
Transforming Telco: AI in Telecommunications
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
The worldwide conversational AI market is set to be worth $45.5 billion by 2030 according to research from McKinsey & Well. But what are the reasons behind such a high demand?
In this guide I explore the benefits and different business use cases for intelligent generative AI based chatbots, one of the main types of solution using conversational AI.
Enterprises in most industries are already using chatbots for a wide range of scenarios. For example:
Every industry can benefit from adopting GenAI, according to PwC. In some sectors, the potential increase in operating profit margin is almost 20%.
GenAI-driven conversational e-commerce is already having a real impact for businesses adopting it. According to BCG:
Chatbots can understand and respond to text or speech. By learning from conversations and complicated queries over time and drawing on proprietary information from knowledge bases, a well-trained model can provide a strong user experience.
However, many simple chatbots fail to meet expectations. Without rigorous planning, design, and knowledge-base implementation basic chatbots can misinterpret user needs, overlook the context, and fail to provide empathetic responses. Such chatbots decrease satisfaction levels and fall short of enterprise-specific requirements.
Therefore, it’s crucial to create chatbots that understand user intent. They must provide empathetic, personalized support – tailoring interactions to each user’s unique needs.
High-quality conversational AI solutions use machine learning, natural language processing (NLP) and foundation models to accurately replicate the qualities of regular conversations.
By leveraging GenAI, it can understand complex queries and adapt over time to a user’s style of speech. It can even convey emotion – for example, striking an appropriate empathetic tone for sensitive topics.
Whereas pre-GenAI solutions required programming in advance with answers to anticipated questions, the latest models can produce fresh content. Outputs can include a combination of text, image, and sound, further replicating natural interactions.
Such a solution can handle multiple languages and translate content into other languages, making it suitable for multi-market use.
It uses retrieval-augmented generation (RAG) and large language models (LLMs). But implementing a solution that uses RAG is not enough alone – finding relevant information from a company’s knowledge base helps ensure accurate, informed responses.
Furthermore, this type of solution can perform analysis tasks – summarizing content and making logical predictions. Over time, the model continues to learn and improve automatically, using algorithms to incorporate learnings from previous interactions.
By 2025, most business executives – 85% – say their customers will interact with GenAI, according to IBM.
There are many different scenarios where chatbots can provide efficiency through automation. Here are some of the most popular examples:
Let’s take a look at some practical examples of chatbots providing benefits for businesses:
Neurons Lab provided Solar Manager with a solution that leverages Anthropic Claude 3.5 architecture to increase customer support team productivity. The objective was to implement a chatbot assistant on AWS infrastructure. This initiative aimed to streamline support ticket responses, improve efficiency, and maintain high customer satisfaction.
We connected Solar Manager’s data and knowledge base to the architecture, ensuring seamless integration and AI validation. Our solution demonstrated the capabilities of GenAI and machine learning in optimizing customer support operations.
Results for the Solar Manager customer support team included:
Xauen offers a virtual CISO (Chief Information Security Officer) evolving Laguun platform. As part of this, users fill in a questionnaire about their company’s security practices, with around 80 questions.
Xauen aimed to innovate how companies assess their security by leveraging AI for a conversational security assessment chatbot. The chatbot needed to offer an intuitive, human-like interaction experience to help CISOs evaluate their company’s security practices.
Neurons Lab developed an AI-powered conversational chatbot that transformed Xauen’s cybersecurity questionnaire into an interactive text and voice-based assessment. The chatbot provides a seamless and engaging user experience while ensuring thorough coverage of all security aspects.
We used the following AWS architecture to achieve this:
Integration with Deepgram and Amazon Polly enables voice-to-text and text-to-voice functionality.
Developed by Neurons Lab during a tight timeframe of only a few weeks, the innovative chatbot was ready in time for an AWS Summit, where Xauen successfully showcased it.
High chatbot response accuracy was essential for ensuring users received reliable and relevant information throughout the assessment process. Low latency was crucial for providing a smooth and engaging user experience.
By minimizing the time users wait for the chatbot to respond, Xauen can ensure that the assessment process remains interactive and efficient.
Consistently meeting the latency target will help to maintain user satisfaction and encourage the adoption of the chatbot for cybersecurity assessments.
More info: Building an innovative voice and text-based cybersecurity chatbot for Xauen
The tournament organizer needed to improve digital engagement and the customer experience with a generative AI-powered chatbot, requiring a partner able to:
The project began only six weeks before the tournament started, so the delivery deadline for a ready-to-launch chatbot was very tight.
The project vision emerged from the following business challenges:
Neurons Lab created a chatbot that uses GenAI powered by AWS Bedrock in just six weeks. Key features include:
The final product, delivered in record time, was a GenAI-based chatbot capable of:
More info: Enhancing the customer experience for a Top-10 tennis tournament with an advanced AI chatbot
Without thorough development, chatbots can struggle to accurately understand user requirements. But with preparation in planning, design, and knowledge integration, chatbots like the ones detailed above can be very advanced.
It’s essential to develop chatbots capable of accurately understanding the intentions of different customers or users. AI assistants should offer empathetic, personalized support by accurately tailoring their responses to address each user’s specific requirements:
The use cases are both customer-facing and internal, with wide-ranging efficiency benefits for enterprise employees across all industries.
Neurons Lab delivers AI transformation services to guide enterprises into the new era of AI. Our approach covers the complete AI spectrum, combining leadership alignment with technology integration to deliver measurable outcomes.
As an AWS Advanced Partner and GenAI competency holder, we have successfully delivered tailored AI solutions to over 100 clients, including Fortune 500 companies and governmental organizations.
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
We explore advanced attack techniques against LLMs, then explain how to mitigate these risks using external AI guardrails and safety procedures.
We cover some of the most common potential types of attacks on LLMs, explaining how to mitigate the risks with security measures and safety-first principles.
The recently released SWARM framework offers a simple yet powerful solution for creating an agent orchestration layer. Here is a telco industry example.
Traditional chatbots don't work due to their factual inconsistency and basic conversational skills. Our co-founder and CTO Alex Honchar explains how we use AI agent architecture instead.