Tag: IVR

Gen AI: Where should telcos start?

The key Gen AI concepts

ChatGPT defines Generative AI as:

“a class of artificial intelligence models and algorithms that have the capability to generate new, original content. Unlike traditional AI models that operate based on predefined rules or patterns, generative AI models can produce novel data that resembles the patterns observed in the training data. Generative AI models can generate content in various domains, such as natural language, images, music, and more”.

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The diagram below describes the range of models, concepts and uses that are seen in discussions around Gen AI.

• Blue boxes describe the main models, architectures and concepts that underpin various Gen AI capabilities (e.g., large language models).

• Orange boxes describe the general capabilities of these models (e.g., natural language generation).

• Grey arrows show the main models used to create capabilities in an orange box, and smaller black arrows show where other models can also be used (e.g., diffusion models provide image generation capabilities).

• Red text gives some of the uses made of the capabilities shown in the yellow boxes (e.g., generation of novel text).

• Red boxes highlight some of the popular foundational models for these uses (e.g., ChatGPT).

Concepts in Gen AI

Source: Charlotte Patrick Consult

Definitions of terms in the graphic:

• Generative models create something new based on examples they are given.

• Foundational models introduce a significant breakthrough, a new architecture or a novel approach that paves the way for subsequent advancements in the field.

• Parallel dataset is a data set which provides exact translations of all words in one language to the other.

• Discriminative model is a type of machine learning or statistical model that classifies input data points into different categories or classes.

• GPT (Generative Pre-trained Transformer) is a foundational model which can generate text responses.

• LaMDA is a Google project to provide a language model designed to allow more free-flowing conversations.

• dALLe is an OpenAI system that creates realistic images and art from natural language.

• Whisper is an automatic speech recognition system with improved ability to understand accents, technical language and background noise.

• BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based language model for text generation.

• PaLM is a Google model that works on advanced reasoning tasks including code, mathematical problems, classification and question answering.

Table of contents

  • Executive Summary
    • After the hype of Generative AI (Gen AI)
    • The most compelling uses for Gen AI in telcos
    • How to scope a Gen AI project
    • 4 core recommendations
    • Next steps
  • Introduction
  • What does Gen AI bring to the telco?
  • Gen AI use cases in a telco
    • 1. Content creation
    • 2. Human-machine interactions
    • 3. Human-human interactions
    • 4. Knowledge management
    • 5. Process improvements
    • 6. Data management
    • 7. Intelligence improvements
  • Where is the value in Gen AI?
    • Important types of Gen AI
    • Use cases for Gen AI
  • Conclusion
  • Index

Related research

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A3 in customer experience: Possibilities for personalisation

The value of A3 in customer experience

This report considers the financial value to a telco of using A3 technologies (analytics, automation and AI) to improve customer experience. It examines the key area which underpins much of this financial value – customer support channels – considering the trends in this area and how the area might change in future, shaping the requirement for A3.

Calculating the value of improving customer experience is complex: it can be difficult to identify the specific action that improved a customer’s perception of their experience, and then to assess the impact of this improvement on their subsequent behaviour.

While it is difficult to draw causal links between telcos’ A3 activities and customer perceptions and behaviours, there are still some clearly measurable financial benefits from these investments. We estimate this value by leveraging our broader analysis of the financial value of A3 in telecoms, and then zooming in on the specific pockets of value which relate to improved customer experience (e.g. churn reduction).

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The diagram below illustrates that there are two parts of the customer journey where A3 will add most value to customer experience:

  1. The performance of the network, services, devices and applications is increasingly dependent on automation and intelligence, with the introduction of 5G and cloud-native operations. Without A3 capabilities it will be difficult to meet quality of service standards, understand customer-affecting issues and turn up new services at speed.
  2. The contact centre remains one of the largest influencers of customer experience and one of the biggest users of automation, with the digital channels increasing in importance during the pandemic. Understanding the customer and the agent’s needs and providing information about issues the customer is experiencing to both parties are areas where more A3 should be used in future.

Where is the financial benefit of adding A3 within a typical telco customer journey?

A3 customer experience

Source: STL Partners, Charlotte Patrick Consult

As per this diagram, many of the most valuable uses for A3 are in the contact centre and digital channels. Improvements in customer experience will be tied with trends in both. These priority trends and potential A3 solutions are outlined the following two tables:
• The first shows contact centre priorities,
• The second shows priorities for the digital channels.

Priorities in the contact centre

A3 Contact centre

Priorities in the digital channel

A3 Digital channel

Table of Contents

  • Executive Summary
  • The value of A3 in customer experience
  • Use of A3 to improve customer experience
  • The most important uses of A3 for improving the customer experience
    • Complex data
    • Personalisation
    • Planning
    • Human-machine interaction
    • AI point solution
  • Conclusion
  • Appendix: Methodology for calculating financial value
  • Index

Related Research:

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