Gen-AI in Telcos: Strategies, Challenges & Impact

The slides by Torry Harris Integration Solutions explores how telecommunication companies (telcos) can leverage Generative Artificial Intelligence (GenAI) to create new value from data. It delves into key strategies, challenges, and impacts of implementing GenAI, highlighting its role in enhancing customer operations, sales, marketing, and network security. The content discusses foundational capabilities like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), applications such as digital twins, synthetic data, and churn modeling, and addresses challenges including data quality, costs, system compatibility, and regulatory compliance. It underscores the importance of partnerships and strategic planning for successful GenAI adoption and emphasizes the need for robust data management practices and building customer trust.

1. Gen-AI in Telcos: Strategies, Challenges & Impact

2. GenAI implementation strategies for telcos Telecommunication companies (telcos) can create new value from data using Generative Artificial Intelligence ( GenAI ). With access to vast customer datasets, telcos can train GenAI models to enhance customer operations, sales, marketing, and network security. Key Benefits of GenAI : Complex behavior simulation: Adapt and generate personalized content in real-time. Robust performance: Effective even with data scarcity. Market Insights: 64% of CSPs have found unique applications for GenAI (Altman Solon, AWS). GenAI market in telecom is projected to reach $4,883.78 million by 2032, with a CAGR of 41.59% (Precedence Research).

3. Building foundational capabilities Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are popular generative models used to create data that mimic real-world data. GANs are designed to produce synthetic data that is indistinguishable from real data, for realistic outpiuts . The VAE model can be utilized first to compress data into a compact form and then extract highlights to generate new data. An important use case for the VAE model is anomaly detection. Telcos are exploring both these models to create foundational capabilities that go into building business applications.

4. Digital twins Synthetic data Churn modeling Digital twins simulate real-world systems and operations, optimizing predictive network management and energy usage. Example: China Telecom uses digital twins to visualize the effects of network changes on performance before implementation. Algorithms generate synthetic data to enhance machine learning training and testing, improving testing speed and security. Recent development: Vodafone tests synthetic data for its ML models in a new proof of concept. GenAI addresses customer churn by enhancing prediction accuracy and providing personalized insights, outperforming traditional models. Focus area: Essential for reducing dissatisfaction with services or pricing.

5. Navigating GenAI implementation challenges GenAI enhances data quality, reduces costs, and ensures regulatory compliance through automation. It integrates seamlessly with existing systems, addresses ethical concerns by promoting transparency, and builds customer trust with advanced security and personalized interactions. Challenges: Poor data quality High operating costs Compatibility issues with existing systems Regulatory compliance Ethical concerns Customer distrust

6. It’s about data Quality is key for effective AI outcomes, emphasizing accuracy, completeness, and consistency in structured and unstructured data. Addressing the crucial role of unstructured data, organizations need comprehensive practices to capture, cleanse, store, label, and classify data. GenAI aids in automating data governance tasks like labeling sensitive data, ensuring compliance with privacy laws and enhancing security against exposures. Integrating GenAI improves data management, ensuring high-quality and secure data essential for reliable AI operations.

7. Compatibility with existing systems Integrating AI tools with legacy systems can be complex and costly. Effective AI tools should blend seamlessly into current workflows, boosting performance and encouraging adoption. For successful implementation, telcos should choose tools based on speed, security, and scalability, train staff for optimal use, and start with small pilot projects to evaluate and expand. Example: Deutsche Telekom's Ask Magenta chatbot effectively combines a decision tree with an LLM, enhancing response accuracy—80% of its functions are decision-tree-based, while 20% utilize the LLM.

8. Addressing Costs in GenAI Implementation Leaders often misjudge GenAI costs by viewing it merely as an add-on, neglecting the extensive ongoing expenses for deployment and maintenance. Boston Consulting Group advises adopting a strategic pricing model—subscription-based, consumption-based, or outcome-based—tailored to align with an organization’s specific business objectives.

9. Strengthening customer trust Recent findings by EY indicate that 68% of telco leaders believe they are not effectively managing the unintended consequences of AI. This oversight often leads to increased risk exposures. Gartner's Advice: Implement a governance framework focused on Trust, Risk, and Security Management ( TRiSM ) for Gen AI technologies. Forrester's Recommendations: Prioritize addressing issues related to data leakage, lineage, observability, and privacy. Improve communication with customers by highlighting security measures in an accessible manner. Best Practice: Educate both employees and customers on the importance of rigorous security protocols, including the zero trust approach, to build and maintain trust.

10. Managing regulatory concerns AI is under increased regulatory scrutiny worldwide, which will impact telcos. The proposed EU AI Act suggests fines for violating guidelines on responsible and ethical AI, potentially reaching up to 6% of annual revenue for non-compliance. Telcos need to protect sensitive data to avoid non-compliance, using anonymization techniques like just-in-time anonymization or obfuscation. Bain & Company highlights that doing this well and quickly can build trust among consumers, employees, and investors, making ethical AI a competitive edge for telcos.

11. The road ahead Opportunity: GenAI is set to open new possibilities for telcos and enable them to differentiate themselves in a highly competitive market. Approach: Consider unique challenges and create a roadmap for sustained gains. Technology partners with deep expertise in AI and industry knowledge can extend the support telcos need to succeed in their GenAI journey.

12. Contact us Website: https://www.torryharris.com/ Email: [email protected]