks of tomorrow.

Ready to start?

Contact Google Cloud Sales to schedule a Data Readiness Workshop and explore our reference architecture for Autonomous Network Operations.
Reach out to DigitalRoute to learn how their Usage Engine on GKE can solve your specific format disparity and volume challenges.

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The rise of the autonomous network: How GraphML is redefining telecom operations

Wed, 04 Mar 2026 08:00:00 +0000

The complexity of modern telecommunications is exploding. Communication Service Providers (CSPs) are no longer managing static, isolated networks; they are orchestrating massive, multi-layer ecosystems that span 5G radio access, transport fiber, edge compute, and centralized cloud cores.

To manage this complexity, CSPs are building autonomous networks — self-managing telecommunications networks that use AI, machine learning, and closed-loop automation to self-configure, self-optimize, self-heal, and self-secure with minimal human intervention. According to Google Cloud’s autonomous networks framework, a critical building block for an autonomous network is a high-fidelity, real-time digital twin. You can then use advanced graph machine learning (GraphML) such as graph neural networks (GNNs) on the network digital twin to analyze, predict and proactively remediate potential problems on the live network.

This approach is not new at Google. It is the same architectural philosophy we use to manage our planet-scale network and power complex systems like Waymo’s self-driving car technology. GraphML enables this by understanding the geometry of the world around it, and the relationships of the objects in it, enabling it to make safe, autonomous decisions. Today, through a strategic collaboration with GraphML-based AIOPs solution provider NetAI, we’re bringing this same relationship-aware intelligence to the telecom industry. At Mobile World Congress (MWC) 2026, Spanish telecommunications provider MasOrange is showcasing a Proof of Concept (PoC) that explores this concept as a way to drive fully managed AIOps.

The digital twin foundation: Modeling your network

The first step in building an autonomous network is to move from a static inventory to a dynamic digital twin.

Traditional operational models rely on fragmented data lakes, where topology is often just another table. In our framework, we use Spanner Graph to build a dynamic digital twin of the network, explicitly modeling live network relations and topology. This twin captures billions of dependencies — mapping exactly how a specific optical transponder supports a particular IP interface, for example, which in turn supports a customer’s 5G slice.

This graph structure is not just for visualization; it provides the topological context as input for advanced GNN-based AI models.

GNNs vs. traditional ML

For years, the industry applied traditional machine learning (ML) to network operations, often with mixed results. To understand why GNNs are the superior choice for autonomous networks, let’s look at how they treat data compared to traditional methods.

Standard ML algorithms, such as random forests and basic deep learning, generally interpret network data as simplified, flat vectors or disconnected time-series sequences. This perspective results in models that:

Are topology-blind: Standard models do not inherently understand network topology. They don't "know" that Router A is physically connected to Switch B, meaning they miss the one of the most important features of a network failure: propagation.
Are symptom-based: Traditional ML models excel at detecting that a metric has spiked (anomaly detection) but struggle to understand why.
Detect correlation, not causality: These models rely on statistical correlation. If CPU usage spikes at the same time as packet loss, they assume a link. However, in a complex network, correlation is often coincidental or a downstream symptom of a distant failure.

GNNs are fundamentally different because they are structure-aware. This enables:

Native topology processing: GNNs ingest the network graph directly. They don't just look at the data on the nodes; they process the connections between them.
Deterministic reasoning: Instead of guessing based on statistics, GNNs use message passing. If a fiber cut occurs, the model mathematically propagates that "fault signal" along the known physical paths in the digital twin to predict exactly which upper-layer services will fail.
Precise anomaly detection: By understanding relationships between neighbors, a GNN can distinguish between a local anomaly (one chaotic router) and a structural anomaly (a sub-graph failure), drastically reducing false positives.

NetAI GNN on Google Autonomous Network stack

The technology stack: Google tf-GNN and NetAI

The MasOrange PoC is an exploration of GraphML, an integrated stack that combines Google’s infrastructure with NetAI’s domain expertise.

Google tf-GNN Library: Google developed and open-sourced TensorFlow GNN (tf-GNN), a production-tested library designed to build GNNs at massive scale. This is the same class of modeling Google uses to analyze complex graph structures in its own products, enabling developers to easily work with graph data and build large-scale GNN models.
NetAI fine-tuning: NetAI explored these foundational tf-GNN models and, in collaboration with Google, developed advanced GNN models that are significantly more tailored to the telecom use case. For example, the fine-tuned models understand specific telco behaviors such as distinguishing between BGP session flaps, optical signal degradation, and congestion-induced latency.
Managed AIOps: NetAI wraps these models in a fully managed platform that handles the end-to-end lifecycle, from automated network discovery and graph construction to the final root cause analysis.

Executive perspectives

"As we scale our operations, the ability to pinpoint root causes across millions of interconnected components is no longer optional. Partnering with Google Cloud and NetAI on this GraphML-driven approach allows us to explore and transform our observability into a proactive engine for service reliability." - Roberto González Librán, Head of Observability and Automation, MasOrange

"Our autonomous networks framework is designed to handle the intense data demands of modern telcos. By integrating GraphML capabilities with partners like NetAI, we are providing CSPs with the deterministic reasoning they need to run truly autonomous, self-healing networks." - Muninder Sambi, VP & GM of Networking, Google Cloud

Want to learn more?

The transition to autonomous networks requires more than just better monitoring; it requires a fundamental shift in how we model and reason about our infrastructure.

Ready to explore GraphML? Talk to your Google Cloud account team or contact NetAI to learn how GNN-powered digital twins can transform your network operations.

Scaling the autonomous network: Introducing the Data Steward and Core Network Agents

Wed, 04 Mar 2026 08:00:00 +0000

Last year, Google Cloud unveiled the Autonomous Network Operations framework, a comprehensive blueprint designed to help Communication Service Providers (CSPs) transition from manual management to zero-touch operations. As part of that launch, we introduced a RAN AI agent as a Proof-of-Value (PoV) asset, demonstrating how generative AI could autonomously optimize radio access networks (RAN).

Today, we’re announcing the evolution of our framework. Google Cloud. In partnership with Future Connections, we’re expanding the framework's capabilities to move beyond isolated tasks toward agentic AI that reasons and executes across the entire telecom stack. We’re introducing two critical innovations integrated directly into the umbrella: the Autonomous Data Steward and the Core Network VoLTE Agent, which New Zealand telecom provider One NZ has deployed into its operations.

"When we launched the Autonomous Network Operations Framework, our goal was to provide a blueprint for the future of telecom. Today, we are solidifying that framework by developing and launching critical new agents under the Autonomous Network umbrella. One NZ is deploying the Core Network VoLTE Agent, which is a testament to this progress, demonstrating how we are empowering operators to move beyond isolated use cases and truly operationalize AI at scale." - Muninder Sambi, VP & GM of Products, Google Distributed Cloud

The industry bottleneck: Why agents struggle to scale

While the industry has successfully deployed individual agents, scaling to a fully autonomous ecosystem has revealed a fundamental infrastructure challenge: data access is broken. Currently, every organization developing a new AI agent faces the same hurdles:

Data silos: Data is locked in rigid schemas and scattered across domains. For example, RAN and Core in BigQuery or Probes in Cloud Storage.
Semantic complexity: Without semantic context, data scientists can spend months deciphering cryptic vendor-specific counters (e.g., "pmCounterNumber_487") rather than building models.
Access issues: Each new agent requires a custom integration project, making rapid scaling challenging.

To solve this, we must first activate the data infrastructure’s intelligence. This requires a dual-layered approach: an intelligent agent to govern the data foundation and specialized agents to act upon it.

Expanding the framework: The Autonomous Data Steward

The Autonomous Data Steward is a Gemini-powered agent that serves as the intelligent backbone of the framework. It doesn’t just store data; it dynamically provisions it on demand for other agents to use.

Built on Google Cloud, the Steward utilizes a "zero-copy" architecture. Using Dataplex Universal Catalog, it stores metadata pointers rather than duplicating massive datasets. This approach reduces storage costs by up to 70% while helping ensure agents can access real-time telemetry.

The Autonomous Data Steward further automates the DataOps lifecycle by leveraging Google’s advanced reasoning models to bridge the gap between human intent and technical execution. This enables:

Semantic understanding: When a user or agent asks for data (e.g., "cell drop rate"), Gemini interprets the natural language intent and maps it to specific telecom counters using Dataplex Universal Catalog.
Dynamic ETL creation: If the dataset doesn't exist, the Steward autonomously writes and executes Python code (via Dataproc) to extract, clean, and join the data — reducing weeks of engineering to minutes.
Cross-domain unification: The agent can generate federated queries that join RAN performance, Core session failures, and Probe errors into a single view, transparently uniting isolated domains.

The Core Network (VoLTE) Agent

We are moving from reactive monitoring to self-healing networks. OSS remediation agents don’t just alert humans to problems; they diagnose and solve them. By breaking down data silos, we are building a cognitive layer that turns months of engineering expertise into milliseconds of automated action.

The IMS Quality Agent (VoLTE Agent) is our specialized core network specialist. Built on the foundation of the Autonomous Data Steward, it provides excellent voice service in three ways:

Real-time monitoring: It continuously tracks critical metrics like Call Setup Success Rates, Mean Opinion Scores (MOS), and CSCF performance.
Intelligent root cause analysis: Powered by Gemini’s multimodal reasoning, the agent correlates complex signaling data (SIP/Diameter) with Core performance to instantly identify issues like CSCF overload.
Autonomous recommendations: The agent recommends corrective actions, such as adjusting call routing or rebalancing load.

When the VoLTE Agent detects a dropped call, it queries the data to instantly fetch correlated IMS signaling and probe captures — data that previously lived in disconnected systems.

One NZ is already deploying this agent to proactively manage voice quality, moving beyond manual maintenance toward a fully autonomous network.

"We're excited to partner with Future Connections and Google Cloud, using Gemini's advanced capabilities to deploy agentic workflows into our network. This is another meaningful progress step on our journey towards fully autonomous network operations." - Kieran Byrne, CTO, One NZ

Accelerating the journey to autonomous operations

These launches represent an exciting leap forward in the framework. By integrating the infrastructure intelligence of the Autonomous Data Steward agent with the domain-specific logic of the VoLTE Agent into the Autonomous Network Operations framework, we’re delivering:

For operations teams: A critical MTTR accelerator, slashing the time required to identify and resolve voice service issues. This helps teams move from detection to resolution in a fraction of the time, maintaining strong network performance.
For data teams: A deployment accelerator, collapsing the time between model development and live production. This allows data teams to bypass traditional bottlenecks of data operations and realize the value of new ML deployments almost instantly.

Together, we’ve open-sourced these core methodologies, allowing operators to build and customize their own agentic workflows.

Ready to get started? If you’re interested in gaining early access to the Autonomous Data Steward or the VoLTE Agent, or want to learn more about One NZ’s deployment of the agent, please reach out to your Google Account Team or Future Connections today.

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From network APIs to network AI agents — building the agentic future of connectivity with Nokia

Tue, 03 Mar 2026 08:00:00 +0000

The telecommunications industry is entering its most significant shift yet: the agentic era. We’re moving beyond siloed automation tools toward a fully agentic ecosystem that can automate entire workflows.

At Mobile World Congress (MWC) Barcelona this week, we’re announcing the integration of Nokia’s Network as Code (NaC) platform with Google Cloud’s optimized agentic AI stack, to transform from network APIs into a fully agentic ecosystem. This delivers AI agents to telecommunications companies that can autonomously observe, program, and optimize the network through natural language and goal-oriented task automation.

The three pillars of the AI-network evolution

To meet the demands of the agentic era, the relationship between AI and the network is shifting from a "connected" model to a "deeply integrated" one. This evolution is unfolding across three distinct fronts:

The autonomous network: The network is transitioning from a manually managed utility to an autonomously orchestrated system with AI embedded directly into its control and data planes. By integrating intelligence into every phase — from initial build-out and planning to real-time fault detection and root cause analysis — the network becomes a self-healing fabric. This transition prepares the infrastructure for the programmable, high-fidelity data needs of the agentic era, ensuring that the network can anticipate and scale for demand before it impacts the enterprise.
Networks optimized for AI: As AI agents take on the "digital chores" of our lives, networks must be architected for heavy uplink data and low-latency "time-to-first-token" requirements. To make technology feel like magic rather than work, mobile networks must become hyper-dynamic, proactively negotiating with the nearest compute nodes like Google Distributed Cloud (GDC) to process tasks at the edge. This localized orchestration manages traffic and compute latency in the background, ensuring that AI assistants respond instantly without the user ever noticing a lag.
AI agents programming the network: While programmable networks have long promised to unlock value for enterprises and telcos, agentic AI is the catalyst that finally makes this accessible by transforming the developer experience. By exposing capabilities from the RAN to the core through a unified API framework, AI agents can now translate natural language prompts into complex, intent-driven network configurations. This shift enables autonomous systems to coordinate tasks and exchange data between agents, allowing the network to dynamically adapt to the specific needs of an application in real-time.

Nokia’s Network as Code platform — now connecting over 70 partners and 20+ network APIs — is becoming agent-enabled through integration with Gemini models and Google Cloud’s agentic framework. By leveraging standardized interaction protocols like A2A and MCP, these specialized agents move beyond simple automation to active reasoning — allowing them to interpret intent and manage complex network tasks autonomously.

The technical bridge: How Nokia and Google Cloud integrate

The shift from simple APIs to network AI agents is powered by a three-layer integration that connects Google Cloud’s intelligence to Nokia’s network expertise:

The exposure layer (Nokia Network as Code): This platform abstracts complex 5G core and RAN functions into standardized, Northbound APIs (CAMARA/GSMA Open Gateway). By accessing these Nokia APIs via Google Cloud Marketplace, developers can tap into global mobile networks and utilize the Network as Code portal to gain standardized access to network functions without the need to navigate underlying technologies.
The intelligence layer (Gemini with Network as Code): We are using Gemini to move from "calls" to "intents." Using MCP, Network as Code AI agents can autonomously identify and utilize network tools such as Quality of Service (QoS) or location verification to achieve specific business goals.
The interaction layer (agent-to-agent): Through the A2A protocol, a business’s agent (e.g., a logistics coordinator) can communicate a goal to a network agent. The network agent then translates that goal into network-specific instructions, orchestrating the network in real-time without manual coding.

Extensibility via the Agent Development Kit (ADK)

To enable a truly bespoke enterprise ecosystem, organizations can use the ADK to extend these capabilities. The ADK allows developers to build and deploy custom multi-agent systems that integrate proprietary business logic with Nokia’s network intelligence. This modularity ensures that specialized business agents can autonomously negotiate network resources to meet unique mission requirements, securely bridging the gap between internal enterprise data and external network capabilities.

From programmable network to intent-driven enterprise

The true power of this integration lies in how it bridges the "API gap." While Network as Code provides the essential programmable foundation, the addition of Agentic AI allows for a zero-code user experience. Developers no longer need to master low-level telco protocols; they simply define the business intent, and the agentic framework handles the underlying code execution.

The agent-enabled enterprise in action

By sensing real-time conditions — like a drop in mission-critical connectivity — the agentic ecosystem can autonomously resolve issues or provision resources before a service is impacted:

Autonomous logistics: A fleet management agent can request optimized connectivity for a future drone mission. The Network as Code agent automatically ensures coverage, quality, and priority throughout the flight path.
Enhanced security: An enterprise device management agent can ask a Network as Code agent to flag any customer subscription, device, or location anomalies in real-time, instantly hardening the security posture of mobile assets.
High-precision monitoring: A security agent can request a dedicated network slice to support multi-modal AI inference from mobile device feeds, ensuring the bandwidth is there exactly when the data starts flowing.

Why this matters for the telco ecosystem

This partnership bridges the gap between the complex world of network standards and the fast-moving world of cloud developers. For telco providers, it provides a clear path to transform the network from a cost center into an innovation engine. By making network capabilities discoverable and actionable within the Google Cloud Marketplace, telcos can finally monetize their 5G investments at scale.

“The network is no longer a bottleneck or a black box — it is a programmable, intelligent partner in our AI journey. With an intuitive intent-driven interface built into Nokia’s Network as Code, developing and deploying innovative services that leverage the network intelligence is possible at a much quicker pace, reducing time-to-revenue significantly.” - Vladimir Liulka, CEO, Blocksport

“Unlocking a seamless, intuitive developer experience is critical to the growth of monetizable services that leverage the full power of programmable networks and network APIs. Agentic AI framework, with agents providing the developer interface, provides the critical infrastructure to enable this experience. Nokia’s Network as Code platform, which is already integrated with Deutsche Telekom Network APIs, is a pioneer in enabling the transformation for programmable networks to the agentic world.” - Dr. Chathurangi Wickramasinghe, Senior Vice President, Magenta Business API, Deutsche Telekom

Experience the future at MWC

The shift to building the agentic future of connectivity is happening now. We invite you to join Nokia and Google Cloud at MWC to see these live use cases and discover how the Network as Code platform is turning the complexity of the network into a competitive advantage for the agentic era.

From framework to scale: Accelerating autonomous networks at MWC 26

Mon, 02 Mar 2026 08:05:00 +0000

Last year, we unveiled our Autonomous Network Operations framework — a blueprint for Communication Service Providers (CSPs) to move beyond siloed automation toward self-healing, "zero-touch" networks. Today, we’re pivoting from networks that merely use AI for insights to intelligent agents capable of sensing, reasoning, and taking autonomous action.

As we head into Mobile World Congress (MWC) Barcelona, we’re demonstrating how this shift is becoming a reality. By embedding AI into the heart of operations, companies like Deutsche Telekom and Vodafone are reducing operational complexity and turning connectivity from a utility into a value-creating engine.

Product innovation: The engine of the agentic telco

A key goal of the industry is to achieve Level 4 to 5 autonomy: a network that identifies, diagnoses, and fixes its own problems without human intervention. To do this, the underlying data platform must be as dynamic as the network itself. Over the last year, we’ve evolved our Cloud Spanner Graph and Vertex AI to handle the "dual nature" of telcos: the need for high-speed, real-time response for alarm correlation, combined with deep, historical pattern detection.

Today, we’re refining our platform to support these autonomous operations through:

The network digital twin: The network digital twin has evolved from a static map into a dynamic, temporal graph that represents the network’s live physical and logical state. It captures real-time performance and fault conditions while allowing agents to query historical states — such as the network’s appearance five hours or days ago — to perform instant, accurate root-cause analysis.
Unified graph data layer: We’re breaking down the silos between operational and analytical data. By leveraging Spanner Graph for digital twins and federated graph analytics through BigQuery, telcos can interoperate between real-time updates and deep historical analysis without complex, slow ETL (Extract, Transform, Load) processes.
Real-time predictions with GNN: Operators can now train Graph Neural Networks (GNNs) on their network digital twin data in Vertex AI. They can then use the trained GNN models, along with Spanner’s ML.PREDICT capability and the real-time data in the network digital twin, to move from monitoring to predicting, mathematically tracking how a failure might propagate, and resolving it before it impacts subscribers.

Solution updates: Accelerating time-to-value

One of the biggest hurdles to achieving Level 4 to 5 autonomy is manual delays caused by disconnected legacy systems. We’re launching new tools to replace bottlenecks with a unified, automated system:

Open-source data foundation: To accelerate adoption, we’re releasing our telco data pipeline and data models source code on GitHub. CSPs can now implement unified industry-standard ontologies without manual schema mapping.
New telco agents: In partnership with FutureConnections, we’re launching two new proof-of-value agents:
- Data steward agent: An agentic workflow that automates data governance to ensure digital twins remain accurate.
- Autonomous network agents: Currently being trialed by One NZ, these agents manage voice core and OSS networks, moving beyond monitoring to active execution — like independently rerouting traffic or resetting network settings to restore call quality the moment a drop is detected.

Ecosystem momentum: Innovation in action

True autonomy requires a vibrant ecosystem. This year, we’re highlighting several key milestones:.

MasOrange & NetAI: We’re working with NetAI to deliver GraphML-based AIOps. Together, we’ve launched a pilot project with MasOrange that demonstrates how specialized partner models can run on Google Cloud’s AI stack to resolve network incidents while providing the confidence for autonomous action.
Nokia "Network as Code": We’re partnering with Nokia to make networks fully programmable by turning complex technical code into AI agents that understand everyday language. This allows telcos to simply ask the network to perform complex tasks — like prioritizing network resources for critical services like emergency response or remote healthcare — without needing any manual engineering.

Looking ahead

The agentic AI era is here. By embedding AI into the fabric of a telco’s network, we’re helping operators transform from connectivity utilities into intelligent service providers that continue to delight customers.

Join us at MWC: Visit booth #2H40 in Hall 2 to see these solutions in action, including live demonstrations of multi-agent systems, digital twins, and physical AI robots. You can also dive deeper into our approach by downloading our latest whitepaper.

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How Fastweb + Vodafone reimagined data workflows with Spanner & BigQuery

Thu, 22 Jan 2026 17:00:00 +0000

Editor’s note: Fastweb + Vodafone, a leading telecommunications provider in Italy, created its Customer 360 platform to support faster and more personalized customer experiences across channels. By rebuilding serving layers and governance with Google Cloud services like Spanner, BigQuery, and Gemini, the company simplified its architecture and introduced AI-assisted engineering workflows. Fastweb + Vodafone now delivers real-time insights across the organization and is preparing for expanded AI agent use cases in the future.

Following the acquisition of Vodafone Italy by Swisscom in 2025, and looking toward our official merger in January 2026, we saw an opportunity to rethink how we serve our customers.

As a leading telecommunications provider in Italy, we aim to deliver timely, personalized experiences across mobile, broadband, and digital channels. Our team’s job is to give everyone at our business access to the customer insights they need, when they need them. Those insights rely on a robust Customer 360 platform where data from thousands of sources is unified and made available in real-time.

Both companies had already begun modernizing these customer data workflows on Google Cloud, especially with BigQuery. However, combining ecosystems exposed the limits of our existing setup. The architecture required more upkeep than we wanted and didn’t offer the flexibility our unified organization needed. It was time for us to rethink how we served and governed customer data.

Choosing a connected platform, not a single tool

When we stepped back and looked at what Fastweb + Vodafone needed, it was clear we weren’t searching for a single tool. We wanted a connected suite of products that could support real-time serving, stronger governance, and new AI-assisted workflows without adding operational weight.

Figure 1: Architecture diagram for the Customer 360 Serving Layer

We were covered on the warehouse side: BigQuery was already in place and handling core analytical workloads. What we lacked was a serving layer that could match BigQuery’s speed and flexibility while simplifying our architecture. We also wanted tighter integration across the stack so we could more easily surface insights and begin incorporating AI.

Google Cloud brought all these capabilities together in a unified way. With native integrations between products, it was the right choice. We could spend less time connecting systems and more time building the features our business actually needs.

Spanner accelerates serving without the overhead

Giving every channel real-time access to accurate customer data meant finding a serving layer that kept pace with telecom traffic. Spanner was the answer. Spanner delivers low-latency reads, horizontal scalability, high availability, and a fully managed environment with zero ops overhead.

Spanner’s native integration with BigQuery was the real game-changer. Before Spanner, we relied on several custom layers built solely to move warehouse data into downstream systems. But the direct interoperability between Spanner and BigQuery has made most of that complexity disappear. With Spanner as the front-end serving layer, we were able to migrate ten applications in two weeks.

Spanner also transformed how we monitor and maintain our workflows. Four separate monitoring processes collapsed into a single view, making troubleshooting and optimization far faster. And with Apigee exposing Spanner data to the call center, digital channels, and partner systems, the improvements were felt across the entire organization — not just within engineering.

Making sense of thousands of data flows

With Spanner in place as our serving layer, the next challenge was making sense of the thousands of batch and real-time ingestion flows that power Fastweb + Vodafone Data platform. Understanding how data moves across these pipelines is essential for quality, compliance, and day-to-day engineering. We needed something faster and far more intuitive than our previous solution.

Spanner’s multi model capabilities gave us that foundation.

Spanner Graph allowed us to map lineage in a way that reflects how our platform actually works: which tables drive specific jobs, how transformations cascade, and where dependencies sit. From there, vector search and full-text search added a richer discovery layer. This made it easier to surface results quickly, even in a large and complex ecosystem.

Together, these features gave us the governance experience we wanted — not only more accurate, but much easier for engineers and analysts to work with.

Figure 2: Spanner’s Graph model allowed us to rapidly build a scalable, easy-to-navigate visualization of complex data lineage for improved Data Governance

Effortless documentation

We also wanted to make development smoother across our organization’s large and varied codebase. Many services had limited documentation, especially older systems that had evolved over time. Gemini helped us close those gaps.

With Gemini, our teams can generate clear documentation directly from the code, which saves hours of manual work. We store this output in Spanner as vector embeddings, which creates a searchable knowledge base that grows alongside our platform.

To make that knowledge even easier to use, we built a chat interface powered by Gemini. Developers can ask natural-language questions — say, about a function or how a change might ripple through the system — and get a context-aware answer in seconds. It’s a far more intuitive way to explore the codebase and reduces the time spent deciphering legacy logic or chasing down subject-matter experts.

Figure 3: Code Documentation augmentation and search with Gemini and Spanner

This AI-assisted workflow is now a regular part of how we build. It speeds up onboarding and gives us a scalable way to improve productivity even as our systems evolve.

Engineering at the speed of business

Rebuilding our Customer 360 platform with Google Cloud services has already changed how Fastweb + Vodafone works.

Workflow monitoring is simpler, pipelines are leaner, and real-time serving is now the norm.

The gains from this work are also visible across the business: Call centers now see more complete, up-to-date customer information, digital channels can rely on consistent data without custom integrations, and partners can access what they need with low latency through Apigee. Together, it all creates a smoother, more personalized customer experience — exactly what we imagined at the start of this process.

And we’re just getting started. We are now exploring Spanner Graph capabilities to better understand customer relationships through householding and social indexing. We also see more potential for AI agents to help teams improve analysis and decision-making.

Google Cloud didn't just simplify our engineering – it changed how our entire organization operates.

Learn more:

Explore Spanner, a fully managed relational database for global-scale workloads, and the power of Spanner Graph!
Learn how BigQuery simplifies analytics with built-in performance and scalability.
Bring AI assistance into engineering workflows with Gemini for Google Cloud.

How Ericsson achieves data integrity and superior governance with Dataplex

Fri, 07 Nov 2025 17:00:00 +0000

Data is the engine of modern telecommunications. For Ericsson's Managed Services, which operates a global network of more than 710,000 sites, harnessing this data is not just an advantage, it's essential for business growth and leadership. To power the future of its autonomous network operations and deliver on its strategic priorities, Ericsson has been on a transformative data journey with governance at the center of its strategy.

Ericsson moved from foundational practices to a sophisticated, business-enabling data governance framework using Google Cloud’s Dataplex Universal Catalog, turning data from a simple resource into a strategic asset.

From a new operating model to a new data mindset

Ericsson’s journey began in 2019 with the launch of the Ericsson Operations Engine (EOE), a groundbreaking, AI-powered operating model for managing complex, multi-vendor telecom networks. The EOE made one thing clear: to succeed, data had to be at the core of everything.

This realization led Ericsson to develop its first enterprise data strategy, which established the core principles for how data is collected, managed and governed. However, building a strategy is one thing — operationalizing it at scale is another.

To move beyond theory to address real-world challenges, Ericsson needed to:

Build trust: Provide discoverable, clean, reliable, and well-understood data to the teams deploying analytics, AI, and automation.
Balance defense and offense: Ensure compliance with contracts and regulations (defensive governance) while empowering teams to innovate and create value from data (offensive governance).
Ensure data integrity: Ericsson users see data integrity as the core principle for effective data management. Data quality, which is essential for reliable, trustworthy data throughout its lifecycle, is a key quality indicator (KQI) for measuring effectiveness. Any quality deviations must be managed like a high-priority incident with clear Service Level Agreements (SLA) for restoration and resolution.

To realize this vision, Ericsson sought a platform that could match its ambition for global-scale governance and innovation — and Dataplex Universal Catalog emerged as the ideal choice.

Ericsson made its selection based on four key criteria.

First, its capabilities aligned perfectly with Ericsson’s requirements for cloud-native transformation, business principles, and a long-term governance vision, underpinned by Ericsson’s strategic partnership with Google Cloud. Second, from a technical standpoint, Dataplex provided a tightly integrated, end-to-end ecosystem as a native Google Cloud solution, translating to faster time-to-market for use cases and reduced integration overhead.

Third, the platform offered a practical operating model that enabled quick learning, adaptation, and self-sufficiency, supporting an agile approach where Ericsson could fail fast and iterate. Finally, as an existing Google Cloud customer, Dataplex presented a clear and manageable Total Cost of Ownership (TCO), serving as a natural extension of Ericsson’s existing environment and providing a clear, manageable cost profile for both storage and compute extension with governance capabilities.

Putting governance into practice: Key capabilities in action

With Dataplex Universal Catalog as the governance foundation, Ericsson began implementing the core pillars of its governance program, moving from manual processes to an automated, intelligent data fabric.

More specifically, Ericsson established a unified business vocabulary within Dataplex. This transformative first step eliminated ambiguity and ensured their teams — from data scientists to data analysts — were speaking the same language. These glossaries also captured tribal knowledge and became the foundation for creating trusted data products.

In addition, Dataplex's catalog is at the heart of the data governance solution, making data discovery simple and intuitive for authorized users. Ericsson uses its tagging capabilities to enrich the data assets with critical metadata, including data classification, ownership, retention policies, and sensitivity labels. Dataplex’s ability to automatically visualize data lineage, down to the column level, is another game-changer. Different data personas can instantly understand a dataset's origin and its downstream impact, dramatically increasing trust and reducing investigation time. Furthermore, trustworthy AI models are built on high-quality data. For proactive data quality, Ericsson uses Dataplex to run automated quality checks and profiles on its data pipelines. When a quality rule is breached, an alert is automatically triggered, creating an incident in its service management platform to ensure data issues are treated with the urgency they deserve.

These capabilities are all underpinned by Ericsson's Data Operating Model (DOM), a framework that defines the policies, people, processes, and technology needed to translate its data strategy into tangible value, comprising several facets when working with data.

Enterprise data architecture: Managing data flow, enterprise data modeling and best practices for data collection till consumption
Technology and tools: Business glossary, master, reference and metadata management, data modeling, and data quality management
Roles and responsibilities: Roles to manage and govern data (i.e., end-to-end data lifecycle and stewardship)
Data and model assurance: Data pipelines monitoring, data observability, and data quality monitoring
Governance: Manage data compliance, risk and security management, managing operational level agreement, objective and key results, and audit management
Processes: Data governance, data quality, data management, and data consent related processes

Looking ahead: The future is integrated and intelligent

As a global technology leader, Ericsson is committed to shaping the future of AI-powered data governance. Technology, especially in the AI space, is evolving at a breathtaking pace and both the data and AI governance practices must keep up.

These developments are guiding Ericsson’s future priorities, which include bridging the gap between data and AI governance, especially with the rise of generative and agentic AI. These plans include evaluating using generative AI capabilities in BigQuery and Dataplex to simplify governance and pursuing solutions that ensure transparency, explainability, fairness and manage risk in the deployment of AI models.

In addition to harnessing the power of AI for at-scale governance, Ericsson will also include usage of governance workflows, glossary-driven data quality policies, at-scale assignment of terms to assets, bulk import and export of glossaries, AI-powered glossary recommendations, and data quality re-usability functionalities. Ericsson is also aligning its architecture with data fabric and data mesh principles, empowering teams with self-service access to high-quality, trusted data products.Finally, Ericsson will be assessing the use of more granular, policy-based access controls to complement existing role-based access, further strengthening its data security, protection and privacy.

For any organization embarking on a similar path, Ericsson’s experience offers several key lessons:

Governance is a value enabler, not a blocker: A modern data governance program is focused on business enablement first, driving value and innovation, to complement policies, rules and risk management.
It's a journey, not a destination: Be prepared to fail fast, learn, and adapt. The landscape is constantly changing at breakneck speed.
Focus on business outcomes, not tools: Technology is a critical enabler, but the conversation is about the business value you’re creating. Simplify the story, speak the language of the business, and unpack the hype.
Culture is everything: For governance to be effective, it’s the responsibility of everyone. This requires strong leadership, sponsorship, and a "data-first" mindset embedded throughout the organization.

By partnering with Google Cloud and tapping into the power of Dataplex Universal Catalog, Ericsson is building a data foundation that is not only compliant and secure but agile and intelligent — ready to power the next generation of autonomous networks.

How AI can scale customer experience — online and IRL

Tue, 14 Oct 2025 21:00:00 +0000

Customer service teams at fast-growing companies face a challenging reality: customer inquiries are growing exponentially, but scaling human teams at the same pace isn’t always sustainable.

Intelligent AI tools offer a new path forward. They handle routine questions automatically so employees can focus on more complex customer service tasks that require empathy, judgment, and creative problem-solving.

LiveX AI enables businesses to build and deploy advanced AI systems that deliver natural conversational experiences at scale. These can show up as chat bots, call center agents — even 3D holographic personas in live settings.

To handle thousands of concurrent, real-time interactions with low latency requires infrastructure that is both powerful and elastic, especially when seamlessly escalating complex issues to human agents.

In this joint technical post, we'll share the technical blueprint LiveX AI uses to build and scale its intelligent customer experience systems on Google Cloud, demonstrating how the right combination of services makes this transformation possible.

Why this architecture matters: Proven ROI

This architecture delivers measurable business impact.

90%+ self-service rate for Wyze: Smart home leader Wyze deployed LiveX AI to achieve a 90%+ self-service rate, enabling their support team to focus on complex cases that require human expertise while improving the overall customer experience.
3x conversion for Pictory: The video creation platform Pictory saw a 3x increase in conversions by using LiveX AI to proactively engage and qualify website visitors.

These results are only possible through a sophisticated, scalable, and secure architecture built on Google Cloud.

Platform capabilities designed for scale

The LiveX AI platform is designed to be production-ready, enabling companies to easily deploy intelligent customer experience systems. This is possible through key capabilities, all running on and scaling with Google Cloud's Cloud Run and Google Kubernetes Engine (GKE):

AgentFlow orchestration: The coordination layer that manages conversation flow, knowledge retrieval, and task execution. It routes routine queries automatically and escalates complex issues to human agents with full context.
Multilingual by design: Built to deliver native-quality responses in over 100 languages, leveraging powerful AI models and Google's global-scale infrastructure.
Seamless integration: Connects securely to internal and external APIs, enabling the system to access account information, process returns, or manage subscriptions, giving human agents complete context when they step in.
Customizable knowledge grounding: Trained on specific business knowledge to ensure accurate and consistent responses aligned with team expertise.
Natural interface: Deployed via chat, voice, or avatar interfaces across web, mobile, and phone channels.

Figure 1: LiveX real-world 3D assistants

The technical blueprint: Building intelligent customer experience systems on Google Cloud

LiveX AI's architecture is intelligently layered to optimize for performance, scalability, and cost-efficiency. Here's how specific Google Cloud services power each layer.

Figure 2: LiveX AI customer service agent architecture on Google Cloud

The front-end layer

Managing real-time communication across web, mobile, and voice channels requires lightweight microservices that handle session management, channel integration, and API gateway services.

Cloud Run is the ideal platform for this workload. As a fully managed, serverless solution, it automatically scales from zero to thousands of instances during traffic spikes, then scales back down, so LiveX AI only pays for the computation they actually use.

The orchestration and AI engine

The platform's core, AgentFlow, manages the conversational state, interprets customer intent, and coordinates responses. When issues require human expertise, it routes them to agents with complete context. The system processes natural language input to determine customer intent, breaks down requests into multi-step plans, and connects to databases (like Cloud SQL) and external platforms (Stripe, Zendesk, Intercom, Salesforce, Shopify) so both AI and human agents have complete customer context.

Cloud Run for orchestration automatically scales based on request traffic, perfectly handling fluctuating conversational loads with pay-per-use billing.

GKE for AI inference provides the specialized capabilities needed for real-time AI:

GPU management: GKE's cluster autoscaler dynamically provisions GPU node pools only when needed, preventing costly idle time. Spot VMs significantly reduce training costs.
Hardware acceleration: Seamless integration with NVIDIA GPUs and Google TPUs, with Multi-Instance GPU (MIG) support to maximize utilization of expensive accelerators.
Low latency: Fine-grained control over specialized hardware and the Inference Gateway enable intelligent load balancing for real-time responses.

With this foundation, LiveX AI can serve millions of concurrent users during peak demand while maintaining sub-second response times.

The knowledge and integration layer

From public FAQs to secure account details, the knowledge layer provides all the information the system needs to deliver helpful responses.

The Doc Processor (on Cloud Run) builds and maintains the knowledge base in the vector database for the Retrieval-Augmented Generation (RAG) system, while the API Gateway manages configuration and authentication. For long-term storage, LiveX AI relies on Cloud SQL as the management database, while short-term context is kept in Google Cloud Memorystore.

Putting it all together

Three key advantages emerge from this design: elastic scaling that matches actual demand, cost efficiency through serverless and managed GKE services, and the performance needed for real-time conversational AI at scale.

Looking ahead: Empowering customer experience teams at scale

The future of customer service centers on intelligent systems that amplify what human agents do best: empathy, judgment, and creative problem-solving. Businesses that adopt this approach empower their teams to deliver the personalized attention that builds lasting customer relationships, freed from the burden of repetitive queries.

For teams evaluating AI-powered customer experience systems, this architecture offers a proven blueprint: start with Cloud Run for elastic front-end scaling, leverage GKE for AI inference workloads, and ensure seamless integration with existing platforms.

The LiveX AI and Google Cloud partnership demonstrates how the right platform and infrastructure can transform customer service operations. By combining intelligent automation with elastic, cost-effective infrastructure, businesses can handle exponential inquiry growth while enabling their teams to focus on building lasting customer relationships.

To explore how LiveX AI can help your team scale efficiently, visit the LiveX AI Platform.
To build your own generative AI applications with the infrastructure powering this solution, get started with GKE and Cloud Run.

From legacy to cloud: How Deutsche Telekom went from PySpark to BigQuery DataFrames

Thu, 14 Aug 2025 16:00:00 +0000

In today's hyper-competitive telecommunications landscape, understanding and maximizing the Customer Lifetime Value (CLV) metric isn't just a nice-to-have, it's a strategic imperative. For Deutsche Telekom, accurate CLV calculations are the bedrock of informed decisions, driving crucial initiatives in customer acquisition, retention, and targeted marketing campaigns. The ability to predict and influence long-term customer relationships directly translates to sustained profitability and a competitive edge.

Initially, Deutsche Telekom's Data Science team processed data within an on-premises data lake environment, leveraging Jupyter notebooks and PySpark. However, this reliance on legacy on-prem data lake systems was creating significant bottlenecks. These systems, designed for a different era of data volume and complexity, struggled to handle the massive datasets required for sophisticated CLV modeling. The result? Extended processing times, limited agility in data science experiments, and a growing gap between potential insights and actionable results.

This challenge demanded a transformative solution, leading Deutsche Telekom to embrace the power of modern cloud infrastructure, specifically Google Cloud's BigQuery, to unlock the full potential of their data and accelerate their journey towards data-driven innovation.The core of this transformation was the migration of critical data science workloads, beginning with the CLV calculations, to BigQuery.

Deutsche Telekom decided that for distributed Python data processing, they wanted to move off of PySpark-based code and adopt BigQuery DataFrames a.k.a. BigFrames. BigFrames is an open-source Python library offered by Google that scales Python data processing by transpiling common Python data science APIs to BigQuery SQL. You can read more about BigFrames in the official introduction to BigFrames and can refer to the public git repository.

This decision was driven by three factors:

Keep it simple: By moving all the data processing to BigQuery, the company would be standardizing on a single data processing technology. This helps in administration and standardization across the organization.
Bet on universal skills: Python and Pandas are universal data science skills, but bringing in Spark would introduce additional learning. BigFrames is Pandas-like — it pushes processing to BigQuery. The move reduces the upskilling required to work on data science tasks.
Focus on business logic: Data science teams can focus on core business logic and less on the infrastructure required to make that logic work.

In other words, this move was not just a technical upgrade — it was a strategic shift towards a more agile, efficient, and insight-driven future. By leveraging BigQuery's ability to process massive datasets quickly and efficiently, along with the tight integration of BigQuery DataFrames and its compatibility with familiar pandas and scikit-learn APIs, Deutsche Telekom aimed to eliminate the bottlenecks that had hindered their data science initiatives. This solution, centered around BigQuery and BigQuery DataFrames, provided the foundation for faster insights, improved decision-making, and ultimately, enhanced customer experiences.

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The migration journey

To realize these benefits, Deutsche Telekom meticulously planned a two-phase technical migration strategy, designed to minimize disruption and maximize the speed of achieving tangible business results.

The actual effort required to execute the two phases above was one person-week. Conversion with Gemini was 95% accurate and worked as expected. In the first phase, the most time was spent on manual data validation. Furthermore, around 70% of the pandas code automatically functioned as BigQuery DataFrames code as well. Some adjustments were required for data types and scalar functions.

Phase 1: Accelerated transition with AI-powered code conversion
The initial phase focused on rapidly converting existing code to a format compatible with Google Cloud's environment. This was significantly accelerated by leveraging advanced AI tools like Gemini, which streamlined the code conversion process. This allowed the team to focus on validating results and ensuring business continuity, rather than getting bogged down in lengthy technical rewrites.

Phase 2: Optimizing for cloud scalability and performance
The second phase involved adapting the data processing to fully leverage BigQuery. This step was crucial for eliminating the performance bottlenecks they had experienced with the legacy systems. By aligning the data processing with BigQuery's capabilities, Deutsche Telekom was able to unlock significant improvements in processing speed and scalability, allowing for faster and more insightful data analysis.

Key business benefits of the technical approach:

Deutsche Telekom’s technical migration strategy was not just about moving data; it was about strategically enabling faster, more scalable, and more reliable data-driven decisions. Among the benefits that they saw from this approach were:

Faster time to insight: The accelerated code conversion, powered by AI, significantly reduced the time required to migrate and validate the data, enabling quicker access to critical business insights.
Improved scalability and performance: The transition to BigQuery's cloud-native architecture eliminated performance bottlenecks and provided the scalability needed to handle growing data volumes.
Reduced operational risk: The structured, two-phase approach minimized disruption and ensured a smooth transition, reducing operational risk.
Leveraging existing expertise: The use of familiar tools and technologies, combined with AI-powered assistance, allowed the team to leverage their existing expertise, minimizing the learning curve.

Of course, a project of this scale presented its own set of unique challenges, but each one was addressed with solutions that further strengthened Deutsche Telekom’s data capabilities and delivered increased business value.

Challenge 1: Ensuring data accuracy at scale
Initially, the test data didn't fully represent the complexities of their real-world data, potentially impacting the accuracy of critical calculations like CLV.

Solution: During the test phase, they relaxed the filters on the data sources to overcome the data size problem. They implemented the changes in both old and new versions of the code to reliably compare the outputs.

Challenge 2: Maintaining robust security and compliance
Balancing the need for data access with stringent security and compliance requirements was an important consideration. BigQuery DataFrames documentation highlights the need for admin-level IAM privileges for some tasks like Remote Functions, which may not be possible in enterprise environments.

Solution: Deutsche Telekom developed customized IAM roles that met its security standards while enabling data access for authorized users. This helped ensure data security and compliance while supporting business agility.

By addressing these challenges strategically, Deutsche Telekom not only had a successful migration, but it also delivered tangible business benefits. Deutsche Telekom now has a more agile, scalable, and secure data platform, enabling them to make faster, more informed decisions and ultimately enhance their customer experience. This project demonstrates the power of cloud transformation in driving business value.

Deutsche Telekom's successful migration to BigQuery was a strategic transformation, not just a technical one. By overcoming the limitations of their legacy systems and embracing cloud-based data processing, they've established a robust foundation for future innovation. This project underscores the power of strategic partnerships and collaborative problem-solving, showcasing how Google Cloud's cutting-edge technologies and expert consulting can empower businesses to thrive in the data-driven future.

Ready to unlock the full potential of your data?

Whether you're facing similar challenges with legacy systems or seeking to accelerate your data science initiatives, Google Cloud's data platform can provide the solutions you need. Explore the capabilities of BigQuery and DataFrames, and discover how our expert consultants can guide you through a seamless cloud migration.

Contact Google Cloud Consulting today to discuss your specific needs and embark on your own journey towards data-driven innovation.

_{A special thanks to Googler Rohit Naidu, Strategic Cloud Engineer, for his contributions to this post.}

Engineering Deutsche Telekom's sovereign data platform

Tue, 15 Jul 2025 07:00:00 +0000

Imagine transforming a sprawling, 20-year-old telecommunications data ecosystem, laden with sensitive customer information and bound by stringent European regulations, into a nimble, cloud-native powerhouse. That's precisely the challenge Deutsche Telekom tackled head-on, explains Ashutosh Mishra. By using Google Cloud's Sovereign Cloud offerings, they've built a groundbreaking "One Data Ecosystem."

When we decided to modernize our telecommunications data ecosystem at Deutsche Telekom, we faced a daunting task. Over 40 legacy systems, each an ecosystem (data warehouse or data lake), held terabytes of customer, network, and operational data. Each system had 5,000 to 10,000 users who had built their workflows around these isolated silos over decades of use.

The result? What I lovingly call a "spaghetti mess” of data distribution and an undetermined cost of value creation.

The technical challenge of building our One Data Ecosystem (ODE) was one thing — consolidating disparate systems always is. It’s the regulatory puzzle that made it genuinely interesting.

As a telecommunications company in Germany, we handle some of the most sensitive data imaginable: call data records (CDRs), network telemetry, and customer location data. Under GDPR and Germany's Telecommunications and Telemedia Data Protection Act regulations, this data simply can not leave sovereign borders or risk exposure to foreign legal frameworks.

Here's where it gets technically fascinating: Traditionally, regulated industries solve this by building expensive on-premises encryption and pseudonymization infrastructure. You process your sensitive data locally, strip it of identifying characteristics, and then send sanitized versions to the cloud for analytics.

This approach costs millions in dedicated hardware and creates a fundamental innovation bottleneck. We wanted something radically different: cloud-native processing of sensitive data, without compromise.

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Engineering sovereignty at cloud scale

The breakthrough came with Google Cloud's approach to digital sovereignty and their Germany Data Boundary by T-Systems offering (formerly known as Sovereign Controls by T-Systems). The architecture is elegant in its simplicity: Deutsche Telekom maintains complete cryptographic control through external key management (EKM) while using cloud-native data services.

Here's how the technical magic works. T-Systems manages our encryption keys entirely outside Google's infrastructure. This creates sovereign protection against foreign legal frameworks, and ensures they are able to control access to their data and deny access for any reason.

Meanwhile, we use format-preserving encryption (FPE) algorithms that maintain data utility for analytics while ensuring privacy protection.

The core innovation is our custom pseudonymization layer, which comprises C++ modules with Java wrappers that handle real-time data transformation during ingestion. This eliminates the traditional need for separate preprocessing infrastructure while maintaining analytical value.

Choosing our data format was crucial. After extensive POCs, we settled on Apache Iceberg, and here's why that matters for anyone building similar platforms: Iceberg solves the polyglot analytics problem beautifully. Our data scientists prefer working in Python notebooks, our engineers use Spark, and our business analysts work with SQL.

While traditional approaches force you to pick sides or maintain multiple data copies, Iceberg provides us with a single source of truth that speaks every language fluently.

The three-layer architecture we built around Iceberg is worth replicating: Raw data lands directly in Cloud Storage, flows through an Atomic layer for normalization and schema evolution, and then surfaces in an Analytic layer optimized for specific use cases. BigQuery, Spanner, BigTable, and Cloud SQL each serve their optimal workloads while sharing the same underlying Iceberg foundation.

Performance and scale in production

We are migrating from more than 40 legacy systems to reinvigorate our business demands. We have ingested over 200 source systems in just six months. However, the real validation came recently when one of our use cases, running live on the new platform, achieved a 22x performance improvement over its legacy predecessor.

That number represents the compound effect of eliminating data silos, reducing ETL complexity, and using cloud-native autoscaling. When you can process overnight analytics jobs in minutes instead of hours, you fundamentally change how business decisions get made.

What makes this platform genuinely scalable isn't just the technical architecture; it's the operational model. We've implemented a GitOps approach with policy-as-code onboarding through GitLab CI/CD pipelines, where infrastructure and governance policies are defined declaratively and deployed automatically. This means onboarding a new system takes hours instead of months, and compliance becomes automatic rather than manual.

Additionally, we're already running agentic AI use cases on the public side of our platform. The unified data model we've built positions us perfectly for the next wave of AI innovation. As more AI services become available with sovereign controls, we'll be ready to expand our deployment at scale.

The key insight: Build your data foundation with AI in mind, even if you can't implement every AI capability immediately. Clean, unified, well-governed data is the prerequisite for everything that's coming.

A blueprint for the future

This is one of the largest and most comprehensive data platforms built on Google Cloud’s Data Boundary — but it won't be the last. The architectural patterns we've developed, external key management, format-preserving encryption, unified data formats, policy-as-code governance, are replicable across any regulated industry.

The business case is also compelling: Eliminate expensive on-premises preprocessing infrastructure while gaining cloud-scale analytics capabilities. The technical implementation is proven. What's needed now is the willingness to engineer sovereignty, rather than simply accept traditional trade-offs.

For my fellow data architects in regulated industries, you don't have to choose between innovation and compliance. With the right technical approach, you can achieve both and build platforms that position your organization for the AI-driven future that's rapidly approaching.

The maturity and integration of Google Cloud's data and AI capabilities, combined with our intensive collaboration between engineering teams, has made this transformation possible. We're not just customers: We're co-creating the future of sovereign cloud platforms.

Strengthening network resilience with the Sol transatlantic cable

Wed, 09 Jul 2025 10:00:00 +0000

Today, we’re announcing Sol, a new transatlantic subsea cable system to connect the U.S., Bermuda, the Azores, and Spain. Sol translates to “sun” in Spanish and Portuguese, a nod to the cable system’s landing points in warmer climates. Alongside the Nuvem subsea cable, Sol completes our unique investment in transatlantic resiliency — with the two systems interconnecting terrestrially in the U.S. and in Iberia, as well as in Bermuda and the Azores.

The Sol cable will be manufactured in the U.S. and, once operational, bolster capacity and reliability for our growing network of 42 Google Cloud regions around the world, helping meet growing customer demand for Google Cloud and AI services across the U.S., Europe, and beyond.

Palm Coast, Florida, will anchor the Sol cable in the U.S. We’ll partner with DC BLOX to land the cable and establish a new connectivity hub in the Sunshine State. We’ll also develop a terrestrial route linking Palm Coast to our South Carolina cloud region.

When it’s complete, Sol will be the only in-service fiber-optic cable between Florida and Europe. In Spain, we’ll partner with Telxius to provide the necessary infrastructure to land the Sol cable in Santander, further integrating the Google Cloud region in Madrid into our global network.

"Florida is once again at the forefront of strengthening our nation's digital infrastructure. The anchoring of the Sol subsea cable in our state is a testament to our state's commitment to fostering an environment that is ripe for technological investment and innovation. We are proud to partner with companies like Google and DC BLOX on strategic infrastructure projects that will support the future of AI innovation and bring tangible benefits to our communities and the entire Sunshine State." – J. Alex Kelly, Florida Secretary of Commerce

"This is a landmark moment for Palm Coast, Flagler Beach, and Flagler County, and it's a clear signal that we are a community of the future, investing in our economic development and vitality. The Sol subsea cable is more than just infrastructure; it's a gateway to unprecedented global connectivity that will attract further high-caliber industries that our residents deserve. We are not just putting Palm Coast and our community on the map; we are building a direct route to the world's digital economy, ensuring a prosperous and dynamic future for our community." – Vice Mayor Theresa Carli Pontieri, Palm Coast, Florida

“DC BLOX is proud and honored to expand the foundational digital infrastructure that is vital to Florida’s growing economy. Supporting Google’s Sol cable, along with the capacity for additional cables, the new Palm Coast Cable Landing Station campus enhances Florida’s position as a nexus for global communications.” - Chris Gatch, Chief Revenue Officer, DC BLOX

“Digital connectivity is today a strategic factor for the economic and social development of any city. At Santander City Council, we are committed to facilitating this type of infrastructure because its implementation attracts investment, talent, and technology companies, in addition to generating employment and positioning us as a city prepared for remote work, advanced services, and the Smart City model. That's why we appreciate the interest that Google has shown in our coast and for choosing Santander as a transatlantic connection point!” - City Council Mayor Gema Igual, Santander, Spain,

“Telxius is delighted to support Google in bringing the Sol cable to Spain, a key milestone that will boost transatlantic connectivity with enhanced capacity, reliability, and resilience. Through our submarine landing services, we accelerate project implementation and help expand global networks for our customers.” - Mario Martín, CEO, Telxius

“The Government of the Azores welcomes this important investment in the region with great enthusiasm and congratulates Google on its visionary, pioneering, and strategic approach, which fully aligns with our own vision: that the Azores are an important and strategic hub for digital connectivity in Europe and the North Atlantic.” - Artur Lima, Vice President, Government of the Azores

“Promoting the expansion and modernization of submarine cable infrastructure, with a view to strengthening the country's international connectivity and digital competitiveness, is an essential goal for the government to the development of our country. The project for Google's new "Sol" cable, with 16 fiber optic cable pairs and the potential to provide redundancy to the "Nuvem" cable, also from that company, will increase resilience and respond to the growing demand for digital infrastructure in Portugal and Europe.”- Miguel Pinto Luz, Minister of Infrastructure and Housing, Portugal

“Bermuda welcomes Google’s continued investment with the announcement of a second cable ‘Sol,’ further establishing Bermuda as a digital hub in the Atlantic. Bermuda greatly values its positive relationship with Google. This second cable highlights the benefits of this Government’s strategy to develop Bermuda as a progressive world leading jurisdiction for technology companies.” - Alexa Lightbourne, Minister of Home Affairs, Bermuda

Sol will add capacity, increase reliability, and decrease latency for Google users and Google Cloud customers around the world. Alongside cable systems like Nuvem, Firmina, Equiano, and Grace Hopper, Sol further establishes key locations across the Atlantic as connectivity hubs, strengthening local economies and bringing AI’s benefits to people and businesses around the world.

Autonomous Network Operations framework: Unlock predictable and high-performing networks

Thu, 12 Jun 2025 13:00:00 +0000

Over the past year, an exponential surge in data, the widespread rollout of 5G, and heightened customer expectations have placed unprecedented demands upon communications service providers (CSPs). To thrive in this challenging landscape, telecommunications leaders are rethinking traditional network management, embracing digital transformation, and using the power of AI to build smarter, more efficient, and self-managing networks.

Today, to help CSPs address these pressures, we are announcing the Autonomous Network Operations framework — enabling CSPs to enhance service reliability, proactively detect and resolve network issues, and turn fragmented data into value. This new framework takes an AI-first approach, leveraging the latest in Google Cloud AI, infrastructure, and analytics products to understand and make sense of complex network data, risks, and operations. The framework also offers an extensive ecosystem to help deploy these solutions, including partners and Google Cloud Consulting.

The Autonomous Network Operations framework draws on Google’s extensive expertise in operating its own global network, which has leveraged AI at scale for more than 25 years and is one of the industry’s most advanced and resilient autonomous networks. CSPs are already using the framework to improve service reliability and minimize mean time to repair (MTTR) by 25%, and now we’re making it broadly available to accelerate their autonomous network operations journeys.

Navigating network complexity in the AI era

Managing complex telecom networks is a costly and resource-intensive undertaking for CSPs. Legacy infrastructure, often built for previous generations of mobile technology, struggles to keep pace with the immense data demands of 5G and beyond. This has led to several challenges:

Increased operational costs and network demands: Manual tasks like alarm triage, troubleshooting, configuration, and service provisioning across diverse systems consume significant resources for CSPs, hindering innovation and modernization.
Sub-optimal customer experience: Network operations traditionally focus on technical KPIs (utilization, latency, etc.) without real-time visibility into how specific network events or degradations are actually affecting the quality of experience for individual subscribers or services. Taking action usually happens reactively, leading to eroded customer satisfaction and increased churn.
Fragmented and siloed data: In many legacy platforms, vital network-performance and customer-experience data reside in separate systems, and often are difficult to integrate. But without a unified view, correlating network events with their impact on the customer experience is hard, and can lead to ineffective resource prioritization and delayed root cause analysis.
Difficulty implementing advanced technologies: Adopting autonomous networking operations can be resource intensive and costly, presenting challenges such as integration with existing infrastructure, data management, cybersecurity, upskilling talent, and identifying a clear path to a positive return on investment.

Yet, we've heard from our customers that successfully embracing autonomous network operations has the potential to dramatically improve service uptime for subscribers, significantly reduce network complexity, and unify fragmented data for actionable insights.

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Google Cloud’s Autonomous Network Operations framework

Google Cloud's Autonomous Network Operations framework supports CSPs’ strategic pathways to achieving true network autonomy, building on our unique strengths in AI, infrastructure, and global expertise.

The framework integrates critical Google Cloud products to transform operations, enhance service reliability, and unlock new value in three key ways:

1. Differentiated building blocks for sophisticated use cases

Google Cloud uniquely helps CSPs build intelligent networks with cutting-edge, AI-powered tools tailored to their specific needs. At its core is Cloud Spanner, Google Cloud’s globally distributed database that acts like a real-time virtual copy (a "digital twin") of national networks. Spanner tracks billions of changing data points across all network components, providing a single, reliable record that even retains historical network conditions. This is crucial for advanced analysis and quickly pinpointing the root cause of issues. BigQuery then adds data analysis that can handle massive amounts of live network information. Finally, Google's Gemini, our most capable AI model available through Vertex AI, and specialized Graph Neural Network (GNN) models, deeply understand the network's complex and evolving connections.

Together, these tools let CSPs move beyond simple automation to tackle advanced autonomous network operations like finding problems before they happen, linking issues across different network areas, and making quick, precise decisions based on live information, at any scale.

2. Extensive integration to accelerate time-to-value

What truly sets Google Cloud’s framework apart is how smoothly all of its pieces fit together, reducing complexity and helping CSPs get things done much faster. Our services are designed to work hand-in-hand from the start, cutting down on time-consuming data engineering. For instance, BigQuery can directly access data in Cloud Spanner, providing a unified view of current operations and historical trends. Additionally, with BigQuery ML, CSPs can build and deploy AI models using simple SQL commands, leveraging powerful AI capabilities like Gemini through Vertex AI. This tight integration across our data storage, analytics, and AI tools allows CSPs to quickly pilot, launch, and expand their AI initiatives. The entire system is AI-ready from day one, facilitating the ingestion of live network data and even enabling automated problem resolution, unlocking value in days, not months.

3. Google’s AI and network operations expertise through Google Cloud Consulting

Google’s global network, one of the most advanced and resilient networks in the world, has leveraged AI at scale for more than 25 years. Through Google Cloud Consulting, we bring this operational expertise directly to CSPs to help them design and implement their own autonomous network operations frameworks. Our teams work closely with CSPs to tailor the framework to their environments. This includes everything from setting up data pipelines to operationalizing use cases like predictive maintenance, fault correlation, and closed-loop automation — helping to ensure rapid and reliable data activation.

To help ensure these intelligent, automated operations remain secure, we also bring in Mandiant, Google’s frontline cybersecurity team. Mandiant helps CSPs embed security by design into our framework — securing data flows, detecting adversarial AI threats, and protecting automated decision loops from compromise. With this unified approach, CSPs can scale autonomous operations with the same level of resilience, visibility, and protection that underpins Google’s own global infrastructure.

A tightly integrated, open ecosystem

We strengthened the Autonomous Network Operations framework with a deep ecosystem of leading independent software vendors (ISVs) and global system integrators (GSIs), who bring specialized expertise and solutions to accelerate CSPs' transformation journeys.

For example, Amdocs, Ericsson, and Nokia now offer their own autonomous network solutions as comprehensive offerings built on the Autonomous Network Operations framework’s capabilities, enabling their customers to easily adopt and accelerate their journey toward network autonomy. These partners bring crucial expertise in handling diverse network data from various vendors, facilitating the creation of a unified data model. This unified model is essential for building sophisticated, AI-driven automation.

“As CSPs navigate the complexities of modern networks—ranging from high operational costs to the need for enhanced resiliency and uptime—intelligent automation and the evolution to autonomous networks become essential. By leveraging Google Cloud’s AI infrastructure, our Amdocs Network AIOps solution and the network agents it includes empower CSPs to proactively manage their networks through predictive analytics, automated workflows, and closed-loop operations. This collaboration enables a transformative shift toward autonomous networks, enhancing efficiency and delivering superior customer experiences.” - Anthony Goonetilleke, group president of Technology and head of Strategy, Amdocs

“The transformation to full autonomy will shape the success of CSPs, paving the way for a transition to next-generation technologies. Ericsson and Google Cloud are committed to empowering this transformation. Our collaboration is driving a fundamental shift in how mobile core networks are built and operated on public cloud infrastructure. Ericsson and Google continue to combine their expertise on multiple fronts — technology innovation, streamlined delivery models, and, most importantly, a shared culture of relentless innovation — to empower operators in realizing their vision of autonomous networks.” -- Razvan Teslaru, head of Strategy, Cloud Software and Services, Ericsson.

“The industry needs to work together to realize the benefits of Level 4/5 autonomous networks. Nokia has a long history of meaningful innovation in network automation and applied Telco AI. We’re excited about deepening our collaboration with Google Cloud, which is already delivering tangible benefits to CSPs on their own, unique journeys to fully autonomous networks.” - Kal De, senior vice president, Product and Engineering, Cloud and Network Services, Nokia.

Complementing the ISVs, GSIs including Accenture and Capgemini act as the execution arm for the CSP, playing a pivotal role in helping create the specific autonomous networking deployments, and scaling these autonomous operations across the entire organization.

Customers embracing the framework with Google Cloud

CSPs are already transforming their operations and enhancing customer experiences with the Google Cloud AI, infrastructure, and expertise provided in the Autonomous Network Operations framework:

Bell Canada achieved a 25% reduction in customer-generated reports and increased software delivery productivity by 75%. By leveraging Autonomous Network Operations framework capabilities such as Spanner Graph to dynamically assess network relationships and changes in traffic and Google Cloud AI to identify and prioritize network issues before they escalate, Bell's new AI operations (AI Ops) solution enables faster detection and resolution of network problems, improving network performance.
Deutsche Telekom ensures high service uptime for its customers, even during peak demand, with the RAN Guardian agent built using capabilities from Google Cloud's Autonomous Network Operations framework. This RAN Guardian is a multi-agentic system that constantly analyzes key network details in real time to predict and detect anomalies. It also prioritizes network issues by combining data from monitoring, inventory, performance, and coverage. Then, it automatically implements fixes, such as reallocating resources or adjusting configurations, to keep service quality high.
Telstra and Google Cloud are also co-developing a new approach to optimizing its radio access network (RAN) with an AI-powered agent. This agent uses Telstra's network data to rapidly pinpoint incidents and detect anomalies before they impact service. This project is a key step in Telstra's ambition for an autonomous network. If successful, it will unlock a future of advanced AI capabilities, enabling dynamic RAN optimization and intelligent capacity management to deliver a more resilient and higher-performing network.
Vodafone is also at the forefront of leveraging Google Cloud for autonomous networks. Their next phase focuses on a shared agentic architecture and agent-to-agent (A2A) framework, enabling intelligent automation for everything from investment planning to operational efficiency and customer experience. This builds on their extensive AI Ops deployment, evolving with gen AI and multi-agent systems to improve first-time resolution and reduce incidents.
Customers such as MasOrange, Orange, and VMO2 have also expressed interest in leveraging advanced autonomous network capabilities to enhance their operations and customer experiences.

“By achieving a 25% reduction in customer-generated reports and boosting software delivery productivity by 75%, we're transforming our operations into a customer-centric ‘techco’ model. This lean approach, with the customer as our #1 priority, is paving the way for full network autonomy. This future-forward strategy promises not only self-healing and resilient systems but also significant cost efficiencies.” – Mark McDonald, EVP and Chief Technology Officer, Bell Canada

“Transforming our network operations is fundamental to delivering best-in-class connectivity and services. By deeply integrating Google Cloud's cutting-edge capabilities like Spanner Graph with its robust data and AI tools that we use today — such as BigQuery and Vertex AI — we will better understand network behavior and anticipate service incidents. This integration is key to achieving a truly autonomous operation in our future NOC, ensuring the best experience for MasOrange customers.” - Miguel Santos Fernández, Chief Technology Officer, MasOrange

Unlock autonomous network operations today with Google Cloud

If you are a CSP who is looking to enhance service reliability, proactively detect and resolve network issues, and turn data into value, the Autonomous Network Operations framework can help. Contact a Google Cloud account manager or explore our framework on our telecommunications industry page to learn more about starting a proof-of-concept with the Google Cloud Autonomous Network Operations framework.

Pluto AI: Revolutionizing AI accessibility and innovation at Magyar Telekom

Fri, 30 May 2025 16:00:00 +0000

In today's rapidly evolving technological landscape, artificial intelligence (AI) stands as a transformative force, reshaping industries and redefining possibilities. Recognizing AI's potential and leveraging its data landscape on Google Cloud, Magyar Telekom, Deutsche Telekom’s Hungarian operator, embarked on a journey to empower its workforce with AI knowledge and tools. This endeavor led to the creation of Pluto AI — an internal AI platform that has grown into a comprehensive framework for diverse AI solutions.

As one of Hungary's largest telecommunications operators, Magyar Telekom's ultimate vision is to embed AI into every aspect of its operations, empowering every employee to leverage AI's potential. Pluto AI is a significant step toward achieving this goal, fostering a culture of innovation and data-driven decision-making.

Magyar Telekom's leadership recognized that AI proficiency is now essential for future success. However, the company faced challenges, including employees with varying levels of AI understanding and a lack of accessible tools for experimentation and practical application. As a result, Magyar Telekom aimed to democratize AI knowledge and foster a culture of experimentation by building a scalable solution that could adapt to its evolving AI needs and support a wide range of use cases.

To enable business teams across Magyar Telekom to utilize generative AI, the Pluto AI team developed a simple tool that provided a safe and compliant way to prompt large language models (LLMs). They also created educational content and training for business teams on how to use gen AI and what opportunities it brings. This approach provided other teams with the building blocks to quickly construct the AI solutions they needed.

With Pluto AI, Magyar Telekom spearheaded the successful adoption of gen AI across the company, quickly expanding the platform to support additional use cases without the need for the central platform team to have a deep understanding of them.

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Developing Pluto AI

Magyar Telekom’s AI Team partnered with Google Cloud Consulting to accelerate the development of Pluto AI. This collaboration ensured that the platform was built on best practices, aligned with industry standards, and met security and compliance requirements of a regulated industry.

Here are some of the key features and functionality of Pluto AI:

1. Modular framework

Pluto AI's modular architecture allows teams to seamlessly integrate, change, and update various AI models, tools and various architectural patterns. This flexibility enables the platform to cater to a wide range of use cases and rapidly evolve alongside Magyar Telekom’s AI strategy.

The core modules of Pluto AI include:

Large language models: Pluto AI integrates with state-of-the-art LLMs, enabling natural language understanding, text and image generation, and conversational AI applications.
Code generation and assistance: The platform supports code generation, autocompletion, and debugging, boosting developer productivity and code quality. Pluto AI provides both a coding model, accessible via its user interface, for all development levels and IDE integration for experienced coders.
API: Pluto AI’s models can be called via API, enabling all parts of Magyar Telekom to utilize and integrate AI capabilities into their existing and new solutions.
Retrieval augmented generation (RAG) with grounding capabilities: RAG combines LLMs with internal knowledge sources, including multimodal content like images and videos. This enables teams to build AI assistants that can access and synthesize information from vast datasets and add evidence like extended citations from both corporate and public data to their responses.
Customizable AI assistants: Users can create tailored, personalized AI assistants by defining system prompts, uploading documents, and fine-tuning model behavior to meet their business needs.

2. Technical implementation

Pluto AI runs on Compute Engine using virtual machines, providing scalability, reliability, and efficient resource management. The platform also utilizes foundation models from the Model Garden on Vertex AI, including Google’s Gemini, Imagen, and Veo models, Anthropic’s Claude 3.5 Sonnet, and more. Magyar Telekom also deployed ElasticSearch on Google Cloud to store the knowledge bases necessary for enabling RAG workflows.

In addition to these core components, Pluto AI also utilizes other Google Cloud services to help develop production-ready applications, such as Cloud Logging, Pub/Sub, Cloud Storage, Firestore, and Looker.

3. User interface and experience

Pluto AI's intuitive interface makes AI tools accessible to users with varying technical expertise. A dropdown menu allows users to easily navigate between different modules and functionalities. The platform's design prioritizes user experience, ensuring that employees can leverage AI capabilities without a steep learning curve.

Impact and adoption

Pluto AI has seen impressive adoption rates, with hundreds of daily active users across different departments. The platform's user-friendly design and practical applications have garnered positive feedback from Magyar Telekom employees.

In addition, Pluto AI has enabled the development of various AI assistants, including legal and compliance assistants that accelerate contract review, identify compliance risks, and analyze legal documents. Knowledge management assistants have enhanced knowledge sharing and retrieval across the organization, while software development has benefited from code generation and assistance tools. Additionally, AI-powered chatbots that handle routine inquiries have significantly improved customer support experiences.

Magyar Telekom has seen quantifiable results since rolling out Pluto AI. These include hundreds of daily unique users, tens of thousands of API calls, an estimated 20% reduction in the time spent reviewing legal documents, and a 15% decrease in code defects.

Vision and future roadmap for Pluto AI

Magyar Telekom sees Pluto AI as a key part of its AI strategy going forward. To maximize its impact, the company intends to expand the platform to more markets, business units, and departments within the organization. Additionally, Magyar Telekom is looking into the possibility of offering Pluto AI as a service or a product to other Deutsche Telekom markets. The company is also planning to build a library of reusable AI modules and frameworks that can be easily adapted to different use cases.

Magyar Telekom is pursuing several key initiatives to enhance Pluto AI and expand its capabilities. These efforts include investigating the potential of agent-based AI systems to automate complex tasks and workflows, adding a language selector for multilingual support to cater to a diverse user base, and developing an enhanced interface for managing RAG solutions, monitoring usage, and tracking performance metrics. Magyar Telecom also plans to continue developing dashboards for monitoring and optimizing cloud resource usage and costs.

Pluto AI has transformed Magyar Telekom's AI landscape, making AI accessible, practical, and impactful. By providing a user-friendly platform, fostering experimentation, and delivering tangible business value, Pluto AI has set a new standard for internal AI adoption.

The AI-driven telecom: A new era of network transformation

Wed, 21 May 2025 16:00:00 +0000

The telecommunications industry is undergoing a profound transformation, with AI and generative AI emerging as key catalysts. Communication service providers (CSPs) are increasingly recognizing that these technologies are not merely incremental improvements but fundamental drivers for achieving strategic business and operational objectives. This includes enabling digital transformation, fostering service innovation, optimizing monetization strategies, and enhancing customer retention.

To provide a comprehensive and data-driven analysis of this evolving landscape, Google Cloud partnered with Analysys Mason to conduct an in-depth study “ Gen AI in the network: CSP progress in adopting gen AI for network operations. This research examines CSPs' progress, priorities, challenges, and best practices in leveraging gen AI to reshape their networks, offering quantifiable insights into this critical transformation.

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Key findings: A data-driven roadmap

The Analysys Mason study offers valuable insights into the current state of gen AI adoption in telecom, providing a data-driven roadmap for CSPs seeking to navigate this transformative journey:

1. Widespread gen AI adoption and future intentions

Demonstrating the strong momentum behind gen AI, 82% of CSPs surveyed are currently trialing or using it in at least one network operations area, and this adoption is set to expand further, with an additional 9% planning to implement it within the next 2 years.

2. Strategic importance of gen AI

Gen AI empowers CSPs to achieve strategic goals within the network: 57% surveyed see it as a key enabler of autonomous, cloud-based network transformation initiatives, 52% for the transition to new business models like NetCo/ServCo and more digitally driven organizations, and all with the aim of enhancing customer experience and driving broader transformation.

3. Key drivers for gen AI investment

CSPs are strategically prioritizing gen AI investments to achieve a range of network objectives, including optimizing network performance and reliability, enhancing application quality of experience (QoE), and improving network resource utilization, recognizing gen AI's potential to move beyond a productivity tool and become a cornerstone of future network operations and automation..

4. Challenges in achieving model accuracy

While gen AI offers significant potential, the study found that 80% of CSPs face challenges in achieving the expected accuracy from gen AI models, a hurdle that impacts use case scaling and ROI. These accuracy issues are linked to data-related problems, which many CSPs across different maturity levels are still working to resolve, and the complexity of customizing models for specific network operations.

5. Addressing the skills gap

With over 50% of CSPs citing it as a key concern, employee skillsets represent a major challenge, highlighting the urgent imperative for CSPs to invest in upskilling and reskilling initiatives to cultivate in-house expertise in AI, gen AI, and data science related fields.

6. Gen AI implementation strategies

While many CSPs begin their gen AI implementation by utilizing vendor-provided applications with embedded gen AI capabilities (the most common approach), the study emphasizes that to fully address their diverse network needs, CSPs also seek to customize models using techniques like fine-tuning and prompt engineering; this customization, however, is heavily reliant on a strong data strategy to overcome challenges such as data silos and data quality issues, which significantly impact the accuracy and effectiveness of the resulting gen AI solutions.

7. Deployment preferences

While 51% of CSPs indicated hybrid cloud environments as the predominant deployment choice for gen AI platforms in network operations, reflecting the need for flexibility and control, a significant 39% of CSPs show a strong preference for private cloud-only deployments specifically for their data platforms, driven by the critical importance of data security and control. Public cloud deployments are preferred for AI model deployments.

Recommendations for CSPs

In summary, to secure a competitive edge, CSPs will need to prioritize gen AI use cases with clear ROI by adopting early-win gen AI use cases while developing a long-term strategy, transform their organizational structure and invest in upskilling initiatives, develop and implement a robust data strategy to support all AI initiatives and cultivate strong partnerships with expert vendors to accelerate their gen AI journey.

Google Cloud: Your partner for network transformation

Google Cloud empowers CSPs' data-driven transformation by providing expertise in operating planetary-scale networks, a unified data platform, AI model optimization, professional services for gen AI, hybrid cloud solutions, and a rich partner ecosystem. This is further strengthened by Google Cloud's proven success in driving network transformation for major telcos, leveraging infrastructure, platforms, and tools that deliver the required near real-time processing and scale.

To kickstart your AI-powered journey for network transformation visit Google Cloud for Telecommunications.

Companies achieve stronger results with Customer Engagement Suite, plus new AI-enabled capabilities

Wed, 12 Mar 2025 10:00:00 +0000

The demands for top-notch customer service have never been greater — but so are the rewards for those companies that can deliver on the promise. Indeed, organizations with higher customer loyalty scores have delivered 3.5 times more cumulative shareholder return over 10 years, according to Bain & Company.

While technology has largely fueled these rising customer expectations, it’s also delivering the tools to meet the moment. Notably, generative AI provides unprecedented capabilities that have enabled organizations of all sizes and across all industries to transform their contact center operations.

Whether it’s how customers interact with brands or supporting team functions behind the scenes, AI is significantly enhancing customer experiences and resulting in higher customer loyalty scores. Many leading organizations are realizing these gains through the implementation of Google Cloud’s Customer Engagement Suite with Google AI.

Since its introduction last September, Customer Engagement Suite has helped companies deliver greatly improved customer service and business impact. They have boosted self-service with Conversational Agents, empowered customer service representatives with Agent Assist, and modernized contact center operations with Conversational Insights, Quality AI, and Google Cloud Contact Center-as-a-Service (CCaaS).

To help existing users and new ones achieve even more, we’re excited to announce the latest advancements and features coming to the Customer Engagement Suite.

These new capabilities make building AI agents easier, help customer service representatives be more productive, and streamline contact center operations. Read on to discover how leading businesses worldwide are already transforming the way they connect with their customers using Customer Engagement Suite — as well as what the platform can do for you.

Companies are seeing strong business results

From boosting customer satisfaction to streamlining operations and reducing costs, Google’s Customer Engagement Suite is helping companies deliver significant business impact.

TTEC is a leading provider of business-process outsourcing (BPO) for customer experience (CX) operations. They support 15 BPO clients, as well as TTEC’s own internal contact center operations. More than 1,400 of their customer care representatives use the Customer Engagement Suite to support use cases such as HR and IT help desks, customer care, and sales.

With Conversational Agents, TTEC has been able to automate up to 40% of customer interactions across payroll, a benefits help desk, HR, new hire onboarding, equipment returns, emergency hotlines, and employee call out use cases.
Agent Assist has led to a 40% reduction in escalations and Knowledge Assist capabilities have led to a 11% reduction in the average handle time, with new hires seeing 18% improvement.
Call Summarization has led to a 30 second reduction in average handle time and after-call work time (the latter is the time agents spend completing tasks after an interaction). One client reported seeing a 4 minute reduction in after-call work time.
Data analytics from Conversational Insights helped reduce manual quality assurance processes, and a further 10% to 20% reduction in average handle times from analytics-led process improvements.
You can learn more about TTEC’s AI-driven transformation in the Smarter CX, Bigger Impact: Transforming Customer Experience with Google AI webinar.

loveholidays is one of the fastest growing online travel agencies in the UK. They were looking to cost-effectively scale their contact center without impacting the support experiences of their rapidly expanding customer base.

55% of their customers get an answer to their question in less than a minute by engaging with Sandy, their self-service AI agent built using Conversational Agents.
The multi-language capabilities of Google’s Customer Engagement Suite enabled them to scale operations to new markets in Europe using their existing workforce, resulting in operational savings of £3 million ($3.8 million) per year.
You can learn more about their AI-enabled transformation at New Way Now: loveholidays powers smoother travel experiences with Google Cloud AI.

Google’s YouTube business utilizes Customer Engagement Suite to improve user satisfaction, provide greater consistency in metrics, and gain data-driven insights.

With Conversational Agents, YouTube was able to improve information gathering experiences over voice and SMS channels. Combining this with Agent Assist, they achieved a 23% reduction in average handle time.
Through scaling their people, processes, and leveraging Google’s Customer Engagement Suite, YouTube dramatically improved the customer experience, reducing abandons in queue by 75%.

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Announcing new AI-enabled capabilities

Today, we are happy to introduce our latest Customer Engagement Suite innovations that will enable users to build hybrid conversational agents at scale, enhance employee coaching and training, and improve contact center operations.

New Conversational Agents capabilities
- Natural sounding HD voices: Our latest voice models provide human-sounding emotions and intonations, with steerable voice instructions and configurable pauses, pronunciation, and emotion to provide life-like conversational experiences. There are now 30 new voices with various tones to choose from. We can’t wait for you to hear them.
- Unified console to build hybrid agents is now generally available: By combining generative AI and rules-based controls, the new Conversational Agents console enables users to rapidly build AI agents for self-service experiences, with realistic, natural-sounding inflection and expressive conversations that are grounded in both business and customer logic. The console provides users with new evaluation capabilities to benchmark agent performance, understand single turn and multi-turn conversational metrics, and build feedback loops to help create and manage their AI agents.
  
  To improve reliability and quality at the scale large enterprises need, we also introduced observability, evaluation, and test-case instrumentation into the Conversational Agents console. This enables customers and partners to validate agent quality at scale, with tools to consistently monitor and improve their self-service experiences.
- Prebuilt agents: These quick start reference agents allow users to get started easily with consumer commerce. We are initially launching four prebuilt agents: flight booking, movie ticketing, shopping assistance, and appointment booking. They demonstrate various capabilities including: geolocation API use; connecting to Google products like BigQuery for data retrieval; Google Calendar for appointment booking; and rich text formatting to build beautiful interfaces for end users.
- Out-of-the-box connectors: We are making it easy to expand AI agent capabilities by introducing more than 30 data retrieval connectors to augment the knowledge of your agent, and 70-plus action connectors to allow your agent to take actions with or without human-in-the-loop for approvals. Connectors include common data sources like BigQuery, Salesforce, SharePoint, Jira, ServiceNow and more. With the new Conversational Agents connectors, customers get out-of-the-box, click-to-deploy tools that speed up time to production, provide seamless customer personalization, and quickly help realize the business impact of Conversational AI.
New Agent Assist and Conversational Insights capabilities
- AI Coach: This Gemini-powered feature is designed to improve employee productivity with real-time and contextual coaching and guidance to speed up time to resolution and improve customer satisfaction. It provides contextual step-by-step agent coaching to guide customer service representatives through complex interactions, upsell opportunities, compliance steps, automated information retrieval, and follow up steps. Customer service representatives always have a partner at the ready! This feature is available in public preview.
- AI Trainer: This feature provides AI-powered simulation, personalized training, and real-time coaching to address service challenges, information overload, and difficulty handling feedback. This allows new hires and less experienced support staff to learn in a safe, controlled environment to avoid inadvertently delivering lower quality responses and risking negative impact on an organization’s brand, while providing self-service tools for them to continually improve on quality. Agent Trainer boosts business performance and customer satisfaction, delivering ROI through lower training costs, tailored training recommendations, faster agent proficiency, and better customer satisfaction and average handle time scores, ultimately reducing turnover. This feature is available in public preview.
New Contact Center-as-a-Service (CCaaS) capabilities
- New web and voice co-browse: This feature helps customer service representatives see what customers see, so they can provide more effective service, lower abandonment rates, and help improve both customer satisfaction and loyalty. Co-browse delivers an improved digital experience with real-time, collaborative browsing that allows support representatives to deliver seamless digital customer experiences, and improve first contact resolution and customer satisfaction.
- Customizable dashboarding and reporting: Enhance access to information and help supervisors and managers better measure and manage their operations. Enhanced reporting, powered by Looker that’s embedded in the product, provides new customizable dashboarding and reporting tools to improve access to information and help supervisors and managers better measure and manage their operations.
- Standalone agent desktop application: The new standalone agent desktop client provides an out-of-the-box, omnichannel, and multimodal interface. By seamlessly integrating with Agent Assist capabilities, like generative knowledge assist and post-interaction summarization, it helps improve agent efficiency and productivity. It provides a flexible, customizable layout, new UI modules that integrate with third-party data sources, and easily integrate with a system of record like a customer relationship management (CRM) system.

The Future is Now

Generative AI's latest developments are clearly accelerating customer experience and contact center transformation throughout the customer journey. Great customer experience improves customer engagement, retention, and brand loyalty, and it can boost sales by lowering drop-offs and cart abandonment in online commerce.

Conversational AI and automation are crucial for improving customer experience and operational efficiency because the solution involves many touchpoints, systems, and channels. Google Cloud is helping lead this transformation by providing AI-powered use cases and applications at scale.

Take the next step

Schedule a free consultation with Google’s AI specialists to identify specific use cases and applications that will help your organization deliver similar business impact results.
Come meet with us at Enterprise Connect (March 17-20) in Orlando. See these products in action in our booth and speak with our AI and customer experience experts and partners. Be sure to check out the Google keynote, “5 Ways AI Will Transform Customer Experience and Collaboration”, along with six panel sessions Google is speaking on at the event.

Dynamic 5G services, made possible by AI and intent-based automation

Fri, 28 Feb 2025 15:00:00 +0000

The emergence of 5G networks opens a new frontier for connectivity, enabling advanced use cases that require ultra-low-latency, enhanced mobile broadband, and the Internet of Things (IoT) at scale. However, behind the promise of this hyper-connected future lies an equally critical challenge: managing the complexity of 5G networks. With dynamic demands on bandwidth, latency, and reliability, traditional static configurations and manual operations are no longer sufficient.

Enter intent-based service management, a pioneering approach that combines AI and automation with service orchestration and assurance. With intent-based service management, network resources in a 5G network scale and adjust dynamically according to real-time demand, based on the business intent, the 5G network resources are orchestrated dynamically and adjusted just as they are for cloud computing, storage, and networking and streaming services.

Together, Ericsson and Google Cloud are pushing the boundaries of this technology, exploring technology that not only simplifies operations but redefines how telecommunications companies can deliver on the promise of 5G.

The power of intent-based autonomous operations in 5G

Intent-based networking has entered the spotlight as Communications Service Providers (CSPs) recognize that they must master substantial network complexity with sophisticated automation if they want to create, deliver, and sustain services, especially in cloud-native network environments like standalone 5G. Automation of networks and related operations cannot evolve and mature to full closed-loop autonomy without intent-based implementations.

Take for example a hospital that needs dedicated 5G connectivity, provided by a 5G network slice, in order to support emergency communications with ultra-low latency. The required configuration in such a scenario should not be a static configuration; in order to provide optimal service experience it should evolve with the network conditions and the demand. The network layer needs to understand the intent behind every service request e.g. “provide ultra-low-latency video services” — and translate it into actionable configurations across the network, which then are dynamically updated based on real-time factors such as changes in traffic conditions, underlying network status, and so on.

With Ericsson’s Service Orchestration and Assurance and Google Cloud’s Vertex AI, this process becomes far easier. The solution takes high-level intents, expressed in natural language, and maps them to precise technical requirements like bandwidth, latency, and throughput; then orchestrates the creation of a tailored network slice in real time. The quality of network-slice-based service provisioned is assured via the intelligent closed loop automation mechanism.

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TMF standardization for intent-based operations

To integrate different solution components with one another without expensive and fragile integration solutions, you need a standards-based interface and model.

The Telemanagement Forum (TMF) has specified APIs and a model with common intents to enable this sort of integration. TMF provides intent models and interfaces at the business, service, and resource layers, which derive from CSP-defined business objectives and follow the Autonomous Networks Reference Architecture. Ericsson and Google Cloud’s approach is fully compliant to these standards and intent models.

The Role of AI and automation

At the heart of this solution lies AI-driven closed-loop automation. Once a service is instantiated, AI agents continuously monitor its performance against defined KPIs such as quality of service (QoS). Going back to our hospital example, if latency in the hospital’s network slice exceeds the acceptable thresholds, the system automatically identifies the issue and proposes corrective actions, such as reallocating RAN resources or Core Network.

This closed-loop system isn’t just reactive — it’s predictive. By analyzing data from the network in real time, it can anticipate issues and resolve them before they impact users. For telcos, this means not just operational efficiency but also delivering a superior quality of service.

Ericsson’s closed-loop system leverages Google Cloud’s AI agents for the intent translation, proposal and evaluation agents for slice assurance and remediation. The AI agents reason on how to best achieve a goal based on inputs and tools at their disposal, leveraging models, tools, and orchestration.

In addition to Gemini-based AI agents, Ericsson Service Orchestration and Assurance uses Vertex AI to implement predictive AI services for the remaining closed-loop agents (such as assurance, measurement, and actuation).

Why Ericsson Service Orchestration and Assurance and Google Cloud

Ericsson Service Orchestration and Assurance is a multi-domain, multi-technology platform that enables cross-domain orchestration across access, transport, and core networks. It addresses CSPs’ critical need for simplification of open and programmable networks by automating the service lifecycle, ultimately boosting revenues and profitability.

The platform provides an open, standards-based architecture that fosters innovation and multi-vendor integration in the telecom ecosystem. It allows CSPs to design services with ease across both network and IT environments, reducing time-to-market and enabling collaboration across diverse teams and use cases with fewer manual errors.

Furthermore, intent-driven orchestration accelerates the testing and launch of new services in multi-vendor environments, potentially increasing the number of services that can be launched. SLA commitments are enhanced through closed-loop automation, improving customer satisfaction and operational efficiency. Designed to be vendor-, technology-, and service-agnostic, Ericsson’s platform empowers CSPs to implement multi-domain service orchestration effectively, enabling service expansion and revenue growth.

To support this sophisticated framework, Google Cloud provides a foundation to meet the demands of modern telecom networks:

Scalability for dynamic networks: As 5G networks expand, Google Cloud’s elastic compute power helps ensure even the most resource-intensive processes, such as intent translation and real-time assurance, run seamlessly without performance bottlenecks.
AI-first infrastructure: Leveraging Vertex AI, intelligent agents translate intents into actionable configurations while optimizing network performance. With AI tools like BigQuery ML, telcos can move beyond basic analytics to actionable insights, enabling smarter decision-making.
Vertex Model Garden for open innovation: Pre-trained models and open-source LLMs in Vertex AI Model Garden empower telcos to innovate and customize highly specialized 5G service management solutions.
Hardware acceleration: Google Cloud’s Tensor Processing Units (TPUs) and Nvidia GPUs provide the computational power necessary to efficiently train and deploy complex AI models for intent-based service management.
Hybrid and edge capabilities: 5G networks demand hybrid setups across cloud and edge environments. Google Cloud’s edge-to-cloud orchestration helps ensure operations, whether optimizing central data centers or delivering ultra-low-latency services at the edge.
Security and compliance: Operating in a highly regulated environment, telcos benefit from Google Cloud’s secure, reliable infrastructure tailored to meet the stringent compliance requirements of telecom workloads.
Gemini for advanced reasoning: Gemini’s multimodal models enhance intent interpretation by processing natural language and network telemetry data. This enables nuanced and precise intent translation, leading to more effective automated actions, higher efficiency, and improved reliability of 5G service management.

Together, Ericsson Service Orchestration and Assurance and Google Cloud are exploring technology to meet these challenges together, combining automation, intelligence, and scalability to empower CSPs to deliver innovative, efficient, and profitable 5G services.

Real-world impact: A hospital use case

Let’s return to the hospital scenario. A request for a network slice is received from hospital users through the system’s intent translator: "Provide video service for patient doctor interactions having ultra low latency."

The solution springs into action:

Translating intent: The request is converted into technical configurations for the RAN and core network.
Creating the slice: Using orchestration tools, the system allocates resources and configures the network slice with Quality of Service (QoS) goals across the 5G network.
Assuring performance: With closed-loop automation, AI agents continuously monitor quality of service elements such as latency or packet loss. If a deviation that violates the intent occurs or is forecasted to occur, corrective actions are proposed and executed automatically.

In this scenario, the hospital’s needs are met dynamically, helping to ensure critical operations proceed without disruption.

Beyond efficiency: Redefining 5G Innovation

Intent-based service management enables telcos to innovate at scale. By automating complex tasks, telcos can focus on creating new services, entering new markets, and unlocking revenue streams. For instance, they can offer differentiated slices tailored to industries like gaming, manufacturing, and healthcare, creating a competitive edge in a crowded market.

Moreover, the integration of custom gen AI models like Gemini helps these solutions be tailored to telcos’ unique needs. These LLMs, trained on telecom-specific data, enable intelligent automation that feels intuitive and responsive.

Ericsson and Google Cloud’s collaboration showcases the potential of intent-based service management. By combining Ericsson’s technical expertise with Google Cloud’s AI infrastructure, telcos can navigate the complexities of 5G with confidence.

As 5G adoption accelerates, solutions like this will become the cornerstone of telecom operations, enabling seamless, dynamic networks that deliver on the promise of connectivity.

The future of 5G isn’t just about faster speeds or lower latency — it’s about intelligent, intent-driven networks that adapt to the world’s needs in real time. And together, Ericsson and Google Cloud are making that future a reality.

Demonstrating the AI-driven telecom at Mobile World Congress

Thu, 27 Feb 2025 17:00:00 +0000

Telecoms, like all businesses, are wondering how AI can transform their businesses. And there’s no better way to display how to build the AI-driven telecom than with demos. Join us at Mobile World Congress 2025, March 3-6 in Barcelona Hall 2 Booth #2H40, where we’ll be highlighting key agent use cases where AI is becoming an imperative:

Customer experience
Employee productivity
Field operations
Network operations

We know that the future of telecom isn’t just about gen AI — it’s also about data and security. We will show how to execute on cloud migrations and implementation of our core security practices.

We can’t do this alone. Across our 12 demonstrations, we have collaborations with key customers and partners on display including: Amdocs, Bell, Deutsche Telekom, Ericsson, MasOrange, Nokia, TELUS, and Vodafone.

Lastly, we will showcase the power of cloud-native networks and the ability to monetize those networks. Discover how we harness the power of Open Gateway Initiative (OGI) APIs, expose them to Firebase developers, and enable a new way to implement electronic phone number verification (ePNV). You’ll see lots of amazing progress by the telecom community!

Here are the demos that will be on display:

Creating AI Agents: Share your requirements on the spot and we will use Gemini on Vertex AI and AI Studio to build your agent within minutes.
Employee Productivity with Agentspace: Discover how Agentspace leverages Gemini's advanced reasoning and Google-quality search to put all your enterprise knowledge at your employees' fingertips, dramatically boosting productivity and unlocking expertise.
CX with Amdocs: Discover how Google Cloud's collaboration with Amdocs on amAIZ, powered by Gemini's generative AI, is revolutionizing both customer experience management and autonomous network operations in telecom.
Developing AI Apps: Discover how developers can leverage a suite of Google tools, including Flutter, Firebase, Gemini, and Vertex AI, to create intelligent app experiences that dramatically improve the customer journey, featuring AI-powered phone-plan selection.
User Identity with Firebase and OGI: Experience a more secure and reliable user identity service built on Firebase and the Open Gateway Initiative, offering developers expanded reach and enhanced authentication capabilities.
Ericsson Networks with AI: Ericsson has a long-standing relationship with Google Cloud. In this demonstration, we will showcase how the two companies are reimagining 5G networks with AI agents driven by intent-based service management using Vertex AI.
Load Balance for Inferencing: Experience a gamified challenge against our AI-powered Cloud Load Balancer, then dive into the Google Cloud console to see how you can use custom metrics to optimize GPU utilization and achieve lightning-fast inference response times.
AI for Cybersecurity: This demo showcases how Google Threat Intelligence provides visibility into threats and how it delivers detailed and timely threat intelligence to security teams around the world. Gemini in Threat Intelligence acts as a force multiplier, immediately surfacing threats most relevant to your unique risk profile and summarizing complex threat reports.
AI Network Operations: This AI network operations demo shows advanced AI/ML-powered tools and capabilities to help CSPs understand the impacts of network issues, detect them, and resolve them faster through automated end-to-end correlation and root-cause analysis. The solution leverages several native Google Cloud services.
Nokia Network on Demand: This demo features a collaboration between Nokia AVA and Google Cloud to show customers how Nokia delivers an innovative approach to energy efficiency by combining AI-driven insights with gamification, engaging users and driving sustainability in telecom networks.
Gen AI Field Operations: Last but not least, discover how Google Cloud is helping CSPs supercharge field-agent productivity, elevate service quality, and reduce costs through the power of multi-modal gen AI and democratized data access.

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And if you are up for some fun, come by and get behind the wheel of the Formula E simulator, powered by Google Cloud AI. Experience the same cutting-edge technology that's transforming motorsport through our partnership with Formula E. Feel firsthand how AI and data are revolutionizing racing, and the future of telecommunications.

Engage with the technologies reshaping the industry and discover how Google Cloud can help you drive innovation. Don't just hear about the future of telecom — build it with us at Mobile World Congress 2025, March 3-6 in Barcelona Hall 2 Booth #2H40. The future of telecommunications awaits!

The AI-driven telecom: How we’re powering transformation

Thu, 27 Feb 2025 14:01:00 +0000

AI is driving unprecedented change and evolution across every industry, and the telecommunications sector is at a particularly interesting crossroads: The industry is standardizing on 5G; data is growing exponentially; and customer expectations are quickly changing. Leading communication service providers (CSPs) are working with Google Cloud to adopt AI and lead the industry forward in terms of innovation.

At Google Cloud, we believe that AI, harnessed boldly and responsibly, has the potential to transform how CSPs operate on planetary scale — and we’re deeply committed to delivering the technologies, tools, and resources that help them do so. From building a strong data foundation, to optimizing network operations, to enhancing customer experiences, our AI-powered solutions and strategic partnerships enable CSPs to innovate and grow their businesses.

Heading into Mobile World Congress 2025 next week, let’s look at the announcements we’ve made with leaders in this industry to build AI-driven telecoms.

Data migrations are key to adopting AI

CSPs typically need to move their data into one place to use AI effectively. Google Cloud helps with this by offering tools like BigQuery and Looker, which can store and organize vast amounts of data, creating scalable data lakes and data oceans that can be analyzed by data scientists and businesspeople alike. Our open approach allows integration with existing systems, streamlining data management and getting benefits faster. Combined with our AI infrastructure, CSPs can gain actionable insights, create personalized experiences, and build new services.

DNA, part of Telenor, for instance, announced it has selected Google Cloud to help accelerate its transition to the cloud and support the delivery of fast and reliable services to its customers, allowing it to gradually migrate its on-premises workloads to Google Cloud and pave the way for generative AI adoption.

Vodafone Italy also announced it has modernized its data architecture by building a new AI-ready platform on Google Cloud called Nucleus. This gives the company enhanced process efficiency, scalability and real-time data processing. In partnership with Amdocs and Google Cloud, Vodafone re-engineered its data pipelines to optimize operational workflows and support business-critical functions. The migration to Nucleus was achieved in only 12 months without interruption or issues. The company’s modern architecture delivers key benefits, including enhanced agility, more efficient reporting, cost optimization, and improved real-time processing.

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From reactive to proactive network operations with AI

Telecommunications providers face the constant challenge of maintaining optimal network performance and reliability in a dynamic and increasingly complex environment. Traditional reactive network-management approaches can lead to service disruptions, increased operational costs, and diminished customer satisfaction. Google Cloud's AI-powered platform, which includes Vertex AI and Network Intelligence Center, provides intelligent automation for telecommunications providers, enabling them to analyze network data with BigQuery to identify anomalies, predict outages, and optimize traffic flow in real time. This shift from reactive to proactive management allows CSPs to not only improve reliability and reduce costs, but also create self-healing networks that dynamically adapt to changing conditions.

Bell Canada, for example, announced its using Google Cloud's AI technologies to automate network issue detection and resolution with its new AI operations (AI Ops) solution. Partnering with Google Cloud has enabled Bell to simultaneously boost software delivery productivity by 75%, accelerating time to market, and reduce customer-reported issues by 25%, significantly enhancing customer experience and solution reliability. And Deutsche Telekom is using Google Cloud's Gemini 2.0 in Vertex AI to develop a network AI agent called RAN Guardian, which can analyze network behavior, detect performance issues, and implement corrective actions to improve network reliability and customer experiences. This collaboration aims to create autonomous, self-healing networks that proactively optimize RAN performance and reduce operational costs.

Then there’s Amdocs, a leading provider of software and services to communications and media companies, which in partnership with Google Cloud, announced recently that it has launched a new Network AIOps solution to help 5G network providers automate complex network operations, enhance service reliability, and improve customer experiences.

Through AI-powered insights, CSPs can deliver superior service quality while streamlining their operations, ultimately driving greater customer satisfaction and loyalty.

Empowering field technicians with AI

CSPs often face challenges in field service operations, such as inefficient dispatching and a lack of real-time diagnostics, leading to longer resolution times and increased customer downtime. Google Cloud's platform, including a multimodal field-tech AI assistant and Customer Engagement Suite, empowers field technicians with real-time diagnostics, predictive maintenance, optimized routing, and agentic workflows powered by Gemini. This leads to fewer worker deployments and faster issue resolution. AI-driven diagnostics proactively identify and resolve potential issues. Predictive maintenance models anticipate equipment failures, enabling scheduled repairs. Agentic workflows and Gemini provide technicians with a comprehensive knowledge base for faster troubleshooting. Smart routing helps ensure efficient dispatch. Together, these AI-powered services help create a streamlined, customer-centric field service operation, providing faster and more effective support.As an example, TELUS' NEO Assistant, an innovative multimodal AI copilot that leverages advanced machine learning and AI technologies like Google Cloud's Gemini model, provides field technicians with instant access to critical information and streamlines workflows. Since its launch, NEO Assistant has been adopted by three-quarters of TELUS field technicians, contributed to the creation of nearly 7,000 jobs, and significantly increased efficiency by allowing tasks to be completed in under a minute.

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Delighting customers with AI-driven insights

CSPs are facing increasing pressure to deliver exceptional customer service while managing costs and navigating complex customer journeys across multiple channels. Google Cloud's AI platform, featuring the Customer Engagement Suite, Vertex AI, and Vertex AI Search, empowers CSPs to create exceptional customer experiences. By harnessing AI-driven insights about customer behavior, CSPs can anticipate needs, proactively address issues, and deliver personalized recommendations and seamless interactions, ultimately boosting satisfaction and revenue.

Chunghwa Telecom is using Google Cloud’s AI to enhance strategic decision-making and data analysis, exemplified by tools like NotebookLM Enterprise for strategic decision-making and data analysis, and a Customer Agent using Gemini 2.0 that slashes response times and is projected to reduce billing-related calls by 25% annually. Additionally, automating roaming charge verification with Document AI streamlines operations and reduces costs by eliminating manual invoice processing, leading to potential cost savings for both the company and subscribers.

Glance leverages Google’s suite of AI models, including Gemini and Imagen, to deliver visually captivating and AI-powered experiences that position consumers as the 'heroes' of their digital world. GlanceAI offers an immersive shopping experience from inferred user interests and images uploaded by consumers, drawing consumers in and helping them visualize themselves in various styles so they can make real-time purchase decisions. Here, AI transforms a quick glance at a device into an interactive shopping opportunity, whether on a smartphone lock screen or an ambient TV screen.

Unlocking new revenue streams through APIs

CSPs face high network deployment costs and pressure to innovate, leading them to explore APIs and engage developers to monetize their networks. Google Cloud's developer-centric platform simplifies network asset monetization by providing necessary tools and infrastructure. Initiatives like GSMA Open Gateway foster collaboration and developer access to network capabilities.

Built on the Open Gateway vision, yesterday we announced a new Firebase phone number verification service, providing more than three million Firebase developers with access to critical network CSP APIs such as phone number acquisition and verification. We’re partnering with Deutsche Telekom, Orange, Telefónica, T-Mobile, Telenor, Verizon, Vodafone and others to offer this service to improve developer and end-user experiences, and strengthen security by mitigating fraud — creating new revenue opportunities for CSPs and developers alike.

The future of AI in telecommunications

MWC 2025 marks a pivotal moment for AI-driven telecom innovation, and Google Cloud is at the forefront of this transformation. As CSPs continue to embrace AI in the coming months, our platform ensures they unlock new efficiencies, elevate customer experiences, and drive business growth.

For more information, see our press page or visit Google Cloud at Mobile World Congress (MWC) from March 3-6 in Hall 2, Booth #2H40.

How our partners are enhancing CSP operations with AI solutions

Thu, 27 Feb 2025 14:00:00 +0000

The telecommunications industry has always been on the leading edge of technology — but rarely has it been changing as quickly as it is today. Increased 5G adoption, the spread of edge computing, new monetization models, and growing consumer expectations are presenting opportunities and hurdles in equal measure.

Since its inception, Google Cloud has made supporting telecommunications innovation a priority — and a key part of that has been fostering a partner ecosystem that can help meet the constant evolution that communication service providers (CSPs) are undergoing.

The AI era has made such collaboration more important than ever, so ahead of Mobile World Congress (MWC), we’re showcasing the breadth of our ecosystem of telecom partners and the ways they are enabling customer success with Google Cloud. Our partners — spanning global system integrators (GSIs), specialized ISVs, and network equipment providers (NEPs) — are using AI to help CSPs optimize network performance, personalize customer experiences, automate operations, and identify new revenue streams.

Improving network operations

AI is enabling CSPs to move from reactive to proactive network management, with capabilities that span predictive maintenance, automated troubleshooting, and real-time optimization. Our partners are bringing the next-generation network to life by using Google Cloud’s Vertex AI and Network Intelligence Center. Together, we’re providing the necessary tools and technologies for building and deploying AI solutions across the modern network.

Amdocs yesterday announced Network AIOps, an AI-powered solution built on Google Cloud's Vertex AI and BigQuery, that can help telecommunications companies automate and optimize their 5G network operations, thus enhancing reliability and customer experiences.
CARTO, a geospatial analytics platform provider, uses BigQuery to enable telecoms to visualize and analyze massive location-based datasets, leading to more effective network planning and optimized service delivery.
Ericsson, a leading telecommunications company, is building AI agent-driven network slice instantiation and assurance for telecom customers with Google Cloud’s AI.
Fortinet, a cybersecurity leader, collaborates with Google Cloud to enhance real-time threat detection and response capabilities with AI, providing network security and resilience for telecom providers.
MATRIXX, a leading provider of cloud-native business support services (BSS) solutions, is using Google Cloud's AI to simplify and automate tasks, helping telecoms reduce costs and deliver a better customer experience.
Nokia is collaborating with Google Cloud to enhance its energy saving application, wherein telcos can include their subscribers as part of their energy savings goals.

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Optimizing IT transformation and productivity

Improving IT systems is important for telecom companies to work faster and smarter. Using tools like BigQuery, Looker, and Vertex AI, our partners are helping telecom providers automate tasks and optimize the value they get from data and AI projects while improving overall IT capabilities.

BMC Helix Software uses Vertex AI to enhance its service assurance capabilities, helping telecoms streamline IT processes, predict and prevent outages, and automate resolutions.
Datatonic is building solutions for telecoms with Vertex AI and Google Cloud’s generative AI technology, helping it better understand customer behavior, personalize service recommendations, enhance network analytics, and boost employee productivity.
Zinkworks is using Vertex AI, Gemini models, and additional Google Cloud technology to develop and implement solutions that automate network operations, significantly reducing operating costs and enhancing efficiency for telecom workers in the field.

Enhancing the customer experience

AI helps CSPs better understand customer needs, personalize interactions, and provide proactive support. Partners are using Vertex AI, BigQuery, Customer Engagement Suite, and Telecom Subscriber Insights to develop solutions that deliver personalized and engaging experiences to telecom customers.

commercetools is helping telco businesses build agile, scalable, and flexible commerce on top of Google Cloud.
Pega is an AI decision-making and workflow automation platform that helps CSPs and other enterprises transform their operations, personalize customer engagement, and continuously adapt to change with the help of Google AI.
Sprinklr offers a unified customer experience management platform powered by Google AI that helps front-office teams and contact center agents to make every customer experience extraordinary.
Zeotap offers a secure customer data platform (CDP) on Google Cloud that empowers telco and other brands to integrate and orchestrate customer data, enabling targeted marketing while protecting customer privacy, all designed for now and for when cookies are no longer used.

Revamping monetization tools

AI plays a key role in optimizing pricing strategies, personalizing offers, and automating billing processes. Partners are using AI and network APIs to create more effective monetization models with solutions that can analyze customer data to identify preferences, predict churn, and recommend optimal pricing strategies.

Aduna, a venture between Ericsson and leading CSPs, is simplifying global access to network APIs, making it easier for Google Cloud developers to build new applications leveraging mobile networks to accelerate digital transformation across industries.
Beyond Now offers an AI-powered digital marketplace for CSPs and technology providers to co-create and bundle partner solutions, helping support their customers’ digital transformation journeys.
Eureka.AI provides actionable market and risk intelligence products for industry verticals to help CSPs modernize their data monetization.
Glide Identity provides a platform built on Google Cloud for identity and anti-fraud products, helping telecoms create new revenue opportunities and delight users.
Kloudville offers a marketplace powered by Google Cloud that enables telecoms to offer digital products and services to their SMB customers, creating new revenue opportunities and enhancing the customer experience.
Nokia’s Network-as-Code platform simplifies the use of advanced network capabilities by exposing them as developer-friendly interfaces, enabling applications to work seamlessly across multiple public and private networks, and fostering new value creation and network monetization.

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Enabling customer success with services partners

Services partners play a critical role in providing customers with the expertise and support needed to plan, deploy, and optimize AI projects. Many of these partners have launched services specifically for telecoms and are continuing to demonstrate their proven ability to help customers transform with AI and other Google Cloud technology.

Accenture empowers CSPs to reinvent customer engagement, drive revenue growth, and enhance network operations through AI, machine learning, and gen AI agents, while prioritizing cost reduction and sustainability.
Capgemini helps CSPs develop autonomous network capabilities and next-generation field operations, as well as strength in AI-powered marketing, customer care, and lifetime-value management.
Prodapt is helping the global telecom and technology industry use Google Cloud products, including generative AI tools and solutions, to modernize technology infrastructure, fast-track data migration, improve productivity, and make operations seamless and cost-efficient.
TCS offers innovative end-to-end business solutions that encompass agentic AI frameworks for CSPs across customer experience, customer service, field service, and network operations; it also creates tailored offerings such as TCS CX Transformer for Telcos, which combines its vast industry expertise and Google Cloud’s data analytics and AI technology.
Tech Mahindra partners with CSPs to scale at speed by enabling them to simplify and modernize their business and technology, as well as to monetize their assets for greater revenue growth, and all of this is powered by AI.

Supporting sustainable practices

On top of navigating evolving customer demands and technology needs, CSPs are juggling pressure to decarbonize and comply with ever-changing global regulations. AI is helping telecom organizations optimize energy consumption, reduce waste, and minimize their carbon footprint.

Geotab provides vehicle and driver behavior data services for more than 55,000 fleets and 4.7 million vehicles around the world. Using Google Cloud’s data analytics and machine learning, Geotab helps customers improve fleet productivity, optimize fleet management, and achieve strong compliance.
Watershed helps companies manage climate and ESG data, producing audit-ready metrics for voluntary and regulatory reporting using Google Cloud tools.

Google Cloud is committed to fostering a collaborative and mutually beneficial ecosystem where our partners can thrive and contribute to the success of our CSP customers. We believe that together, we can empower the telecom industry to embrace the full potential of digital transformation and shape the future of connectivity.

Visit Google Cloud at Hall 2, Booth #2H40 at Mobile World Congress (MWC) to learn more about how Google Cloud and our partners can help you transform your telecommunications business. We hope to see you in Barcelona!

Telecommunications

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The two paths to network modernization

Engineering the "telco-grade" foundation

Shifting from "saving" to "solving" with AI

Your journey, your terms

Fueling the autonomous network: Google Cloud and DigitalRoute simplify data readiness for AI

Moving from "data swamps" to digital twins

From data to outcome: Enabling autonomous agents

Accelerate your autonomous network mission

The rise of the autonomous network: How GraphML is redefining telecom operations

The digital twin foundation: Modeling your network

GNNs vs. traditional ML

NetAI GNN on Google Autonomous Network stack

The technology stack: Google tf-GNN and NetAI

Executive perspectives

Want to learn more?

Scaling the autonomous network: Introducing the Data Steward and Core Network Agents

The industry bottleneck: Why agents struggle to scale

Expanding the framework: The Autonomous Data Steward

The Core Network (VoLTE) Agent

Accelerating the journey to autonomous operations

From network APIs to network AI agents — building the agentic future of connectivity with Nokia

The three pillars of the AI-network evolution

The technical bridge: How Nokia and Google Cloud integrate

Extensibility via the Agent Development Kit (ADK)

Why this matters for the telco ecosystem

Experience the future at MWC

From framework to scale: Accelerating autonomous networks at MWC 26

Product innovation: The engine of the agentic telco

Solution updates: Accelerating time-to-value

Ecosystem momentum: Innovation in action

Looking ahead

How Fastweb + Vodafone reimagined data workflows with Spanner & BigQuery

Choosing a connected platform, not a single tool

Spanner accelerates serving without the overhead

Making sense of thousands of data flows

Effortless documentation

Engineering at the speed of business

Learn more:

How Ericsson achieves data integrity and superior governance with Dataplex

From a new operating model to a new data mindset

Putting governance into practice: Key capabilities in action

Looking ahead: The future is integrated and intelligent

How AI can scale customer experience — online and IRL

Why this architecture matters: Proven ROI

Platform capabilities designed for scale

The technical blueprint: Building intelligent customer experience systems on Google Cloud

The front-end layer

The orchestration and AI engine

The knowledge and integration layer

Putting it all together

Looking ahead: Empowering customer experience teams at scale

From legacy to cloud: How Deutsche Telekom went from PySpark to BigQuery DataFrames

The migration journey

Key business benefits of the technical approach:

Ready to unlock the full potential of your data?

Engineering Deutsche Telekom's sovereign data platform

Engineering sovereignty at cloud scale

Performance and scale in production

A blueprint for the future

Strengthening network resilience with the Sol transatlantic cable

Autonomous Network Operations framework: Unlock predictable and high-performing networks

Navigating network complexity in the AI era

Google Cloud’s Autonomous Network Operations framework

A tightly integrated, open ecosystem

Customers embracing the framework with Google Cloud

Unlock autonomous network operations today with Google Cloud

Pluto AI: Revolutionizing AI accessibility and innovation at Magyar Telekom

Developing Pluto AI

Impact and adoption

Vision and future roadmap for Pluto AI

The AI-driven telecom: A new era of network transformation

Key findings: A data-driven roadmap

Companies achieve stronger results with Customer Engagement Suite, plus new AI-enabled capabilities

Companies are seeing strong business results

Announcing new AI-enabled capabilities

The Future is Now

Take the next step

Introducing Customer Engagement Suite with Google AI

TMF standardization for intent-based operations