• Autonomous Network Management: How can AI Agents be utilized to automate complex network management tasks such as troubleshooting, incident resolution, and real-time configuration adjustments to ensure optimal performance?
• E2E Service Quality: How can AI Agents orchestrate and optimize end-to-end service quality, dynamically adapting to changing network conditions and traffic patterns?
• Self Learning Capabilities: How can AI incorporate self-learning capabilities to continuously improve network operations, ensuring resilience in heterogeneous and multi-layered network environments?
• Enhanced Network Observability: How can AI improve network observability, detect configuration drift in real-time, and ensure efficient management of telco edge cloud and edge computing?
• AI at the Edge: How might we leverage AI at the edge to enhance network performance and user experience?
• Human AI centricity: How can AI agents interact with human to provide intelligent recommendations augmenting human decision making in network operations?
• AI RAN use cases: How might we implement AI-driven improvements in Radio Access Networks?
• Network Operation Center:  How can AI be used to make the NOC more effective?

• Metadata Automation: How can GenAI automate the generation and enrichment of metadata for network datasets, ensuring high data quality, lineage tracking, and discoverability for downstream AI applications?
• Computation Effort: How can we reduce the computation effort of GenAI to ensure its application in network management remains cost-effective and scalable?
• Data Discovery: How can GenAI assist in automatically discovering and cataloguing network data assets, making it easier for data scientists to find the data they need?

• Society Resilience: How might we leverage telco networks and AI to enhance societal resilience, for example, through networked sensing and early warnings during crises?
• Emerging Technologies: How can AI enhance the deployment and performance of emerging technologies like AR/VR while ensuring regulatory compliance, ethical practices, and sustained leadership in network innovation?
• Monetization Strategies: How might we leverage AI to monetize network capabilities, optimize resource allocation (e.g., network slicing), and generate actionable insights to predict and reduce customer churn?

• AI for continued network leadership: How might we leverage AI to maintain and enhance our position as a network leader?
• Novel approaches for churn modeling: How might we develop innovative AI methods to predict and reduce customer churn?
• Identity drivers and recommendation solutions for churn: How might we use AI to identify drivers of churn and recommend solutions to retain customers?
• AI for network slicing management: How might we utilize AI to efficiently manage network slicing and optimize resource allocation?
• AI for Regulatory and Compliance: How might we ensure our AI solutions comply with regulatory standards and promote ethical practices?

• Planning & Streamlining Permits: How can digital tools and AI optimize the planning processes for fiber rollout projects and speed up permitting coordination with local authorities in a federal system?
• Construction Support & Management: AI-powered solutions that facilitate seamless collaboration with construction partner, allow real-time quality control, and automated project management that enhance efficiency, minimize errors, and accelerate deployment?
• Documentation: How might we utilize AI to streamline and enhance the documentation process in fiber rollout projects?
• Improving Community Communication: How can tools enhance transparency with residents and businesses about fiber rollout timelines?
• Identify opportunities for bundling and retention: How might we leverage bundling opportunities to improve customer retention?

Imagine a TelCo network where sustainable power consumption is at the heart of everything from early design to operations. This includes everything from the integrated use of renewable energy to load-dependent, and most efficient use of electricity and available resources. AI in its various forms can help.

The idea space can range from SW management solutions for operations. It can also include assistance for better network design, identification of ineffiencies and reusability opportunities in the given installed base.

To enable our network to run in an energy conscious way, as well general design principles, like “green AI”, “green coding”, etc. need to get applied.

In any case, such a solution must also be sustainable and maintain superior customer experience. And any proposed solution would need to be able to be integrated into existing networks in place in a simplistic manner or considered in upcoming and future technologies in a very tangible way with a clear path.

The following exemplary questions only indicate some parts of the search area addressed. It is about the full TelCo-stack and the entire lifecycle of a network. The size of the addressed lever for the diversity of DT's sustainability goals and its implementability will make a difference.

• RAN: How can AI enhance the integration of renewable energy sources with RAN components and how can we leverage the benefits of energy flexibility?
• Transport: Identify inefficient links and optimize routing to minimize energy use while maintaining network performance?
• End-to-End Network Energy Management: Integrate data from passive infrastructure (e.g., cooling systems, power supplies) with active network elements to provide a holistic energy management solution using AI
• Identify network power savings: How can we reduce network power consumption and increase efficiency?
• AI for facility management: How might we leverage AI to improve the efficiency and management of our facilities?
• Energy savings for AI infrastructure: How can we optimize the energy consumption of our AI infrastructure to achieve significant energy savings?

We are on a mission to enhance our sales and service capabilities with innovative AI solutions. Our focus lies on four key performance indicators: conversion rate, customer satisfaction (NPS), "first-time-right" process effectiveness, and productive working minutes. We are particularly interested in AI solutions that can boost sales, elevate customer satisfaction, improve our first-time-right performance, or make our work processes more efficient. Below, you'll find detailed questions to illustrate the solutions we seek.

Training/Coaching/Guidance:

• How can AI help train employees to maximize sales opportunities and guide our sales activities effectively?
• What AI methods can assist in retaining critical knowledge when employees leave the company?
• How can we create a realistic, avatar-based virtual training environment for employees to practice sales and service calls before engaging with real customers?

Sales Support

• In what ways can AI support our employees during sales pitches?
• How can AI facilitate autonomous sales processes to drive automatic sales?
  Information Management:
• What methods and technologies can optimize information retrieval and delivery for our frontline employees?

Real Time Agent Support:

• How can AI assist employees in customer interactions (e.g., in shops or field service) without causing distraction or disruption?
• How feasible is real-time translation in voice/phone calls to enable customer and agent to communicate in their native languages?
• How can sentiment analysis be applied to monitor and address employee stress levels?
  Automation:
• How can we reduce manual work in routing and responding to emails and other written customer communications?
• What new technologies can automatically identify and authenticate customers, ensuring smooth interactions

Customer Profiling:

• What AI solutions can better guide agents with next-best-offer or next-best-action recommendations based on customer characteristics?

Field Service:

• How can AI provide personalized podcasts or videos to prepare field technicians for upcoming tasks, using travel time efficiently?
• Are there solutions that can offer real-time technical advice to field service employees, particularly through context-sensitive data interpretation?

Bot Detection:

• How can we distinguish between bot and human callers during inbound calls?
  Bot Orchestration:
• What methods can streamline the development, deployment, and management of multiple sales and service bots to maximize productivity and minimize risk?
• How can we implement multimodal chatbots that understand text, images, and voice to improve customer experience?

Text-to-Video/Text-to-Image:

• How can AI enable automatic generation of personalized videos for sales and service use cases, such as onboarding and troubleshooting videos?

Additional Opportunities:

• Are there any other AI use cases ready for implementation in the next 1-2 years that could significantly improve our sales and service activities?
• Identify reasons why care would be contacted for churn non-network related?
• Identify business sales opportunities.
• Identify areas to upsell and cross-sell the network
• How to use network slicing to increase sales?
• How to improve the customer in store experience using AI?
• How can AI be used to help or provide proactive suggestions in order improve the customer experience?

Build the AI Powered Future with Cutting-Edge Network-APIs

Innovate with our diverse set of advanced APIs! We encourage developers to explore the following use cases and come up with innovative AI solutions using these APIs:

• Fraud Detection: Implement measures using AI to detect and prevent fraudulent activities using APIs like SIM Swap or KYC Match.
• Anomaly Detection: Build AI systems to identify irregularities in user behavior or network data.
• Identity Verification: Use Number Verify or KYC Match APIs with AI technology to streamline and secure the identity verification process.
• Location Services: Utilize the Location Verification and Roaming Status APIs to develop AI powered location-based services without GPS reliance.
• Communication Services: Extend the telco communication capabilities for devices, apps and systems to build AI enabled services.
• Network Performance: Enhance the network performance for AI services using Quality on Demand (QoD) and Edge Services.

If you want to learn more about our APIs you can find more information here:

• https://developer.telekom.com/en
• https://devedge.t-mobile.com/

In this focus area we are seeking visionary technologies to strengthen cybersecurity & network security defenses through advanced solutions in protection, detection, and response. If you’re building the future of intelligent cybersecurity & network security, we want to help you bring your innovations to market. Join us to create a safer digital world.

PROTECT

In protection, we’re looking for the next evolution of security tools, from adaptive firewalls to intelligent malware detection and endpoint protection solutions, all powered by AI. Our goal is to stop threats before they take root, leveraging AI & machine learning to continuously evolve and stay one step ahead of attackers.

DETECT

In the detection phase, we’re focused on security software that uses AI/ML-based engines to build and refine threat models, capitalizing on vast data sets, real-time traffic analysis, and user behavioral patterns to uncover even the most complex or previously unknown threats. We aim for solutions capable of proactive, real-time threat monitoring to identify trends, suspicious behaviors, and emerging threats within large log data.

RESPOND

For incident response, we’re seeking AI-driven tools designed to enhance security teams’ effectiveness. These solutions should reduce noise and false positives, provide actionable recommendations, and even automate certain mitigation actions to streamline responses to incidents. The following AI-driven cyber security use cases are of special interest for us. If you have additional cases in development, we would be pleased to hear from you as well.

• AI powered device compromise detection: How might we identify compromised devices using advanced AI techniques?
• AI powered remediation: How might we use AI to streamline and automate the remediation process?
• AI powered security advisors/agents: How can we develop AI-driven advisors to assist in making informed security decisions?
• Network traffic anomaly detection: How might we identify unusual patterns in network traffic before they become a threat?
• AI-based evolution of NG Firewalls: How can we enhance next-generation firewalls to adapt in real-time to new threats?
• AI-based CISO assistant: How might we support Chief Information Security Officers with AI-driven insights and decision-making tools?
• AI powered deception networks: How can we use deception technologies to mislead and trap attackers within our network?
• AI driven intrusion prevention: How might we proactively prevent intrusions using advanced AI techniques?
• AI based phishing prevention: How might we detect and prevent phishing attempts using AI technologies?