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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.

Catalyst Champions Include:

Browse Catalyst Projects

AI-driven product configuration and provision

AI-driven product configuration and provision

For CSPs, provisioning services for enterprises typically involves lengthy, customized development and numerous offline processes, with limited reusability. To address these challenges, CSPs need a standardized design and automated orchestration to rapidly integrate business and operational support systems (BSS and OSS) to meet the needs of enterprise customers. This Catalyst will develop an approach based on PSR (product-service-resource) to lower the ready-to-launch time from months to minutes and achieve an 76% improvement in product deployment efficiency. By adopting a componentized design, AI algorithms, and flexible assembly of atomic services, CSPs can better adapt to diverse business needs, improve flexibility and scalability, reduce risks, optimize resource usage, and automate operation and maintenance management. Drawing on the TMF GB922 specification, the Catalyst will use a hierarchical decoupling method to design telecoms products rapidly by reusing objects and capabilities prepared at lower levels, eliminating the need to start from scratch each time. The Catalyst will also convert the design content into a unified IT language and distribute it in the form of orchestration packages, which can be automatically loaded and compiled by the OSS. At the same time, it will employ a PSR-based automatic generation algorithm to further optimize service design efficiency. This algorithm will be designed to reduce labor costs and network/device configuration learning costs, and streamline the overall process. The Catalyst will use a number of metrics to track its progress. For example, the effectiveness of the PSR modeling approach can be measured by tracking component reusability, while the readiness of each system's capabilities can be measured by the one-time success rate of service provision. Similarly, the effectiveness of PSR-based service design can be measured by tracking the service development cycle. Decoupling OSS and standardizing network capabilities should help CSPs' network management systems to achieve better fault and performance management, as well as faster service provision. Moreover, enterprises will be able to develop new applications based on these network capabilities, and accelerate application innovation and the digital transformation of their industry.

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URN: C24.0.643
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LOKI - LLM O&M Knowledge Integrator

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.

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URN: C24.0.628
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AI chat agent: The game-changer for telecoms

AI chat agent: The game-changer for telecoms

AI chatbots are becoming increasingly central to enterprise customer service efforts. When combined with large language models (LLMs), AI chatbots can be trained on datasets from diverse service areas, enabling them to provide swift, precise, and personalized assistance. This not only enhances the customer experience but also offers to drive significant revenue growth. The telecom sector, in particular, is ripe for adoption of such technology, using it to effectively discern customer intent and tailor services accordingly. This Catalyst seeks to create an AI-based customer service system which can provide the best possible user experiences across common user scenarios. The aim of the project is to engineer advanced conversational AI chatbots that can understand and use comprehensive telco-specific data to execute defined and targeted tasks, and engage interactively on demand with end users. The core architecture will integrate enhanced LLMs that have been fine-tuned with specialized telco datasets. The system will incorporate a hybrid framework to coordinate a rule-based system, natural language understanding (NLU) modules, and LLM technologies to ensure high performance and adaptability in complex business scenarios. The Catalyst seeks to show how LLM-based chatbots can determine consumer intentions and proactively resolve current issues, such as recommending personalized data plans, explaining products or initiating direct subscriptions. The outcome will be to improve telco customer satisfaction and reduce long-term overheads.

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URN: M24.0.713
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AI-driven EBITDA mastery: Revolutionizing customer journeys

AI-driven EBITDA mastery: Revolutionizing customer journeys

CSPs continue to search for mechanisms to improve EBITDA in the face of high competition and formidable network investment requirements. Generative AI is transforming the revenue management lifecycle, and this catalyst partnership provides an opportunity to turn our top operational challenges into avenues of profitability (EBITDA Growth) and operational efficiency. Our goal is to collaborate with the catalyst team to validate the costliest aspects of the customer and service journey and to apply a designed Generative AI solution that drives operational efficiency and secures a notable lead in both EBITDA growth and market competition. We will address and select use cases that span three key target categories: customer experience, service operations, and product development. Utilizing Generative AI and Intent Analysis to remodel revenue management and enrich conversational commerce, our goal is to create hyper-personalized natural language customer journeys that integrate sales and service engagements, improving both revenue and service assurance whilst dramatically reducing the cost-to-serve. We aim to apply proactive intelligence for early customer engagement to reframe and automate the customer experience to new levels. This will allow organizations to: * Engage proactively with customers and stakeholders directly through natural language to reduce otherwise costly service engagement. * Tailor acquisition and upsell strategies to individual contexts to maximize customer profitability. * Optimize packages, pricing, and offers based on customer context in real-time. * Drive sales with intelligence and customer intent rather than relying solely on post-sale profitability analysis. * Ensure revenue assurance operates in parallel with customer engagement to enhance customer value. * Refine retention management strategies to align with customer lifetime value and move away from generic and potentially unprofitable policies Resources README FIRST! Here, you'll discover videos and resources showcasing the AI architecture designed in this catalyst, along with its use cases and business value. 1. Executive Introduction - Provides a high-level introduction to the catalyst 2. Business Track - Provides Business Intro, Business Value Analysis, and CurateFX Business Information 3. Technical Track - Provides Solution Intro, Use Case Demos, and CurateFX Tech Information 4. Vendor Products - Provides information on the vendor products behind the solution

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URN: M24.0.708
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Interoperable logical resource management platform

Interoperable logical resource management platform

CSPs have long struggled with Logical Resource Management, a critical but uncharted capability. The challenge has grown with each wave of new network technologies and services, from IP addresses to the surge of mobile identifiers, and now the exploding world of IoT numbers. Achieving a standardized resource porting environment remains a significant hurdle for our industry - the inherent variability of the process, driven by geographically diverse regulations and product-specific requirements, makes it sound to have a one-size-fits-all solution unrealistic. Most incumbent CSPs across the globe work closely with their peers and regulatory bodies to traverse its way through a mesh of complicated regulatory requirements , non-standard processes and architecture. While navigating their way through this hurdle they have commitment to their customers to deliver services quickly and seamlessly. To meet these commitments it is necessary to standardize logical resource management and portability processes. This Catalyst aims to build a reusable logical resource management and porting platform that empowers CSPs to operate more effectively, and foster interoperability across the industry and with regulatory bodies. By establishing a standardized approach that leverages scalable, cloud-native orchestration, this platform gives CSPs more complete control of their logical resources, streamlining management within their own network and with ecosystem partners. TMF Open Digital Architecture (ODA) principles were a central focus in the design of this catalyst. Through deep collaboration amongst the champions and participants involved in this program, we conducted a rigorous analysis of related open APIs, including those yet to be published, and this analysis benchmarked the APIs against typical industry use cases, validating their effectiveness. Furthermore, valuable insights from this analysis led to recommendations for potential API modifications, enhancing their adaptability. The end-result of this program will deliver a comprehensive suite of API enhancements, along with a detailed description of a platform that strictly adheres to TMF standards. Additionally, we will provide a minimum viable working model to demonstrate the platform's functionality

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URN: C24.0.694
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DBM: Securing the supply chain - Phase VII

DBM: Securing the supply chain - Phase VII

Extending ODA standards to enable B2B2x frictionless trading could increase average revenues per customer by 2 to 5 times or more AND reduce operating costs by 50-75% … … and enable cyber security requirements for Zero Trust attestation handshakes per device (with all supply chain partners’ keys) + Zero Touch … for both activation and in-life! Eight years since publishing the Platform Revolution (Profs Parker, Van Alstyne, Choudary) digital platform players continue to massively outperform traditional companies. BUT, everything needs connectivity and telcos are uniquely placed to participate in this growth. DBM Phases 1 - 6 solved key challenges and demonstrated deploying cameras as secure trusted endpoints leveraging Zero Trust, Zero Touch, & Frictionless Trading across all partners in the supply chain. But while these B2B2x techniques are based on TM Forum ODA standards, the ODA standards do NOT articulate B2B2x capabilities ! DBM7’s focus is to help uplift the ODA standards to enable telcos to operate with and like the platform players, to partner and trade frictionlessly with multiple organisations B2B2x, rather than trading manually or using proprietary techniques. DBM7 uses two B2B2x partnering use cases to explore what needs to be done to enable the ODA to articulate B2B2x: * VR gaming services, where on top of the 5G core, the partner provided 5G slices are used by roaming gamers * securing the supply chain for multi-partner sourced solutions such as Drone operation & AI for Smart Manufacturing. DBM7’s architectural governance insights, key ODA B2B2x gaps (TR302 et.al) and proposed ODA extensions required for B2B2x partnerships and multi-party ecosystems are being worked with ODA core teams using B2B2x capable API call sequence flowcharts. Examples include launching partner offers bundled with native data services, ensuring consistency across Partner Agreement, Contract to Assure process e.g. Customer contract, Product Inventory, partner settlements, etc.

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URN: C24.0.710
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Incident co-pilot

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
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ReNOVATE AI: Rejuvenating network build and operations through AI

ReNOVATE AI: Rejuvenating network build and operations through AI

In the dynamic telecom landscape driven by evolving customer demands, telcos can tailor customized bundles using existing product components and flexible contract terms in order to monetize available resources. By harnessing the power of GenAI, this solution will empower telcos to build bespoke products, bundles, and networks quickly, strengthening their operational agility and automation, ensuring seamless customer journeys from initial onboarding to continuous service delivery of these agile products. This will enable telcos to address emerging demands such as 5G, edge computing, and value-added bundled services through strategic partnerships with other telcos, hyperscalers, and OEM vendors. We also intend to target the challenges in network operations faced by telcos within their brownfield ecosystems. The exponential scale of network events requires an automated approach to address issues like alarm noise, correlating alarms and events, root-cause analysis for the right-sized assurance outcomes. This solution will enable an industry-standard (TMF ODA)-based framework that will leverage AI-OPS to address such requirements satisfactorily with minimum disruption, leading to OPEX optimization and improved customer experience. The solution approach involves implementing a framework for automated analysis of network events for anomaly detection against pre-defined KPIs and feeding corrective actions into the automation framework to enable a closed-loop operation. This solution will also deliver AI-driven assistance to the operations team in NOC for expedited resolution of network incidents and leverage relevant hyperscaler infrastructure for accelerated development and ubiquitous access. With the help of AI, we will also demonstrate use cases to improve energy efficiency by continuously scanning energy usage metrics leading to selective hibernation of under-utilized network elements and movement away from less efficient networks.

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URN: C24.0.664
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Responsible AI

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.

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URN: M24.0.698
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Mighty Minions: Unleashing domain-specific GenAI via SLMs

Mighty Minions: Unleashing domain-specific GenAI via SLMs

Large language models (LLMs) are enabling development of highly capable generative AI applications. But these generic models can be expensive and energy-intensive to run, prompting growing interest in bespoke smaller language models (SLMs) that promise greater cost-efficiency, deployment flexibility and enhanced privacy control. While fine-tuning LLMs on smaller datasets for specific use cases is a prolonged and resource-intensive process, fine-tuning pre-trained SLMs with domain-specific data can be accomplished swiftly. For example, an insurance company could fine-tune a pre-trained SLM with its policy documents in just two to three hours. Using an SLM allows for the implementation of a generative AI model on devices with relatively low processing and memory requirements, reducing overall cost of ownership by around 30%. As they can draw on customer, network, operations and billing data, CSPs could build SLMs both for internal use and for enterprise customers, opening up new revenue streams. This Catalyst plans to introduce an architectural framework wherein all CSP data is securely centralized on a single platform, facilitating the creation of clean and pre-processed datasets. This end-to-end framework would empower CSPs to extend this service to other enterprises, which could use their proprietary data to efficiently and effectively create their own generative AI models. CSPs could expose pre-trained SLMs arising from the framework as APIs so that enterprises can access and use them seamlessly, without needing a team of technical experts. Until now, implementing generative AI has required specialized skills in machine learning, data science and AI development. Enterprises may struggle with a shortage of talent or expertise in these areas, making it challenging to develop and deploy AI solutions, while also addressing ethical concerns and regulatory requirements. Once complete, this Catalyst project will help CSPs and businesses overcome these challenges by enabling them to harness pre-trained, domain-specific models that perform better than generic LLMs, while offering lower latency and reduced power consumption.

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URN: M24.0.620
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BiMa: Billing integration for marketplaces - Phase II

BiMa: Billing integration for marketplaces - Phase II

As CSPs move into adjacent sectors and support IoT deployments, they must increasingly work with various partner organizations to provide customers with additional services – and it is far more convenient for the end customer if these services are all billed through a single invoice. This Catalyst is therefore developing a solution to consolidate various CSP and partner bills through a shared platform. The aim is to support intricate billing patterns and post-billing activities while using TM Forum assets for seamless integration. Building on Phase I, this iteration of the project caters specifically for diverse B2B2X environments, focusing on the distinctive demands of respective marketplaces, thereby enhancing scalability and operational efficiency. The solution will enable CSPs and marketplaces to leverage the native billing capabilities of involved partners, including complex pricing models such as usage-driven and tier-based pricing, by dynamically and seamlessly integrating the bills at cycle time. It will also employ event-driven standardized integration patterns to simplify post-billing operations across partners and CSPs. The aim is to hide these complex integrations and flows from customers, who simply receive a single consolidated invoice served by a single entity. Ultimately, the Catalyst plans to produce a novel and scalable solution catering to the evolving demands of the communication industry. Designed to be flexible, holistic and versatile, the solution will support the onboarding of CSPs and partners, product onboarding, managing account relationships, bill cycle time integrations and post-billing scenarios. The project will measure the solution’s effectiveness by tracking subsequent reductions in billing errors, with a target of at least 30%. It will also measure the time saved when onboarding new partners and onboarding partner products with complex pricing models - the targets for these metrics being reductions of 20% and 50% respectively. The Catalyst is also aiming to lower operational costs of billing processes by 20%.

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URN: C24.0.622
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