Where is the value for telco in Generative AI?

As the saying goes, “if all you have is a hammer, everything looks like a nail”.

The current hype levels around generative AI make it hard to build a clear picture of where it can be used in a telco.  The diagram below builds a picture of 7 main capabilities (in the rows) and their uses across the telco (in the columns):


The 7 categories are:

  1. Content creation – the area currently generating most headlines with models such as ChatGPT.  Includes creating original content (eg. blog posts, product descriptions for marketing) and audio, image and video content creation.  Also, a range of other related capabilities such as translation and proofreading of text
  2. Human-machine – centred around improvements in natural language understanding and generation (NLU/NLG) providing better human-machine communications within chatbots, IVRs and digital*.  Also, improved accessibility and a number of Gen AI improvements to sentiment and emotion analysis
  3. Human-human – the improvements in digital assistants will be at its most important in the contact centre, helping agents reduce time and improve customer satisfaction when engaged in complex interactions.
  4. Knowledge management – a category relating to all telco catalogues and knowledge bases – for example, in contact centres or field services.  Gen AI capabilities will bring improvements from NLU/NLG (similar to chatbot functionality).  Also, the improvement of the knowledge base (gap analysis and summarisation of material).
  5. Process improvements – A catch-all category which includes the creation of code by Gen AI, its use in testing to generate test data and create specific test types such as adversarial testing.  There are also some use cases around the management of processes such as API call creation and the improvement of process documentation.
  6. Data management – this category includes the management of underlying data and activities such as governance.  The primary use cases today appear to be around the generation of synthetic data and the augmentation of existing data sets to improve quality; although, likely that more uses will appear over time.
  7. Intelligence improvements – Lastly, the use of Gen AI to improve the intelligence within a telco.  It is at its strongest, currently, in anomaly detection – but can also improve other models for prediction and personalization.