AI is starting to pay: Time to scale adoption

AI, coupled with a data-centric approach and automation, looks like it is starting to pay back the operators who have led in this field. Where can industry leaders go next, and what are the key lessons for others on how to ‘jump the curve’?

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AI adoption yields positive results

Over the last five years, telcos have made measurable progress in AI adoption and it is starting to pay off.  When compared to all industries, telcos have become adept at handling large data sets and implementing automation. Over the last several years the telecoms industry has gone from not knowing where or how to implement AI, to having developed and implemented hundreds of AI and automation applications for network operations, fraud prevention, customer channel management, and sales and marketing. We have discussed these use cases and operator strategies and opportunities in detail in previous reports.

• Network AI: The state of the art
• Telco AI: How to organise and partner for maximum success
• The value of analytics, automation and AI for telcos: The telco A3 application map
• A3 for telcos: Mapping the financial value
• Telco data monetisation: What’s it worth?
• The future of assurance: How to deliver quality of service at the edge

For the more advanced telcos, the challenge is no longer setting up data management platforms and systems and identifying promising use cases for AI and automation, but overcoming the organisational and cultural barriers to becoming truly data-centric in mindset, processes and operations. A significant part of this challenge includes disseminating AI adoption and expertise of these technologies and associated skills to the wider organisation, beyond a centralised AI team.The benchmark for success here is not other telcos, or companies in other industries with large legacy and physical assets, but digital- and cloud-native companies that have been established with a data-centric mindset and practices from the start. This includes global technology companies like Microsoft, Google and Amazon, who increasingly see telecoms operators as customers, or perhaps even competitors one day, as well as greenfield players such as Rakuten, Jio and DISH, which as well as more modern networks have fewer ingrained legacy processes and cultural practices to overcome.

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Telecoms has a high AI adoption rate compared with other industries

Source: McKinsey

In this report, we assess several telcos’ approach to AI and the results they have achieved so far, and draw some lessons on what kind of strategy and ambition leads to better results. In the second section of the report, we explore in more detail the concrete steps telcos can take to help accelerate and scale the use of AI and automation across the organisation, in the hopes of becoming more data-driven businesses.

While not all telcos have an ambition to drive new revenue growth through development of their own IP in AI, to form the basis of new enterprise or consumer services, all operators will need AI to permeate their internal processes to compete effectively in the long term. Therefore, whatever the level ambition, disseminating fundamental AI and data skills across the organisation is crucial to long term success. STL Partners believes that the sooner telcos can master these skills, the higher their chances of successfully applying them to drive innovation both in core connectivity and new services higher up the value chain.

Contents

• Executive Summary
• Introduction
• Developing an AI strategy: What is it for?
  • Telefónica: From AURA and LUCA to Telefónica Tech
  • Vodafone: An efficiency focused strategy
  • Elisa: A vertical application approach
  • Takeaways: Comparing three approaches
• AI maturity progression
  • Adopt big data analytics: The basic building blocks
  • Creating a centralised AI unit
  • Creating a new business unit
  • Disseminating AI across the organisation
• Using partnerships to accelerate and scale AI
  • O2 and Cardinality
  • AT&T Acumos
• Conclusion and recommendations
• Index

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