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

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

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

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

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

Transforming Techco landscape through open digital experience to achieve sustainable business growth

This Catalyst wants envisions the future of Techco under cutting-edge technology digital technologies such as AI, Foundation Models, GenAI. , aimed at crafting a robust digital experience for all stakeholders to foster sustainable growth in a diverse market environment. • The Landscape embodies an ecosystem containing multiple blocks joining together to form a sustainable environment, to foster the on-going development of the ecosystem. Its dynamic nature ensures ongoing expansion into groundbreaking areas and regions. •ODE: Open Digital Experience enables superior experience to stakeholders within and beyond the company, including customers, partners, and suppliers. Its openness allows for the seamless integration of new stakeholders, serving targeted customers effectively. Leveraging Data Centric Decision capabilities and AI support, Techco embarks on revitalizing its business landscape. Through an open digital experience, it gauges its health from an outside-in perspective and prepares for future endeavors. With initiatives supporting Smart City and Smart Country endeavors, coupled with Techco's evolving capabilities, it sets a new course towards sustainable business growth.

URN: C24.0.663

Project details

GenAI powered language agnostic business operations

The global telecommunications market is ~300B USD growing at an avaerage rate of 5-8% annually. The worldwide outsourcing market for business services is ~110B USD, growing at an average CAGR of 5-7% just for the Telecommunications industry (Gartner). Moreover, according to IDC, demand for Customer Experience related services is expected to grow at 6.0% during the 2023–2027 forecast period, from $353.0 billion in 2022 to $473.3 billion in 2027 This provides immense opportunities for telcos to expand into geographically, and also create shared and global business operations in different countries. Traditional approaches of shared services or global business operations centres struggle with language barriers and need for acquiring talents with local language skills. These local language skills are critical in all customers facing processes due to interactions with end customers requiring 100% accurate communication. Tackling this evolving operational nuance requires a suitable technology solution to help the CSPs overcome any bottlenecks, so that they are able to utlise outsourcing and near/offshoring solutions at scale. The specific problems that we seek to address include: * Difficulty Finding and Skilling Employees on multiple languages & cultures * Limitations in recording / transcribing tacit knowledge of SMEs * Difficulty Designing Multilingual Systems * Limitations in automating region-specific and language-specific systems and processes * Difficulty Understanding structured and unstructured content during offline and online conversations * Limitations managing multi-lingual Customer Support services in real-time To address this challenge we are harnessing the power of Generative AI to facilitate seamless communication and data processing across languages. We are demonstrating a Gen AI powered language agnostic operations assistant that will not only handle communications in different languages, but also support locale-agnostic data processing and multilingual content generation. The LLM model will also be trained with process specific terminology to enable contextual and accurate communication. The solution addresses the following nuances: * Contextual Capture of customer emails and conversations in a foreign language and translating them to native languages where the business operations centers are located. Context includes sentiments, localized abbreviations and enterprise business terminologies * Minimising the time spent consolidating email conversations, transaction records and creating a comprehensive incident provenance record * Providing prescriptive guidance that is customized based on the organizational requirements with the capability to learn and adapt. * Ability to generate workflows, imbibe the output from the workflow action sets and help generate a consolidated respons * Organizing data sources that are relevant to the domain such as procedures, transactions and generating data for AIOps * Ability to assess the yield in terms of number of steps taken, number of emails for the same customer and using customer satisfaction indices to help build a learning model

URN: M24.0.659

Project details

Incident co-pilot

In today's complex network environments, incident management is a critical task for ensuring business continuity and minimizing downtime. However, traditional incident management tools often fall short in providing the necessary correlation and next-best-action guidance to effectively manage incidents. This is where generative AI (GenAI) can play a transformative role. Problem: Lack of Correlation and Next-Best-Action Guidance The root cause (RCA) of incidents is often difficult to determine, especially when multiple sources of data, such as alarms, tickets, and customer feedback, are involved. Traditional incident management tools struggle to correlate this disparate data and provide actionable insights. Additionally, these tools often lack the ability to suggest next-best-actions, leaving incident management teams to rely on their experience and intuition. Solution: GenAI for Incident Co-pilot GenAI can address these challenges by providing a more comprehensive and proactive approach to incident management. GenAI algorithms can analyze vast amounts of data from various sources to identify patterns, anomalies, and potential root causes. This ability to correlate data effectively is essential for determining the true RCA of incidents. Furthermore, GenAI can go beyond simply identifying root causes and provide next-best-action guidance to incident management teams. By analyzing historical incident data and current network conditions, GenAI can recommend the most effective course of action to resolve incidents and prevent future occurrences. The proposed solution is a human-AI co-pilot system that leverages Generative AI (GenAI) to bridge the gap between AI algorithms and human trust. This system addresses the "Catch 22" problem by providing a transparent and understandable explanation of AI-generated insights, enabling human users to gradually build trust in the AI's recommendations. GenAI holds immense potential to revolutionize incident management by providing tools like Incident Co-pilot with the ability to correlate data effectively, identify root causes accurately, and provide next-best-action guidance. By leveraging GenAI, organizations can significantly improve their incident management capabilities, reducing downtime, enhancing customer satisfaction, and minimizing business disruptions.

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URN: C24.0.636

Project details

Responsible AI

Introduction AI has the potential to generate immense value across the telecommunications industry. From customer care to network operations, the possibility to generate new insights, new revenue and deliver efficiencies should not be underestimated. However, AI also comes with a number of risks which telecommunications leaders, technology teams and customers are becoming increasingly aware of. Our ambition is to bring together a diverse team of contributors from multiple Telecommunications and Technology organizations in order to define an approach to Responsible AI which highlights the potential benefits to the Telco industry, whilst also highlighting the key risks associated with traditional and generative AI. From here we look at how specific AI use cases align with key Governance, Risk and Compliance frameworks. By assessing input from each of the participating organizations, we have developed a consistent approach to AI Governance which covers three critical areas: 1: Risk management 2: Pre production design and evaluation 3: Post production monitoring This approach then leads into an overview of the key considerations, processes and technical approaches / tools required in order to identify, manage and mitigate AI risks. The key outcomes we aim to prove are as follows: * A view of the business opportunities presented by AI. * An understanding of the potential AI related risks faced by the Telecommunications industry. * A framework and approach to understanding and mitigating against these risks. * A view of the technical advancements which enable organizations to address these risks at scale and where possible in an automated manner. * A view of the business benefits associated with the adoption of an AI Governance function across the organisation. The "Responsible AI Moonshot Catalyst" underscores the imperative of investing in responsible AI governance for CSPs, emphasizing that neglecting this aspect could lead to detrimental consequences. By showcasing the risks associated with inadequate governance, the initiative aims to drive home the message that responsible AI practices are essential for mitigating risks and maximizing revenue and efficiency gains, projecting a potential 30% EBIDTA improvement for CSPs globally.

URN: M24.0.698

Project details

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