Telecommunications Industry Blog articles

Evolving the Network Operations Agent Framework: Driving the Next Wave of Autonomous Networks

rickliev — Fri, 27 Feb 2026 14:23:07 GMT

The original announcement of the Network Operations Agent (NOA) Framework outlined a bold mission: provide telecom operators with a modular, multiagent foundation to accelerate the journey toward autonomous networks. NOA combined intelligent agents, unified data access, and strong governance to help operators modernize complex, cloud scale environments.

A follow-up is timely. Over the past year, customer deployments, industry collaboration, and Microsoft’s own internal learnings have fueled a rapid evolution of the framework. Telecom operators face skyrocketing event volumes, rising operational costs, and a deepening skills gap—conditions echoed in the challenges documented by Microsoft’s own NetAI. Against this backdrop, NOA’s enhancements are designed to deliver more automation, more intelligence, and more resilience—while maintaining safety and human oversight.

What’s New: Key Enhancements in NOA v2

Adoption of NetAI Concepts and Best Practices

Although NOA is not NetAI, its latest iteration incorporates proven architectural principles and operational practices from the NetAI program—including intelligent agents, curated context, engineered prompts, and deterministic workflows. NetAI’s focus on scaling automation without scaling headcount, and its multi‑agent roles such as diagnostics and fiber repair, informed several NOA improvements.

This infusion strengthens NOA’s ability to support autonomous incident detection, diagnosis, and remediation while preserving telco-grade safety and predictability. And the partnership is symbiotic, as NetAI is leveraging NOA’s modern, Foundry based architecture in future iterations.

The UI for AI: Deep Integration with Microsoft 365 Copilot and Teams

NOA now embraces Microsoft Teams, Outlook, and Copilot as the primary user interface for AI agents—turning everyday collaboration tools into a real-time operations cockpit.

Operations engineers can ask questions such as “What’s causing the latency spike in region X?” directly in Teams and receive agent generated diagnostics, summaries, or recommended actions. Agents proactively post alerts, help draft incident summaries, and allow supervisor approvals—merging human and AI workflows seamlessly.

This “UI for AI” approach dramatically reduces friction, shortens response cycles, and boosts adoption across operations teams.

Migration to Microsoft Foundry & the Microsoft Agent Framework

A cornerstone update is NOA’s full alignment with Microsoft Foundry technology, including:

Microsoft Agent Framework – An extensible, open‑source orchestration layer providing standardized agent communication, tool use (via MCP), A2A, observability, and hybrid deployment options.
Foundry Agent Service – A secure runtime for deploying and scaling NOA’s multi‑agent workflows.
Foundry Observability – Built-in memory, traceability, and monitoring for every action and interaction.
Foundry IQ – Intelligence services enabling context retrieval, semantic grounding, and safe decision-making.

These enhancements give NOA a more deterministic, governed, and auditable operational backbone—crucial for regulated telco environments.

Integration of TM Forum Open APIs via MCP

The NOA Framework’s alignment with TM Forum standards has expanded significantly since the initial release. In addition to its broader support for TM Forum’s Autonomous Network principles, NOA now incorporates TM Forum Open APIs for trouble ticketing, delivered through the Model Context Protocol (MCP) integration layer.

A key enhancement is the explicit support for the TMF621 Trouble Ticket Management API, the industry standard interface for creating, updating, querying, and resolving trouble tickets in OSS/BSS environments. By exposing TMF621 operations through NOA agents and MCP toolchains, the framework enables:

Automated creation of standardized TMF621‑compliant trouble tickets
Agent driven correlation of telemetry, diagnostics, and ticket history
Seamless interoperability with existing OSS/BSS and service desk systems
Consistent, vendor neutral workflows for incident lifecycle management

This deeper TMF621 alignment ensures NOA agents can participate directly in ticket-driven operational processes while maintaining full compliance with telco grade open standards.

Strengthened Security, Governance, and Compliance Controls

NOA’s governance model has been expanded with:

Read-only defaults and restricted permissions, enforced through managed identities and RBAC
Detailed action logging for auditability
Operator defined policy gates requiring human approval for sensitive tasks
Support for hybrid and multi-cloud deployments with consistent identity and compliance

This ensures telcos can deploy autonomous agents without compromising regulatory responsibilities or operational safety.

Real-World Impact: How Azure Accelerates Autonomous Operations

NOA’s evolution is driven not only by design, but by field usage across operators and large‑scale networks.

Microsoft’s internal Azure Networking success

Microsoft used NetAI to automate fiber incident management across the global Azure backbone—achieving:

60% reduction in time-to-detect fiber issues
25% improvement in repair times
These measurable gains demonstrate the potential of agentic operations at hyperscale.

These learnings, best practices, concepts, and designs are at the core of NOA.

Alignment with broader telco transformations

Far EasTone Telecom (FET) exemplifies how leading operators are turning this architecture into real operational impact. FET is leveraging the NOA framework to redefine cloud native network operations by embedding agentic AI across its NOC and change management workflows. Today, nearly 60% of its NOC operations are AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents now handle largescale alarm correlation and root cause analysis in seconds, supporting nearly 7,000 monthly operational queries with an average response time of 16 seconds, and enabling most maintenance actions to complete within one minute. This shift has significantly reduced human error, accelerated recovery times, and allowed engineers to focus on higher value work. Built on Azure cloud native and hybrid data principles, FET can scale network intelligence securely across on premises and cloud environments, deploying capabilities closer to customers while maintaining carrier grade reliability, performance, and regulatory confidence—demonstrating how adaptive cloud and AI can turn network operations into a strategic advantage.

Vodafone is working with Microsoft to apply a proven AI‑powered blueprint for autonomous network operations across transport infrastructure and field‑force management. The collaboration combines Vodafone’s deep network expertise with Microsoft Foundry and the NOA framework to modernize how large‑scale telecom networks are operated.

This blueprint is built on Microsoft’s own experience running autonomous agents across its global Azure transport network, where AI continuously monitors performance, identifies root causes, and autonomously manages more than 65% of fibre‑break field dispatches—improving time to repair by up to 25% and accelerating root‑cause analysis by 80%. By applying these proven capabilities to Vodafone’s transport network, the two companies are accelerating the shift toward intelligent, automated transport network operations across the telecom industry.

“By working with Microsoft, we’re combining deep network expertise with proven AI‑powered operations to create something greater than either could achieve alone. Together, we’re building intelligent, automated transport network operations that empower our teams and deliver faster, more resilient connectivity networks for our customers.”

—Alberto Ripepi, Chief Network Officer, Vodafone

Other operators, including AT&T, T-Mobile, Telefónica, and MEO, are adopting Microsoft Foundry as a blueprint for scaling agentic AI across complex, multi-vendor networks.

Case studies from global operators leveraging Foundry and Azure AI capabilities—though not NOA specific—demonstrate similar patterns of AI driven operational gains:

AT&T unifies and analyzes massive volumes of network data with Microsoft Azure—particularly Azure Databricks, Power BI, and cloud-scale AI analytics—in a secure lakehouse architecture, enabling faster AI-driven insights, improved network-informed decision-making, and more efficient, scalable operations across its telecom network.
T‑Mobile leverages Microsoft Azure, data, and AI analytics platform—combining services like Azure Data Explorer, Azure Databricks, and AI-driven analytics—to ingest and analyze trillions of network data points in near real time, giving teams deep visibility into network performance, proactively identifying and resolving issues, and continuously optimizing the customer experience across its nationwide network.
Telefónica España applies Microsoft Azure’s big data, AI, and automation capabilities—such as Azure Data Explorer and Azure Databricks—to analyze massive volumes of network data in real time, detect anomalies, and enable self‑optimizing 4G/5G networks that improve performance, reliability, and customer experience while reducing operational costs.
MEO uses Microsoft AI systems to improve technician efficiency and transparency in operational processes.

These customer achievements reinforce the architectural direction taken with the NOA Framework, leveraging Azure data and AI capabilities like Azure Databricks, Fabric, and Foundry to operate world-class networks.

Architecture & Key Components

Based on the latest Foundry capabilities, the NOA reference architecture emphasizes four key subsystems, detailed in the next sections.

UI for AI

The UI for AI is the human interaction layer through which operators engage with the NOA system using natural language. In the NOA architecture, this UI is delivered through familiar enterprise surfaces—the WebApp, Microsoft Teams, and Microsoft 365 Copilot—allowing users to initiate workflows, review agent findings, approve actions, and observe outcomes without switching tools or learning new interfaces. Teams based agent interactions and supervisor controls now form a key architectural pillar.

What it enables

Conversational interaction with the NOC Manager and specialist agents
Human-in-the-loop approvals for diagnostics, remediation, and ticket actions
Consistent experience across web, Teams, and Copilot while preserving enterprise identity and permissions

Agentic Governance

Agentic Governance is the policy, safety, and control layer that enforces Responsible AI, security, compliance, and observability across all NOA agents and workflows. In the NOA architecture, this governance is provided by the Foundry Control Plane and associated guardrails, evaluations, audit logs, and operator views.

What it enables

Runtime guardrails for content safety, PII protection, prompt injection, and tool misuse
Human-in-the-loop escalation and approval controls
Auditability, compliance reporting, and policy enforcement across all deployed agents

Foundry Control Plane

Foundry Control Plane functions as the governance + observability + safety enforcement layer that sits above the multi-agent system, ensuring agent workflows can run in production with the right controls. It is where governance, observability, security, and Responsible AI controls are enforced for agent workflows. It’s presented as the mechanism that turns the solution into a scalable, governed, and enterprise-ready operations framework.

Concretely, the Foundry Control Plane provides:

Guardrails (safety controls): a central place to apply and manage protections against harmful content, PII leakage, prompt injection, and off-topic drift—plus the ability to create custom guardrails tailored to telecom needs (e.g., restricting sensitive config exposure or limiting high-risk tool calls during incidents).
Evaluations (quality and readiness checks): benchmarking agents via “evaluation runs” using built-in evaluators (accuracy, safety, coherence, domain quality), helping validate integrations (like MCP / Fabric) and catch regressions before rollout.
Operator view for fleet monitoring: centralized monitoring across deployed agents/channels, including fleet health (uptime, errors, sessions), performance (latency, token usage, throughput), and compliance signals (guardrail violations, policy alerts).
Asset inventory and lifecycle management: a unified inventory of deployed agents, models, and tools (e.g., MCP servers, Fabric Data Agents), supporting versioning, staging/rollback, usage analytics, credential rotation, and model policy enforcement.
Compliance management: centralized management of guardrails, policy templates, audit logs, and risk dashboards to produce audit-ready governance across the agent estate.

Foundry Control Plane is the control-and-monitoring system that makes autonomous, multi-agent incident workflows safe, auditable, and operable at enterprise scale.

Agent 365

Agent 365 functions as the enterprise management plane for AI agents—the place where agents are registered, controlled, observed, and secured at scale. Agent 365 is positioned as the central control plane that provides:

Unified registry / inventory of AI agents (a single place to manage what agents exist across the enterprise).
Access control so only the right users/roles can use particular agents and tools.
Observability to monitor agent usage and behavior across deployments.
Enterprise security controls applied consistently across agents and where they run.

In short, Agent 365 is the “management hub” for governing and operating the organization’s agent estate, complementing (and working alongside) the Foundry Control Plane’s runtime guardrails/evaluations and the broader multi-agent orchestration.

Agentic Framework

The Agentic Framework is the orchestration layer that enables stateful, multi‑agent workflows within the NOA system. Built on the Microsoft Agent Framework, it allows a NOC Manager (Niobe) to coordinate specialized agents (e.g., Troubleshooting, Telemetry, Ticketing, Field Ops) through delegated tasks, shared context, and ordered execution.

What it enables

Vertical orchestration of specialized agents under a single supervisory control
Durable workflows spanning diagnosis, remediation, validation, and ticketing
Agent-to-Agent (A2A) communication and integration with external agents (e.g., ServiceNow)

Microsoft Agent Framework

The Microsoft Agent Framework functions as the foundation layer for building the Telco NOA solution’s stateful, multi-agent system, providing the structure for how agents are defined, orchestrated, and operated end-to-end.

Specifically, the Microsoft Agent Framework enables:

Stateful, multi-agent workflows for NOC operations: it underpins the NOA platform, letting agents collaborate across a single incident while retaining shared context over time (not just one-off prompts).
A centrally orchestrated (“vertically orchestrated”) model: a NOC Manager (Niobe) manages task delegation, sequencing, and shared context across the workflow, while specialized agents (Troubleshooting, Incident Management, Field Ops, SONiC, Security & Compliance) execute domain-specific tasks.
Secure cross-platform agent collaboration via A2A (agent-to-agent) communication: external agents (example given: ServiceNow Now Assist) can plug into the ecosystem through A2A to enable coordinated actions across platforms.
Operationalization of multi-agent systems with durable workflows: an open-source SDK used for designing, orchestrating, and operationalizing multiagent systems with durable, stateful workflows.

All of that positions Microsoft Agent Framework as the core multi-agent runtime/SDK layer that makes the NOA incident flow (supervisor + specialist agents + external A2A agents) possible and maintainable as a workflow-driven system.

Foundry Workflows

Foundry Workflows function as the orchestration layer that defines, executes, and governs the end-to-end sequence of multiagent actions required to resolve network incidents in the Telco NOA Framework. The workflow models how tasks flow across agents—from initial intent capture through diagnostics, remediation, ticketing, verification, and closure—under the supervision of a central orchestrator agent.

Specifically, the workflow encodes the ordered handoffs and decision logic between agents such as the Supervisor Agent (Niobe), Network Telemetry Analyzer, Troubleshooting Agent (Pal Locke), SONiC Agent, Field Ops Agent, and Ticketing Agent. Rather than relying on static scripts or point integrations, the workflow graph visually and operationally represents how the Supervisor dynamically delegates tasks, routes context, and coordinates execution across specialized agents.

Foundry Workflows also provide a testable and observable execution environment. Workflows can be previewed in a sandbox mode, allowing presenters to simulate real incident flows, trigger agent interactions via natural language prompts, and validate that tool calls (for example, MCP-based ServiceNow or TM Forum APIs) execute correctly before production deployment.

During execution, the workflow enables full traceability and auditability. Debug and conversation detail views expose each step in the workflow, including routing decisions by the Supervisor, tool invocations, intermediate responses, and final outputs. This makes workflows not just an automation mechanism, but a governance artifact that supports troubleshooting, cost analysis, and compliance review.

Finally, Foundry Workflows act as the deployment unit for operationalizing agentic solutions. Once validated, the same workflow can be published with one click to Microsoft Teams or Microsoft 365 Copilot, preserving guardrails, evaluations, RBAC, and monitoring. This allows the exact same orchestrated logic to run consistently across chat, Copilot, and custom channels without re‑engineering.

Telco IQ

Telco IQ is the intelligence layer that grounds agent reasoning in telecom specific operational knowledge. In the NOA architecture, this intelligence is delivered through Foundry IQ and Fabric IQ, which provide retrieval augmented reasoning across structured telemetry, operational data, and unstructured domain knowledge.

What it enables

Telecom aware reasoning using KPIs, SOPs, policies, and historical incidents
Multi-hop retrieval and citation backed answers
Reduced hallucinations through grounded, policy aware context

Fabric IQ

Fabric IQ functions as the semantic intelligence layer that helps agents make better decisions by organizing operational + analytical data into business concepts, so the agents can reason over it more effectively (not just retrieve raw records). This dramatically simplifies connecting to data while improving the quality of the results.

Foundry IQ

Foundry IQ functions as the unified knowledge and retrieval layer that grounds the NOC agents with relevant, policy-aware context during incident troubleshooting. Foundry IQ is described as:

A Foundry Knowledge Base (built on Azure AI Search) that unifies content such as network security policies, troubleshooting guides, and SOP knowledge articles across sources including Microsoft 365/SharePoint, Fabric IQ/OneLake, Azure Blob Storage, ADLS Gen2, ServiceNow tables, and the web.
The capability that automates key RAG pipeline steps—including query planning, multi-hop reasoning, and answer synthesis with citations—so each agent doesn’t have to rebuild chunking, indexing, and connector logic per project.
A mechanism for enterprise-grade security and governance, explicitly leveraging Entra ID governance and respecting Microsoft Purview sensitivity labels, while reducing hallucinations through grounded retrieval.
The knowledge backbone agents rely on in the workflow: for example, the NOC Agent leverages the Foundry IQ knowledge base to retrieve operational insights and summarize likely causes, and other specialized agents (e.g., troubleshooting and security compliance).

Universal Data Access

Universal Data Access is the data foundation that unifies real-time, structured, and unstructured data sources into a single, governable knowledge fabric for NOA agents. The architecture explicitly combines Microsoft Fabric Eventhouse, Azure Cosmos DB, ADLS Gen2, Azure Blob Storage, ServiceNow tables, and Microsoft 365 sources, all accessed through governed tools and identity passthrough.

What it enables

Real‑time telemetry access for diagnostics and validation
Persistent conversational memory and incident history
Secure, identity aware access across enterprise and operational systems

NOTE: While the NOA Framework sample implementation and demo utilize this specific data architecture, the Framework can integrate into any existing data environment. The focus of NOA is to simplify and accelerate the development of network AI agents by leveraging your existing data estate.

With the included Microsoft Fabric connectors and OneLake virtualization, NOA can reason over:

Real‑time telemetry
OSS/BSS data
Ticketing systems
Multi-cloud or on-prem data stores

These updates collectively make NOA more scalable, more open, and easier to operationalize across diverse network environments.

Meet the Agents

The following agents are the core “cast” of the NOA. Each one represents a specialized capability (telemetry analysis, troubleshooting, device interaction, compliance validation, ticketing, and field operations) coordinated by a central supervisor.

NOC Manager Agent (Niobe)

The central orchestrator and human interface: manages task delegation, sequencing, and shared context across workflows; coordinates the other specialized agents through Foundry’s runtime. This agent flags/triages critical tickets, coordinates with connected agents, and uses Foundry IQ to retrieve operational insights and summarize likely causes for the operator.

Network Telemetry Analyzer Agent

Analyzes network performance metrics (e.g., packet loss, throughput) and uses the Fabric Data Agent to generate/execute KQL queries against the Fabric (Eventhouse/KQL DB) to support diagnostics and post-mitigation verification.

Network Troubleshooting Agent (Pal Locke)

Runs diagnostic playbooks and remediation steps; retrieves insights from Foundry IQ and proposes fixes via the SONiC Agent for device-level commands.

SONiC Agent

Interfaces directly with the network elements (e.g. SW-TOR-05 device) to execute commands and retrieve system-level data/telemetry (e.g., OS details, PFC watchdog stats, logs).

Network Security Compliance Agent

Validates post-mitigation QoS/SLA metrics (e.g. latency and jitter) using Foundry IQ, ensuring fixes remain within defined SLAs.

Ticketing Agent

Manages incident tickets and documentation: creates/updates tickets, logs agent actions, supports auditing/handovers, and integrates via an MCP server exposing ServiceNow tools and TM Forum Trouble Ticket Open API to retrieve history for accurate categorization and updates.

Field Ops Agent (Miles Dyson)

Handles physical/field issues (e.g. fiber cuts) and collaborates with the appropriate teams via email.

Building the Autonomous Network Ecosystem

NOA is intentionally designed as a partner-extensible framework, not a closed product. As a result, many partners are adopting NOA as a reference implementation for agentic operations—then integrating it into their existing agent framework, OSS/BSS, assurance, and automation portfolios to deliver differentiated autonomous network solutions for operators.

How partners are adopting NOA

Starting point for agentic architecture: using NOA’s supervisor + specialist agent pattern as the baseline for incident, problem, and change workflows.
Accelerator for telco-grade governance: adopting the guardrails, approvals, observability, and auditability concepts to meet operator safety and compliance expectations.
Reference integration blueprint: mapping NOA’s tool and API-driven approach (MCP, TM Forum Open APIs) onto their own integration adapters and connectors.
Blueprint for the “UI for AI” in operations: embedding agents into Teams/Copilot experiences to reduce swivel-chair work and drive practitioner adoption.
Foundation for packaged offerings: creating repeatable solution bundles (templates, playbooks, and connectors) that can be deployed across multiple operators with configuration, not re-engineering.

Integrating NOA into existing autonomous network solutions

Most partners integrate NOA by keeping their domain platforms (assurance, orchestration, inventory, ticketing, observability) as the systems of record, while positioning NOA as the agentic orchestration and experience layer that coordinates people, tools, and workflows end-to-end. Practically, this integration typically follows a repeatable sequence:

Choose the first high-value workflow (for example: P1 incident triage, RAN anomaly investigation, transport fault isolation, or trouble ticket enrichment) and define clear success metrics (MTTD/MTTR reduction, ticket quality, deflection rate).
Connect tools through MCP and Open APIs, exposing partner and operator capabilities (queries, actions, validations) as governed tools the agents can call deterministically.
Ground agents in partner/operator knowledge by connecting SOPs, topology, inventory, prior incidents, and KPI definitions via retrieval and curated context.
Implement policy gates and RBAC so that high-risk actions (config changes, mass remediation, ticket closure) require explicit human approval and are fully logged.
Operationalize with testing and observability, using evaluation runs, traces, and runbooks to validate correctness and monitor agent behavior in production.

Where partners extend NOA to differentiate

Partners extend NOA to differentiate on top of an open agentic foundation by combining Microsoft’s orchestration with deep domain expertise and proprietary IP. Packaged solutions, multi‑vendor interoperability, and a consistent Teams/Copilot‑based operations experience ensure scalability while preserving a familiar NOC workflow.

Domain-specialized agents: adding RAN, core, transport, edge, and security agents tailored to vendor-specific telemetry, counters, and remediation procedures.
Proprietary reasoning and models: incorporating partner algorithms (anomaly detection, RCA graphs, topology analytics) and selecting models suited to latency/cost/regulatory needs.
Closed-loop automation: integrating with orchestration/controllers to move from “recommend” to “execute” in bounded, policy-approved scenarios (for example, self-healing with automatic rollback).
Vertical solution packaging: delivering repeatable “packs” (connectors + prompts + workflows + dashboards) for specific operator pain points.
Multi-vendor interoperability: normalizing data and actions across heterogeneous network domains using TM Forum Open APIs and partner mediation layers.
Operations experience: embedding NOA into partner portals and NOC tooling while maintaining a consistent Teams/Copilot experience for daily operations.

Co-innovation and operating model

Successful partner implementations treat NOA as a living operations capability that is improved continuously—not a one-time integration. Partners typically establish a shared lifecycle with operators that covers solution design, governance, deployment, and ongoing optimization.

Clear RACI: which actions agents may take autonomously vs. which require NOC supervisor approval vs. which must be escalated to engineering.
Evaluation and release management: versioned prompts/workflows, pre-production evaluation runs, and rollback plans aligned to telco change-control practices.
Safety-by-design: read-only defaults, least-privilege tool access, and explicit policy gates around configuration, customer impact, and data handling.
Observability as a first-class requirement: tracing, action logging, and dashboards for quality, latency, and cost—plus incident reviews that include agent performance and tool outcomes.
Knowledge ops: continuously curating SOPs, updating topology/inventory context, and incorporating learnings from resolved incidents to reduce repeat failures.

Taken together, these adoption patterns show how NOA is becoming a common architectural baseline that partners can integrate into their portfolios and extend with domain expertise—helping operators move faster toward safe, scalable autonomy while preserving differentiation where it matters most.

Partner Solutions

Microsoft is actively working with a number of GSI and ISV partners on integrating NOA concepts into their solutions. Partners include:

Global Systems Integrators

Provide professional services to build, connect and ground you agent orchestration and data modernisation with Microsoft Fabric and Microsoft Foundry.

Independent Software Vendors

Turnkey, packaged solutions that include RAN & Core optimization agents, telco ontologies, or network insights which integrate or leverage the NOA architecture.

Kenmei announced its collaboration with Microsoft to help operators accelerate their path toward autonomous networks by combining Kenmei’s telecom intelligence offer with Azure and Microsoft Fabric to enable scalable analytics and agentic AI–powered operations. Already in use at leading operators like Telefónica and etisalat (e&), this collaboration brings proven deployments into a broader cloud and AI ecosystem designed to reduce manual effort, speed decision‑making, and unlock new levels of network automation.

Amdocs and Microsoft have further strengthened their strategic partnership by combining Amdocs’ aOS with Microsoft’s NOA Framework, amplifying the capabilities of the Amdocs Network Digital Twin. Leveraging Microsoft Fabric IQ’s unified data and intelligence platform, the Digital Twin evolves into a dynamic, AI-ready foundation that contextualizes inventory, assurance, analytics, and cross-domain data in real time. Together, the companies deliver intelligent network operations agents that detect anomalies, assess service impact, conduct root-cause analysis, and validate remediation scenarios prior to execution. This collaboration enables transparent, agent-driven automation that dismantles operational silos, improves efficiency, and accelerates CSPs’ advancement toward truly autonomous networks.

Advancing the Journey

Looking ahead, Microsoft remains committed to advancing telco autonomy through:

Continued expansion of agent capabilities

Future releases will bring additional agent roles, deeper coordination patterns, and broader integration with OSS/BSS systems—reflecting the trajectory of NetAI’s evolving multi‑agent ecosystem.

Accelerated partner and customer enablement

A downloadable NOA Framework accelerator, combining reference architectures, prompt libraries, agent templates, and deployment assets, will be made available in April 2026.

Reinforcement of openness

NOA will continue to support:

Third-party agent onboarding
Interoperability via MCP and TM Forum Open APIs
Hybrid network environments
Open‑source extensibility through Foundry components

This ensures operators retain maximum flexibility while adopting autonomous operations.

Closing: The Road to Autonomous Networks

As telcos navigate increasing complexity and rising expectations, the enhanced NOA Framework provides a practical, modular, and secure path toward autonomous operations.

By combining the intelligence of multi‑agent systems, the familiarity of Teams and Copilot, the power of Microsoft Foundry, and the safety of strong governance, NOA helps operators boost reliability, reduce MTTR, and simplify operations at scale.

We invite you to explore the documentation, demos, and community resources at Mobile World Congress 2026 to learn how NOA can accelerate your autonomous network journey.

Accelerating revenue in telecommunications through Agentic Sales Processes

rodekirk — Thu, 26 Feb 2026 19:06:30 GMT

Executive Summary

Telecommunications providers are under unprecedented pressure to reignite revenue growth amid market saturation, commoditization of core services, rising infrastructure costs, and intensifying competition from digital‑native players. At the same time, customers demand seamless, personalized, digital‑first experiences—while regulatory constraints, legacy systems, and talent gaps limit agility and innovation. These forces require a fundamental shift in how telecoms generate, manage, and scale revenue.

Whitepaper

This whitepaper presents a practical framework for accelerating revenue generation through agentic AI—intelligent, autonomous agents embedded across sales, marketing, and post‑sale processes. These agents augment human teams by analyzing data, orchestrating workflows, recommending next‑best actions, and automating routine tasks across the lead‑to‑cash lifecycle. By integrating agentic capabilities into existing CRM and business systems and aligning with TM Forum ODA and eTOM standards, service providers can modernize commercial operations without disrupting core platforms.

The result is faster deal velocity, more personalized customer engagement, improved conversion and retention, and a scalable foundation for monetizing 5G, IoT, and emerging services. Agentic AI enables telecom leaders to move from reactive, cost‑driven models to intelligent, outcome‑driven revenue engines built for long‑term growth.

eBook

The eBook explores how agentic AI, based on Copilot Studio, can fundamentally reinvent telecom sales, marketing, and customer engagement by shifting organizations from reactive, manual processes to autonomous, intelligent, and continuously learning sales systems.

Specifically, this eBook examines:

Why traditional telecom sales models are failing.
What “agentic sales systems” are.
How agentic AI transforms the sales lifecycle.
The business impact for telecom operators.

Agentic AI is redefining how telecom providers grow, engage, and compete in an increasingly digital market. By moving beyond reactive, manual sales models and embracing intelligent, autonomous and adaptive agents, operators can unlock faster revenue growth, deeper customer relationships, and more agile go‑to‑market execution. The journey does not require a wholesale transformation—success starts with focused, modular deployments that deliver measurable impact and scale over time. Those who act now will position themselves as future‑ready, customer‑centric organizations, equipped to lead in the next era of telecom sales and engagement. Start small, scale fast, and lead the next wave of telecom innovation.

The Rise of Agentic BSS in the IQ Era: from Systems of Record to Systems of Outcome

rodekirk — Fri, 20 Feb 2026 16:14:52 GMT

Authors: Rode Kirk and Rick Lievano

Telecom Business Support Systems (BSS) are entering their most consequential transformation since digital billing. As networks become programmable, products become composable, and customers expect real‑time, personalized experiences, the traditional BSS stack—designed for linear processes and human‑driven workflows—has reached its limits. Agentic AI changes this equation.

Agentic BSS represents a new operating model where intelligent agents continuously sense, decide, and act across the commercial lifecycle—turning BSS from a system of record into a system of outcome.

What the BSS Layer Signifies in Telecom

The BSS layer is the commercial and customer‑facing brain of a communications service provider. It governs how services are designed, sold, priced, ordered, billed, assured, and monetized across consumer, enterprise, and partner ecosystems. Core BSS domains include:

Product and offer catalog management.
Customer and account management
Order management and orchestration.
Charging, billing, and revenue management
Partner and ecosystem settlement

The BSS Layer in Telecom

Modern BSS determines a Communications Service Provider’s (CSP) ability to launch new services quickly, monetize 5G and IoT, support B2B2X ecosystems, and deliver personalized digital experiences.

Why BSS Is Both Vital—and Inherently Complex

BSS complexity is not accidental; it reflects telecom reality:

Extreme process coupling across sales, fulfillment, billing, and care.
High‑volume, real‑time transactions at massive scale
Regulatory and financial precision with zero tolerance for error
Multi‑vendor stacks accumulated over decades.
Constant change driven by new pricing models, partners, and technologies.

Legacy BSS platforms were never designed for continuous optimization or autonomous decision making. As a result, CSPs often experience slow product launches, high operational costs, and fragmented customer journeys, as legacy technical debt persists—even after modernization efforts.

The Top Five Agentic BSS Use Cases

Agentic BSS introduces goal‑driven AI agents that operate across BSS domains, rather than within single applications.

Autonomous Quote‑to‑Order Orchestration - Agents interpret intent, validate eligibility, configure products, orchestrate orders across domains, and resolve exceptions in real time—dramatically reducing cycle times (see T-Mobile US Retail story).

Intelligent Revenue Assurance & Leakage Prevention - Agents continuously monitor usage, billing, and settlement patterns, detect anomalies, and initiate corrective actions before revenue is lost.

Adaptive Product & Pricing Optimization - Agents analyze demand, usage, and margin signals to dynamically recommend pricing, bundles, and promotions aligned to customer and market conditions.

Proactive Customer Lifecycle Management - Agents predict churn, trigger retention actions, personalize offers, and coordinate care interventions across channels.

Partner & Ecosystem Automation - Agents manage onboarding, contract compliance, usage settlement, and dispute resolution across B2B2X ecosystems—at machine speed.

These use cases shift BSS from reactive processing to continuous value optimization.

AI-powered agents are transforming T‑Mobile US retail operations by acting as real-time copilots for frontline employees, helping them serve customers faster, smarter, and with greater confidence. Embedded directly into point-of-sale and service workflows, these agents surface personalized recommendations, explain complex plans and promotions in plain language, and guide associates’ step‑by‑step through upgrades, activations, and troubleshooting. By instantly pulling from customer history, device data, and current offers, agents reduce training dependency, shorten transaction times, and ensure consistent, policy‑aligned interactions across stores. The result is a more empowered retail workforce—able to focus less on navigating systems and more on delivering the high‑touch, consultative experience that drives customer satisfaction, loyalty, and sales.

To learn more, see the full interview with T-Mobile’s Brian Hodel

How Microsoft Copilot Studio Enables Agentic BSS

Microsoft Copilot Studio (MCS) provides the orchestration layer for building, governing, and scaling agentic business processes. MCS democratizes AI by empowering every line‑of‑business owner to create AI agents that streamline and automate their work, turning domain expertise into measurable outcomes.

It enables organizations to:

Design goal‑oriented agents that reason across BSS workflows.
Connect securely to enterprise systems using connectors and APIs.
Govern actions with enterprise‑grade security, identity, and compliance.
Deploy agents across channels including Microsoft Teams and digital front ends.

With Model Context Protocol (MCP) now generally available in Microsoft’s Copilot Studio, agents can dynamically connect to external tools, knowledge servers, and APIs—automatically inheriting actions as systems evolve.

MCP enables AI agents to easily connect and use enterprise tools and applications

This significantly reduces time to value, eliminates brittle point integrations, and allows BSS innovation to move at cloud speed.

TM Forum, Open APIs, and Interoperability at Scale

Microsoft is an active participant in the TM Forum Open Digital Architecture (ODA) ecosystem. TM Forum Open APIs provide standardized interfaces for customer, product, order, and billing domains—forming the interoperability foundation for Agentic BSS.

Microsoft and TM Forum have demonstrated how Copilot technologies accelerate TM Forum Open API development, reducing boilerplate code and improving consistency across multi‑vendor environments.

In practice:

Copilot Studio agents invoke TM Forum Open APIs via secure connectors.
MCP enables dynamic discovery and execution of BSS actions.
Agents remain decoupled from vendor‑specific implementations.

TM Forum states that MCP is becoming a foundational requirement for telecom, particularly for AI‑driven automation across BSS systems. As telecom operators and vendors experiment with agentic AI, the limiting factor is no longer AI’s ability to understand intent—but its inability to reliably and safely act on BSS systems without extensive custom integration. As explored in a recent article, MCP is a protocol must for telecommunications.

TMF621 Open APIs registered in Copilot Studio via MCP

IG1445 Open API & Model Context Protocol v1.0.0 explores how MCP complements and accelerates the vision of the TM Forum's Open Digital Architecture (ODA) Open API program.

Leading BSS Vendors in the Agentic Ecosystem

By leveraging connectors, exposed APIs or MCP Servers, Copilot Studio easily and securely integrates with leading third‑party BSS platforms, including:

These platforms increasingly expose TM Forum aligned Open APIs and MCP Servers, enabling agentic orchestration without replacing core systems.

Where MCP Accelerates Innovation

Model Context Protocol (MCP) acts as the agentic control plane between Copilot Studio and the BSS ecosystem. MCP enables:

Real‑time access to BSS tools and data
Automatic synchronization as APIs evolves.
Secure, governed, low‑maintenance integrations.

This allows CSPs and partners to innovate above the BSS layer, without destabilizing it—unlocking faster experimentation, safer automation, and measurable business outcomes.

Putting it All Together: Microsoft Copilot Studio in Action

In partnership with Exos Systems, we have created an agentic BSS platform that demonstrates how AI agents built in Microsoft Copilot Studio can streamline and modernize core telecom BSS workflows. Refer to Exos Systems’ blog for additional insight into the TM Forum Open API MCP & API configuration and integration.

Exos Systems delivers telecom‑focused IT services and BSS/OSS integration, powered by Exosphere—its cloud‑native, AI‑ready digital platform built on TM Forum Open API and ODA standards and strengthened through a Microsoft partnership.

By integrating with TM Forum Open APIs and exposing capabilities through Microsoft 365 Copilot and Teams, the platform shows how conversational, human‑in‑the‑loop experiences can coexist with selective autonomous execution for event‑driven scenarios. The platform uses the Model Context Protocol (MCP) to decouple agents from vendor‑specific BSS implementations, enabling portability across BSS environments while maintaining governance, orchestration, and enterprise-grade access through Copilot Studio.

“With Microsoft Copilot Studio, telcos can rapidly create BSS agents that connect via MCP servers to leverage TM Forum Open APIs, delivering interoperability and simplicity—turning industry standards into real‑time, intelligent customer and business experiences in days rather than months. Exos Systems is delighted to partner with Microsoft to facilitate this journey.”

-Saleh Bari, CTO & Co-Founder

The initial scope is anchored by three role‑aligned agents that illustrate practical, high‑value BSS use cases:

Product Expert. Provides conversational access to product catalogs, eligibility, and guided upgrades.
Billing Expert. Help customer service representatives explain billing anomalies by correlating bills and usage data.
Order Expert. Supports both conversational order inquiries and autonomous remediation of failed orders.

Together, these agents demonstrate how agentic AI can reduce friction across products, billing, and order operations while accelerating time to value. To see Copilot Studio in action and learn more about how agentic BSS can transform telecom operations, visit the Microsoft booth at Mobile World Congress for a live demo – and see how everyone in your organization can create agents that transform their work.

Fleet of Agentic BSS agents for product management, bill management, and order management

Build Your Own Agent @ Mobile World Congress 2026

Step into the Microsoft booth and experience the future of AI firsthand with Build Your Own Agent. This interactive, hands-on experience puts you in the driver’s seat—showing how quickly you can design, customize, and deploy an AI agent tailored to your real business needs. In minutes, you will move from idea to action, connecting data, workflows, and intelligence to create an agent that works the way you do. Whether you are exploring AI for the first time or looking to scale agentic solutions across your organization, this is your chance to build, test, and walk away with a practical understanding of how AI agents can deliver real outcomes.

Join us at MWC for a fast-paced, hands-on workshop where you will build a fully functional Billing Analysis AI Agent from scratch using Microsoft Copilot Studio. Move beyond the hype and build agents to solve complex telecom challenges using no-code/low-code orchestration.

In this hands-on session, you will use Microsoft Copilot Studio to create an enterprise-grade agent that analyzes telecom billing data, detects anomalies, flags churn risks, and delivers role-specific insights—then publish it and access directly into Microsoft 365 Copilot.

What you will accomplish

Accelerated launch: You will build, fine-tune, test, and deploy the agent in under 45 mins.
Insight Focused: Train your agent to do real-world tasks like detect anomalies, predict churn risks, generate reports and more.
Advanced Orchestration: Ground agents in data and fine-tune prompt reasoning.
Enterprise Activation: Publish your agent across organization and access using Microsoft 365 Copilot (UI for AI).

Walk away with a proven, repeatable architectural pattern you can apply to countless AI use cases across your business.

Hardware

Microsoft will provide laptops for all participants. Participants may use their own devices if preferred; however, Microsoft will be unable to support any connectivity or device-related issues on personal equipment.

REGISTER HERE or visit the Microsoft booth (Hall 3 3H30) during the event if you are interested in participating in the workshop.

The Strategic Takeaway

Agentic BSS is not about replacing BSS platforms. It is about elevating them—from transaction engines to intelligent, outcome‑driven systems.

With Copilot Studio, TM Forum Open APIs, and MCP, Microsoft enables telecom providers to industrialize AI across the commercial core—driving speed, agility, and sustainable differentiation in the AI‑native telecom era.

AI-Powered RAN and the Intelligent Edge: Microsoft’s Vision for the Future of Telecom

Yongguang Zhang — Wed, 18 Feb 2026 16:15:41 GMT

Artificial intelligence (AI) is rapidly converging with telecommunications infrastructure, promising to transform how networks are built, optimized, and monetized. Nowhere is this more evident than in the radio access network (RAN) – the crucial “last mile” that connects our devices to the digital world. At Mobile World Congress, Microsoft is sharing a strategic vision for AI in the RAN (AI-RAN) and intelligent edge computing. This vision centers on harnessing cloud and AI technologies to make telecom networks smarter, more efficient, and ready for new services. With decades of wireless research and a broad AI ecosystem spanning Azure to Copilot and Microsoft Foundry, Microsoft is partnering with the telecom industry to enable a new generation of AI-powered networks.

A New Era of AI-RAN: AI Meets the Radio Network

The concept of AI-RAN captures a threefold innovation in telecom networks, where AI and RAN technology intersect:

AI for RAN: using advanced machine learning and AI algorithms to improve how RANs operate. By leveraging AI-based analytics and control, operators can dynamically optimize spectrum usage, network performance, and energy efficiency, leading to lower operational and capital expenditures. In practice, this means mobile networks that self-optimize – automatically adjusting parameters to reduce interference, enhance coverage, and cut power consumption without human intervention.
AI on RAN: turning the RAN itself into a distributed AI computing engine. In this paradigm, the thousands of cell sites and edge data centers in a network can host AI inference workloads closer to end users. This intelligent edge approach allows telecom providers to offer new AI-driven services – from real-time translation to AR/VR and interactive gaming – with the ultra-low latency and data sovereignty that cloud alone cannot achieve.
AI and RAN: creating a shared infrastructure where AI platforms and the RAN co-exist and collaborate. By co-locating AI resources with telecom network functions, operators unlock synergies like integrated sensing and communications (for example, using 5G cells as distributed sensors), and they can support “physical AI” use cases such as autonomous robots and smart factories at the edge. This convergence of AI and telecom infrastructure not only improves the network itself but also opens new revenue streams through innovative services delivered over 5G and future 6G networks.

Microsoft envisions AI-infused RANs that are more than just communication channels – they become intelligent platforms for innovation. For instance, in recent trials, Microsoft researchers demonstrated AI systems that detect radio interference in real time by turning a 5G base station into a wideband spectrum analyzer. Similarly, an AI-driven anomaly detection system can continuously learn a network’s normal behavior and spot irregularities before they cause outages, helping prevent failures and improve reliability. These examples illustrate how applying AI to RAN data can translate into more resilient networks and better user experiences.

Edge AI: Bringing Cloud Intelligence Closer

The push for edge AI in telecom is about extending the power of the cloud out to the network’s edge, closer to where data is generated and consumed. This is crucial for applications that demand instantaneous processing and response, or that must keep data local for privacy and security. In a traditional setup, complex AI models live in the cloud, and lightweight AI runs on devices. But many new scenarios – such as unmanned aerial vehicle, autonomous mobile robot, or industrial IoT – require a middle ground. The telecom network’s edge (for example, in 5G base stations or nearby edge data centers) can serve as that ideal “in-between” AI execution layer.

Edge AI offers several strategic advantages for operators and enterprises:

Ultra-low latency: By processing data on edge servers just one “hop” away from end-users, critical applications (like autonomous driving or remote robotic control) can respond in milliseconds, far faster than sending data to distant cloud servers.
Data sovereignty and privacy: Keeping sensitive data (such as video feeds, industrial sensor data, or health information) within local networks or on-premises helps meet regulatory and privacy requirements. AI at the edge can analyze data without that data ever leaving the telecom’s domain.
Bandwidth optimization: By processing and filtering data locally, only the most important insights (or lightly compressed data) are sent to the cloud. This reduces backhaul traffic and lowers costs.
Resilience and continuity: Edge AI systems can continue to operate even when connectivity to cloud is limited, ensuring critical services remain available.

In short, intelligent edge computing transforms telecom networks into platforms for innovation. A prime example is the concept of “physical AI” – where AI-driven services control physical devices in real time via the network. Imagine factory robots or autonomous drones connected to a 5G network: with edge AI, heavy computation (like computer vision or coordination algorithms) can run on nearby servers, leveraging GPUs at the base station or aggregation site. Microsoft’s research has shown that offloading robotics AI workloads from onboard devices to edge GPUs can improve response times dramatically – in one scenario, cutting inference latency from over a second on a device to under 100 milliseconds at the edge. This kind of performance boost can make previously impossible applications feasible, from real-time hazard detection in smart cities to advanced augmented reality experiences.

Unifying Cloud and Telecom through Microsoft’s AI Ecosystem

Achieving the AI-RAN and edge vision requires more than just ideas – it demands a cohesive platform that brings cloud technology into the heart of telecom networks. This is where Microsoft’s broad AI and cloud ecosystem plays a pivotal role.

Azure’s cloud platform provides the robust, scalable foundation. Telecom operators can run key network functions in Azure (such as 4G/5G core networks) and leverage Azure’s global infrastructure for high performance and elasticity. At the same time, Azure’s capabilities extend on-premises and to the edge via Azure Arc, enabling a single pane of glass for managing resources across public cloud, private data centers, and network edge sites. This means operators can deploy and manage AI models or applications on distributed RAN edge servers as easily as in the cloud – achieving “zero-touch” automation and unified operations across their entire network.

Microsoft’s AI platforms and tools further empower telecom innovation. With Azure Machine Learning and the new Microsoft Foundry platform, operators and partners can train, fine-tune, and deploy state-of-the-art AI models for their unique needs. In fact, Microsoft’s AI ecosystem includes thousands of advanced models – from the latest OpenAI GPT-5.2 and domain-specific models, to a vast catalog of open-source models from partners like Anthropic, Meta, and Mistral – all available through Foundry for use in custom solutions.

Likewise, Microsoft’s growing family of Copilot experiences and AI agent services can be harnessed to improve telecom operations and customer experiences. For example, the Network Operations Agent (NOA) Framework demonstrates how a service desk AI agent might assist network engineers by intelligently parsing through network alerts and suggesting fixes, while different agents could help automate customer support with industry-specific expertise. Under the hood, developers have access to powerful frameworks like the Semantic Kernel and Azure’s AI libraries to build their own telecom-focused AI applications and xApps (RAN applications) that run on cloud or edge infrastructure. Microsoft’s vision is to make developing AI-driven network solutions as seamless as any cloud application development – develop in Azure, deploy to the RAN.

Crucially, all these capabilities are grounded in an open, standards-based approach. Microsoft is working closely with the industry to support Open RAN standards and has collaborated with leading operators and vendors on initiatives like Project Janus – an open RAN programmability platform that exposes rich RAN telemetry and control to AI algorithms. By embracing open interfaces and partnering across the telecom ecosystem, Microsoft ensures that AI solutions can plug into existing networks and equipment regardless of vendor, protecting operators’ investments while extending their capabilities. Microsoft is also a founding member of the global AI-RAN Alliance, a cross-industry effort to accelerate AI-native RAN technologies and establish best practices for integrating AI into next-generation networks.

From Research to Reality: Innovation with Partners

Microsoft’s leadership in AI and cloud is backed by deep research and real-world experimentation. Microsoft Research has been pushing the boundaries of wireless networking for over 20 years. Today, that research is yielding dividends in the form of new telecom technologies:

Microsoft’s researchers have constructed a live AI-RAN testbed network across two global innovation hubs. This 24/7 private 5G network – spanning more than 30 cloud-controlled cell sites on Microsoft’s Redmond (USA) and Cambridge (UK) campuses – serves as a blueprint for the future RAN. It is fully software-defined, cloud-managed, and open, allowing internal teams to develop and test advanced 5G/6G capabilities like AI-driven optimization, edge robotics, and healthcare applications in a real-world environment.
Insights from these efforts are shared with the industry and academy, helping define 6G-era concepts such as real-time RAN intelligent control and AI-native RAN architectures. Microsoft’s research prototypes (including reference designs and proofs-of-concept) offer operators a head start in understanding how to implement AI in their networks – from intelligent resource allocation to network slicing and beyond.
Collaboration is key: Microsoft works hand-in-hand with major communication service providers (CSPs), network equipment manufacturers, and startups to bring these innovations to production. Joint trials and proof-of-concepts have demonstrated use cases like interference detection, energy-efficient RAN automation, and near-real-time network anomaly detection in live networks. By co-innovating with the telecom community, Microsoft ensures that its AI solutions align with real operational needs and can be deployed in multivendor environments.

A Strategic Path Forward for the Telecom Industry

As the telecom sector looks to the future, the message is clear: AI and the network are no longer separate – they are becoming one and the same. Operators that embrace AI-powered RAN and edge computing stand to benefit from significant gains in efficiency and customer experience. They will be able to optimize network performance in ways not possible before, from squeezing more capacity out of spectrum to slashing energy usage during off-peak hours. At the same time, these intelligent networks can unlock new revenue opportunities by offering differentiated services – think of carriers providing AI-powered insights or automation services to enterprise customers, or delivering rich digital experiences (from cloud gaming to mixed reality) with quality guaranteed by AI-driven network slices.

Microsoft’s role is to serve as a platform and partner for this industry-wide transformation. By bringing its unparalleled cloud and AI ecosystem to the telecom domain, Microsoft is helping operators transform into hyperscale tech-driven enterprises. That means Azure infrastructure for carrier-grade reliability and scale, Azure ML and data platforms to train models on telecom data, Copilot and agent technologies to augment both network operations and customer-facing services, and the Foundry catalog of AI models and tools to jumpstart innovation. All of these building blocks are designed to work in a hybrid, open environment – spanning public and private clouds, the network core, and the far edge – so that AI can run wherever it creates the most value, even directly in the RAN.

The convergence of AI and telecom infrastructure is poised to define the next decade of networks. Microsoft’s strategic investments in AI-RAN and edge computing, combined with deep partnerships across the telecom ecosystem, position it as a key enabler of this transformation. As the industry gathers at MWC to discuss what’s next, Microsoft reaffirms its commitment to helping telecom operators and partners harness the power of AI, from the cloud to the intelligent edge, and to jointly create a future where networks aren’t just faster or more open – but truly smarter.

Introducing Microsoft’s Network Operations Agent – A Telco framework for Autonomous Networks

bryangrimm — Wed, 19 Nov 2025 18:19:43 GMT

How NOA Works: Multi-Agent Intelligence with Human Oversight

At its core, NOA is a multi-agent system tailored for telecom operations. It hosts a suite of specialized agents, each with a focused domain expertise – for example, one agent might handle network provisioning, another oversees software updates, and another focus on fault management. These agents continuously gather and interpret data from across the network and IT systems and feed their insights to a higher-level coordinating “planner” agent (NOA itself).

The planner agent synthesizes inputs from all the specialists and generates real-time recommendations and insights for the operations team throughout the service lifecycle. In practice, this means many routine issues can be anticipated or resolved autonomously, with examples such as:

Proactive deployment checks: During a new service rollout, a provisioning agent can automatically scan configuration scripts and flag anomalies or errors before they cause incidents, preventing outages caused by human error and improving overall network reliability.

Accelerated incident response: If a network fault occurs, a service assurance agent springs into action to diagnose the issue. It can correlate telemetry and logs to pinpoint the root cause in seconds, then suggest the best remediation steps to engineers – massively reducing time to restore service. This shrinks the mean time to detect and repair issues, improving uptime.

Crucially, NOA keeps humans in the loop. All agent-initiated actions operate under strict governance and operator-defined policies. Any automated fix or change recommended by an agent can be gated behind approvals, and every action is logged for audit compliance. This ensures that even as more tasks become automated, network engineers retain control and regulatory requirements are met. In short, NOA’s agents do the heavy lifting, but people set the guardrails.

Key Components of the NOA Framework

NOA brings together several Microsoft technologies into an integrated solution. Three foundational components make this telco agent framework powerful:

Unified Data Access with Microsoft Fabric

Effective AI agents require access to all relevant data, wherever it resides. NOA leverages Microsoft Fabric to break down data silos across the telco environment. Fabric acts as a unified data mesh for the network: it connects real-time telemetry streams, operational support system (OSS/BSS) databases, ticketing systems, and more into a single logical data layer.

- - Broad data connectors: Fabric provides prebuilt connectors for Microsoft 365, Graph API, Dynamics 365, as well as telecom OSS/BSS and third-party systems. This means agents can directly tap into data ranging from network device metrics to customer trouble tickets, without custom integration work.
  - Virtualized lakehouse (“OneLake”): Through OneLake, Fabric virtualizes multi-cloud and on-premises data into one scalable data lake. Whether the source is Azure Data Lake Storage, Amazon S3, Google Cloud Storage, or on-prem SQL servers, NOA’s agents can read and reason over it in real time without needing to physically relocate the data.
  - Cross-domain data sharing: Fabric’s data virtualization and mirroring allow agents to combine insights across domains (e.g., correlating network performance data with service desk logs or even sales data) to make more informed decisions.

By unifying telemetry and business data, NOA accelerates troubleshooting and decision-making. Agents and human analysts get a full picture of the network’s state and context instantly, improving accuracy of insights and enabling faster root-cause analysis. For the business, this means less downtime and more informed strategy, since decisions are based on comprehensive, up-to-date data.

The framework is also aligned with industry standards like the TM Forum’s Autonomous Networks model, providing a common blueprint that fits into existing OSS/BSS processes. Microsoft has made available TM Forum–aligned templates, reference architectures, GitHub assets, and even Azure-hosted sandbox environments so that telcos can prototype and deploy their own agent-based solutions rapidly.

Multi-Agent Orchestration with Azure Agent Framework

A highlight of NOA is its multi-agent orchestration engine, built on the Azure Agent Framework. This open-source platform (part of Microsoft Foundry) provides the runtime environment and tooling to deploy, manage, and coordinate all the AI agents working in the system. In essence, it’s the “brain” that makes sure the right agent does the right task at the right time, and that they can communicate and work together seamlessly.

Key capabilities of the Azure Agent Framework include:

- - Standardized agent communication: Agents can talk to each other and to external services using open protocols. For example, Agent-to-Agent (A2A) messaging and the Model Context Protocol (MCP) allow dynamic tool use and data sharing between agents. This means a fault-management agent can trigger a troubleshooting agent automatically when needed, or an agent can call external APIs via OpenAPI definitions.
  - Agent catalog and SDKs: Azure Agent Framework comes with a catalog of pre-built agent templates for common telco scenarios (provisioning, fault management, repair, etc.). Developers can also create custom agents using its SDK (with support for integration into existing apps), leveraging familiar tools like Visual Studio and GitHub for development and CI/CD. This drastically shortens the time to build new agents and integrate them into the NOA system.
  - Built-in memory and observability: The framework provides long-term memory storage for agents and robust tracing/monitoring capabilities. This means agents “remember” past interactions and learn over time, and operations teams can monitor agent decisions and interactions in detail – crucial for refining agent behavior and troubleshooting any issues. It also includes enterprise-grade logging of agent actions (tying into the governance mentioned earlier).
  - Enterprise security & hybrid readiness: Governance and security are baked in at the platform level. Agents can be deployed in a fully isolated manner (e.g. within Azure Virtual Networks), use managed identities for auth, and respect role-based access controls. The framework supports running agents in Azure or connecting to external/on-prem agent hosts, enabling hybrid and multi-cloud deployments out of the box.

By using Azure Agent Framework, NOA ensures that a telco’s autonomous operations are running on a proven, secure, and extensible orchestration layer. (For more detail, see the Azure AI blog post “Introducing Microsoft Agent Framework” and the open-source Agent Framework repository on GitHub which provide deeper dives into these capabilities.)

“UI for AI” – Copilot Integration in Teams and Outlook

A distinguishing feature of Microsoft’s approach is making AI collaborative and user-friendly. Rather than confining insights to a dashboard, NOA integrates its agents into the tools where humans already work. This creates a “Copilot”-style experience for network operations.

Through Microsoft Teams, Outlook, and the Copilot platform, NOA agents interact with engineers and managers in natural language:

- - Conversational interface: An operations engineer can chat with the network AI agents as if they are teammates. For example, in a Teams channel, one could ask, “NOA, what’s causing the latency spike in region X?” and the agent would respond with its analysis, backed by data. Agents can also proactively post alerts or recommendations in chat when certain conditions are detected.
  - Integrated into daily workflow: Within Outlook or Teams, if an incident occurs, an agent might automatically draft an incident summary or recommend next steps via a Copilot card, which the engineer can approve or tweak. This turns everyday collaboration tools into a unified operations cockpit where monitoring, troubleshooting, and decision-making happen collaboratively in real-time.
  - Supervisor visibility and control: Managers can use the same interface to get high-level summaries, see trends (e.g., a weekly digest of recurring issues or network KPIs), and intervene when necessary. For instance, a supervisor could override an automated recommendation directly from within Teams if they see fit, or provide feedback to train the agents.

With Microsoft 365 Copilot as the control system for these interactions, the learning curve is low – the AI fits into existing workflows. This “UI for AI” approach has proven to be a “killer app” internally at Microsoft: it dramatically improved productivity and response times in Microsoft’s own network operations by making human-AI collaboration seamless. The bottom line is that NOA’s advanced AI capabilities remain accessible and transparent to the people running the networks, rather than a black box.

Open and Secure by Design

The Network Operations Framework is built to be open and extensible. It’s not a closed system limited to Microsoft-only tools. Operators can integrate third-party or custom-built agents into NOA’s orchestration layer just as easily as first-party ones. For example, if a telecom has an existing AI solution or an OSS tool they want to include, they can wrap it as an agent and plug it into the framework. Microsoft’s AI Gateway service in Azure helps manage the security and identity of all agents (including third-party agents) via the MCP standard, ensuring consistent authentication, authorization, and compliance policies across the board. This open ecosystem approach means telcos can leverage their current investments and expertise, augmenting them with NOA, rather than starting from scratch.

At the same time, NOA is secure by design. As mentioned, every agent action can require approval and is logged. The framework enforces read-only defaults for agents unless explicitly granted permissions. It uses restricted service accounts and integrates with existing access control systems (AAA/TACACS) to ensure agents only do what they’re permitted to do. Built-in guardrails prevent unsafe operations on network devices. This level of governance is critical in telecom environments, which are often highly regulated and sensitive. Automation is controlled – it operates within the bounds set by the network operators.

Real-World Impact: Azure Networking’s Success Story

Microsoft itself has been a “customer zero” for NOA, applying this framework to manage its vast global Azure network. The results demonstrate the transformative impact of autonomous operations. Microsoft’s Azure Networking team deployed multiple agents using the NOA framework to handle fiber optic incidents worldwide. These agents act as copilots and even fully autonomous responders for network fiber cuts and degradations – a traditionally labor-intensive domain.

The outcome has been remarkable: Azure Networking achieved a 60% reduction in time-to-detect fiber issues and a 25% improvement in repair times. In other words, faults that used to take hours to even notice are now identified within minutes, and the restoration of service is significantly faster. Such improvements translate to higher network uptime and better customer experience. This example underscores how NOA’s combination of data-driven agents and automation can drastically improve operational efficiency in practice.

Conclusion: A Blueprint for Telecom Autonomy

The Microsoft Network Operations Agent Framework (NOA) offers telecom operators a pragmatic path to achieve autonomous networks. It’s modular, open, and built on proven technology – from AI agents and data fabric to collaboration tools – that operators may already use.

Whether you are looking to modernize a Network Operations Center (NOC), automate fiber-optic repairs, or build a self-healing, self-optimizing network, NOA provides the foundation and tools to get started. It brings the promise of AI-driven autonomy within reach of network operators – augmenting human teams with intelligent agents to handle complexity at cloud scale. By adopting this framework, telecoms can improve reliability and performance today, while setting the stage for the fully autonomous networks of the future.

Learn more:

Read the NetAI White Paper https://aka.ms/netai_wpdl
Check out the Microsoft Azure Blog announcement on the Agent Framework for the developer side of this technology.
Explore the Agent Framework on GitHub to see how multi-agent systems are built.
Read Microsoft’s Tech Community blog on Introducing Teams Mode for Microsoft 365 Copilot which illustrates the power of bringing agents into collaborative workflows.
Review the Azure API Management AI Gateway documentation details how third-party AI agents can be securely managed in this ecosystem.

With NOA, Microsoft is delivering a telco-specific blueprint for autonomous operations – and inviting the industry to build upon it.

Reimagining Network Operations: How Microsoft NetAI Tackles Hyperscale Challenges

rickliev — Wed, 18 Feb 2026 10:41:47 GMT

The Business Imperative: Why Network Operations Must Change

Modern network operators face a perfect storm of challenges:

Exponential Growth in Events and Maintenance

Network events and maintenance activities are increasing at an unprecedented rate. According to Microsoft’s analysis, weekly network events are projected to grow from hundreds to thousands over the next five years. Maintenance activities are expected to follow a similar trajectory. Without automation, this growth would require a dramatic—and unsustainable—increase in staffing.

Rising Operational Costs

Dense Wavelength Division Multiplexing (DWDM) operations, which are critical for high-capacity fiber networks, are both costly and labor-intensive. The global spend on Network Operations Center (NOC) services exceeds $5 billion annually, with total network operations costs reaching $250 billion per year. As networks expand, these costs threaten to spiral out of control.

Human-Centered Workflow Limitations

Manual processes are slow, error-prone, and unable to keep pace with the scale and speed of modern networks. Organizational inertia, fragmented tooling, and siloed systems further impede efficiency. Engineers are often bogged down by device-specific command-line interfaces and isolated management systems, slowing onboarding and cross-functional collaboration.

Safety and Reliability Concerns

Early attempts to automate network operations with AI revealed critical gaps. Traditional AI models struggled with limited context, leading to unpredictable outcomes and eroding trust. Machine learning models often generated false positives, overwhelming operations teams with unnecessary alerts. The risk of unsafe command execution—where an autonomous agent might inadvertently disrupt service—remained a constant concern.

The Talent Crunch

As network complexity grows, so does the demand for skilled engineers. Yet, hiring and training enough talent to keep up with operational demands is neither cost-effective nor sustainable. The industry faces a widening gap between operational needs and available expertise.

NetAI: A Strategic Shift Toward Autonomous Operations

Microsoft NetAI is not just another automation tool—it’s a strategic framework for transforming how networks are managed. By leveraging intelligent agents, curated context, and modular workflows, NetAI enables the Azure Networking team to move from reactive, manual processes to proactive, AI-driven automation.

Key Objectives of NetAI

Achieve Fully Autonomous Network Operations: NetAI aims to eliminate the need for manual intervention in the majority of network incidents, allowing intelligent agents to detect, diagnose, and resolve issues independently.
Minimize Human Involvement in Incident Lifecycle: By automating detection, root cause analysis, and repair, engineers can focus on higher-order tasks like agent enablement and system design.
Scale Operations Without Scaling Headcount: As network events grow exponentially, NetAI maintains a flat staffing curve by automating repetitive and time-consuming tasks.
Ensure Deterministic and Reliable AI Behavior: NetAI emphasizes deterministic workflows, engineered prompts, and stateful context management to guarantee consistent and safe outcomes.
Enable Role-Based Agent Collaboration: Specialized agents operate within defined scopes, enhancing reliability and accountability.
Support Organizational Transformation: NetAI redefines the role of network engineers, shifting their focus from manual operations to automation enablement and system governance.

How NetAI Addresses Business Challenges

NetAI’s architecture is designed to tackle the most pressing operational challenges head-on:

Scalability: By automating incident handling, NetAI enables organizations to manage more events without increasing headcount.
Cost Efficiency: Automation reduces the need for expensive, labor-intensive operations, delivering significant cost savings.
Reliability and Safety: Deterministic workflows, strict guardrails, and role-based access controls ensure that automation is both reliable and safe.
Organizational Agility: By freeing engineers from repetitive tasks, NetAI empowers them to focus on innovation and strategic initiatives.

A summary table from the whitepaper highlights the breadth of challenges addressed, from exponential event growth and DWDM inefficiency to fragmented tooling and repair delays.

The Measurable Benefits of NetAI

The impact of NetAI on Microsoft’s global network operations has been transformative. Here are some of the most notable outcomes:

40% More Incidents Handled Per Person

AI agents manage detection, diagnosis, and resolution, allowing engineers to handle 40% more incidents per person. This shift enables human operators to focus on higher-value activities such as agent enablement, prompt refinement, and system design.

80% Faster Root Cause Analysis

With agents like Pal leveraging topology, telemetry, and historical data, the time required to isolate and understand complex issues has dropped by 80%. This acceleration not only improves service reliability but also reduces the operational burden on Tier 2 support teams.

25% Reduction in Time to Repair (TTR)

Autonomous agents like Miles initiate and manage fiber repair workflows without waiting for human coordination, streamlining the resolution process and minimizing service disruption.

Flat Staffing Curve Despite 10x Event Growth

Perhaps most impressively, NetAI has enabled Microsoft to maintain a flat staffing curve even as the number of incidents and maintenance tasks has increased dramatically. This decoupling of scale and headcount is a critical advantage in hyperscale environments.

Improved Consistency and Reliability

Deterministic automation reduces false positives and operational noise, improving consistency and reliability across the board.

Cultural Transformation

Beyond the numbers, NetAI has fostered a cultural shift within Microsoft. Engineers are no longer just responders—they are automation architects, designing and refining the systems that drive autonomous operations. This evolution enhances job satisfaction, reduces burnout, and positions the workforce for long-term success in an AI-driven future.

Strategic Collaboration and Industry Impact

The success of NetAI is not just a product of internal innovation—it’s also shaped by Microsoft’s active collaboration with network operators around the world. Through joint workshops, pilot deployments, and feedback loops, Microsoft works closely with partners to tailor the agentic framework, workflow orchestration, and safety protocols to real-world conditions. This collaborative approach accelerates the maturity of NetAI while empowering operators to modernize their network operations.

To further accelerate adoption, Microsoft has introduced the Network Operations Agent (NOA) Framework—a deployment and enablement toolkit that packages NetAI’s best practices, engineered prompt libraries, architectural blueprints, and modular components into a reusable format for operators.

As NetAI continues to evolve, Microsoft is focused on expanding agent roles, enhancing multi-agent coordination, and deepening integration with operational systems. The vision is clear: smarter, safer, and more strategic operations that redefine what’s possible in network management.

Download the Full Whitepaper

Ready to dive deeper? The full Microsoft NetAI whitepaper explores the strategic vision, technical architecture, and real-world impact of autonomous networking. Download it here to learn how your organization can benefit from the next generation of network operations:

⬇️Download the Microsoft NetAI Whitepaper

Reimagining Telco with Microsoft: AI, TM Forum ODA, and Developer Innovation

rickliev — Tue, 09 Sep 2025 22:43:18 GMT

The telecom industry is undergoing a seismic shift—driven by AI, open digital architectures, and the urgent need for scalable, customer-centric innovation. At the heart of this transformation is TM Forum Innovate Americas 2025, a flagship event bringing together global leaders to reimagine the future of connectivity.

Microsoft’s presence at this year’s event is both strategic and visionary. As a key partner in the telecom ecosystem, Microsoft is showcasing how its technologies—spanning AI, cloud, and developer tools—are enabling Communication Service Providers (CSPs) to modernize operations, accelerate innovation, and deliver exceptional customer experiences.

🔑 Key Themes Shaping the Conversation

Connected Intelligence: Microsoft is championing a new model of collaboration—one where AI systems, teams, and technologies work together seamlessly to solve real-world problems. This approach breaks down silos and enables intelligent decision-making across the enterprise.
AI-First Mindset: From network optimization to customer service, Microsoft is helping telcos embed AI into the fabric of their operations. The focus is on building shared data platforms, connected models, and orchestration frameworks that scale.
Customer Experience & Efficiency: With rising expectations and increasing complexity, CSPs must deliver faster, smarter, and more personalized services. Microsoft’s solutions are designed to enhance agility, reduce friction, and elevate the end-user experience.

As the event unfolds, Microsoft’s sessions and showcases will highlight how these themes come to life—through real-world implementations, collaborative frameworks, and developer-first tools.

Thought Leadership & Sessions

At TM Forum Innovate Americas 2025, Microsoft is not just showcasing technology—it’s sharing a bold vision for the future of telecom. Through a series of thought-provoking sessions led by industry experts, Microsoft is demonstrating how AI, open standards, and developer tools can converge to drive meaningful transformation across the telco ecosystem.

From enabling intelligent collaboration through the Azure AI Foundry, to operationalizing AI and Open Digital Architecture (ODA) for autonomous networks, and empowering developers with GitHub Copilot, Microsoft’s contributions reflect a deep commitment to innovation, scalability, and interoperability.

Each session offers a unique lens into how Microsoft is helping Communication Service Providers (CSPs) modernize their IT stacks, accelerate development, and deliver exceptional customer experiences.

Microsoft Thought Leadership Sessions

CASE STUDY: Connected Intelligence: multiplying AI value across the enterprise

📅Sep 10 1:30pm CDT

Peter Huang, Senior Director, Technology, Network Data and AI
T-Mobile
Andres Gil, Industry Advisor/Business Developer, Telco, Media and Gaming Industry
Microsoft

CASE STUDY: From hype to impact: operationalizing AI in telco with TM Forum’s ODA and Open APIs

📅Sep 11 1:30pm CDT
Puja Athale, Director - Telco Global Azure AI Lead
Microsoft

Connected Intelligence & Azure AI Foundry: Scaling AI Across the Telco Enterprise

T-Mobile and Microsoft are spotlighting a transformative approach to enterprise AI: Connected Intelligence. The joint session explores how telcos can break down silos and unlock the full potential of AI by enabling strategic collaboration across systems, teams, and technologies.

The core challenge they address is clear: AI in isolation cannot answer even the simplest customer questions. Whether it's billing, device performance, or network coverage, fragmented systems lead to blind spots, duplication, and poor customer outcomes. To overcome this, they propose a unified framework that blends technology and culture—because tech alone doesn’t scale, and culture alone doesn’t transform.

Azure AI Foundry: The Engine Behind Connected Intelligence

At the heart of this vision is Microsoft’s Azure AI Foundry, a shared AI platform designed to scale intelligence across the enterprise and a core component of Microsoft’s recently announced Network Operations Agent Framework. Connected Intelligence integrates:

Agent Frameworks and Agent Catalogs for modular AI deployment
Hundreds of TBs of daily data from network switches, device logs, and location records
Enterprise-grade orchestration and data governance
AI/ML models aligned with customer-level time series events

This architecture enables reuse, speed, and alignment across people, organizations, and systems—turning data into actionable intelligence.

Model Context Protocol (MCP): AI-to-AI Collaboration

A standout innovation is the Model Context Protocol (MCP), which goes beyond traditional APIs. While APIs connect systems through data, MCP connects intelligence through context. It allows AI agents to dynamically discover and chain APIs without custom coding, enabling real-time collaboration across network operations, device management, and deployment workflows.

By integrating MCP into the API fabric, Microsoft is laying the groundwork for agentic AI—where intelligent systems can autonomously interact, adapt, and scale across the telco ecosystem.

From Hype to Impact: Operationalizing AI in Telco with TM Forum’s ODA and Open APIs

The telecom industry is moving from hype to impact by operationalizing AI through TM Forum’s Open Digital Architecture (ODA) and Open APIs. The session, From hype to impact: operationalizing AI in telco with TM Forum’s ODA and Open APIs, explores how telcos can build AI-ready architectures, unlock data value for automation and AI agents, and scale responsibly with governance and ethics at the core.

Microsoft’s collaboration with TM Forum is enabling telcos to modernize OSS/BSS systems using the ODA Canvas—a modular, cloud-native execution environment orchestrated with AI and powered by Microsoft Azure. This architecture supports plug-and-play integration of differentiated services, reduces integration costs by over 30%, and boosts developer productivity by more than 40% with GitHub Copilot.

Learn how leading telcos like Telstra are scaling AI solutions such as “One Sentence Summary” and “Ask Telstra” across their contact centers and retail teams. These solutions, built on Azure AI Foundry, have delivered measurable impact: 90% of employees reported time savings and increased effectiveness, with a 20% reduction in follow-up contacts. Telstra’s success is underpinned by a modernized data ecosystem and strong governance frameworks that ensure ethical and secure AI deployment.

From Chaos to Clarity with Observability

Despite advances in operational tooling, fragmented observability remains a persistent challenge. Vendors often capture telemetry in incompatible formats, forcing operations teams to rely on improvised log aggregators and custom parsers that drive up costs and hinder rapid incident resolution. Microsoft’s latest contribution to the Open Digital Architecture (ODA) initiative directly tackles this issue with the ODA Observability Operator, now available as open source on GitHub. By enforcing a standardized logging contract, integrating seamlessly with Azure Monitor, and surfacing health metrics through TM Forum nonfunctional APIs, the operator streamlines telemetry across systems. Early trials have shown promising results—carriers significantly reduced the time needed to detect billing anomalies, enabling teams to shift from reactive troubleshooting to proactive optimization.

Accelerating TM Forum Open API Development with GitHub Copilot

As the telecom industry embraces open standards and modular architectures, Microsoft is empowering developers to move faster and smarter with GitHub Copilot—an AI-powered coding assistant that’s transforming how TM Forum (TMF) Open APIs are built and deployed.

Why GitHub Copilot for TM Forum Open APIs?

TMF Open APIs are a cornerstone of interoperability in telecom, offering over 100 standardized RESTful interfaces across domains like customer management, product catalog, and billing. But implementing these APIs can be time-consuming and repetitive.

GitHub Copilot streamlines this process by:

Autocompleting boilerplate code for TMF endpoints
Suggesting API handlers and data models aligned with TMF specs
Generating test plans and documentation
Acting as an AI pair programmer that understands your code context

This means developers can focus on business logic while Copilot handles the heavy lifting.

Real-World Uses

Telco developers benefit from powerful features in GitHub Copilot that streamline the development of TMF Open API services. One such feature is Agent Mode, which automates complex, multi-step tasks such as implementing TMF API flows, running tests, and correcting errors—saving developers significant time and effort. Another key capability is Copilot Chat, which provides conversational support directly within the IDE, helping developers debug code, validate against TMF specifications, and follow best practices with ease. Together, these tools enhance productivity and reduce friction in building compliant, scalable telecom solutions.

For example, when building a Customer Management microservice using the TMF629 API, Copilot can suggest endpoint handlers, validate field names against the spec, and even help write README documentation or unit tests.

📈 Proven Productivity Gains

CSPs like Proximus have reported significant productivity improvements using GitHub Copilot in their Network IT functions:

20–30% faster code writing
25–35% faster refactoring
80–90% improvement in documentation
40–50% gains in code compliance

Other telcos like Vodafone, NOS, Orange, TELUS, and Lumen Technologies are also leveraging Copilot to accelerate innovation and reduce development friction.

Best Practices for TMF API Projects

To get the most out of Copilot:

Use it for repetitive tasks and pattern recognition
Always validate generated code against TMF specs
Keep relevant spec files open to improve suggestion accuracy
Use Copilot Chat for guidance on security, error handling, and optimization

GitHub Copilot is more than a coding assistant—it’s a catalyst for telco transformation. By combining AI with TMF’s open standards, Microsoft is helping developers build faster, smarter, and more consistently across the telecom ecosystem.

Learn more about how to configure and use GitHub Copilot in your own TMF Open API projects in our latest tech community blog.

Microsoft’s Broader Vision for Telco Transformation

Microsoft’s contributions reflect a comprehensive strategy to reshape the telecom landscape through scalable intelligence, open collaboration, and developer empowerment.

At the core of Microsoft’s vision is the idea that AI must be connected, contextual, and reusable. The Azure AI Foundry and Model Context Protocol (MCP) exemplify this approach by enabling telcos to:

Harness massive volumes of time-series data from networks, devices, and customer interactions
Deploy modular AI agents that can collaborate across systems
Orchestrate workflows that adapt in real time to changing conditions

This architecture transforms fragmented data into actionable insights, allowing CSPs to move from reactive operations to proactive intelligence.

Conclusion: Microsoft’s Strategic Alignment with TM Forum

Microsoft’s participation at TM Forum Innovate Americas 2025 reflects a deep commitment to transforming the telecom industry through AI-first innovation, open collaboration, and developer empowerment.

From T-Mobile’s vision for Connected Intelligence, to Microsoft’s roadmap for operationalizing AI and ODA, and the developer-centric acceleration enabled by GitHub Copilot, Microsoft is helping Communication Service Providers (CSPs) move faster, scale smarter, and deliver better customer experiences.

By aligning with TM Forum’s goals—standardization, interoperability, and autonomous operations—Microsoft is not just participating in the conversation; it’s helping lead it.

📣 Call to Action

Join Microsoft and other industry leaders at TM Forum Innovate Americas 2025 to explore the future of telco transformation. Whether you're a strategist, technologist, or developer, this is your opportunity to connect, learn, and shape what’s next.

Supercharge Your TM Forum Open API Development with GitHub Copilot

JayantMishra — Tue, 09 Sep 2025 14:46:48 GMT

Developing applications that implement TM Forum (TMF) Open APIs can be greatly accelerated with the help of GitHub Copilot, an AI-based coding assistant. By combining Copilot’s code-generation capabilities with TMF’s standardized API specifications, developers can speed up coding while adhering to industry standards. In this blog post, we’ll walk through how to set up a project with GitHub Copilot to write TMF Open API-based applications, including prerequisites, configuration steps, an example workflow for building an API, best practices, and additional tips.

Introduction: GitHub Copilot and TM Forum Open APIs

GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It integrates with popular editors (VS Code, Visual Studio, JetBrains IDEs, etc.) and uses advanced language models to autocomplete code and even generate entire functions based on context and natural language prompts. For example, Copilot can turn a comment like “// fetch customer by ID” into a code snippet that implements that logic. It was first introduced in 2021 and is available via subscription for developers and enterprises. Copilot has the ability to interpret the code and comments in your current file and suggest code that fits, essentially acting as an AI pair programmer.

TMF Open APIs refers to a set of standardized REST APIs for telecom and digital service providers. The APIs are designed to enable seamless connectivity and interoperability across complex service ecosystems. In practice, the TMF Open API program has defined over 100 RESTful interface specifications covering various domains (such as customer management, product catalog, billing, etc.). These APIs share a common design guideline (TMF630) and data model, ensuring that services can be managed end-to-end in a consistent way.

Why use GitHub Copilot for TMF Open API development?

Integrating Copilot with TMF Open API streamlines telecom app development. Copilot helps generate boilerplate code, suggests API handling snippets, and provides usage examples, all in line with TMF specs. For developers building services like Customer Management or Product Catalog, Copilot autocompletes endpoints, models, and business logic based on learned standards, maintaining TMF consistency. Developers review and edit outputs, but Copilot eases repetitive tasks. The following sections will guide you on setup and practical use with TMF Open API.

"With GitHub Copilot, TM Forum members can accelerate API development — reducing boilerplate coding, improving consistency with our Open API standards, and freeing developers to focus on innovation rather than routine tasks. We’d love to hear from members already experimenting with Copilot — your experiences, lessons, and best practices will help shape how we embed AI-assisted coding into the wider TM Forum Open API community."

- Ian Holloway, Chief Architect, TM Forum

Prerequisites for Setting Up the Project

Before configuring GitHub Copilot in your project, make sure you have the following prerequisites in place:

GitHub Copilot Access: You will need an active GitHub Copilot subscription or trial linked to your GitHub account. Copilot is a paid service (with a free trial for new users), so ensure your account is signed up for Copilot access. If you haven’t done this, go to the https://github.com/features/copilot and activate your subscription or trial.
Supported IDE or Code Editor: Copilot works with several development environments. For the best experience, use a supported editor such as Visual Studio Code, Visual Studio 2022, Neovim, or JetBrains IDEs (like IntelliJ, PyCharm, etc)
GitHub Account: Obviously, you need a GitHub account to use Copilot (since you must sign in to authorize the Copilot plugin). Ensure you have your GitHub credentials handy.
Programming Language Environment: Set up the programming language/framework you plan to use for your TMF Open API application. Copilot supports a wide range of languages, including JavaScript/TypeScript, Python, Java, C#, etc., so choose one that suits your project.
TMF Open API Specification: Obtain the TMF Open API specifications or documentation for the APIs you plan to implement. TM Forum provides downloadable Open API (Swagger) specs for each API (for example, the Customer Management API, Product Catalog API, etc.).
Basic Domain Knowledge: While not strictly required, it helps to have a basic understanding of the TMF Open API domain you're working with. For example, know what “Customer Management API” or “Product Catalog API” is supposed to do at a high level (reading the TMF user guide can help). This will make it easier to prompt Copilot effectively and to validate its suggestions. For more training, please refer to the TM Forum Education Programs.

With these prerequisites met, you’re ready to configure GitHub Copilot in your development environment and integrate it into your project workflow.

Step-by-Step Guide: Configuring GitHub Copilot in Your IDE

Setting up GitHub Copilot for your project is a one-time process. Here is a step-by-step guide using Visual Studio Code as the example IDE:

Step 1: Install the GitHub Copilot Extension. Open Visual Studio Code and navigate to the Extensions view (you can click the Extensions icon on the left toolbar or press Ctrl+Shift+X on Windows / Cmd+Shift+X on Mac). In the Extensions marketplace search bar, type “GitHub Copilot”. You should see the GitHub Copilot extension by GitHub. Click Install to add it to VS Code. This will download and enable the Copilot plugin in your editor.

Step 2: Authenticate with GitHub. After installation, Copilot will prompt you to sign in to GitHub to authorize the extension. Click “Sign in with GitHub”. Log in with your GitHub credentials and grant permission to the Copilot extension.

Step 3: Enable Copilot in your Workspace/Project. Now that Copilot is installed and linked to your account, you should ensure it’s enabled for your current project. In VS Code, open the command palette (Ctrl+Shift+P / Cmd+Shift+P) and type “Copilot”. Look for a command like “GitHub Copilot: Enable/Disable”. Make sure it’s enabled (it should be by default after installation).

At this point, GitHub Copilot is fully configured in your development environment. The next step is to actually use it in developing a TMF Open API application. We will now walk through writing code with Copilot’s assistance, focusing on a TMF Open API use case.

Writing TMF Open API Apps Using GitHub Copilot

Now for the fun part – using GitHub Copilot to help write an application that implements a TMF Open API. In this section, we’ll provide a step-by-step example of how you might develop a simple service using a TMF Open API (say, a Customer Management API) with Copilot’s assistance. The principles can be applied to any TMF API or indeed any standard API.

Scenario: Let’s assume we want to build a minimal Customer Management microservice that conforms to the TMF629 Customer Management API (version 5.0) – which manages customer records. We will implement a simple endpoint to retrieve customer information by ID, as defined in the TMF API spec. We’ll use Node.js with an Express framework for this example, but you could choose Python (FastAPI/Flask) or Java (Spring Boot) similarly. The emphasis is on how Copilot assists with the coding.

Step 1: Referring to TMF Open API GitHub API specifications

Before coding, ensure you have the TMF629 API specification open or accessible for reference. For example, the spec might say there’s a GET operation at /tmf-api/customerManagement/v5/customer/{id} for retrieving a customer, and defines a Customer data model. If you have the YAML/JSON file, open it in a VS Code tab – this provides Copilot with a bunch of context (resource paths, field names, etc.). Copilot can use this textual context to inform its suggestions.

The spec files can be downloaded from below link (needs a TM Forum registration and login):

Customer Management API REST API v5.0
Open API Directory (Link for all API specifications)

Step 2: Set up the project scaffolding. Initialize a new Node.js project (e.g., run npm init -y for a Node project, and install Express by running npm install express). Then create a file index.js (or app.js). In that file, start with the basic Express server setup:

const express = require('express');

const app = express();

app.use(express.json());

// Start server on port 3000

app.listen(3000, () => {

console.log('TMF Customer API service is running on port 3000');

});

As you type the above, Copilot may autocomplete parts of it. For instance, after writing app.listen(3000, () => {, you might see it suggest a console.log line. It’s standard boilerplate, so nothing magical yet, but it confirms Copilot is active.

Step 3: Implement an API endpoint using Copilot.

Consider the TMF629 Customer Management API

Customer Management API TMF629-v5.0

Now, according to the TMF specification, the GET Customer by ID endpoint should be something like: GET https://host:port/tmf-api/customerManagement/v5/customer/{customerId} -> returns customer details.

Let’s write a handler for this. Start typing the Express route definition. For example:

// GET customer by ID

app.get('/tmf-api/customerManagement/v5/customer/:id', (req, res) => {

});

The moment you write the path string and arrow function, Copilot is likely to recognize this as a request handler and may suggest code inside. It has context from the route path (which is quite specific and likely uncommon except from the TMF spec) and the comment. Copilot might suggest something like: fetching the customer by ID from a database or returning a placeholder. Since we haven’t defined a database in this simple scenario, let’s see what it does. Often, for a new route, Copilot might guess you want to send a response. It could for example suggest:

// ... inside the handler:

const customerId = req.params.id;

// TODO: fetch customer from database (this is a Copilot suggestion comment)

res.status(200).json({ id: customerId, name: "Sample Customer" });

});

Of course, this is just an example of what Copilot might do. In practice Copilot may complete the code differently. The key is that Copilot can help stub out the logic. If it doesn’t automatically fill it, you can nudge it by writing a comment or function description inside the handler, such as:

// Find customer by ID and return as JSON

After writing that comment, pause and see if Copilot suggests a code block that finds a customer. If we had more context (like a Customer array or database connector imported), it might try to use it. For now, you can accept a basic implementation (like returning a dummy object as above).

Accepting the suggestion, our route becomes:

// GET customer by ID

app.get('/tmf-api/customerManagement/v5/customer/:id', (req, res) => {

const customerId = req.params.id;

// For demo, return a dummy customer object

res.json({ id: customerId, name: "John Doe", status: "ACTIVE" });

});

Here we assumed Copilot suggested returning an object with some fields. If the TMF spec defines fields for a Customer (e.g., name, status), and especially if the spec file is open in another tab, Copilot might use actual field names from the spec in its suggestion because it “saw” them in the YAML. This is a huge win: it helps ensure your code uses correct field names and structure as per the standard. For instance, if the spec says a Customer resource has id, name, status, Copilot might include those. Always verify against the spec, but it often aligns.

You continue this way for other operations (PUT/PATCH to update a customer, etc.), each time leveraging Copilot to write the initial code which you then adjust. Copilot can also help with non-HTTP logic: for example, if you need a function to validate an email address, just write the function signature and a comment, and it will likely fill it in (because such patterns are common in its training).

Step 5: Use Copilot for documentation and examples. Copilot can even assist in writing documentation-like content or tests for your API. For instance, you could create a README.md for your project.

Step 6: Iterate and refine with Copilot Chat (if available). GitHub Copilot includes a Chat mode (Copilot Chat) in VS Code, which acts like an assistant you can converse with in natural language. If you have Copilot Chat enabled, you can ask it things like “How do I implement pagination in this API according to TMF guidelines?” or “Suggest improvements for error handling in my code”. The chat can analyze your code base and provide guidance or even write code snippets to apply.

GitHub Copilot provides the capability to choose your own model (e.g. GPT-4.1, GPT-4o, GPT-5 or Claude 3.5 Sonnet, etc.). This provides additional flexibility to Telco developers building solutions on TM Forum (TMF) Open APIs. This flexibility means developers aren’t limited to one generic AI assistant – they can select the model best suited to each coding task, whether for rapid code suggestions or complex problem-solving.

Step 7: Test and validate against the TMF spec. Once you have your endpoints coded with Copilot’s help, it’s crucial to test them against the TMF specification to ensure correctness. Use tools like Postman or curl to call your API endpoints. For instance, GET http://localhost:3000/tmf-api/customerManagement/v5/customer/123 should return either a dummy customer (if using in-memory data as above) or a 404 if not found, as per spec expectations. Compare response structures to the TMF API definition. If something is missing or named incorrectly (say Copilot used customerName but spec expects name), adjust your code accordingly. Copilot is not guaranteed to produce 100% correct or updated spec implementations – it provides a helpful draft, but you are responsible for aligning it exactly with TMF’s definitions.

During testing, you might encounter bugs or mismatches. This is another point where Copilot can assist: if you get an error or exception, you can paste it into Copilot Chat or as a comment and prompt Copilot to help fix it. For example, if you see your server crashes on a null reference, you can write a comment // Copilot: fix null reference in customer lookup near the code, and it might suggest a null-check.

Best Practices and Tips for Using Copilot with TMF Open APIs

To use GitHub Copilot efficiently for TMF Open API development, follow these key practices:

Apply Copilot for Repetitive Tasks: When implementing endpoints with similar logic (e.g., CRUD operations), use an initial example as a template. Copilot will recognise patterns and help adapt code for new entities.

Prompt Clearly and Iterate: Refine prompts to get better suggestions; add specifics in comments for improved results. If output isn't right, adjust your instructions for more detail.

Verify Against TMF Standards: Copilot's knowledge may not reflect the latest TMF specs. Double-check generated code against official documentation and provide context from newer specs when necessary.

Incorporate Security and Quality Checks: Always validate Copilot’s code for security and proper input handling. Use Copilot Chat for advice on improving validation and ensure you meet industry standards (e.g., OAuth).

Learn From Suggestions: Use Copilot to expand your skills, especially if you're new to a language or framework, but confirm that its examples suit your use case.

Don’t Over Rely on Automation: Copilot is best for boilerplate and common patterns; customise business logic and architecture-specific code yourself.

Keep Relevant Files Open: Copilot works best with focused context. Close unrelated files to improve suggestion quality.

Update Copilot Regularly: Keep your extension up-to-date and try different AI models for improved performance.

Following these principles will help make Copilot a productive partner in TMF Open API projects, offering speed while maintaining adherence to standards.

CSPs Leveraging GitHub Copilot

Multiple Telco customers across the globe have adopted GitHub Copilot and have achieved a significant boost in their developer productivity.

In particular, Proximus has achieved below productivity benefits by adopting GitHub Copilot in their Network IT function.

Code					Test
Write Code	Refactor	Code Documentation	Code Review	Code Compliance	Unit Test
↑20-30%	↑25-35%	↑80 - 90%	↑5-10%	↑40 – 50%	↑20-30%

More details here: (2) Transforming Telecommunications with Generative AI: Proximus and TCS's GitHub Copilot Journey | LinkedIn

Other Telco Customer Stories

NOS empowers developer collaboration and innovation on GitHub | Microsoft Customer Stories

Orange: creating value for its lines of businesses in the age of generative AI with Azure OpenAI Service and GitHub Copilot | Microsoft Customer Stories

With GitHub, Canadian company TELUS aims to bring ‘focus, flow and joy’ to developers - Source

https://github.com/customer-stories/telus

Lumen Technologies accelerates dev productivity, sees financial gains with GitHub Copilot, Azure DevOps, and Visual Studio | Microsoft Customer Stories

Vodafone

What's Next?

Agent mode to autonomously complete tasks

Telco developers can boost productivity with GitHub Copilot’s Agent Mode, which acts as an autonomous coding partner. Agent Mode handles multi-step coding tasks—such as implementing TMF Open API flows—reducing manual effort and speeding up feature delivery. It automates complex processes like file selection, testing, and error correction, allowing developers to concentrate on higher-level design while routine tasks run in the background.

Write and execute test plans

GitHub Copilot Chat can quickly generate test plans. Acting as an AI pair-tester, Copilot produces unit tests from your existing code or specs. Telco developers can highlight a method, request test generation, and instantly receive comprehensive test suggestions for different scenarios.

Conclusion

Setting up GitHub Copilot for TMF Open API projects streamlines productivity. This blog covered Copilot’s setup, its application to TMF-compliant services, and provided best practices like offering context and reviewing AI-generated code. Copilot speeds up development by handling boilerplate and suggesting standard patterns so you can focus on business logic. It fits seamlessly into your workflow, producing helpful suggestions when guided with clear specs and prompts. Developers report saving time and reducing complexity.

Still, Copilot shouldn’t replace understanding TMF APIs or good engineering habits; always verify code accuracy. Combining your expertise with Copilot’s capabilities leads to efficient, high-quality implementations. Explore features like Copilot CLI and keep up-to-date via TM Forum resources, including the Open API Table and community forums.

With the right setup and practices, you’re ready to develop robust TMF Open API apps, leveraging AI for faster results.

Unifying Data and AI in Telecom: Inside the Telco Analytics PoC Accelerator

rickliev — Thu, 12 Jun 2025 07:37:53 GMT

As telecom operators race to modernize their operations and deliver personalized, data-driven services, the need for a unified, intelligent analytics platform has never been greater. The Telco Analytics PoC (TAP) Accelerator is Microsoft’s answer to this challenge: a deployable, open-source solution that brings together the power of Microsoft Fabric, Power BI, Azure AI, and Purview to help telcos unlock the full potential of their data.

What Is the TAP Accelerator?

The TAP Accelerator is a pre-packaged, cloud-native PoC environment that enables telcos to explore and showcase AI-powered analytics in action. It includes:

A demo web app
Power BI dashboards for tracking telco specific KPIs and reports
Microsoft Fabric Lakehouses with a telco data model
Sample telco data
ML notebooks
Real-time telemetry via Eventhouse
Automation scripts and ARM templates for deployment

It’s designed to be deployed in a customer’s Azure subscription, allowing hands-on customization and exploration of real-world telecom scenarios.

Why It Matters

Telcos face a unique set of challenges:

Fragmented data across legacy and cloud systems
Slow, manual decision-making processes
Difficulty demonstrating the ROI of AI and analytics

The TAP Accelerator addresses these by offering a unified, governed, and scalable platform that brings together data from across the business—network, finance, customer service, and sales—and applies AI to drive actionable insights.

Core Capabilities

Unified Data Integration

Connects on-prem and cloud data sources via Microsoft Fabric and OneLake

Uses a data mesh approach to unify domains without duplication

Real-Time and Historical Analytics

Eventhouse enables real-time telemetry (e.g., call center, network KPIs)
Fabric Lakehouses support historical trend analysis and forecasting

AI-Driven Insights

Azure AI Foundry powers predictive models (e.g., churn, campaign ROI)
Data Agents enable Copilot-style natural language querying

Governance and Security

Microsoft Purview ensures compliance, access control, and auditing
Built-in security best practices for customer deployments

Key Components

Layer	Description
Fabric Lakehouses	Bronze, Silver, and Gold layers for structured data processing
Power BI Reports	Dashboards for Call Center, Finance, Network, Sales, CEO views
Eventhouse	Real-time KQL-based telemetry ingestion and analytics
Semantic Models	Structured telco semantic models for Power BI and Copilot
Data Agents	Natural language interface for querying data
Azure AI Services	Cognitive services and ML models for advanced analytics

Telco Data Model

The TAP Accelerator uses the Azure Synapse database template for wireless that helps to redefine how data is managed and utilized within the telecommunications sector. This model is designed to streamline operations, foster innovation, and enable a more seamless integration of services across the industry. It's a comprehensive telco data model consisting of more than 20 domains, and thousands of tables. For example, the Network domain alone contains 399 tables defining every entity and attribute commonly used in network use cases.

The Accelerator also includes guidance for modeling the required telco data schema in Synapse, and importing it into your Microsoft Fabric environment.

Business Value

The TAP Accelerator delivers measurable outcomes across the telco value chain:

Customer Experience: Personalized engagement and faster issue resolution
Operational Efficiency: Streamlined workflows and reduced overhead
Financial Control: Real-time visibility into revenue, disputes, and liabilities
Strategic Agility: Faster, AI-informed decision-making
Security & Compliance: Enterprise-grade governance across all data flows

Deployment and Access

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Monetizing generative AI: How telecoms are unlocking new revenue streams

pujaathale — Mon, 09 Jun 2025 23:10:09 GMT

Introduction

As someone deeply engaged in AI-driven transformations within the telecoms industry, I’ve witnessed firsthand how telecoms are spearheading the adoption of generative AI (GenAI). By leveraging AI-powered solutions and forming strategic alliances, telecoms are enabling enterprises to streamline operations, enhance customer experiences, and—most importantly—unlock new revenue opportunities.

A recent survey of telecom executives underscores the growing momentum behind AI adoption, with nearly 50% of telcos reporting tangible benefits from GenAI—double the adoption rate from the previous year. Early adopters are already seeing meaningful returns, achieving cost efficiencies and enhancing customer engagement through AI-driven hyper-personalization. For example, one telecom refined its upselling techniques using GenAI, resulting in a 5–15% increase in average revenue per user (ARPU). Another deployed an AI-powered help desk bot, reducing per-call costs by 35% while increasing resolution rates by 60%.

The urgency to monetize AI

GenAI is becoming essential for tackling industry challenges such as streamlining operations and reducing costs through automation, accelerating growth via hyper-personalized marketing and customer insights, enhancing customer service through AI-driven virtual assistants and chatbots, and evolving telcos into “techcos” that deliver AI-driven services beyond basic connectivity.

AI’s Impact on Business Strategies and Revenue Models

Generative AI is reshaping the telecom landscape by optimizing pricing models, enabling personalized customer interactions, and reducing churn rates. AI-driven analytics can proactively identify customers at risk of switching providers, allowing telcos to deploy personalized retention strategies such as loyalty programs and customized pricing tiers. Modern monetization models require flexibility, allowing telcos to integrate a mix of subscription-based, usage-based, and one-time payment plans that cater to evolving customer demands.

Monetizing AI in telecoms

AI-Optimized Computing Services / GPU as a Service:

With the demand for GPUs far exceeding supply, telcos can monetize their data centers by offering AI computing power to enterprises and government entities seeking sovereign AI solutions.

AI-Driven Customer Engagement Platforms:

Telecoms can package their AI-enhanced customer service capabilities as enterprise solutions for companies managing high-volume call centers.

Intelligent Network Optimization:

AI-powered Radio Access Network (RAN) solutions are improving network performance through real-time analytics, predictive maintenance, and dynamic resource allocation.

Centralized AI Platforms / LLM as a Service:

Leading organizations are developing centralized AI platforms that serve as repositories of proven and maintained AI/gen AI modules, APIs, tools, and code snippets. This platform approach helps drive quicker implementation of successful use cases while maintaining consistent guardrails and leveraging proven architectures and use-case “recipes.” For example, by building a GenAI platform with ~50 reusable services, one telecom successfully reduced the time it took to build new use cases from months to about two weeks. This ensured that all similar use cases used consistent architectures and that best practices and learnings were shared in a common repository.

AI-powered fraud detection and risk management:

AI can analyze vast volumes of transactional and behavioral data in real time to detect anomalies and prevent fraud. By offering fraud detection as a service or embedding it into enterprise solutions, telcos can reduce revenue leakage and enhance customer trust—both of which directly impact the bottom line.

AI-enhanced personalized marketing:

By leveraging customer data and behavioral insights, telecoms can use AI to deliver hyper-personalized offers, upsell opportunities, and loyalty programs. These targeted campaigns increase conversion rates and average revenue per user (ARPU), making marketing spend more efficient and profitable.

AI-driven field operations optimization:

AI can streamline field service operations by predicting equipment failures, optimizing technician dispatch, and automating maintenance workflows. These efficiencies reduce operational costs and improve service reliability—both of which contribute to margin expansion and customer satisfaction.

Success stories: AI-driven transformation in telecoms

Several telecoms are already seeing significant ROI from their AI investments. One achieved an in-year ROI of more than 2x from GenAI implementations, contributing to a multibillion-dollar cost-reduction target. Another reported an ROI of 9x to 12x from its proprietary AI tool, which optimizes customer support and internal workflows. A third launched a platform to democratize AI adoption, with real-world applications showcased at a major global event.

High-ROI AI applications for telecoms

AI is proving to be a game-changer across several key areas. In operational efficiency, AI-driven automation reduces workloads and enhances workforce productivity. In customer engagement, personalized AI-driven interactions improve satisfaction and retention rates. For network optimization, AI-powered analytics predict outages and enhance service reliability. In product innovation, AI enables more customized offerings, increasing customer loyalty. For fraud prevention, AI’s pattern-recognition capabilities enhance fraud detection and mitigate risks. In sustainability initiatives, AI helps telecoms minimize their carbon footprint by optimizing energy use and device recycling programs.

Conclusion

Generative AI is fundamentally reshaping the telecom industry, ushering in a new era of automation, intelligence, and monetization. By investing in robust AI frameworks and monetization strategies, telecoms can improve efficiency, expand revenue streams, and secure their leadership in the evolving digital economy.

How Microsoft partners are helping telecoms transform with industry-focused AI solutions

Pabhanda — Thu, 22 May 2025 15:27:17 GMT

In today's rapidly evolving digital landscape, the telecom industry stands at a pivotal moment. The integration of artificial intelligence (AI) is not just an innovation—it's a transformation. With a partner ecosystem of over 500,000, Microsoft partners are playing a crucial role in revolutionizing the telecom industry by leveraging cutting-edge AI capabilities. In fact, it is predicted that 95% of all customer interactions will be through channels supported by artificial intelligence (AI) technology. Across the globe, Microsoft partners are at the forefront of helping telecom companies reimagine operations, customer engagement, and network optimization through cutting-edge AI capabilities. These advancements are not generic—they are finely tuned to telecom providers' unique challenges and opportunities.

Transformation with AI

The telecom sector is experiencing seismic shifts as demand for faster connectivity, better customer experiences, and more efficient network management grows. Microsoft partners are harnessing AI to help operators modernize legacy systems, reduce operational costs and unlock new revenue streams.

For example, AI-powered platforms are redefining customer engagement, enabling telecom providers to deliver more personalized, responsive, and seamless interactions across every touchpoint. Through Microsoft Azure AI and Copilot technologies, partners are deploying tools that transform how customers experience services at scale. A recent example is Amdocs’ collaboration with PLDT, which just implemented the Customer Engagement Platform to enhance personalization and responsiveness.

In addition to Amdocs, several other Microsoft partners are also demonstrating the transformative potential of AI in telecom:

Nokia is advancing the vision of Level 5 Autonomous Networks with AI and generative AI, helping operators automate and self-optimize complex network environments.
D-ID is working with Microsoft to introduce agentic AI avatars, revolutionizing customer communications and enabling human-like interactions at scale.
PwC Spain, through its Mas Orange case study, showcases how AI is driving end-to-end digital transformation in a major European telecom operator.
Icertis, a leader in contract intelligence, is using generative AI to transform enterprise contracting—a critical function for telecom companies managing large-scale vendor and service agreements.

Industry-focused solutions

Unlike one-size-fits-all offerings, Microsoft's partner ecosystem delivers AI solutions tailored specifically for the telecom industry. These industry-focused applications address critical priorities such as 5G monetization, edge computing, network automation and customer lifecycle management.

One example is the integration of AI into Business Support Systems (BSS) and Operations Support Systems (OSS), systems, enabling smarter billing, provisioning and service assurance. These solutions are grounded in real-world telecom use cases and refined through close collaboration with operators.

Collaborative efforts

The power behind these transformations lies in partnership. Microsoft’s deep collaboration with its partner ecosystem enables rapid innovation and solution deployment. Through programs like the Microsoft AI Cloud Partner Program and ISV Success, partners gain the tools, support and co-innovation opportunities needed to scale their impact in telecom.

This spirit of co-innovation was highlighted in Microsoft's recent 50th-anniversary partner blog, emphasizing its partner ecosystem's evolution and future. As noted in the March 2025 blog post, Microsoft is doubling down on strategic industry collaborations to empower every partner to succeed in the age of AI.

Future outlook

Looking ahead, AI will continue to be a critical enabler for telecom operators navigating the challenges of scale, complexity, and customer expectations. Microsoft and its partners are working to define the next generation of telecom services—whether through autonomous networks, AI-enhanced cybersecurity or more innovative service delivery models.

As AI capabilities mature, the opportunities for telecom transformation will only expand. With strong foundations built on collaboration, industry expertise, and advanced technology, Microsoft and its partners are helping telecom companies turn AI potential into measurable outcomes.

Supporting programs that accelerate innovation

To further empower telecom-focused innovation, Microsoft offers several key programs for partners:

Microsoft AI Cloud Partner Program: Provides resources, training, and go-to-market support for partners building AI-driven industry solutions.
ISV Success Program: Helps independent software vendors (ISVs) scale their offerings through Azure with technical guidance and marketplace visibility.
Co-Innovation Labs: Enables collaborative solution development with Microsoft engineers and domain experts.
Azure Marketplace: A platform for partners to publish and monetize telecom solutions, reaching global customers.

By leveraging these tools and fostering deep industry partnerships, Microsoft is enabling a new era of AI transformation in telecom. Microsoft has always been a partner-led company. Our partners are core to our heritage and our future. Their innovation and collaboration have driven real transformation and customer success and will continue to shape the future of industries around the world.

To learn more about how AI is transforming customer engagement in telecom, explore the Microsoft AI Playbook for Microsoft Partners.

AI beyond Chatbots

PrajaktDeotale — Fri, 25 Apr 2025 13:58:41 GMT

Artificial intelligence (AI) has rapidly evolved from narrow automation tools to autonomous, intent‑driven agents that perceive environments, interpret high‑level objectives, and execute complex tasks with minimal human intervention. This shift — known as agentic AI — represents the next frontier of generative AI, empowering telecom operators to transform customer engagement, network management, and operational efficiency.

According to McKinsey, the global telecom industry could capture up to $250 billion in value by 2040 through advanced AI and agentic deployments. Microsoft, at the forefront of this revolution, is enabling telcos to leverage GenAI to enhance customer engagement, optimize networks, secure operations, and drive new revenue streams. By leveraging Microsoft’s Copilot Studio and Azure AI capabilities, telecom CTOs can move beyond conversational chatbots to build intelligent, self‑optimizing workflows that drive measurable outcomes across the enterprise.

The Agentic AI Advantage

Agentic AI goes well beyond today’s conversational chatbots: it comprises autonomous systems that perceive their environment, interpret high‑level goals, plan and execute multi‑step workflows, and continuously learn to improve outcomes. In telecom, agentic AI is rapidly moving from pilot projects to strategic priority. A recent McKinsey survey found that 64% of telco C‑suite executives have made scaling agentic use cases a top priority for 2025, and nearly 75% are targeting customer service first.

Early adopters are already seeing material ROI: one North American operator reduced network capital expenditure by 10% by deploying an autonomous optimization agent, and a leading European telco cut cost per call by 35% while increasing first‑contact resolution by 60% with an AI‑powered help‑desk agent.

According to a recent IDC white paper, telecom and media companies are seeing nearly four times the return on investment (ROI) on every dollar invested in AI.

These results demonstrate that agentic AI isn’t merely a technological upgrade—it’s a transformative capability that automates complex processes, drives significant efficiency gains, and delivers measurable financial impact across the telecom value chain.

For Chief Technology Officers (CTOs), the question isn’t whether to integrate AI into their operations but how to best implement these tools to achieve measurable results. In this exploration, we’ll examine Microsoft’s GenAI offerings and their role in reshaping the telecom landscape.

Cracking the Code on Fraud: AI’s Role in Network Security

Fraud is a persistent and costly issue for telecom operators, with industry losses nearing $39 billion globally in 2023. Traditional fraud detection systems, dependent on static rules, struggle to keep up with the rapidly evolving techniques used by attackers. GenAI and AI agents are proving to be game-changers in combating it. These agents continuously monitor vast volumes of network and transactional data in real-time, using pattern recognition, anomaly detection, and predictive analytics to identify suspicious behavior as it unfolds. Unlike traditional rule-based systems, AI agents can adapt to evolving fraud tactics, flagging irregularities such as sudden call spikes, unusual roaming activity, or identity mismatches. They can also trigger automated responses—like blocking transactions, flagging accounts, or alerting fraud teams—within seconds. This autonomous, always-on defense enables telcos to detect and prevent fraud faster, reduce financial losses, and protect customer trust.

At the heart of Microsoft’s fraud prevention strategy is Azure OpenAI Service, integrated into platforms like Nokia’s NetGuard Cybersecurity Dome. These systems leverage GenAI models trained on extensive datasets to detect and neutralize threats more effectively. For example, Microsoft’s Extended Detection and Response (XDR) framework aggregates and enriches data from core, RAN, and transport domains. This telco-specific context enables the system to identify anomalies and threats with greater precision.

By reducing the time needed to detect and respond to fraud by up to 50%, these solutions enhance network security and scalability. Additionally, their adaptability ensures that telcos remain ahead of emerging threats without needing constant manual updates.

Beyond detection, Microsoft employs Confidential Computing on Azure, which ensures sensitive data remains encrypted during processing. This approach not only aligns with stringent global privacy regulations like GDPR but also builds customer trust in data-intensive applications.

Enhanced Use Cases for GenAI in Telecom Security

Proactive Risk Mitigation: GenAI models continuously evolve by learning from historical data and real-time events, enabling predictive analysis to preempt potential vulnerabilities.
Dynamic Network Behavior Analysis: By analyzing user behavior and device activity, these systems detect deviations that might signal fraud, such as unauthorized access or abnormal data usage patterns.
Automated Remediation: Once a threat is identified, GenAI-driven systems and AI agents can automatically initiate countermeasures, such as blocking suspicious transactions or isolating compromised network segments.

Voice AI: Redefining Customer Engagement with GenAI

For years, voice has been the backbone of customer interactions in telecom. GenAI is now transforming these experiences by integrating advanced voice capabilities that create seamless, personalized, and efficient customer engagements.

Microsoft’s collaboration with Norwood Systems and their CogVoice platform highlights how GenAI elevates voice interactions. By integrating Azure OpenAI Service and Azure AI Translator, Norwood’s solutions provide real-time transcription and multilingual support, allowing telcos to serve a diverse customer base with minimal latency.

Norwood Systems' CogVoice Agentic Network IVR represents the next generation of interactive voice response (IVR) systems, powered by advanced AI. This solution merges AI with voice technology to enable natural, smart conversations, replacing rigid menus with fluid, context-aware interactions. Key features include an intelligent memory system for continuity across multiple conversations, real-time interruptible conversations, and multilingual support with seamless language switching.

AT&T has implemented GenAI-powered voice solutions to reduce spam calls and deliver predictive customer service. Their Visual Voicemail platform not only filters unwanted calls but also uses analytics to anticipate user needs, offering targeted responses. This system operates on a foundation of Azure Speech Service, which processes vast amounts of voice data to provide real-time, context-aware insights.

Additional Technical Capabilities

Custom Neural Voice, a feature of Azure Cognitive Services enables telcos to create branded AI voices that maintain a consistent tone and identity across all customer interactions.
Contextual Integration: GenAI-powered voice systems can integrate with CRM platforms to provide agents with real-time insights during calls, enhancing customer satisfaction.

These innovations are not just about efficiency—they represent an opportunity for telcos to redefine their customer engagement strategies, setting themselves apart in a competitive market.

Boosting Worker Productivity with AI-Infused Tools

Telcos face ongoing challenges with workforce productivity, particularly in roles that involve repetitive or administrative tasks. Microsoft 365 Copilot is revolutionizing how telecom employees work by automating these processes and freeing up time for higher-value activities.

Telcos can also build their own agents or enhance Microsoft 365 Copilot with Microsoft Copilot Studio using an intuitive natural language interface that doesn’t require coding expertise. Developers can further extend with Microsoft 365 Agents SDK to publish agents across multiple channels including Microsoft Teams, the web, and more. Additionally, developers can craft scenarios that leverage code-first experiences in Azure AI Foundry, a trusted, integrated platform to design, customize, and manage AI applications and agents

Lumen Technologies, for instance, reduced sales proposal preparation time from four hours to just 15 minutes, saving an estimated $50 million annually. This is achieved through Microsoft Graph APIs, which aggregates data from multiple sources like emails, documents, and CRM systems. The result is contextually relevant insights delivered directly to employees, allowing them to focus on strategic objectives.

KT Corporation is leveraging Microsoft's advanced AI to drive efficiency and innovation.

“The Microsoft AI-driven solutions have enabled KT Corporation to improve its work efficiency and drive significant work innovation. By introducing Microsoft 365 Copilot, KT Corporation empowered over 11,000 employees with the latest AI solutions. Additionally, by developing AI agents built on solutions such as Microsoft Sustainability Manager and Copilot, KT reduced task completion time by 50% and improved infrastructure efficiency by 20%.” Phil Oh, CTO, KT Corporation

Vodafone, another Microsoft partner, expanded its use of Copilot to 68,000 employees across departments, including legal teams and customer service. For customer-facing roles, Copilot summarizes previous interactions, equipping representatives with the knowledge they need to resolve issues more effectively. This has driven Net Promoter Scores (NPS) from low single digits to the high 30s, highlighting the impact of AI-driven tools on customer satisfaction. Vodafone has developed AI-powered tools like "SuperAgent" to assist customer care agents in handling complex inquiries. Built using Microsoft Azure AI Foundry, Azure OpenAI Service, and Microsoft Copilot, SuperAgent enables agents to access relevant information swiftly, improving response times and customer satisfaction.

NTT DATA is leveraging Microsoft AI to build agentic AI workloads.

“NTT DATA leverages Microsoft Copilot Studio to deliver agentic AI advisory, implementation, managed services, and connectivity. By providing industry-specific automation and utilizing our integrated managed services platform, we support clients throughout their agents’ lifecycle. This collaboration is pivotal in achieving our clients’ outcomes, enabling us to deliver tailored, efficient, and innovative solutions that drive business success and enhance decision-making processes.”

Aishwarya Sing, SVP, Global Head of Digital Collaboration, NTT

T-Mobile is harnessing the power of agentic AI through Microsoft Copilot Studio to empower its customer service representatives (CSRs). A key implementation is the “PromoGenius” app, enhanced by an AI-driven agent that connects to over 20 device manufacturers’ websites. This AI agent enables CSRs to ask natural language questions and receive instant, structured answers — including detailed product specs and side-by-side comparisons — without leaving the customer conversation. Enhancements underway will soon allow CSRs to generate customer-specific PDF reports and automatically email them via Power Automate, while upcoming voice capabilities will make access to data even faster. Remarkably, this powerful AI-powered app — which would typically take nine months to build — was delivered in just one week, underscoring the agility and innovation AI agents bring to telecom operations.

Scalability and Integration

Microsoft 365 Copilot integrates seamlessly with Azure Entra ID and Power Automate, enabling telcos to scale these solutions across global operations while maintaining security and compliance.

Network Optimization Through Azure Programmable Connectivity

Telecom networks are increasingly complex, requiring operators to manage integrations across multiple providers and platforms. Microsoft’s Azure Programmable Connectivity (APC) simplifies this process by offering standardized APIs that abstract network-specific complexities.

With 5G slicing support, APC enables developers to build applications that leverage low-latency, high-throughput network segments. This is particularly valuable for use cases like autonomous vehicles, remote surgery, and immersive AR/VR experiences. Additionally, APC’s compatibility with Azure Kubernetes Service (AKS) makes it easy for telcos to deploy containerized applications in hybrid cloud environments.

Azure Programmable Connectivity (APC), when integrated with AI agents, offers transformative capabilities for network optimization in the telecommunications sector. By providing a unified interface across multiple operator networks, APC enables AI agents to dynamically allocate network resources, predict and mitigate potential issues, and ensure compliance with regulatory standards. This integration facilitates real-time analytics, allowing telecom providers to monitor network performance, detect anomalies, and make data-driven decisions to enhance service reliability and efficiency.

A Closer Look at IoT Deployments

In the IoT space, APC accelerates deployment timelines by reducing the need for operator-specific customizations. For example, a smart city project can connect thousands of sensors and devices across different telecom networks without disruption, ensuring consistent performance and reliability.

AI-Driven Analytics and GenAI for Operational Insights

The vast amounts of data generated by telecom operations can overwhelm traditional analytics platforms. AI agents can empower telcos to transform operational insights into intelligent, real-time actions that optimize both network performance and customer experience. By continuously analyzing data from across the network and customer interactions, AI agents enable proactive network monitoring, automatically detecting and resolving issues before they affect users. They support predictive maintenance by identifying early signs of equipment failure and scheduling repairs to avoid outages. Through intelligent resource allocation, agents dynamically manage bandwidth and capacity based on usage trends to ensure consistent service quality. Critically, they also drive customer experience enhancement by using segmentation and behavioral insights to personalize services, proactively resolve customer issues, and tailor offers or interactions. This data-driven personalization improves satisfaction and loyalty while reducing churn. Combined, these agentic AI capabilities allow telecom operators to evolve from reactive operations to an automated, customer-centric, and insight-led model.

GenAI-powered solutions like those offered by Microsoft bring clarity to this complexity, transforming raw data into actionable insights.

Yobi, a platform built on Azure Machine Learning, demonstrates the power of GenAI analytics. By analyzing millions of data points in real time, Yobi provides telcos with insights into customer behavior, network performance, and operational efficiency. This enables operators to proactively address service issues, predict churn, and optimize marketing strategies.

AT&T has also harnessed Microsoft’s AI capabilities to streamline field operations. The Ask AT&T platform uses GenAI to analyze technician routes, reducing fuel consumption and increasing daily service capacity. These optimizations not only improve customer experiences but also contribute to sustainability efforts by minimizing environmental impact.

One NZ is using Microsoft Fabric for real-time analytics from unified data sources. With the integration of multiple systems and visualizing insights on a single pane, One NZ has rapidly streamlined processes and proactively addressed growth opportunities.

A CTO’s Blueprint for GenAI Integration

To capture agentic AI’s full potential, telecom CTOs need a structured, action‑oriented roadmap. Here are five high‑impact steps to guide enterprise‑wide GenAI adoption:

Assess and Modernize Infrastructure: Run a cloud readiness audit using tools like Azure Advisor to identify gaps in compute, networking, and security. Prioritize hybrid deployments with Azure Arc for seamless integration of on‑premises systems and public cloud.
Start with High-Impact Use Cases: Focus first on customer service, network optimization, and fraud detection — domains where telcos have reported 10–15% capex savings and 35% cost‑per‑call reduction. Develop clear success metrics (e.g., time‑to‑resolution, NPS lift, EBITDA improvement).
Build a Modular AI Platform: Centralize reusable components (APIs, models, data pipelines) in Copilot Studio to accelerate new deployments from months to weeks. Implement LLMOps practices for continuous monitoring, retraining, and governance.
Build Internal Expertise: Launch role‑based GenAI certification programs via Microsoft Learn, targeting data engineers, AI product owners, and frontline managers. Establish an internal GenAI Center of Excellence to curate best practices and accelerate cross‑functional collaboration.
Govern Responsibly, Iterate and Scale: Define guardrails for data privacy, bias mitigation, and model explainability, aligned with GDPR and emerging AI regulations. Adopt agile cycles: deploy pilot → collect usage and performance data → refine workflows → scale gradually.

Building the Future of Telecom with GenAI

The telecom industry is entering a new era where AI isn’t just a tool - it’s a cornerstone of strategy. Microsoft’s GenAI solutions provide telcos with the technical foundation to innovate, compete, and thrive in a fast-changing landscape.

By embedding GenAI across every layer of the business — from customer care and network orchestration to capital planning and new product innovation — telcos can transform cost centers into growth engines. Early adopters are already capturing double‑digit improvements in efficiency, slashing call‑center costs by up to 45%, and boosting capital‑expenditure ROI by 10–15%. More importantly, GenAI unlocks entirely new revenue streams: personalized digital services, on‑demand network slices, and AI‑as‑a‑service offerings that turn connectivity into a strategic asset.

Realizing this future demands a holistic approach: modernize infrastructure for AI‑ready compute, build modular platforms that scale reusable AI components, cultivate AI fluency across the workforce, and govern responsibly to earn stakeholder trust. Telco leaders who move decisively today — executing the blueprint outlined earlier — will not merely survive; they will redefine what it means to compete in a 5G and beyond world.

For CTOs, the time to act is now. Integrating GenAI isn’t just a technological upgrade; it’s a strategic imperative. By leveraging Microsoft’s robust ecosystem of AI tools, telcos can reimagine operations, delight customers, and unlock new revenue streams.

Explore how Microsoft is enabling telecom innovation through agentic and generative AI

For a business-centric point of view on this topic, see our blog on this topic on Telecom Industry Blogs.

Project Janus: Now Open Source

bradunov — Thu, 27 Feb 2025 14:21:52 GMT

We are excited to announce that Project Janus is now available as open-source software on GitHub. This release includes the jbpf library, which implements dynamic service models that can be used to make current O-RAN service models much more flexible. We are also releasing jrt-controller, a reference implementation of a real-time controller that supports the use cases currently under study in the O-RAN RT-RIC study item.

Previously, at Mobile World Congress (MWC) 2024, we announced Project Janus along with leaders across the telecommunications industry. Project Janus uses telco-grade cloud infrastructure compatible with O-RAN standards to draw on fine-grained telemetry from the radio access network (RAN), the edge cloud infrastructure, and other sources of data. This enables a communication service provider (CSP) to gain detailed monitoring and fast closed loop control of their RAN network.

Project Janus helps CSPs optimize RAN performance through visibility, analytics, AI, and closed loop control. To meet this objective, Microsoft and industry collaborators built a set of capabilities including RAN instrumentation tools that can improve the existing E2 O-RAN interface and update its service models to communicate with components of a CSP’s RAN and SMO architecture.

Project Janus’ dynamic service models can add new functionality to operational RAN deployment without disruption. They allow developers to obtain RAN telemetry and, where required, exert control over its behaviour, both at microsecond time scales. The dynamic service models can be directly consumed by xApps on a nRT-RIC, or further coupled with the real-time apps (dApps) on the real-time controller we are also releasing. The flexibility and richness of data allows network operators and developers to harness the full power of AI for RAN, which is further discussed in our paper, “Distributed AI Platform for the 6G RAN.”

This architecture enables several new use cases, such as precise analytics for anomaly detection and root cause analysis, interference detection, and optimizing other RAN performance metrics. The framework also enables new applications of particular interest to macro network deployments, such as fast vRAN power savings, failover, and live migration. It also allows for easy customization of RAN performance for niche requirements in different industrial use cases (as discussed in our paper “The Future of the Industrial AI Edge is Cellular.”). More details about Project Janus and the use cases are available on the project’s website.

Project Janus has already garnered significant interest and support from our partners. They have already built and deployed several new applications on top of it, such as RAN performance optimization through fine grained L1 telemetry, interference mitigation and accurate localization.

Learn more about how our top partners are integrating Project Janus into their product offerings and research:

“The Project Janus framework enhances Mavenir's cloud-native, software-based Open RAN solutions with additional real-time capabilities. This enables the creation of customizable solutions, such as low-latency intelligent controllers. Furthermore, Project Janus serves as a flexible framework for supporting decentralized applications (dApps), playing a crucial role in advancing AI-driven Radio Access Networks (RAN) and laying the groundwork for the development of 6G technology.” – Bejoy Pankajakshan, EVP, Chief Technology and Strategy Officer, Mavenir

“Project Janus introduces innovative ways of collecting RAN data in real-time without causing performance deterioration of RAN. The Project Janus framework has been integrated with 5G RAN solutions to control the behaviour of RAN algorithms in real-time by writing simple codelet to aggregate and compress the large volume of timeseries of data received in real-time and pass it to the external entity (e.g. xApp or analytics software). Capgemini is leveraging this solution to enrich our 5G RAN software frameworks, to boost its xApp capabilities and demonstrate improved RAN performance. This framework has been utilized to demonstrate several state-of-the-art features, such as power saving, mobility load balancing, RU sharing, failsafe fronthaul, and more. Additionally, Project Janus provides real-time graphical insights into execution scenarios, significantly enhancing our analysis and debugging capabilities to further optimizes and improve RAN performance.” – Utkarsh Malik, Senior Director of Product Management, Capgemini

"AI in RAN is the next frontier, and it continues to evolve as the industry further uncovers its potential. This is why a software centric architecture, coupled with this exciting collaboration with Microsoft and Project Janus, demonstrates how you can deploy hardware and software now, while enabling new AI use cases in the future. Project Janus allows real time dynamic access to L1 telemetry, allowing the type of telemetry to be controlled based on new future AI use cases, expanding the developer scope." - Dan Lynch, Senior Director, FlexRAN at Intel

“Project Janus promises to revolutionize real-time network telemetry, programmability and optimization through its flexible, dynamically loadable service models. The ability to implement new functionality and deploy at run-time without affecting RAN operations unlocks a new dimension of RAN efficiency, resilience and performance. We're excited to bring this game-changing functionality to our commercial srsRAN Enterprise partners and to our broad community of open-source srsRAN Project users.” – Paul Sutton, CEO, SRS

"Project Janus makes it easy for a RAN vendor to add our sub-meter positioning capability to their products. It has also allowed us to accelerate our development to come to market at least a year ahead of other 5G positioning solutions and with 10-100x better performance than any other 5G positioning solution.” – Daniel Jacker,CEO, ZaiNar

“EdgeRIC is an open-source platform developed through a collaboration between Texas A&M University and UC San Diego, designed to enable real-time AI-in-the-loop feedback control for radio access networks. By integrating EdgeRIC with Microsoft's Project Janus framework, we are advancing the programmability of open-source 5G platforms, allowing precise, low-latency control at the edge. This integration enhances spectrum efficiency, optimizes resource allocation, and provides a robust foundation for AI-driven network intelligence. Through this collaboration, we are fostering an open ecosystem where researchers and industry leaders can accelerate innovation in intelligent wireless communications.” – Professor Srinivas G Shakkotai, Department of ECE and Department of CSE, Texas A&M University

"RAN Intelligent Controllers standardization is typically slow, requiring multiple vendors to align on a constrained set of information sharing between RAN and RICs. Microsoft open-sourcing Project Janus, which works with existing commercial stacks, enables rapid access to information without requiring standardization changes. UC San Diego and Texas A&M University developed the EdgeRIC platform and micro-apps that support open-source RAN stacks (srsRAN and OAI). EdgeRIC's integration with Project Janus allows apps to be directly translated to commercial stacks, enabling them in more industry-accepted stacks. These tools are key to unlocking novel applications, revenues, and business productivity for enterprises, from AI-driven networking to networking-driven AI to network infrastructure-driven sensing, fostering rapid innovation in the 6G ecosystem. The technology has the potential to lead to a world where our phones and base stations are upgrading every other day, much like software releases for apps," - Professor Dinesh Bharadia, Electrical Engineering, Klein Gilhousen Chancellor's Endowed Faculty Fellow for Next Generation Wireless, University of California San Diego.

“RAN is undergoing a transformation toward data-driven operations. Project Janus provides a key enabling technology to make the RAN data accessible for intelligent monitoring and control applications. As such, we have adopted it as the primary RAN telemetry system in our testbed and research efforts.” – Professor Mahesh Marina, University of Edinburgh

We look forward to seeing the community's contributions and innovations with Project Janus. You can read more about our Project Janus announcement, plus our additional telco industry news, ahead of MWC here.

Empowering Telcos and Connected Industries: Shaping the Future at MWC with AI and Microsoft

Pabhanda — Mon, 24 Feb 2025 14:48:01 GMT

The world of technology is evolving at an unprecedented pace, and this transformation is most evident in the telecom industry. At Mobile World Congress (MWC), the spotlight shines brightly on telecommunications and its pivotal role in the connected industry revolution. As the world becomes increasingly interconnected through advancements in AI, 5G, and IoT, telecoms are at the forefront, enabling seamless communication and smart solutions across various sectors. MWC serves as a global stage where industry leaders, innovators, and startups converge to showcase cutting-edge technologies and explore new possibilities. The event highlights how telecoms are driving the evolution of connected industries, from smart cities and autonomous vehicles to healthcare and manufacturing, shaping a future where connectivity is the cornerstone of progress.

At MWC 2025, startups have a unique opportunity to tap into cutting-edge technologies, engage with enterprise leaders, and position themselves at the forefront of an AI-driven revolution. Microsoft provides the tools, platforms, and ecosystem connections that help startups scale, co-sell, and drive global impact.

AI: The Ultimate Startup Growth Accelerator

AI is a game-changer for startups across every industry. From predictive analytics and automation to hyper-personalized customer experiences, AI is revolutionizing how businesses operate and compete. Microsoft’s AI-powered solutions, such as Azure AI and M365 Copilot, are enabling startups to build smarter, scale faster, and innovate in ways previously unimaginable. Key opportunities include:

Automating workflows to boost efficiency and reduce operational costs.
Hyper-personalization, allowing startups to tailor experiences in real time.
AI-driven security solutions that enhance trust in an increasingly digital world.
Accelerated go-to-market with AI-powered insights into customer and partner engagement.

The Connected Future: 5G, IoT, and Intelligent Infrastructure

The fusion of AI with 5G, IoT, and cloud computing is ushering in a new era of connectivity. Startups at MWC will see firsthand how these technologies are unlocking real-time data insights, edge computing capabilities, and next-gen customer experiences.

Smart manufacturing and supply chains: AI-powered automation combined with 5G ensures seamless, data-driven decision-making.
Intelligent retail and commerce: IoT-enabled insights help businesses personalize experiences and optimize inventory.
Autonomous mobility and smart cities: AI and edge computing drive innovation in transportation, energy, and urban planning.
Healthcare and MedTech: AI-driven diagnostics and real-time patient monitoring are revolutionizing the industry.

Microsoft plays a critical role in bringing these innovations to life, helping startups bridge the gap between technology and business impact.

Partnering with Microsoft: Your Catalyst to Scaling

For startups looking to co-sell, integrate, or scale within enterprise ecosystems, Microsoft for Startups Founders Hub and the Pegasus Program offer unparalleled advantages.

Microsoft for Startups Founders Hub

Microsoft for Startups Founders Hub is a comprehensive platform designed to support startups at every stage of their journey, from ideation to exit. Startups are welcome to self-nominate via startups.microsoft.com. Here are some key benefits:

Access to Resources: Startups receive free cloud credits to build their products on Azure, along with access to essential software and technical advisory.
24x7 Technical Support: Pegasus startups receive a dedicated technical resource to help navigate Microsoft and support their Azure build journey.
Incentives and Credits: Secure access to credits and incentives to scale AI-driven innovation. Founders Hub offers various resources, including cloud credits and technical support, to help startups innovate and grow.
Expert Network: Access to a curated mentor community allows startups to engage with diverse business and technical experts who have been where they are and are ready to help them hit their next milestone.

Pegasus Program

The Pegasus Program within Microsoft for Startups is designed to select strategic startups that have achieved product-market fit and are ready for enterprise engagement. It is an invite-only program and here are some key benefits:

Go-to-Market Acceleration: Engage in co-sell motions with Microsoft and tap into enterprise demand. The Founders Hub helps startups accelerate their go-to-market strategies by providing the necessary tools and support to succeed.
Global Reach: With a presence in over 140 countries, Microsoft for Startups Founders Hub enables startups to expand beyond local markets. This global network allows startups to reach new customers and partners, facilitating international growth and success.
Co-Marketing Opportunities: The Pegasus Program provides co-marketing and other opportunities to help startups grow their business. This includes warm introductions to potential customers and navigating corporate divisions and stakeholders.
Global Reach and Enterprise Engagement: The Pegasus Program helps startups connect with Microsoft’s vast partner ecosystem and enterprise customers, driving sales and accelerating growth.

By leveraging the resources and support provided by Microsoft for Startups Founders Hub and the Pegasus Program, startups can accelerate their growth, innovate with AI-driven solutions, and expand their reach globally.

The Time to Innovate is Now

Are you ready to scale, innovate, and lead the next wave of transformation? Connect with Microsoft at MWC and discover how AI and connectivity can take your startup to new heights.

#Telecommunication #Media #Gaming #MSFTstartups #M12#PartnersMakeMorePossible #MSFTAdvocate #4FYN #MWC

Navigating the Future of AI and Innovation

Pabhanda — Tue, 21 Jan 2025 19:24:51 GMT

We are standing at the precipice of unprecedented transformation. AI, security, and innovation are not just reshaping industries - they are redefining what is possible. As global businesses look to the next 2–3 years, the pace of technological evolution demands a new way of thinking, a reimagining of strategies, and a bold embrace of opportunities. Below, are some of the key insights -

The AI opportunity: productivity, personalization, and new business models

The coming years will be pivotal for businesses as they harness AI to drive growth and innovation. While technology evolves at breakneck speed, some opportunities are crystal clear:

Enhanced productivity
AI is enabling businesses to automate repetitive, time-consuming tasks, freeing up talent to focus on strategic and creative pursuits. Tools like Microsoft 365 Copilot have become indispensable in my daily work, helping me prioritize what truly matters.
Hyper-personalization
Emerging AI technologies are unlocking unprecedented opportunities for businesses to deliver tailored customer experiences. This level of personalization will not only deepen customer relationships but also open new revenue streams, including data monetization.
Revolutionary business models
We are witnessing the rise of AI-driven solutions in areas like robotics, autonomous vehicles, and supply chain optimization. These innovations are creating entirely new ecosystems, powered by secure, AI-enabled infrastructure and fast, reliable networks.

Navigating challenges: security, ethics, and ecosystem shifts

While the opportunities are immense, challenges abound. Cybersecurity remains a top concern, as businesses must protect sensitive data and maintain trust in a rapidly evolving landscape. Ethical considerations are equally critical, requiring companies to adopt responsible AI practices that prioritize transparency and accountability.

But the most profound challenge lies in a fundamental shift: the transition from traditional applications to AI-native architectures. This evolution will redefine how businesses build, deploy, and manage technology, creating a pressing need for robust ecosystems and partnerships to navigate this complexity.

AI as a growth driver: the case for telcos

In the telecommunications industry, AI is already transforming operations and driving growth. From automating network optimization to enhancing customer service through AI-powered support systems, telcos are leveraging AI to reduce costs and improve customer satisfaction.

But AI’s potential extends far beyond efficiency. By delivering personalized promotions and services, telcos are unlocking new revenue streams. As these technologies mature, the ROI for businesses adopting AI will only continue to grow.

The power of ecosystems and partnerships

Ecosystems and partnerships are no longer optional - they are essential. At Microsoft, we’ve built a global partner ecosystem that empowers over 500,000 companies to harness the power of cloud and AI.

AI enables collaboration on a scale previously unimaginable, allowing industries to share data and co-create solutions that address complex customer challenges. The future of competition will not be between individual companies but between ecosystems. SaaS leaders, along with their Private Equity (PE) and Venture Capital (VC) investors and board directors, who invest in building and participating in strong ecosystems will gain a significant competitive edge.

Leadership and governance in an AI-driven world

Scaling a business in today’s environment requires more than technological adoption - it demands a strategic, holistic approach. For leaders, investors, and board directors, I offer three guiding principles:

Stay curious
Foster a culture of continuous learning and encourage teams to embrace the ever-changing landscape of technology.
Balance innovation with safety
Pursue growth while managing risks and adhering to ethical standards. Robust security measures and compliance frameworks are non-negotiable.
Invest in people
Build diverse teams and create an environment of psychological safety. AI can amplify human strengths, but it’s the people driving innovation who will define the future.

Shaping the future together

The dawn of this AI-driven era presents an incredible opportunity for businesses to reimagine what’s possible. By embracing innovation, fostering collaboration, and prioritizing ethical growth, we can shape a future that benefits not just industries but society as a whole.

I encourage leaders, investors, and innovators to embrace this moment with courage and conviction. Together, we can navigate this era of transformation and emerge stronger, more resilient, and poised for long-term success.

For a deeper dive into these insights, listen to my full conversation on the Partner1 Distinguished Expert Speaker Series Podcast with Juhi Saha, CEO of Partner1, to discuss these profound shifts.

To learn more about our AI solutions for telecommunications, click here.

About the author:
Parul Bhandari leads Partner Strategy in Telco, Media, and Gaming worldwide at Microsoft. With decades of executive experience driving transformation and growth, Parul is passionate about helping businesses scale profitably and embrace the opportunities of AI and innovation.

Supercharging Network Operations with GenAI

KenikH — Tue, 17 Dec 2024 17:54:45 GMT

Automation has long been a driver of productivity, but GenAI takes automation to a new level - one that transcends routine tasks and tackles complexity across platforms. In BSS, GenAI unlocks significant growth opportunities by enabling smarter customer interactions, revenue assurance, and fraud management. Power Automate, integrated with GenAI, enhances automation capabilities in network operations by enabling intelligent workflows, reducing manual effort and ensuring timely maintenance and repairs through intelligent triggers and integrations. Services like ServiceNow Telecom Service Management (TSM), also enhance operations through GenAI augmented workflow automation, unifying ticketing, incident resolution, and compliance tracking under a single pane of glass.

Connecting GenAI and existing automation solutions leads to increased efficiency, faster deployment timelines, and improved customer experiences. This complementary relationship is ushering in a new era of telecom innovation - one where networks become more resilient, offerings more dynamic, and every customer interaction more meaningful.

Streamlining Multichannel Interactions

Customer interactions today span email, chat, social media, and phone calls. Managing this multichannel complexity is challenging, but GenAI-powered platforms make it seamless. By integrating with systems like Microsoft Dynamics 365, GenAI aggregates customer data into unified profiles. It doesn't just stop there; it provides contextual recommendations, allowing sales and support teams to deliver personalized experiences. Power Automate can be used to automate workflows between different communication channels, ensuring seamless customer interactions.

For example, Lumen Technologies uses Microsoft Copilot to enhance customer-facing operations. By automating administrative tasks, generating tailored proposals, and creating comprehensive customer profiles, Lumen’s teams save hours of preparation for each interaction. This efficiency can translate into better-prepared teams and faster deal closures.to better-prepared teams and faster deal closures.

“Before, it took a day or two to get through all that data. Now I’m hearing it’s taking an hour or so to get through it, validate it and be able to share it internally as they go into prep calls for customer calls.” - Delvin Holman, Lumen senior lead customer service enablement manager

Fraud Detection and Revenue Assurance

Fraud detection in telecom is traditionally rules-based, relying on static logic to identify anomalies. GenAI transforms this process by analyzing real-time data streams and historical fraud patterns to predict and mitigate risks before they escalate. This reduces revenue leakage and enhances customer trust. Power Automate can be used in conjunction with GenAI for triggering automated workflows to respond to suspicious activities in real-time, reducing manual intervention and accelerating response times.

Technical enablers like Azure OpenAI Service and Azure Synapse Analytics empower operators to implement sophisticated fraud detection systems. These platforms ingest multichannel data, process it at scale, and use predictive models to identify potential fraud with high accuracy. By integrating Power Automate, operators like MTN trigger automated workflows to respond to suspicious activities in real time, reducing manual intervention and accelerating response times. Power BI provides dynamic visualizations and dashboards that give operators clear insights into fraud patterns and trends. A prime example is MTN, which leverages these Microsoft technologies to optimize fraud detection and enhance operational efficiency. With these capabilities, operators can stay ahead of threats, protect revenue, and ensure a secure network environment for their customers.

Revolutionizing OSS: Smarter Networks Through GenAI

While BSS focuses on customer interactions, OSS deals with the core of telecom operations—network performance, maintenance, and optimization. GenAI excels here by automating routine processes, predicting failures, and enabling proactive management.

Predictive Analytics for Network Health

Predictive analytics is one of GenAI's most transformative OSS functionalities. By analyzing data from sensors, historical outage logs, and environmental conditions, GenAI systems predict network disruptions before they occur. Building on innovations like Microsoft’s Aurora, the first large-scale foundation model of the atmosphere, carriers can anticipate weather-related outages, such as those caused by snowstorms or high winds. This foresight enables preemptive repairs, minimizing downtime and ensuring customer satisfaction. Such predictive capabilities ensure network resilience and keep customer satisfaction high, even when the skies are unpredictable. Power Automate can also be integrated to automate preventive workflows triggered by Azure Event Grid alerts, ensuring timely maintenance and repairs.

Technical architecture for such implementations often includes:

Azure AI Foundry: A robust foundation of over 1800 models to accelerate AI development.
Azure IoT Hub: Captures real-time data from network components.
Azure Machine Learning: Processes this data to generate predictive models.
Azure Event Grid: Triggers alerts and automates preventive workflows.
Power BI a n d Copilot in Fabric: Enable intuitive visualization, reporting, and real-time insights, making AI-driven decisions more accessible across the organization.

Automated Network Management

For provisioning, GenAI systems can analyze network demands in real time and, with solutions like Azure Programmable Connectivity, dynamically allocate resources to ensure optimal performance. This reduces the manual effort required to configure new services or adjust existing ones, enabling operators to bring new offerings to market more quickly. Power Automate can be used to automate the provisioning and configuration of network resources, reducing manual effort and speeding up deployment timelines.

In load balancing, partners like Grok AIOps use predictive analytics to anticipate traffic spikes and distribute workloads across network nodes, maintaining consistent performance even under high demand. For fault detection, Neuron7 enhances GenAI’s ability to analyze vast amounts of network telemetry data, pinpointing anomalies and potential issues before they escalate.

How Microsoft enhances GenAI execution

The integration of Azure Arc enhances these capabilities by providing a unified platform for managing adaptive cloud environments. This not only simplifies the deployment of network functions but also ensures compatibility across diverse infrastructures.

By automating tasks, GenAI significantly reduces the risk of human error, which is often a contributing factor in network outages and inefficiencies. It also accelerates deployment timelines, enabling telecom operators to respond more rapidly to customer demands and market changes. Furthermore, optimized capacity planning ensures that network resources are utilized efficiently, reducing unnecessary costs while maintaining service quality. Collectively, these advancements position GenAI as a critical enabler of more agile, reliable, and cost-effective network operations.

Real-World Outcomes: GenAI Driving Double-Digit Improvements

Early adopters of GenAI, such as AT&T and Vodafone , report impressive operational and financial outcomes. A McKinsey survey of 130 global telcos highlighted double-digit improvements in both cost reduction and performance metrics.

For instance:

Contact Center Productivity: A Latin American telecom achieved a 25% productivity boost by equipping agents with GenAI-driven recommendations for navigating customer journeys.
Marketing Efficiency: A European operator increased conversion rates by 40% by using GenAI to personalize customer outreach.

Lumen's Case Study

Lumen’s use of GenAI is particularly noteworthy. With $5 billion in new business already attributed to GenAI, the company expects an additional $7 billion in the near term. The newly established Custom Networks Division exemplifies how telcos can leverage GenAI to offer specialized services. This division focuses on AI-enabled connectivity solutions, including dark fiber and custom fiber routes, tailored for hyperscalers and enterprises with AI-intensive workloads.

By integrating Azure Confidential Computing for secure data handling and Azure OpenAI Service for intelligent automation, Lumen ensures that its offerings are not only innovative but also secure and scalable.

Transforming the Telecom Ecosystem: Broader Implications

The potential of GenAI in telecom extends beyond individual companies—it has the power to reshape the industry. Traditional telecom services have seen stagnation in growth, but GenAI introduces opportunities for entirely new revenue streams.

Emerging Use Cases

AI-Optimized Connectivity: Custom networks tailored for industries like healthcare and autonomous vehicles are a cornerstone of telecom innovation. Solutions like Azure Programmable Connectivity enhance this by enabling real-time network adjustments and robust fail-safes through GenAI. In telemedicine, where ultra-low latency and reliability are critical, GenAI predicts network demands and dynamically optimizes performance to ensure seamless connectivity.
Smart City Integration: Smart cities leverage integration to deploy solutions like intelligent traffic systems, which analyze real-time data from IoT sensors to optimize traffic flow, reduce congestion, and lower emissions. Similarly, energy management systems use GenAI to monitor and control power grids, predict energy usage, and balance supply and demand dynamically. These capabilities not only enhance the quality of urban living but also improve sustainability by optimizing resource usage.
5G Slicing Management: As industries adopt 5G for mission-critical applications, the ability to allocate and optimize network slices dynamically has become essential. GenAI excels in this domain by analyzing network conditions and usage patterns in real-time to ensure that each slice is performing optimally. GenAI allocates resources based on need, balancing load across slices and reallocating capacity as demand fluctuates, ensuring that critical applications like emergency services, industrial automation, and remote monitoring maintain reliable connectivity.

These use cases highlight the shift from reactive to proactive telecom operations, made possible by the scalability and adaptability of GenAI.

GenAI as the Heartbeat of Telecom's Future, Today

Generative AI represents a transformative opportunity for the telecom industry. By integrating GenAI and other automation solutions into BSS and OSS, operators can reduce costs, enhance customer experiences, and unlock new revenue opportunities. Companies like Lumen and Vodafone demonstrate the immense potential of this technology, showcasing real-world results such as reduced operational costs, improved efficiency, and new revenue streams. As the telecom sector evolves, GenAI is poised to become the beating heart of innovative strategies, enabling operators to remain competitive in an increasingly digital and data-driven world.

For CTOs, embracing GenAI is no longer optional - it’s a strategic necessity. By leveraging Microsoft's robust suite of tools, from Azure OpenAI Service to Azure Machine Learning, operators can transform their operations, deliver unparalleled value to customers, and secure their place in the future of telecommunications.

Explore how Microsoft is enabling telecom innovation through GenAI

For a business-centric point of view on this topic, see our blog on this topic on Fierce Network.

The Generative AI Revolution: A new era for partners in the telecom, media and gaming industries

Pabhanda — Tue, 12 Nov 2024 16:00:00 GMT

AI is reshaping telecom, media and gaming

Generative AI is rapidly becoming an integral part of our daily lives and business strategies. By 2027, 90% of telecommunications providers are projected to use generative AI to enhance customer experience scenarios. This transformative technology is creating unprecedented possibilities for individuals, organizations, and society.

For telecom companies, this presents a unique opportunity to leverage Generative AI, and Responsible AI to:

Elevate customer experience: AI-powered chatbots and virtual assistants to provide personalized support, troubleshoot issues efficiently, and offer tailored recommendations, leading to increased customer satisfaction and loyalty.

Optimize business and operations support systems: Generative AI can automate tasks, streamline workflows, and analyze data to identify patterns and insights, leading to increased efficiency and reduced operational costs.

Modernize the network: Generative AI can optimize network planning, resource allocation, and maintenance processes, resulting in improved network performance, security, and reliability.

Unlock new revenue streams: Telecom media and gaming companies can create innovative AI-powered services and applications, personalize offers, and optimize pricing strategies, opening new avenues for revenue generation. Using Azure OpenAI Service to scale personalized customer experiences can lead to an annual increase in average revenue per customer of 3% to 7% and a reduction in annual churn of 20% to 30%.

For media and gaming companies, this presents a unique opportunity to leverage Generative AI to:

Optimize operations: Media and gaming companies can leverage generative AI to optimize operations by streamlining media workflows and automating routine tasks, freeing up valuable time for employees to focus on more strategic and creative work. By automating tasks like generating content for various purposes, summarizing and editing text, facilitating real-time collaboration, managing metadata, enhancing video quality, localizing content, optimizing content delivery, and moderating content for safety and compliance, media companies can improve efficiency, reduce operational costs, and free up their workforce to focus on higher-value activities.

Empower content creators: Content creators are increasingly turning to AI-powered tools to unleash their creativity while simultaneously reducing production costs. Tools like Azure OpenAI and Azure AI Video Indexer facilitate this transformation so that talent can focus on creativity. Azure OpenAI, with its GPT-4 and DALL·E features, provides a powerful platform for generating creative concepts, crafting compelling content, and designing captivating visuals. Azure AI Video Indexer, on the other hand, goes beyond optimizing operations by aiding in content discovery and providing valuable insights for crafting engaging content.

Unlock monetization: Media and gaming companies are utilizing generative AI to unify data, creating new avenues for monetization by fostering deeper audience engagement. Tools like Microsoft Fabric play a pivotal role in this process by offering a centralized platform for managing and analyzing data, enabling companies to break down data silos and gain comprehensive insights into audience behavior and content performance. Azure PlayFab also contributes to monetization by powering games and focusing on retaining and engaging audiences, providing valuable insights for monetization strategies in the gaming sector.

Why Microsoft partners should care

This AI revolution presents a massive opportunity for Microsoft partners, particularly those in the software and services space. The Microsoft AI Cloud Partner Program equips partners with the tools and resources they need to:

Incorporate AI into their offerings: Microsoft provides access to cutting-edge AI technologies like Azure OpenAI Service and Microsoft Fabric, enabling partners to develop AI-powered solutions for their clients.
Grow their business: The Microsoft AI Cloud Partner Program offers various benefits, including training, marketing support, and co-selling opportunities, empowering partners to expand their reach, scale their growth and capitalize on the growing demand for AI solutions.

There has been a nearly 250% surge in generative AI-related partners in the past eight months, underscoring the growing interest and investment in this transformative technology.

Source: From vision to reality: Microsoft's partners embrace AI to deliver customer value - The Official Microsoft Blog

Best practices for ensuring a successful co-selling partnership with Microsoft

For telecom, media and gaming companies, establishing a co-selling partnership with Microsoft can unlock substantial growth and extend market reach. However, maximizing these benefits requires a strategic approach, clear alignment, and consistent engagement with Microsoft’s ecosystem. Here are some key best practices to ensure a successful co-selling partnership with Microsoft

1. Clearly defined value proposition and goal alignment

Microsoft works with numerous partners, so it is essential to articulate a unique and compelling value proposition that resonates with Microsoft sellers, partners, and customers. For telecom, media and gaming companies, this means highlighting how your solution addresses industry-specific challenges or offers innovations that enhance existing Microsoft offerings. A well-defined value proposition that aligns with Microsoft's Industry scenarios and goals can help a solution stand out in co-selling discussions.

2. Clarity on target audience and relevant sales enablement

Understanding a target audience is crucial for effective co-selling with Microsoft. Telecom, media and gaming companies should define which segments are most likely to benefit from their offerings. Having a clear audience in mind helps Microsoft’s sellers understand which prospects or accounts to target and enables your team to create industry-specific messaging that resonates with potential customers. The more precise a company can be about their ideal customer profile, the better Microsoft sellers can match the solution to their accounts, driving faster and more targeted engagement. Companies should provide concise, tailored resources such as pitch decks, one-pagers, case studies, and industry success stories that highlight the benefits and ROI of their solutions. These materials should be easy for sellers to use and speak directly to customer pain points.

3. A strategic mix of direct and Partner to Partner (P2P) co-selling activities

A balanced approach that combines both direct co-selling with Microsoft and partner-to-partner (P2P) co-selling activities can amplify success. Direct co-selling involves working closely with Microsoft sellers to target key accounts and develop coordinated sales efforts. Meanwhile, engaging in P2P co-selling with other Microsoft partners can expand your reach and open additional sales channels. For media and telecom companies, for example, this could mean partnering with video platform providers, AI analytics companies, or 5G solution providers in the Microsoft ecosystem to Co-Sell complementary offerings. By blending these strategies, you can leverage the strengths of different partner types, creating a more resilient and diversified sales approach within Microsoft’s ecosystem.

The time to act is now

Activate your Microsoft partnership:

New partners with a solution on Azure:

Start with the Microsoft AI Cloud Partner Program
Join ISV Success for Co-Sell resources so that you can start selling together with Microsoft
Create a partnership plan and align the Microsoft Industry Areas and Solution Plays
Publish your offer in the Marketplace

Existing partners who are ready to sell with Microsoft:

Create a transactable offer in the Marketplace
Get Co-Sell Ready and achieve Co-Sell Incentivized Status and prepare to share and receive leads
Activate Co-Selling with Microsoft and decrement Customer MACCs to tap into pre-committed cloud spend
Achieve Top Tier Status and partner closely with Microsoft sellers, benefiting from seller incentivization and eligibility to participate in partner-reported Azure consumed revenue (PRACR).
Leverage Incentives and Benefits to accelerate your revenue growth

Microsoft empowers you to transform your business growth

The generative AI revolution is well underway, presenting a massive opportunity for Microsoft telecom, media and gaming partners to innovate, differentiate, and thrive. The Microsoft AI Cloud Partner Program helps partners, including startups, software companies (ISVs) and solutions partners, to incorporate AI into their offerings and grow their businesses. It provides access to innovative AI technologies such as Azure OpenAI Service and Microsoft Fabric. The program also offers training, marketing support, and co-selling opportunities to help partners expand their reach and capitalize on the growing demand for AI solutions.

By partnering with Microsoft and embracing the power of the Microsoft AI Cloud Partner Program, software and services companies in the media, gaming and telecom space can transform their offerings and position themselves as leaders in the new AI-powered era.

Wherever you are in your cloud and AI transformation journey, Microsoft can help you accelerate your business growth. Microsoft is committed to the success of our partners.

#Telecommunication #media #Gaming #Partnersmakemorepossible #msftadvocate

Toward a Distributed AI Platform for 6G RAN

bradunov — Thu, 24 Oct 2024 14:37:04 GMT

by Ganesh Ananthanarayanan, Xenofon Foukas, Bozidar Radunovic, Yongguang Zhang

Introduction to the Evolution of RAN

The development of Cellular Radio Access Networks (RAN) has reached a critical point with the transition to 5G and beyond. This shift is motivated by the need for telecommunications operators to lower their high capital and operating costs while also finding new ways to generate revenue. The introduction of 5G has transformed traditional, monolithic base stations by breaking them down into separate, virtualized components that can be deployed on standard, off-the-shelf hardware in various locations. This approach makes it easier to manage the network’s lifecycle and accelerates the release of new features. Additionally, 5G has promoted the use of open and programmable interfaces and introduced advanced technologies that expand network capacity and support a wide range of applications.

As we enter the era of 5G Advanced and 6G networks, the goal is to maximize the network's potential by solving the complex issues brought by the added complexity of 5G and introducing new applications that offer unique value. In this emerging landscape, AI stands out as a critical component, with advances in generative AI drawing significant interest from the telecommunications sector. AI's proficiency in pattern recognition, traffic prediction, and solving intractable problems like scheduling makes it an ideal solution for these and many other longstanding RAN challenges. There is a growing consensus that future mobile networks should be AI-native, with both industry and academia offering support for this trend. However, practical hurdles like data collection from distributed sources and handling the diverse characteristics of AI RAN applications remain obstacles to be overcome.

The Indispensable Role of AI in RAN

The need for AI in RAN is underscored by AI’s ability to optimize and enhance critical RAN functions like network performance, spectrum utilization, and compute resource management. AI serves as an alternative to traditional optimization methods, which struggle to cope with the explosion of search space due to complex scheduling, power control, and antenna assignments. With the infrastructure optimization problems introduced by 5G (e.g. server failures, software bugs), AI shows promise through predictive maintenance and energy efficiency management, presenting solutions to these challenges that were previously unattainable. Moreover, AI can leverage the open interfaces exposed by RAN functions, enabling third-party applications to tap into valuable RAN data, enhancing capabilities for additional use cases like user localization and security.

Distributed Edge Infrastructure and AI Deployment

As AI becomes increasingly integrated into RAN, choosing the optimal deployment location is crucial for performance. The deployment of AI applications in RAN depends on where the RAN infrastructure is located, ranging from the far edge to the cloud. Each location offers different computing power and has its own trade-offs in resource availability, bandwidth, latency, and privacy. These factors are important when deciding the best place to deploy AI applications, as they directly affect performance and responsiveness. For example, while the cloud provides more computing resources, it may also cause higher latency, which can be problematic for applications that need real-time data processing or quick decision-making.

Addressing the Challenges of Deploying AI in RAN

Deploying AI in RAN involves overcoming various challenges, particularly in the areas of data collection and application orchestration. The heterogeneity of AI applications' input features makes data collection a complex task. Exposing raw data from all potential sources isn't practical, as it would result in an overwhelming volume of data to be processed and transmitted. The current industry approach of utilizing standardized APIs for data collection is not always conducive to the development of AI-native applications. The standard set of coarse-grained data sources exposed through these APIs often fail to meet the nuanced requirements of AI-driven RAN solutions. This limitation forces developers to adapt their AI applications to the available data rather than collecting the data that would best serve the application's needs.

The challenge of orchestrating AI RAN applications is equally daunting. The dispersed nature of the RAN infrastructure raises questions about where the various components of an AI application should reside. These questions require a careful assessment of the application's compute requirements, response latency, privacy constraints, and the varied compute capabilities of the infrastructure. The complexity is further amplified by the need to accommodate multiple AI applications, each vying for the same infrastructure resources. Developers are often required to manually distribute these applications across the RAN, a process that is not scalable and hinders widespread deployment in production environments.

A Vision for a Distributed AI-Native RAN Platform

To address these challenges, we propose a vision for a distributed AI-native RAN platform that is designed to streamline the deployment of AI applications. This platform is built on the principles of flexibility and scalability, with a high-level architecture that includes dynamic data collection probes, AI processor runtimes, and an orchestrator that coordinates the platform's operations. The proposed platform introduces programmable probes that can be injected at various points in the platform and RAN network functions to collect data tailored to the AI application's requirements. This approach minimizes data volume and avoids delays associated with standardization processes.

The AI processor runtime is a pivotal component that allows for the flexible and seamless deployment of AI applications across the infrastructure. It abstracts the underlying compute resources and provides an environment for data ingestion, data exchange, execution, and lifecycle management. The runtime is designed to be deployed at any location, from the far edge to the cloud, and to handle both AI RAN and non-RAN AI applications.

The orchestrator is the component that brings all this together, managing the placement and migration of AI applications across various runtimes. It also considers the developer's requirements and the infrastructure's capabilities to optimize the overall utility of the platform. The orchestrator is dynamic, capable of adapting to changes in resource availability and application demands, and can incorporate various policies that balance compute and network load across the infrastructure.

In articulating the vision for a Distributed AI-Native RAN platform, it is important to clarify that the proposed framework does not impose a specific architectural implementation. Instead, it defines high-level APIs and constructs that form the backbone of the platform's functionality. These include a data ingestion API that facilitates the capture and input of data from various sources, a data exchange API that allows for the communication and transfer of data between different components of the platform, and a lifecycle management API that oversees the deployment, updating, and decommissioning of AI applications. The execution environment within the platform is designed to be flexible, promoting innovation and compatibility with major hardware architectures such as CPUs and GPUs. This flexibility ensures that the platform can support a wide range of AI applications and adapt to the evolving landscape of hardware technologies.

Moreover, to demonstrate the feasibility and potential of the proposed platform, we have internally prototyped a specialized and efficient implementation of the AI processor, particularly for the far edge. This prototype is carefully designed to work with fewer CPUs, optimizing resource use while maintaining high performance. It demonstrates that the AI processor runtime principles can be implemented effectively to meet the specific needs of the far edge, where resources are limited and real-time processing is crucial. This specialized implementation exemplifies the targeted innovation that the platform emphasizes, showcasing how the flexible execution environment can be tailored to address specific challenges within the RAN ecosystem.

Balancing Open and Closed Architectures in RAN Integration

The proposed AI platform is adaptable, capable of fitting into open architectures that adhere to O-RAN standards as well as proprietary designs controlled by RAN vendors. This flexibility allows for a range of deployment scenarios, from a fully O-RAN compliant implementation that encourages third-party development to a fully proprietary model, or to a hybrid model that offers a balance between vendor control and innovation. In each scenario, the distributed AI platform can be customized to suit the specific needs of the infrastructure provider or adhere to the guidelines of standardization bodies.

Concluding Thoughts on AI's Future in 6G RAN

The integration of AI into the RAN is central to the 6G vision, with the potential to transform network management, performance optimization, and application support. While deploying AI solutions in RAN presents challenges, a distributed AI-native platform offers a pathway to overcome these obstacles. By fostering discussions around the architecture of a 6G AI platform, we can guide standards bodies and vendors in exploring opportunities for AI integration. The proposed platform is intentionally flexible, allowing for customization to meet the diverse needs and constraints of different operators and vendors.

The future of RAN will depend on its ability to dynamically adapt to changing conditions and demands. AI is essential to this transformation, providing the intelligence and adaptability needed to manage the complexity of next-generation networks. As the industry progresses towards AI-native 6G networks, embracing both the challenges and opportunities that AI brings will be crucial. The proposed distributed AI platform marks a significant step forward, aiming to unlock the full potential of RAN through intelligent, flexible, and scalable solutions.

Innovation in AI and the commitment to an AI-native RAN are key to ensuring the telecommunications industry and the telecommunications networks of the future are efficient, cost-effective, and capable of supporting advanced services and applications. Collaborative efforts from researchers and industry experts will be vital in refining this vision and making the potential of AI in 6G RAN a reality.

As we approach the 6G era, integrating AI into RAN architectures is not merely an option but a necessity. The distributed AI platform outlined here serves as a blueprint for the future, where AI is seamlessly integrated into RAN, driving innovation and enhancing the capabilities of cellular networks to meet the demands of next-generation users and applications.

For more details, please check the full paper.

Acknowledgements

The project is partially funded by the UK Department for Science, Innovation & Technology (DSIT) under Open Network Ecosystem Competition (ONE) programme.

Towards AI Powered and Secure Carrier-Grade Open RAN Platform

bradunov — Fri, 08 Mar 2024 14:03:59 GMT

Introduction

In the fast-paced world of telecommunications, the race is on to harness emerging technologies that promise to revolutionize the way we connect to the internet. Topping the list of these innovations is Open Radio Access Network (Open RAN), a groundbreaking way of building mobile networks, set to redefine the rules of the game.

Traditional mobile networks have relied on closed hardware and software provided by a select few vendors. These systems were tightly integrated, making upgrades or modifications a costly and cumbersome affair. Open RAN came in as a game-changer, separating the software from the hardware, digital from analogue, thus making mobile networks more adaptable, cost-effective, and open to a broader spectrum of vendors.

Now, think of infusing this Open RAN with the power of Artificial Intelligence (AI). The ability of AI to learn from data and make intelligent decisions could significantly enhance the performance and management of Open RAN. It could detect anomalies, optimize resource allocation, reduce power consumption, and even predict and prevent potential problems before they occur. Imagine securing this AI-powered Open RAN with the robustness of the cloud. This was precisely the vision that led to the inception of the project titled "Towards AI Powered and Secure Carrier-Grade Open RAN Platform".

The project was born out of the collaboration between four industry and academia leaders: Microsoft UK, Intel R&D UK Ltd., Capgemini, and The University of Edinburgh. Each partner brought to the table their unique expertise in cloud technology, AI, cybersecurity, academic research, and telecom infrastructure. The project is co-funded by the UK government DSIT (Department for Science, Innovation and Technology) Future RAN diversification funds. Together, we envisioned a carrier-grade cloud solution that would empower operators to deploy Open RAN network functions easily and securely.

Project goals and achievements

The project set its sights on five key areas: manageability, observability, automation, efficiency, and security. Each of these areas came with its own unique set of challenges and questions.

Building a 5G network is no mean feat. To simplify the process, the project developed hardware and software blueprints that were tailored to meet the unique challenges of building a 5G network, such as successfully connecting and integrating various components (servers, switches, clock sources, radio) together, provisioning and life-cycle management, as well as ensuring consistent connectivity and high-speed connections of many mobile devices.

We deployed a building-wide 5G enterprise network over 5 floors with remotely tens of controllable mobile devices spread around the building. We implemented a remote access to the lab that allowed collaborators from different locations to run experiment on the network, and jointly work on the problems.

One of the challenges that the project tackled head-on was the development of a far-edge RAN platform that could meet various demands of virtualized RAN. This platform needed to support real-time Linux kernel, and to be optimized to minimize latency and support the various low-latency requirements put forth by different vendors. To achieve this, the project team had to dive deep into the complexities of platform performance and develop strategies to optimize every aspect of its operation.

The platform also had to support Kubernetes and provide Kubernetes abstractions that are required by different vendors. These abstractions include isolated CPUs, huge pages, and virtual functions, among others. We spent time understanding how best to express these abstractions and reduce the number of errors that can occur in this provisioning.

To provision and configure the platform in a flexible way, the team developed a system that uses REST APIs for remote provisioning. This system allows for the provisioning of bare metal to a functional far-edge platform in a mere 30 minutes. This is a significant achievement, as it allows networks to be deployed much more swiftly, efficiently and at scale, than traditional methods.

Alongside the platform provisioning, the team also addressed the challenge of installing RAN CNF (Cloud-Native Functions) as an app from a marketplace. Achieving this required building another REST API for RAN provisioning and testing it with a full containerized implementation of Capgemini's 5G RAN deployed through the Azure Arc platform. We further tested the platform API with several other vendors’ RAN CNF. The team also had to create advanced platform features for security and correctness, ensuring a seamless and verifiable deployment of RAN CNF.

All these elements combined to create a far-edge RAN platform that is not only robust and efficient but also flexible and adaptable, able to meet the diverse needs of various vendors. This platform forms the backbone of the project's vision for a next-generation telecom network powered by AI and secured by the cloud.

Increasing energy efficiency was another challenge that the project tackled head-on. We developed applications that not only reduced RAN power consumption, even during peak hours, but also detected external interference and avoided inter-cell interference. These innovations led to more efficient use of network resources and a significant improvement in network performance.

Troubleshooting and system visibility were other key areas the project focused on. Advanced algorithms, driven by AI and machine learning, were used to analyse the vast amounts of data generated by the network, detect anomalies, and provide insights for effective troubleshooting. In the prototype we built, we were able to collect hundreds of telemetry sources from RAN and platform several times per second. We fed this data to our custom-built generative AI model that can process the data in real-time and detect any anomalies and their root causes.

Security is always a significant concern in any network, both in terms of software development and deployment. To address these, we leveraged the best DevSecOps industry practices, including pipelines, unit and system tests, scans, and verifications. Furthermore, we developed a ransomware attack detection app that can swiftly detect a ransomware attack at a telco far-edge, and repair compromised servers.

One of the highlights – dynamic service models

The common point for the applications we discussed is the idea of dynamic service models. These models provided a workaround to the restrictions of current xApps that operate on the non-real-time Radio Intelligent Controller (nRT-RIC) and are limited by static E2 service models. These static models are defined by industry standards and any alteration to them necessitates an extensive standardization process.

The dynamic service model we developed, on the other hand, offer a more flexible solution. They allow xApp developers to utilize Extended Berkeley Packet Filter (eBPF), a Linux-based technology, to create their own methods for collecting internal RAN telemetry. A developer can express their service model through a custom code compiled with the eBPF framework, which is both secure and verifiable, and can be seamlessly deployed inside a RAN at run-time.

To demonstrate this, our team built a prototype called Janus. Integrated with Capgemini and Intel RAN components, Janus showcased the potential of dynamic service models and served as the foundation for several applications. This innovative approach brings a new level of flexibility to service models, opening up a realm of possibilities for the future of Open RAN.

Broader impact and lessons learned

The project's impact was not confined to the lab. It made its presence felt at trade shows like MWC Barcelona, Open RAN world events and IntelON, demonstrating to the wider industry the achievements of the project. The team received recognition in the form of the Light Reading Editor's choice award for AI/ML in RAN in 2023, and we were also finalists in the Fierce Telecom award for AI/Analytics/Automation in the same year.

In terms of academic contributions, the team published three papers in the prestigious ACM Mobicom conference, with one more under review. We also published a white paper aimed for the industry decision-makers, contributing significantly to the body of knowledge on Open RAN. This material is available on the project web site.

During the project, we encountered several challenges, primarily on the supply chain side. Some components experienced long delays of more than 6 months. It was often difficult to purchase Raspberry Pi devices at scale, which we used as mobile clients for our building-wide deployments. Yet, these hurdles only served to underscore the importance of having a diverse supply chain and drove the team to seek out alternative solutions.

In shaping the future of Open RAN, the project also recognized the importance of influencing standards and policies. It ran a dissemination campaign to promote the benefits of the platform and analytics and interacted with ecosystem partners to gain understanding how to shape the project outcomes into standards which aligns with DSIT Open RAN principles.

The project's alignment with the UK DSIT diversification strategy was another achievement worth mentioning. By building the platform with open interfaces, it allows a larger pool of vendors to participate. This not only fosters competition but also drives innovation. The project also has an educational aspect, as its collaboration with a major UK university helps develop UK talent in the area of Open RAN, further supporting the DSIT’s diversification strategy.

In summary, the "Towards AI Powered and Secure Carrier-Grade Open RAN Platform" project is a significant step forward in the development of Open RAN technology. It brings together the power of AI, the security of the cloud, and the flexibility of Open RAN to create a next-generation telecom network. With its significant technical achievements, and support for the UK DSIT Diversification Strategy, the project hopes to leave a lasting impact on the world of telecommunications.

Acknowledgements

The project is partially funded by the UK Department for Science, Innovation & Technology (DSIT) under Future RAN Competition (FRANC) programme.

Microsoft and Industry Leaders Enable RAN and Platform Programmability with Project Janus

bradunov — Mon, 26 Feb 2024 11:54:16 GMT

Open RAN-compatible architecture gives CSPs the blueprints and tools to optimize their networks using AI/ML and dynamic, customizable data

Barcelona – February 26, 2024. Today at MWC 2024, Microsoft announced Project Janus, along with leaders across the telecommunications industry. Project Janus uses telco-grade cloud infrastructure compatible with O-RAN standards to draw on fine-grained telemetry from the radio access network (RAN), the edge cloud infrastructure, and other sources of data. This enables a communication service provider (CSP) to gain detailed monitoring and fast closed loop control of their RAN network. Janus has support and participation from CSPs such as Deutsche Telekom, and Vodafone; RAN and infrastructure providers CapGemini, Mavenir, and Intel Corporation; and RIC vendors and software innovators Juniper Networks, Aira Technologies, Amdocs, and Cohere Technologies.

“We know how vital the performance, security, and automation of the network is for CSPs, and going forward, more accurately optimizing complex networks,” said Yousef Khalidi, Corporate Vice President, Azure for Operators at Microsoft. “That’s why we’re excited to debut Project Janus alongside leading partners and supporters as an O-RAN compatible extension that makes RAN and platform even more programmable and optimized.”

leverage the existing E2 O-RAN interface
update its service models to communicate with components of a CSP’s RAN and SMO architecture including the Distributed Unit (DU), Centralized Unit (CU), and RAN Intelligent Controller (RIC).

RAN, RIC, and xApp and rApp vendors are able to develop and use instrumentation tools to capture RAN data dynamically, and also combine them with platform data from cloud-based platforms hosting the RAN workloads.

Project Janus will be available for everyone to include in their platform and network functions and will be supported natively by Microsoft’s Azure Operator Nexus platform.

To see specific use case examples, visit the “Unlock Operator Value with Programmable RAN & Platform” pod in the Microsoft booth at Mobile World Congress 2024 at 3H30 in Hall 3 during February 29-29, 2024 and check out www.microsoft.com/research/project/programmable-ran-platform/videos. Also read Mavenir, Microsoft and Intel Team for Real-Time Layer 1 vRAN Control white paper.

Telecommunications leaders are sharing support for the collaborative initiative:

Deutsche Telekom – “This initiative shows great promise to increase the pace of innovation and unlock new value through dynamic, customizable RAN data and analytics that can work within an O-RAN compliant framework. We look forward to seeing the participation by even more companies and developers in this burgeoning ecosystem.” – Petr Ledl, Vice President of Network Trials and Integration Lab and Chief Architect of Access Disaggregation program at Deutsche Telekom.

Vodafone – “The dynamic service models enabled by Project Janus are fully aligned with the vision of Open RAN in supporting the scale deployment of software-defined RAN. Access to the correct data at the right time and intelligent algorithms based on AI/ML capabilities will introduce significant performance and capacity benefits for all existing cellular networks and enable real autonomous ones.” - Francisco Martín Pignatelli, Head of Open RAN at Vodafone.

Hear from Microsoft Collaborators:

CapGemini – “CapGemini in collaboration with Microsoft has successfully demonstrated implementation of several use cases such as anomaly detection, energy savings and interference detection using Janus. These efforts have also demonstrated the benefits of being able to combine and reason over dynamic data from RAN, incremental to the predefined data types already available today, with dynamic data from the O-Cloud platform using Janus dynamic service models such as resolving key integration issues between RAN and platform as well as offering the power of leveraging AI/ML applications by developers to more precisely target areas of improvement for the RAN network.” – Rajat Kapoor, Vice President and Head of Software Frameworks at Capgemini.

Mavenir – “Improving RAN visibility and real-time control is essential to a CSP’s network performance and security, and it is Mavenir’s goal to support our customers with state-of-the-art observability. Data from our O-RAN-compliant DU/CU can be easily extracted dynamically and made available within our product management tools for tuning the operation of the Mavenir RAN. We demonstrated an advanced on-site debugging tool and customizable interference detection solution with Janus, which highlighted the flexibility of Janus to solve problems in real-time and improve system performance. With Janus, data from our Open RAN compliant DU can also be made available to an ecosystem of O-RAN focused application developers to provide insights and recommendations to the CSP to address and improve their network performance.” – Bejoy Pankajakshan, EVP, Chief Technology & Strategic Officer at Mavenir.

Intel Corporation – “With Intel FlexRAN reference architecture, Intel has been at the forefront of enabling the industry with virtualized, Open RAN to drive performance, flexibility and innovations, including AI. Microsoft’s Janus builds on FlexRAN’s software programmability to expose new data streams and application capabilities to the next generation of xApp developers, accelerating the adoption of AI in RAN networks to provide even more value to service providers”- Cristina Rodriguez, Vice President and General Manager of Wireless Access Network Division at Intel.

Juniper Networks – “Using the existing E2 O-RAN interface, Janus introduces the capability to bring more timely and customized RAN telemetry to Juniper Near-Real Time RIC. From this, we can enable xApp developers to use the incremental data to more precisely target areas of improvement for the performance and optimization of a RAN network.” – Constantine Polychronopoulos, Group VP of 5G and Telco Cloud at Juniper Networks.

Aira Technologies – “Our mission at Aira as an AI Defined Networking company is to enable the fully autonomous cellular RAN and our application of ML to wireless baseband processing is an industry first. Aira has showcased the use of Janus to collect and forward dynamic RAN data into our near-real time xApp where we apply leading-edge machine learning to drive better channel estimation and prediction to help maximize downlink throughput and range. We look forward to demonstrating, with Microsoft and the growing O-RAN ecosystem, even more innovation built on disaggregated and programmable networks.” – Anand Chandrasekher, Co-Founder and CEO at Aira Technologies.

Amdocs – “As a leading service provider and member of the ARI-5G Consortium, Amdocs is a key proponent of Open RAN and dedicated enabler of RAN intelligence and optimization and we do this today by offering among other things, Amdocs’ xApps such as the massive MIMO xApp. With Janus we look forward to leveraging dynamic service models with our network applications to further accelerate RAN performance and programmability for our CSP customers.” - Oleg Volpin, Division President Europe, Telefonica Global and Network Offering Division at Amdocs.

Cohere Technologies – “Cohere along with key operators and vendors is driving Multi-G ecosystem to enable co-existence of 4G, 5G and 6G and helping operators to do spectrum management in a seamless way. Janus’s dynamic infrastructure helps realize Multi-G’s dynamic infrastructure requirements and helps this vision." - Prem Sankar Gopannan, Vice President of Product Architecture and Software Engineering.

Telecommunications Industry Blog articles

Evolving the Network Operations Agent Framework: Driving the Next Wave of Autonomous Networks

What’s New: Key Enhancements in NOA v2

Adoption of NetAI Concepts and Best Practices

The UI for AI: Deep Integration with Microsoft 365 Copilot and Teams

Migration to Microsoft Foundry & the Microsoft Agent Framework

Integration of TM Forum Open APIs via MCP

Strengthened Security, Governance, and Compliance Controls

Real-World Impact: How Azure Accelerates Autonomous Operations

Microsoft’s internal Azure Networking success

Alignment with broader telco transformations

Architecture & Key Components

UI for AI

Agentic Governance

Foundry Control Plane

Agent 365

Agentic Framework

Microsoft Agent Framework

Foundry Workflows

Telco IQ

Fabric IQ

Foundry IQ

Universal Data Access

Meet the Agents

Building the Autonomous Network Ecosystem

How partners are adopting NOA

Integrating NOA into existing autonomous network solutions

Where partners extend NOA to differentiate

Co-innovation and operating model

Partner Solutions

Advancing the Journey

Closing: The Road to Autonomous Networks

Accelerating revenue in telecommunications through Agentic Sales Processes

Executive Summary

The Rise of Agentic BSS in the IQ Era: from Systems of Record to Systems of Outcome

What the BSS Layer Signifies in Telecom

Why BSS Is Both Vital—and Inherently Complex

The Top Five Agentic BSS Use Cases

How Microsoft Copilot Studio Enables Agentic BSS

TM Forum, Open APIs, and Interoperability at Scale

Leading BSS Vendors in the Agentic Ecosystem

Where MCP Accelerates Innovation

Putting it All Together: Microsoft Copilot Studio in Action

Build Your Own Agent @ Mobile World Congress 2026

AI-Powered RAN and the Intelligent Edge: Microsoft’s Vision for the Future of Telecom

Introducing Microsoft’s Network Operations Agent – A Telco framework for Autonomous Networks

How NOA Works: Multi-Agent Intelligence with Human Oversight

Key Components of the NOA Framework

Unified Data Access with Microsoft Fabric

Multi-Agent Orchestration with Azure Agent Framework

“UI for AI” – Copilot Integration in Teams and Outlook

Open and Secure by Design

Real-World Impact: Azure Networking’s Success Story

Conclusion: A Blueprint for Telecom Autonomy

Reimagining Network Operations: How Microsoft NetAI Tackles Hyperscale Challenges

The Business Imperative: Why Network Operations Must Change

NetAI: A Strategic Shift Toward Autonomous Operations

How NetAI Addresses Business Challenges

The Measurable Benefits of NetAI

Strategic Collaboration and Industry Impact

Download the Full Whitepaper

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Thought Leadership & Sessions

Connected Intelligence & Azure AI Foundry: Scaling AI Across the Telco Enterprise

From Hype to Impact: Operationalizing AI in Telco with TM Forum’s ODA and Open APIs

Accelerating TM Forum Open API Development with GitHub Copilot

Microsoft’s Broader Vision for Telco Transformation

Conclusion: Microsoft’s Strategic Alignment with TM Forum

📣 Call to Action

Supercharge Your TM Forum Open API Development with GitHub Copilot

Introduction: GitHub Copilot and TM Forum Open APIs

Prerequisites for Setting Up the Project

Step-by-Step Guide: Configuring GitHub Copilot in Your IDE

Writing TMF Open API Apps Using GitHub Copilot

Best Practices and Tips for Using Copilot with TMF Open APIs

CSPs Leveraging GitHub Copilot

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