2024 Catalyst Projects

See Innovation Come To Life

At the heart of innovation at DTW24 - Ignite, our 50+ Catalyst projects will debut their groundbreaking innovations live in the Quad and on the Innovation Arena stage.

Harnessing the collaborative global force of over 1000 industry minds from 250 organizations, our Catalyst project teams are pioneering solutions to propel industry innovation and growth through Open APIs, ODA, AI, and automation.

Experience first-hand their inventive and trailblazing demonstrations. Delve into the challenges tackled, use cases explored, and solutions forged. Connect with these visionaries to discover how you can leverage their achievements to align with your business objectives and advance future outcomes.

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Browse Catalyst Projects

GenAI Powered toolkit for network & service management

To serve customers effectively and efficiently, CSPs’ IT departments need an accurate real-time view of how their networks are performing. But the network and service resource inventory in a CSP’s operations support system (OSS) often does not reflect the current context of the network topology. This can result in costly service provisioning re-runs, impairments to the service and customer experience, and the need to reconcile data between systems and database, which takes considerable time and effort. This Catalyst is developing a generative AI-based toolkit, encompassing an adaptor agent and large language model (LLM), which will essentially mediate between the systems, processes and data in the network/service inventory database and the corresponding elements of the IP network. The solution should facilitate timely discovery of information across the network, associated network and service elements with a high degree of granularity. The overarching goal is to enhance customer experience by lowering the time needed to provision a service, and eventually enable autonomous network and service orchestration. The Catalyst plans to pilot the solution for fiber access MPLS-based IP-VPN services and 5G access services, with a view to replicating it across other services. The LLM, which will be fine-tuned across data, systems and process artifacts in relation to the network and service, will draw on the TM Forum’s business process framework and information reference model principles (such as ETOM/SID). The TM Forum assets will act as guardrails, supplemented by preventive tests for hallucination, following a significant pre-training process. The adaptive LLM will use a closed feedback loop, ingressing to the main generative AI adaptive engine, to further improve efficacy. On the operational process side, the project team will measure the service delivery time in relation to a baseline approach. Where diverse AI models are used for diverse scenarios, the team will employ A/B testing to evaluate each model’s effectiveness.

URN: C24.0.648

Project details

WebAssembly canvas - Phase II

As CSPs increasingly employ flexible and scalable cloud-native technologies, they are seeking to harness TM Forum’s open digital architecture (ODA) canvases: execution environments for ODA components and the release automation part of a pipeline in CI/CD (continuous integration and continuous deployment). Now in its second phase, this Catalyst explores how to integrate an ODA canvas based on the WebAssembly (Wasm) open standard with common services, such as identity and observability. Designed to support lightweight, instantaneous processes, W3C's Wasm serves as a stack-based virtual machine for clients and servers, acting as a portable compilation target for high-level languages. The CNCF's wasmCloud open-source project also offers a distributed application runtime that represents an evolutionary step beyond Kubernetes. Phase I of this Catalyst demonstrated the ability to run WebAssembly native components in a WasmCloud-based canvas. Phase II will begin to build the equivalent platform to the ODA Canvas Reference Implementation, written using wasmCloud providers to perform functions of the equivalent software in the Kubernetes-based original. The project team will also demonstrate how components are deployed and how they can be used in conjunction with the current ODA Canvas Reference Implementation. A successful outcome will enable deployment of components to a canvas that demonstrates some level of equivalent functionality to the Reference Implementation, while delivering interoperability between canvases based on different technologies. The project is also designed to highlight how the ODA can adapt to evolving technology, while remaining true to its purpose and allowing CSPs to achieve migration without the need for significant extra integration effort.

URN: C24.0.621

Project details

Sustainable and autonomous IOT ecosystems

The IoT has traditionally connected stationary devices such as surveillance cameras or environmental sensors. But as more parts of the IoT move, either independently (such as drones or vehicles) or with us (such as wearables or mobile devices), the challenge increases for managing deployments to ensure continuous operation. Current management platforms have mostly been designed on the assumption that devices are stationary and are only now grappling with the challenges of a dynamic IoT or ‘internet of moving things’ (IoMT). This requires real-time control and consistent service continuity while connected devices move between edge locations. Enhancing capabilities between devices, edge and cloud can ensure more energy efficient IoT deployments and present new monetization opportunities. This Catalyst aims to demonstrate how to replace current manual operations by developing automation solutions to enable autonomous control and optimization of IoT service delivery operations. The goal is to show how such an approach can improve network support for existing use cases while also providing an infrastructure suitable for future dynamic applications. In demonstrating these automated management capabilities for the IoMT, the Catalyst hopes to remove barriers to autonomous and sustainable IoT ecosystems. Managing the challenges of the IoMT and the new fluidity of the device ecosystem will place more demands on IoT platforms and infrastructure. But, with the introduction of advanced automation capabilities to manage seamless service delivery, device and control management will become more effective and energy efficient. The result will be an autonomous and dynamic IoT in which devices can operate both intelligently and sustainably.

URN: C24.0.700

Project details

Sustainable live streaming

Rising demand for streaming video is driving a significant increase in the amount of data delivered via CSPs’ networks. To handle the peaks in this traffic, CSPs have had to invest in network capacity, increasing costs and energy consumption. This Catalyst will explore how to make more efficient use of the existing network infrastructure. As the most popular videos are consumed by large number of customers, a content delivery network (CDN) - a geographically distributed group of servers – can be used to cache this content close to end-users, rather than retrieving it from distant servers in every case. Similarly, the most efficient way to support simultaneous demand for live video (in which customers are all watching the same content at the same time) could be to employ multicast delivery mechanisms: multicast adaptive bitrate (MABR) technology could reduce traffic by 90% compared to unicast mechanisms, without compromising the quality of the end-user’s experience. This Catalyst will explore how CSPs can open multicast network capacity to providers of live content and spare CDN capacity to providers of non-linear content. The goal would be to enable partnerships between so-called over-the-top content providers and telecom operators that both deliver growth and achieve network and energy efficiency. Bringing content closer to the end-user with open caches (edge caching) and MABR (on-premises caching) can significantly reduce the pressure on peering and backbone/aggregation transport infrastructures in CSPs’ networks. As a result, CSPs can delay and even avoid unnecessary capacity upgrades in their networks. At the same time, the energy consumption of the end-to-end video delivery chain can be kept constant or even reduced if the network equipment is carrying less traffic, despite the growing demand for video content. Sharing open caches and MABR between various content providers can decrease power consumption further. Temporarily or permanently switching off already-deployed equipment (after appropriately re-routing the remaining traffic) can further increase energy efficiency, while reducing the need for maintenance, repair and upgrade operations. There are also performance benefits: when content is cached near the end-user, latency will be reduced, improving the viewing experience.

URN: M24.0.679

Project details

The Big Deal

Many enterprise business units within telcos quote manually. In operating this way, CSPs forego the opportunity to automate quote production (time to quote) and lose deals, configurations cannot be validated, costing needs to be done manually, and quotes are unable to transition seamlessly to become orders, introducing an additional step which can be a source of error as well as adding significant effort. CSPs that undertake transformation over their enterprise business units often use non-Telco focused “Core Commerce” solutions with the intention of solving this problem, starting with a small number of products. However, because of the coding required per product, the usefulness of the solution is limited when only part of the product catalog is available from which to assemble a quote, making it essential to perform all of this within a visually configured Telco-focused catalog. This project sets out to visually model complex configurations and relationships in a TMF-compliant catalog, allowing CSPs to productize and automate all their offers to enterprise customers. The solution addresses the challenges by providing the ability to automate quotes, validate configurations, and transition quotes seamlessly into orders. It also integrates with partners using TM Forum Open APIs to comprehensively manage responsibilities, agreed life cycles, and SLAs. The solution has the following innovative capabilities: - Visually model complex configuration and relationships in a TMF-compliant catalog to productize and automate all CSP offers to enterprise customers. - During bespoke contracting, use Gen AI to assist in drafting the contract and generating commercial considerations for the quote. Gen AI can suggest contractual clauses and analyze the impact of changes, significantly accelerating contracting. This faster contracting brings revenue forward and makes it more predictable for the CSP. - Use Gen AI to guide the process and provide insights, predicting potential issues and likely timelines per site, product, configuration, channel, partner, etc. Manage change and non-happy path use cases as the big deal progresses. - Many CSPs need to cancel and restart parts of orders to deal with change; others manage change but suffer from revenue leakage through uncontrolled change. - Support the channels of choice for sales teams, customers, operations staff, and partners. - Using TM Forum Open APIs, integrate with partners comprehensively to address enterprise challenges, enabling the CSP to effectively share responsibility with the partner, including agreed life cycles and SLAs.

URN: C24.5.734

Project details

LOKI - LLM O&M Knowledge Integrator

Hi there, welcome to LOKI - LLM O&M Knowledge Integrator ! We will be showcasing at Catalyst booth C20! CSPs have traditionally relied heavily on the knowledge and expertise of engineers to solve network issues – as a result, multiple rounds of human interactions may be required to tackle a problem. This manual approach can however no longer cope with CSPs’ increasingly complex operations and maintenance (O&M) requirements. This Catalyst aims to harness data patterns and best practice to build large language models (LLMs) enabled Copilots and AI Agents across the “Monitor and Handle Anomaly” value stream. The project team will focus on developing LLMs to address several specific use cases, such as summarizing work order information, demarcating network faults with the support of digital twins, and recommending next best actions for O&M tasks. Other priority applications will be identifying the root causes of network faults and issues and generating operational reports based on intent. In each case, the objective is to enable engineers to simply ‘ask’ an AI agent, underpinned by an LLM, to complete necessary tasks. Besides the scenario-based innovation with LLMs, the project team also agree that organization, culture and talent challenges need to properly addressed in order to adopt LLM at scale. The overarching goal of the Catalyst is to help CSPs greatly simplify their O&M processes and tasking handling, thereby improving the customer & employee experience and realizing operational excellence. This project will also deliver sustainability impact in terms of decent workplace, inclusion & diversity, reduced carbon emission, etc.

URN: C24.0.628

Project details

GenAI powered toolkit for network & service management - Phase II

Telecom operators face increasing challenges in maintaining reliable networks, especially amid environmental disruptions, natural disasters, and rising customer demands. To address these, this Phase II Catalyst is developing an AI-powered system that will transform network management by integrating fault, performance, and alarm management into a unified, automated framework. This AI-powered system will: - Swiftly diagnose faults by correlating data from network logs and user reports, significantly improving response times and reducing network downtime. - Continuously monitor key performance indicators (KPIs) through machine learning algorithms, to identify patterns, predict potential performance issues, and recommend proactive measures to ensure network efficiency. - Stremline alarm management by intelligently prioritizing alerts based on their severity and impact on service quality, enabling faster resolution and preventing service disruptions. This Phase II Catalyst project builds on the Architectural Framework introduced in its previous phase. It aims to create a Knowledge Graph that maps relationships between network elements, topology, faults, and inventory data. This will enable predictive fault correlation, proactive decision-making, and closed-loop automation. We will also integrate TM Forum APIs into end-to-end service assurance workflows, automating manual processes and aligning with the principles of an Autonomous Network. Moreover, the solution will focus on enabling intent-based RAN self-healing and optimization using event-driven automation and a Mixture-of-Agents approach. The AI system will leverage the Knowledge Graph to determine the next best actions, issue service requests, and manage network configuration autonomously, driving closed-loop operations. The ultimate vision is to empower telecom operators with AI-driven intelligence to not only meet current demands but also anticipate future challenges. By employing machine learning, advanced analytics, and automation, we aim to create a smarter, more resilient telecom infrastructure that can thrive in the face of both environmental and operational challenges.

URN: C24.5.750

Project details

AI-Powered sustainable smart farming

Smart Farming has a huge potential to drive revenues from IoT and telcos advance connectivity enabling B2B2X commercial models. But to unlock it, 5G private networks, network slicing and edge computing are key to support the demanding QoS levels required to transform operations of the companies at Smart Farming. Sustainability is a key challenge when delivering solutions for the agriculture vertical market. In this catalyst we will demonstrate how telco operators can face these challenges through an innovative AI-driven approach at different levels. This transformative approach leverage Data Analytics and GenAI to create bespoke offerings, optimizing network resources, Radio Spectrum, Cloud infrastructure and agriculture supplies in real-time. At the core of this innovation lies Generative AI as a Platform, integrated into IoT Platforms driven by AI. Deployed in automated agriculture environments, this powerful combination optimizes resources such as water and fertilizers, exposing their capabilities via APIs. The synergy between AI driven Catalogue and Smart Farming technologies paints a vivid picture of B2B2X Collaboration. From robotics and agricultural drones to computer vision software, automated farming elevates efficiency across operations, reducing labor costs and environmental impact.

URN: M24.0.699

Project details

INNOVATION PIONEER PROJECT: High-value scenarios for autonomous networks level 4

The development of autonomous networks remains a key focus across the industry, with TM Forum playing a pivotal role in collaboration with Members. These efforts have led to the creation of the AN Reference Architecture, Intent, AN Assessment Levels, and self-healing domains. With over 70 leading companies now supporting the AN Manifesto, momentum continues to grow. The AN L4 Innovation Pioneer Project aims to demonstrate high-value Level 4 scenarios within CSPs and vendors, fostering a common understanding of AN Level 4. This shared vision will help accelerate the development of new assets, driving progress toward the next level of network autonomy.

URN: C24.5.756

Project details

Data-to-NPS: Boosting NPS using Decision Intelligence

> Background According to GSMA intelligence, the global unique mobile subscribers’ penetration rate has reached 69% in 2023 and is expected to reach 73% by 2030, with a CAGR of only 1.7%. This number will be even smaller in developed areas or dense urban areas. Almost all the CSPs are facing increasing competition, making managing existing customers the key to further business growth, and NPS is the key metric because: According to Analysys Mason, detractors are much more likely to churn than neutrals and promoters; and according to TMF report, decreasing the churn rate by 5% increases profitability by 25%~95%. So we consider NPS management will be a long-standing topic to drive sustainable growth in the next decade. > Challenge However, there are some long-standing issues that make it difficult to improve NPS if you only use survey results: (1) It is difficult to find root cause of NPS problems and then solve them, you can only see a general trend, but cannot perform closed-loop management. (2) Since it is impossible to quantify the impact of various management measures on the improvement of NPS results, it is difficult to make decisions on investing in NPS-related projects or platforms, which restricts the healthy development of this field. Accordingly we consider to address the challenge through survey and data collaboration manner. > Solution We developed an data-driven NPS management solution “Data-to-NPS”, set up a bridge between data and NPS, make data aware NPS. We design the solution based on the Engaging, Using, and Evaluating of the customer journey in the telecom industry, and NPS management is divided into three parts: product, network and service. The TMF DT4DI methodology is also used to analyze and solve NPS problems in a data- and AI-driven manner. > Team Collaborations - Special Collaborations on DTW24: (1) During DTW2024, several activities are arranged for the catalyst project promotion, and all team are well collaborated and assigned: (1.1) Day 1 15:00~16:30: Session (Telkomsel CTO): Revolutionizing NPS: Unveiling the Potential of AI and Digital Twin Technologies (1.2) Day 1 12:30~14:00: Lunch Briefing: DT4DI 2.0 (Globe VP): Practice sharing. Attendance from TMF and DT4DI Management are confirmed, all team members are having time to communicate and promote our project to them (1.3) A short video for Data-to-NPS solution is prepared and will be displayed on Session, Huawei booth and Catalyst booth - TMF DT4DI/MAMA Project Collaborations (1) Teams are actively connected with TMF DT4DI/MAMA project, 10+ topics applied by more than 5 parties, 4 use cases contributed, 1 Value Stream Contributed. - Other Routine Team Collaborations: (1) Executive management of Champions and Participants are all attach great importance on NPS, totally 100+ experts/managers/executives are involved (2) All team actively engaged in the routine catalyst team meeting and provided useful ideas and suggestions, and team lead and co-lead drive direction (3) Champions responsible for clarifying challenges, sharing operation experience and making solution verification, while Participants responsible for solution development and optimization (4) Together team review the award submission and final presentation. > TM Forum Assets Usage and Contribution: 20+ TMF assets used to design the solution, 5 TMF assets contributed, to share real practice and give a strong reference for industry. > Proof of Concept: Our solution has been verified by Champions, and the IT system has been developed and applied live, and already generating value now, such as Network NPS improve 3.5% and Revenue uplift 1.2% in Telkomsel. We also integrated the system interface and partially desensitized data into the live demo for on-site demonstration. > Industry Value As the demographic dividend gradually disappears, the retention and value management of existing users have become a problem that telcos must face for sustainable growth. And as user growth slows, the issue will attach increasingly attention. The Data-to-NPS management solution is exactly what the industry needs at present and in the future. It combines with the traditional survey method and adopts the data-driven mode, greatly enhancing the proactivity and certainty of NPS management. It has been verified in Champions and ready for large-scale deployment globally. > Sustainable Innovation - Social impact: (1) Better technical and financial inclusion requires breakthrough in new fundamental theories and methodologies, to address the challenge that user behavior will be affected by the surrounding users, resulting in unproper decision-makings.(2) Long-term investment in the fundamental theoretical and methodologies: UEP (User Evolutionary Process) and LUM (Large User Model). These fundamental theories & methodologies are contributed to IG1307 DT4DI Whitepaper 2.0 - Business growth: As the demographic dividend gradually disappears, the retention and value management of existing users have become a problem that telcos must face for sustainable growth.

URN: C24.0.652

Project details

Accelerated service-centric autonomous network operations

The vision of Catalyst C24.5.755 is to empower champions in their Digital Transformation to Accelerate Service Centric Autonomous Network Operations (ASCANO) to meet their R.I.S.E (Revenue, Innovation, Satisfaction and Experience) differentiated business objectives: 1) Revenue: Revenue Loss Reduction (prioritize trouble tickets to minimise service traffic loss), New Revenue (monetize Customer Experience upsell ‘add-on’ packages, New Premium Transport Resilience B2B Latency as a service) 2) Technology Innovation: Leverage AI & GenAI (Generative AI) Co-Pilot, EDNS (expected demand not served) for service impact and service recovery and DTN (Digital Twin) for Multi-layer visualization, Service status analysis, Network simulation to enable Revenue, Satisfaction and Experience objectives. 3) Satisfaction: to stay ahead of competition in delivering high standards of Customer Experience (reduce churn, increase NPS, reduce customer complaints, increase first call resolution rate, and proactive customer care). 4) Network Experience Improvement: Network Availability Mean Time to Repair (MTTR) reduction and reduced OPEX for Fault Handling. Our solution for ASCANO incorporate the ODA TMForum AI-Native Telco Framework: 1)AI-Native Operations- Monitor and Handle Anomaly Event Value Stream for Cross Domain Closed Loop Automation. 2)AI-Native OSS and BSS – Use of Modular ‘Plug and Play’ Microservices Oriented AI Ready components Service & Network Incident Correlation, Network Recovery Automation and Service Restoration Workaround 3)Open Autonomous Infrastructure – OSS systems collaborate with EMS for autonomous close-loop resolution. Supported by NOC & SOC Collaboration for service-centric operations to remove silos, manual processes, inflexible architectures to streamline trouble ticketing unification, and multi-team convergence. Our proposal provides a pragmatic framework for operation transformation which integrates processes, platforms, people (organizations) and technology. Service-oriented business objectives are measured against metric/KPIs using the TMForum MAMA/Value Operation Framework (VOF) methodology. The Business Value Impact using the VOF methodology clearly shows the before and after innovative solution impact and quantified improvement in indicative and overall VALUE terms.

URN: C24.5.755

Project details

Beyond chatbots – Revolutionizing telecom with advanced generative AI

Revolutionizing Customer Service with “Beyond Chatbots” In today's digital age, customers expect quick and efficient support when contacting service centers. But despite the rise of GenAI chatbots, customers still find themselves navigating through a maze of menu options - only to be left frustrated when the chatbot fails to provide the necessary assistance. Meanwhile, customers who seek human interaction face the tedious task of repeating their concerns as they’re call is forwarded from one department to another. These disjointed and inefficient experiences leave customers feeling undervalued and unsatisfied - and for good reason. Our catalyst - “Beyond Chatbot” - aims to change all that. Beyond Chatbot is a cutting-edge solution that redefines customer service by delivering swift, personalized interactions that revolutionize the way we meet customer demands. Imagine a customer making a call to a service center. Instead of the generic "How can I assist you today?" they are greeted with a precise, "Are you calling about your last bill?" This tailored approach isn't a vision - it's happening right now with our catalyst, Beyond Chatbot, at multiple CSP service centers. By leveraging the capabilities of Generative AI and advanced machine learning algorithms, Beyond Chatbot anticipates the customers' needs, delivering a customized service experience from the very first interaction. No more lengthy menus and repetitive conversations with agents: with Beyond Chatbot, customers experience a "menu of one," where personalized responses are crafted for each customer right from the get-go. But Beyond Chatbot, our catalyst, goes beyond addressing customer issues - it creates positive experiences that nurture lasting connections with customers. 1. Enhancing Customer Service: Our catalyst not only quickly resolves issues via IVR, call centers, and chatbots but also provides predictive, intent-based service at every customer touchpoint. This ensures first-contact resolution, eliminating the need for customers to repeat their concerns or be transferred between departments. By improving the customer experience in this way, Beyond Chatbot reduces the operational costs associated with running service centers, leading to significant savings for organizations. It also fosters trust and loyalty, as customers feel valued and understood. 2. Developing an Industry Blueprint: Recognizing the early stages of GenAI technology and the lack of standard implementation methods, we are dedicated to developing a clear, practical framework for utilizing GenAI effectively. Beyond Chatbot, our catalyst, serves as a blueprint that can be adopted by others in the industry. By promoting widespread adoption and sharing best practices, we aim to drive innovation and efficiency across the customer service landscape. In a world where customer service can make or break a business, our catalyst, Beyond Chatbot, emerges as a transformative solution. By harnessing the power of Generative AI, it delivers personalized, efficient support that exceeds customer expectations and sets a new standard for the industry. With Beyond Chatbot, CSPs can provide their customers with the seamless, intelligent service they deserve while streamlining their operations and reducing costs.

URN: C24.0.678

Project details

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