GENERATIVE AI IN TELCO

AI Adoption in India

Below diagram shows the AI Adoption in India ,From TMT Sector 50% of Industries already implemented AI in their projects , 25% not using , but plan to use in 12 to 24 months & 25% not using but actively exploring the use cases .

A Pragmatic Approach to Generative AI

Telecom executives face rising costs, margin pressures, fierce competition, and disruptive technologies. Communication service providers (CSPs) that can expand their networks, innovate business models, and adopt new technologies while controlling expenses will generate value for stakeholders. The emergence of generative AI presents an opportunity for forward-thinking telecom executives to progress toward these goals

Promising Telecoms Use Cases

Early engagements with telecoms and AWS ecosystem partners have demonstrated how generative AI can reduce revenue leakage and customer churn, improve cross-selling attach rates, decrease customer support resolution times, and streamline network operations while reducing troubleshooting time.

Generative AI enables staff to focus on higher value tasks by automating repetitive tasks such as information searching and discovery, planning and summarization, and report and content generation. By improving job satisfaction, generative AI services support employee retention

The Role of Cloud in Telecom and AI

The cloud has revolutionized telecoms.

First, transitioning from hardware-based network appliances to software applications on cloud infrastructure offers operational simplicity and reduced ownership costs.

Second, operators can monetize assets and kickstart new business opportunities by integrating cloud at the network edge for enterprise applications and creating private networks for industries.

Third, cloud provides the computing, storage, and services to transform valuable telecom data into actionable insights. AI layered on top of a well-managed and modern data platform, can help operators develop new services, improve operations, and enhance customer experiences

Generative AI: A Breakthrough in AI

Generative AI marks an advancement in AI and machine learning (ML), enabled by the affordability of large-scale computing, the availability of extensive data corpora, and the innovative Transformer Architecture. Unlike traditional approaches focused on prediction, classification, or optimization, generative AI, powered by versatile pre-trained foundation models, can create content

The proven value of foundation models, including large language models (LLMs), positions generative AI as a strategic opportunity for telecoms. Quick wins can demonstrate its value, secure leadership commitment, and motivate teams to expand AI usage.

Practicing Responsible AI

Generative AI presents challenges around security, privacy, and ethics. Telecommunication operators are regulated and choosing a trusted cloud partner committed to comprehensive security across physical infrastructure, hardware, software, people, and processes is crucial. Operators should also establish system safeguards, encrypt data, and restrict model access.

To prevent privacy violations, operators must implement strict controls and anonymize customer data for training or fine-tuning. They should also monitor outputs to prevent the leak of sensitive information.

Additionally, deploying tools to ensure accuracy, promote fairness, and reduce biases is essential. Any potentially inaccurate, problematic, or suspicious outputs should be flagged for human review.

Furthermore, operators should support transparency by documenting their methodology, training data sources, and incorporating explain ability. With proper policies, processes, and tools, operators can innovate safely with generative AI.

Paths to Leveraging Generative AI

Operators will consume AI through productivity applications and enterprise software, but the greatest potential value comes from combining private telecom data with generative AI. To achieve this, operators are pursuing multiple approaches:

? Pre-Trained Models: Incorporating private data in prompt contexts for pre-trained foundation models is the quickest way to achieve results without extensive customization.

? Fine-tuning Models: Adapting pre-trained models to telecom use cases by fine-tuning with private data offers a balance between customization and speed, potentially yielding higher accuracy and performance.

? Custom Model Training: Training custom foundation models from scratch using large telecom datasets can provide proprietary capabilities and maximize competitive advantage, but it requires significant time and investment.

We recommend starting with pre-trained models and fine-tuning before embarking on building models. Operators should evolve their approach based on use case requirements, team capabilities, and the maturity of their data modernization efforts.

Telco Use Cases on AWS

1. Customer Service:

Generative AI facilitates quick summarization of past interactions, real time sentiment analysis, and next best action recommendations. This leads to reduced call times and enhanced customer satisfaction.

Quickly analyze and leverage customer data from multiple channels to offer personalized experiences and offers, respond to customer issues, and prevent churn

Benefits:

AI-driven contact centers by reducing issue resolution times and alleviating the cognitive load on call center agents. Additionally, it can enhance cybersecurity by detecting and blocking phishing and spam attempts. Telco specific LLM can also support the development of personal AI assistants capable of transcribing calls and identifying specific actions.

The generative AI-enabled solution delivers real-time churn prediction, customer journey segmentation, next-best experience prediction, personalized communications and marketing, and human-like chatbots and to strengthen customer relationships, engage meaningfully, and increase customer lifetime value while generating incremental value through collaboration between customer care and marketing

1. Field Operations :Utilizing generative AI for troubleshooting, integrated with knowledge bases and technical documentation, reduces the mean time to resolve faults. This improves engineer productivity both in the network operations center and in the field.

Benefits:

Enhance operations and design staff productivity by 30%, reduce operational outages by 10%, and decrease mean time to resolve network issues. By leveraging generative AI with AWS services and reducing the time required to create MOPs(Method Of Procedures) from hours to minutes and providing real-time troubleshooting support during of-hours.?

• Data Operations: Generative AI aids in data analysis, content classification, and data labeling, streamlining the management of diverse and complex data sets within telcos

Benefits

Search for column descriptions without needing privileged access, unlocking the value of these previously inaccessible digital assets, saving significant time and effort.?

• Network Operations: Generative AI, enhanced with data feeds, log files, network traces, and ticket information, can accurately identify network issues and provide root cause suggestions. This accelerates the restoration of services.

Benefits

Dramatic reduction in problem resolution times and increased visibility into network issues, resulting in a decrease in calls, chats, and truck rolls. Furthermore, digital adoption has been boosted, with the self-service success rate across digital channels and support calls showing marked improvement.

Accelerating Telecom Generative AI Adoption through Cloud

Recognizing and adapting to technological shifts is vital, especially in the traditionally conservative yet societally impactful telecom industry. Leaders must understand the substantial change generative AI represents and respond strategically. This involves upskilling staff, recognizing the potential of generative AI, and understanding the capabilities provided by cloud partners like AWS.

Strategic partnerships with foundation model developers and cloud companies like AWS and industry bodies that foster collaboration, such as TM Forum, are vital. The collaboration can accelerate the identification of practical use cases and speed up organization onboarding.

Lastly, adopting a new mindset is necessary. As with cloud adoption, this must permeate the entire organization, from executives to engineers. This workforce transformation is as crucial as the technological shift in ensuring the telecom sector

Conclusion

Generative AI can be a game-changer for telcos, improving customer experiences, minimizing churn, speeding up customer acquisition, reducing operational expenses, and driving innovation