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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

Mighty Minions: Unleashing domain-specific GenAI via SLMs

Mighty Minions: Unleashing domain-specific GenAI via SLMs

LLMs like OpenAI's ChatGPT have gained attention for their capabilities, but the associated expenses and challenges have fueled interest in alternatives. Smaller Language Models (SLMs) offer cost efficiency, deployment flexibility, and enhanced privacy control. Enterprises are shifting to domain-specific SLMs, extracting maximum value by tailoring models to their needs. CSPs, with vast datasets, can construct personalized SMLs, that can be used in conjunction with their network and edge ecosystem offerings supporting enterprises and exploring new revenue streams both for CSPs and their customers (B2B2X) across diverse sectors. Communication Service Providers (CSPs) possess multiple data sources within their environments, encompassing customer data, network data, and OSS/BSS data. These extensive datasets can serve as valuable resources for generating clean and structured data, ideal for pre-training language models. Many GenAI use cases necessitate a fusion of data from diverse systems. The proposed Catalyst solution introduces an architectural framework wherein all Telco data is centralized on a single platform. This consolidated platform enables unified access to unstructured data, facilitating the creation of clean and pre-processed datasets. Moreover, the architecture prioritizes addressing risks and security implications, including considerations for outcome accuracy, data protection, cyber risks, and privacy. The GenAI Framework we propose is designed for CSPs to harness existing pre-trained data and develop their own language models using Small Language Models. This initiative specifically targets domain-specific use cases , allowing CSPs to tailor solutions according to the unique needs of various industries. One key aspect of this framework is its capability to empower CSPs to extend this service to other enterprises. By doing so, enterprises gain the ability to efficiently and effectively create their own General AI (GenAI) models, leveraging their proprietary data. The framework spans the entire lifecycle of model development, encompassing crucial stages from initial training to the ultimate deployment of models. This comprehensive approach ensures that CSPs and enterprises utilizing the framework have the necessary tools and capabilities to navigate each phase seamlessly. Finally, CSPs are envisioned to expose some of the models generated within the GenAI Framework as APIs to enterprises. This strategic move allows enterprises to access and utilize pre-trained small language models seamlessly.

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URN: M24.0.620
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Beyond chatbots – Revolutionizing telecom with advanced generative AI

Beyond chatbots – Revolutionizing telecom with advanced generative AI

This catalyst aims to establish best practices for leveraging integrating generative AI and AI/ML in the telecommunications sector. We'll showcase their combined application in various use cases, such as predicting customer needs, elevating service quality, and strengthening relationships in the Care and Digital sectors, along with other impactful scenarios. Leveraging cutting-edge GenAI and LLM models, we embark on an innovative journey to meticulously analyze care call and chat interactions. The primary focus is on precisely labeling the call reason and key discussion topics, laying the foundation for our ML Intent Score models. This breakthrough approach doesn't stop at mere prediction; rather, it propels us into a realm of even greater impact. Once intents are accurately foreseen, we seamlessly transition to the next frontier — routing the customer to a Virtual Agent, a technological marvel powered by GenAI, capable of flawlessly managing the entire interaction with finesse. What sets this process apart is the establishment of an 'Intent and GenAI Factory.' This visionary operation ensures a continuous cycle of identification, thorough vetting, and dynamic development of additional intent and conversation models. It's an innovation-driven engine that propels us beyond the conventional, consistently refining and expanding our capabilities to deliver unparalleled customer experiences. In our commitment to pioneering advancements, we go beyond self-innovation. We are actively shaping best practices throughout this process, creating a blueprint that will rely on and contribute to TMForum assets, such as open APIs and ODA Best Practices. This blueprint is designed not only to be replicated but also adapted by others in the industry, fostering a collaborative environment for transformative innovation

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URN: C24.0.678
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Empowering Sustainability: Unleashing the potential of AI and automation for data-driven environmental leadership

Empowering Sustainability: Unleashing the potential of AI and automation for data-driven environmental leadership

This proposal aims to revolutionize sustainability practices, delivering clarity and actionable insights with the necessary speed and scalability. By implementing AI and automation into data collection procedures, sustainability leaders gain access to predictive capabilities, optimization tools, and anomaly detection. This translates to informed decision-making, expedited regulatory compliance, and the mitigation of environmental impacts on human systems. Organizations face a significant obstacle in the form of manually collecting vast amounts of complex sustainability data. This manual process impedes their ability to effectively comprehend environmental risks and maintain efficient compliance with regulations. By implementing AI and automation into data collection procedures, sustainability leaders gain access to predictive capabilities, optimization tools, and anomaly detection. This translates to informed decision-making, expedited regulatory compliance, and the mitigation of environmental impacts on human systems. This proposal aims to revolutionize sustainability practices, delivering clarity and actionable insights with the necessary speed and scalability. This approach leverages cutting-edge technologies like AI and automation, transforming opaque data into actionable insights. The integration of geospatial data further enhances resource management, thereby establishing this solution as a novel and innovative tool in the pursuit of sustainability goals.

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URN: M24.0.689
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AI-driven offer lifecycle management

AI-driven offer lifecycle management

Providing tailored offers to every customer is a driving vision for many communication service providers (CSPs) today. In practice this would require significant time and effort, creating added complexity to manage across an organisation. With this Catalyst, using AI, we’re taking key steps towards realizing this vision by addressing two of the key challenges for managing offer lifecycle in a world of mass customisation: Creating tailored offers for every customer to enhance their purchasing experience. * With our AI-based offer design assistant, we’re using AI to dynamically assemble new personalised offers and pricing based on a customer’s context, needs and other data, scoring offers on their success potential. Efficiently managing offer lifecycle in this context of mass customization. * Through our AI-based catalogue rationalisation assistant we are using AI to analyse all created offers based on their commercial success, offering recommendations to CSP product and marketing teams, helping them rationalise their catalogue for easier to management across lines of business. Both uses cases rely on commercial intent information, including customer context, needs and budget, the service provider’s business goals, and commercial offer scoring. The target benefits of our AI-driven offer lifecycle management solutions include: 1. Lowering the time and costs of creating, delivering, and managing personalized customer offers at scale 2. Driving increased sales among new and existing customers 3. Improving customers’ satisfaction and their overall experience See you in Copenhagen!

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URN: C24.0.654
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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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Displaying 1-12 of 57 results