Unifying Data and AI in Telecom: Inside the Telco Analytics PoC Accelerator
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 The solution is open-source and available on GitHub under a Microsoft license. It can be deployed via automation scripts into a customer's Azure and Fabric environment. Get started now!
Monetizing generative AI: How telecoms are unlocking new revenue streams
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.AI beyond Chatbots
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
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.Toward a Distributed AI Platform for 6G RAN
Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. This article attempts to shed light to these challenges and argues that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. Motivated by this lack of solutions, it proposes a generic distributed AI platform architecture, tailored to the needs of an AI-native RAN and discusses its alignment with ongoing standardization efforts.
