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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The value of analytics, automation and AI for telcos Part 1: The telco A3 application map

Getting to grips with A3

Almost every telco is at some stage of trying to apply analytics, artificial intelligence (AI) and automation (A3) across its organisation and extended value network to improve business results, efficiency and organisational agility.

However, most telcos have taken a fairly scatter-gun approach to deploying these three interrelating technologies, with limited alignment or collaboration across different parts of the business. To become more sophisticated in their adoption of A3, telcos need to develop a C-level plan to manage deployments, empower business units supporting A3 to efficiently deploy resources, and create cross-functional implementations of these technologies.

The first report in this two-part report series supports telcos in this aim through a high-level mapping of the application areas which can be developed by a telco. It illustrates the opportunities and forms the foundation of our ongoing research in A3.

In the second part of the series, we estimate the potential financial value of each of the A3 application areas for telcos. The follow up is now available here: A3 for telcos: Mapping the financial value 

This research builds on STL’s previous reports covering telcos’ early efforts in implementing analytics, AI and automation within specific parts of their operations, as well as benchmarking their progress globally:

Introducing the telco A3 application map

The first section of this report goes further into the use of different types of A3 in the Telco A3 applications map. Our analysis focuses in turn on the six types of problems that are being addressed and how automation, analytics and/or AI can provide solutions – and for which types of problems and in which parts of a telco’s business each of these three technologies can have the greatest impact.

Summarising the six types of problems A3 can help with:

  1. Making sense of complex data – using analytics and ML to identify patterns, diagnose problems and predict/prescribe resolutions
  2. Automating processes – where intelligent automation and RPA helps with decision making, orchestration and completing tasks within telco processes
  3. Personalising customer interactions – where analytics and ML can be used to understand customer data, create segmentation, identify triggers and prescribe actions
  4. Supporting business planning – where analytics and ML can be used in forecasting demand and optimising use of existing assets and future investments
  5. Augmenting human capabilities – this is where AI solutions such as natural language processing and text analytics are used to ‘understand’ and act on human intent or sentiment, or surface information to customers and employees more quickly
  6. Frontier AI solutions – cutting edge AI solutions which have specialist uses within a telco, but are not widely adopted yet

Following our analysis of the key application areas, we look at how A3 is used not only for the individual parts of the business illustrated in the map, but how more sophisticated implementations require significant integration and interdependencies between A3 solutions across multiple areas of a telco’s operations.

It should be noted that this two-part series only considers the application of A3 to telcos’ internal operations and we will consider both the external monetisation of such services and their use in telco products in follow-up reports.

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How telcos should use the A3 map

  • Innovation teams within the telco should consider plotting their existing and planned A3 activities on a map such as that shown below
  • This map should be presented to the board and also socialised within IT and support teams such as customer care. It can be used to describe current top-level focus areas and those which are more nascent but considered key in the short and medium-term
  • The map can also be shared with vendor partners and other interested external parties to ensure that they are aware of the company’s priorities.

Table of contents

  • Executive Summary
  • Introduction
  • The A3 problem/solution types
    • Type 1: Complex data uses A3 to conquer size and speed
    • Type 2: Automation to replace or augment human resources
    • Type 3: Personalisation uses algorithms to reveal what’s next
    • Type 4: Bringing optimisation and forecasting into planning
    • Type 5: Augmenting human capabilities focuses on chatbots
    • Type 6: Frontier AI solutions are the leading edge of the A3 future
  • Cross-type applications of A3
    • Concept 1: Sharing data between boxes using a data lake
    • Concept 2: The flow of data across different A3 application areas
  • Appendix 1: Further definition of applications by type
    • Type 1: Making sense of complex data
    • Type 2: Automating processes
    • Type 3: Personalising customer interactions
    • Type 4: Supporting business planning
    • Type 5: Augmenting human capabilities
    • Type 6: Frontier AI solutions
  • Appendix 2

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AI in customer services: It’s not all about chatbots

Introduction

Internet companies like Google, Apple and Amazon and second tier digital disruptors like Airbnb, Spotify and Netflix have been refining advanced machine learning-enabled analytics to offer increasingly personalised and predictive services. This means consumers increasingly expect a seamless, personalised experience, where service providers anticipate their needs, rather than react to problems after the fact.

If telcos want to regain credibility with consumers, they must develop more personalised and frictionless customer experiences. Many telcos believe that, as for digital native companies, artificial intelligence (AI) technologies can play a crucial role in achieving their goal to reduce costs while improving services.

In this report, we assess the potential value and feasibility of different types of AI applications in customer experience and customer care and outline how telecoms operators should prioritise their efforts. It is based on primary research at STL events, and conversations with telcos, vendors and other industry players. It also builds on previous reports on big data and AI:

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Defining AI: An evolution in analytics

In this and following reports, we are using AI as an all-encompassing term for advanced predictive analytics, based on machine learning technologies. Machine learning should be seen as a stage in the evolution of analytics, from basic data analysis, to big data analytics, to fully automated systems where algorithms continually learn from new data to deliver more efficient and effective responses and automated actions.

In our recent report, Big data analytics: Time to up the ante, we emphasized that as telcos have built up their big data programmes, they have gradually replaced siloed legacy infrastructure, where each department has their own databases and data sources, with a horizontally-integrated infrastructure across all departments. This shift enables telcos to run advanced analytics across much broader data sets and use machine learning algorithms to uncover new patterns and correlations across previously segregated data, often described as discovering ‘unknown unknowns’.

Within the field of machine learning, there are three types of algorithms:

  1. Supervised learning: an algorithm is trained on a large set of labelled data (e.g. photos of cats, or examples of spam mail). The algorithm learns to find patterns in the training data, which it then applies to new data samples to predict the correct answer (e.g. one email is spam, another is not). This is good for situations where historical data can predict future events, such as when a customer is likely to churn, or when a location is likely to see a peak in demand for connectivity.
  2. Unsupervised learning: an algorithm is applied to data without labels and with no specific goal, other than to find patterns or structure in the data. This is good for finding new ways to segment customers or detecting outliers, for example fraudulent activity, or an operator’s most and least profitable customers.
  3. Reinforcement learning: an algorithm is given a predefined goal and a set of allowed actions, and is then left to find the most effective way to achieve its goal through trial and error. This is most famously used in gaming, for example by Google’s AlphaGo Zero. Testing different marketing campaigns to achieve the best result is an example of a business application of reinforcement learning.

Deep learning (DL) is an extension of machine learning where there are many more layers in a neural network, allowing the system to work with much larger and more complex data sets, such as images, video, text and audio, in order to identify more subtle patterns.

In most application fields, ML and DL algorithms are not yet at the stage where they can teach themselves what to do, without any guidance and oversight from humans. Most algorithms are also trained for a specific purpose, so their applications are not easily transferrable. But even with these constraints, the benefits of AI for businesses are clear:

  • Self-improving: ML algorithms continuously learn from experience, either from inbuilt rewards systems or guidance from humans
  • High performing: AI can handle huge amounts of data and never sleeps
  • Human-like: it can interact with types of data previously indecipherable by software, enabling it to automate previously uniquely human tasks, e.g. natural language understanding, facial recognition.
  • Wide reaching: AI is suited to automation of both soft (e.g. chatbots, system control) and manual tasks (i.e. robotics)

Contents:

  • Executive Summary
  • Chatbots aren’t an easy win
  • Predictive care: an easier entry point, but with limits
  • So is AI in customer care worth it?
  • Introduction
  • Defining AI: An evolution in analytics
  • STL Partners’ AI Framework: Sizing the opportunity
  • Chatbots: Is it worth the work?
  • What is a chatbot?
  • Most chatbots are not ready to handle customers yet, and vice versa
  • Telenor: Taking an active role in the AI technology revolution
  • T-Mobile Austria (Deutsche Telekom): A more personalised customer experience
  • Recommendations
  • Predictive care & agent assist
  • AT&T is prioritising predictive care
  • Lots of room for improvement in handling customer calls
  • But predictive care can’t solve everything
  • Conclusions

Figures:

  • Figure 1: STL Partners’ AI Framework
  • Figure 2: Defining customer engagement categories
  • Figure 3: Customer experience is telcos’ main current priority with AI
  • Figure 4: The lifecycle of a chatbot/virtual assistant
  • Figure 5: Chatbots are a long way from meeting satisfaction levels of live agents
  • Figure 6: Telecoms operators say they lack the skills to deploy AI
  • Figure 7: Example of a rule-based versus AI-enabled chatbot in financial services
  • Figure 8: DT’s NLU and conversation flow selection and management process
  • Figure 9: Level of customer satisfaction with UK operators’ complaints resolution
  • Figure 10: Top reasons why customers complain to their service providers
  • Figure 11: A clear preference for predictive care
  • Figure 12: Estimate of telco opex breakdown

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