Tag: Virtual assistant

A3 for enterprise: Where should telcos focus?

A3 capabilities operators can offer enterprise customers

In this research we explore the potential enterprise solutions leveraging analytics, AI and automation (A3) that telcos can offer their enterprise customers. Our research builds on a previous STL Partners report Telco data monetisation: What’s it worth? which modelled the financial opportunity for telco data monetisation – i.e. purely the machine learning (ML) and analytics component of A3 – for 200+ use cases across 13 verticals.

In this report, we expand our analysis to include the importance of different types of AI and automation in implementing the 200+ use cases for enterprises and assess the feasibility for telcos to acquire and integrate those capabilities into their enterprise services.

We identified eight different types of A3 capabilities required to implement our 200+ use cases.

These capability types are organised below roughly in order of the number of use cases for which they are relevant (i.e. people analytics is required in the most use cases, and human learning is needed in the fewest).

The ninth category, Data provision, does not actually require any AI or automation skills beyond ML for data management, so we include it in the list primarily because it remains an opportunity for telcos that do not develop additional A3 capabilities for enterprise.

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Most relevant A3 capabilities across 200+ use cases

9-types-of-A3-analytics-AI-automation

Most relevant A3 capabilities for leveraging enterprise solutions

People analytics: This is the strongest opportunity for telcos as it uses their comprehensive customer data. Analytics and machine learning are required for segmentation and personalisation of messaging or action. Any telco with a statistically-relevant market share can create products – although specialist sales capabilities are still essential.

IoT analytics: Although telcos offering IoT products do not immediately have access to the payload data from devices, the largest telcos are offering a range of products which use analytics/ML to detect patterns or spot anomalies from connected sensors and other devices.

Other analytics: Similar to IoT, the majority of other analytics A3 use cases are around pattern or anomaly detection, where integration of telco data can increase the accuracy and success of A3 solutions. Many of the use cases here are very specific to the vertical. For example, risk management in financial services or tracking of electronic prescriptions in healthcare – which means that a telco will need to have existing products and sales capability in these verticals to make it worthwhile adding in new analytics or ML capabilities.

Real time: These use cases mainly need A3 to understand and act on triggers coming from customer behaviour and have mixed appeal to telcos. Telcos already play a significant role in a small number of uses cases, such as mobile marketing. Some telcos are also active in less mature use cases such as patient messaging in healthcare settings (e.g. real-time reminders to take medication or remote monitoring of vulnerable adults). Of the rest of the use cases that require real time automation, a subset could be enhanced with messaging. This would primarily be attractive to mobile operators, especially if they offer broader relevant enterprise solutions – for example, if a telco was involved in a connected public transport solution, then it could also offer passenger messaging.

Remote monitoring/control: Solutions track both things and people and use A3 to spot issues, do diagnostic analysis and prescribe solutions to the problems identified. The larger telcos already have solutions in some verticals, and 5G may bring more opportunities, such as monitoring of remote sites or traffic congestion monitoring.

Video analytics: Where telcos have CCTV implementations or video, there is opportunity to add in analytics solutions (potentially at the edge).

Human interactions: The majority of telco opportunities here relate to the provision of chatbots into enterprise contact centres.

Human learning: A group of low feasibility use cases around training (for example, an engineer on a manufacturing floor who uses a heads-up augmented/virtual reality (AR/VR) display to understand the resolution to a problem in front of them) or information provision (for example, providing retail customers with information via AR applications).

 

Table of Contents

  • Executive Summary
    • Which A3 capabilities should telcos prioritise?
    • What makes an investment worthwhile?
    • Next steps
  • Introduction
  • Vertical opportunities
    • Key takeaways
  • A3 technology: Where should telcos focus?
    • Key takeaways
    • Assessing the telco opportunity for nine A3 capabilities
  • Verizon case study
  • Details of vertical opportunities
  • Conclusion
  • Appendix 1 – full list of 200 use cases

 

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AI is starting to pay: Time to scale adoption

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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.

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

AI pays off

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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AI on the Smartphone: What telcos should do

Introduction

Following huge advances in machine learning and the falling cost of cloud storage over the last several years, artificial intelligence (AI) technologies are now affordable and accessible to almost any company. The next stage of the AI race is bringing neural networks to mobile devices. This will radically change the way people use smartphones, as voice assistants morph into proactive virtual assistants and augmented reality is integrated into everyday activities, in turn changing the way smartphones use telecoms networks.

Besides implications for data traffic, easy access to machine learning through APIs and software development kits gives telcos an opportunity to improve their smartphone apps, communications services, entertainment and financial services, by customising offers to individual customer preferences.

The leading consumer-facing AI developers – Google, Apple, Facebook and Amazon – are in an arms race to attract developers and partners to their platforms, in order to further refine their algorithms with more data on user behaviours. There may be opportunities for telcos to share their data with one of these players to develop better AI models, but any partnership must be carefully weighed, as all four AI players are eyeing up communications as a valuable addition to their arsenal.

In this report we explore how Google, Apple, Facebook and Amazon are adapting their AI models for smartphones, how this will change usage patterns and consumer expectations, and what this means for telcos. It is the first in a series of reports exploring what AI means for telcos and how they can leverage it to improve their services, network operations and customer experience.

Contents:

  • Executive Summary
  • Smartphones are the key to more personalised services
  • Implications for telcos
  • Introduction
  • Defining artificial intelligence
  • Moving AI from the cloud to smartphones
  • Why move AI to the smartphone?
  • How to move AI to the smartphone?
  • How much machine learning can smartphones really handle?
  • Our smartphones ‘know’ a lot about us
  • Smartphone sensors and the data they mine
  • What services will all this data power?
  • The privacy question – balancing on-device and the cloud
  • SWOT Analysis: Google, Apple, Facebook and Amazon
  • Implications for telcos

Figures:

  • Figure 1: How smartphones can use and improve AI models
  • Figure 2: Explaining artificial intelligence terminology
  • Figure 3: How machine learning algorithms see images
  • Figure 4: How smartphones can use and improve AI models
  • Figure 5: Google Translate works in real-time through smartphone cameras
  • Figure 6: Google Lens in action
  • Figure 7: AR applications of Facebook’s image segmentation technology
  • Figure 8: Comparison of the leading voice assistants
  • Figure 9: Explanation of Federated Learning