Telco A3: Skilling up for the long term

Telcos must master automation, analytics and AI (A3) skills to remain competitive

A3 will permeate all aspects of telcos’ and their customers’ operations, improving efficiency, customer experience, and the speed of innovation. Therefore, whether a telecoms operator is focused on its core connectivity business, or seeking to build new value beyond connectivity, developing widespread understanding of value of A3 and disseminating fundamental automation and AI skills across the organisation should be a core strategic goal. Our surveys on industry priorities suggest that operators recognise this need, and automation and AI are correspondingly rising up the agenda.

Expected technology priority change by organisation type, May 2020

technology investment priorities telecoms May 2020

*Updated January 2021 survey results will be published soon. Source: STL Partners survey, 222 respondents.

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Key findings on operators’ A3 strategies

Based on deep dive interviews with 8 telcos, as well as insights from 8 more telcos gathered from previous research programmes.

  • Less advanced telcos are creating a set of basic structures and procedures, as well as beginning to develop a single view of the customer
  • Having a single version of the truth appears to be an ongoing issue for all – alongside continued work on data quality
  • As full end-to-end automation is not a realistic goal for the next few years, interviewees were seeking to prioritise the right journeys to be automated in the short term
  • Reskilling and education of staff was an area of importance for many but not all
  • Just one company had less ambitious data-related aims due to the specialist nature of their services and smaller size of the company – saying that they worked with data on an as-needed basis and had no plans to develop dedicated data science headcount

Preparing for the future: There are four areas where A3 will impact telcos’ businesses

four A3 areas impacting telcos

Source: Charlotte Patrick Consult, STL Partners

In this report we outline the skills and capabilities telcos will need in order to navigate these changes. We break out these skills into four layers:

  1. The basic skillset: What operators need to remain competitive over the short term
  2. The next 5 years: The skills virtually all telcos will need to build or acquire to remain competitive in the medium term (exceptions include small or specialist telcos, or those in less competitive markets)
  3. The next 10 years: The skills and organisational changes telcos will need to achieve within a 10 year timeframe to remain competitive in the long term
  4. Beyond connectivity (5–10 year horizon): This includes A3 skills that telcos will need to be successful strategic partners for customers and suppliers, and to thrive in ecosystem business models. These will be essential for telcos seeking to play a coordination role in IoT, edge, or industry ecosystems.

Table of contents

  • Executive Summary
  • Telcos’ current strategic direction
    • Deep dive into 8 operator strategies
    • Overview of 8 more operator strategies
  • How A3 technologies are evolving
    • Deep dive into 40 A3 applications that will impact telcos’ businesses
    • Internal capabilities
    • Customer requirements
    • Technology changes
    • Organisational change
  • A timeline of telco A3 skills evolution
    • The basic skillset
    • The next 5 years
    • The next 10 years
    • Beyond connectivity

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Network AI: The state of the art

Introduction

This report is part of a series exploring how telecoms operators can leverage artificial intelligence (AI) to improve their business operations, from customer experience to new services. Previous reports on AI in telecoms include:

This report explores the applications of AI for network operations, detailing the prerequisites and stages to implementing AI and automation in networks, real-world examples of what some telcos have done already, and their potential value across different application areas.

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We divide the applications for AI in telecoms networks into three main categories:

  • Fault detection, prediction and resolution: speeding up the process of identifying and resolving network faults, including predictive maintenance. This also includes identifying and mitigating network security risks, although security is a highly specialised field that merits its own report, so we do not cover it in detail here.
  • Network optimisation: optimising the use of network resources to mitigate the impact of network faults and adapt to or anticipate changes in demand. This is also the foundation for automated service provisioning in software defined networks, while insights on network usage and traffic could be valuable for new service creation.
  • Network planning and upgrades: optimising new infrastructure planning as well as the transition from legacy to next generation network solutions.

The first area is critical for all telcos, since service impairments are an inevitable element of running a network. The second is of immediate value for telcos that are still in the process of expanding existing network coverage and density, since it can enable operators to use their existing resources more efficiently. However, it is also increasingly tied into the first area of fault detection, since a large part of the fault resolution process is finding ways to re-route traffic from underperforming to underused assets, a process that is made easier with the adoption of SDN and NFV – processes can only be automated if they are software-based.

Compared with the first two categories, using AI for smarter network planning and upgrades is a nascent field. This is partially because many Tier 1 operators, who are leading the charge in adoption of AI elsewhere in network and business operations, completed the bulk of 4G deployments and have not yet fully embarked on 5G deployments. However, this report also looks at some innovative applications of image recognition models for network expansion in emerging markets.

While most of the data used for training and informing AI systems across network operations comes from operators’ own networks, telcos are also beginning to tap into new data sources to further refine their decision-making, such as using drones and image recognition to inspect towers, weather patterns and social media data.

Laying the foundations for AI in telecoms networks

Before jumping into how telcos are implementing AI for fault detection and resolution and in network operations, it is important to clarify what we mean by AI, and lay out the pre-requisites for any meaningful use of the technology.

What counts as AI? From automation to advanced AI

The term AI is nebulous – everyone has a different definition for it. Is it when a computer can make a faster, more accurate decision than a human?  Is it when a process is fully automated? Is it when the computer learns and continuously improves its decisions in real-time?

Wherever people draw the line between manual processes, (big) data analytics, automation and machine learning (ML) / AI, no company goes directly from manual to AI in one go. The transition is gradual. In this report we therefore use a broad definition of AI in this report, as outlined in Figure 1.

Figure 1: Not all AI is equal

Rules-based automation to machine learning

Source: STL Partners

Two transitions are happening in parallel as operators move from left to right on Figure 1. First, there is a shift towards increasingly intelligent analytics techniques, from rules-based automation, where policies outline if-then sequences of actions for the computer, to machine learning supported automation, where models are trained to fulfil an intent (a goal) based on guidelines from experts and historical data.

The second transition that occurs in the move towards more sophisticated AI systems relates to decision-making. In rules-based automation, computers don’t have any decision-making power, they can only take pre-defined actions in specific circumstances. Making the transition from telling computers how to do something to what you want them to do means giving computers decision-making power. Telcos can do this gradually, by requiring humans to verify and approve recommended decisions before they are implemented. But in the promised future 5G and ‘sliceable’ networks, human approval for routine decisions would require more network engineers than operators could profitably employ, or drastically slow down network operations. This is not just a technical issue for telcos but also a cultural one that demands clarity from management teams on the evolving role of network engineers.

Contents:

  • Executive Summary
  • Making the shift from manual operations to autonomous, intelligent networks
  • Recommendations
  • Introduction
  • Laying the foundations for AI in telecoms networks
  • What counts as AI? From automation to advanced AI
  • AI works at two levels for network operations
  • Data: The bridge between rules-based automation and ML
  • Fault detection, prediction and resolution
  • What is it worth?
  • How does it work?
  • Real-world example of a recommendation model: AT&T Tower Outage and Network Analyzer
  • Next step: From fixed to self-learning policies
  • Optimising network capacity
  • What are self-optimising networks worth?
  • Use case overview
  • How to do it
  • From self-optimising to knowledge-defined networks
  • AI for network planning
  • Telefónica case study
  • Driving automation internally versus partnering with vendors
  • Reasons for developing solutions internally
  • Reasons for partnering with a vendor
  • Vendor profiles
  • How AI fits with SDN/NFV
  • Conclusions and recommendations

Figures:

  • Figure 1: Not all AI is equal
  • Figure 2: Rules-based automation versus machine learning
  • Figure 3: A snapshot of rules-based automation versus machine learning
  • Figure 4: Overview of automation and AI in network operations
  • Figure 5: Telemetry is faster and uses less compute power than SNMP
  • Figure 6: Elisa growth of automated trouble ticket handling
  • Figure 7: Tupl results for automatic customer complaints resolution AI platform
  • Figure 8: Implementing fixed policies for fault detection and resolution
  • Figure 9: Visualisation of network alert clustering tool
  • Figure 10: A self-healing network
  • Figure 11: Elisa self-optimising network results
  • Figure 12: Elisa maintained flat capex intensity throughout 4G deployment
  • Figure 13: Finland 4G network performance, August 2018
  • Figure 14: Self-organising network example use cases
  • Figure 15: Numerous applications of machine learning and AI for 5G networks
  • Figure 16: Break self-optimising networks down into mini loops
  • Figure 17: The knowledge-defined network
  • Figure 18: Facebook TCO savings over traditional multilayer planning
  • Figure 19: Telefónica image recognition for network planning
  • Figure 20: Ciena Blue Planet overview
  • Figure 21: Google SDN layers
  • Figure 22: Overview of cross-industry initiatives relating to network AI and automation
  • Figure 23: Telefónica network automation roadmap
  • Figure 24: Overview of SK Telecom Advanced Next Generation OSS (TANGO)

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Autonomous cars: Where’s the money for telcos?

Introduction

Connected cars have been around for about two decades. GM first launched its OnStar in-vehicle communications service in 1996. Although the vast majority of the 1.4 billion cars on the world’s roads still lack embedded cellular connectivity, there is growing demand from drivers for wireless safety and security features, and streamed entertainment and information services. Today, many people simply use their smartphones inside their cars to help them navigate, find local amenities and listen to music.

The falling cost of cellular connectivity and equipment is now making it increasingly cost-effective to equip vehicles with their own cellular modules and antenna to support emergency calls, navigation, vehicle diagnostics and pay-as-you-drive insurance. OnStar, which offers emergency, security, navigation, connections and vehicle manager services across GM’s various vehicle brands, says it now has more than 11 million customers in North America, Europe, China and South America. Moreover, as semi-autonomous cars begin to emerge from the labs, there is growing demand from vehicle manufacturers and technology companies for data on how people drive and the roads they are using. The recent STL Partners report, AI: How telcos can profit from deep learning, describes how companies can use real-world data to teach computers to perform everyday tasks, such as driving a car down a highway.

This report will explore the connected and autonomous vehicle market from telcos’ perspective, focusing on the role they can play in this sector and the business models they should adopt to make the most of the opportunity.

As STL Partners described in the report, The IoT ecosystem and four leading operators’ strategies, telcos are looking to provide more than just connectivity as they strive to monetise the Internet of Things. They are increasingly bundling connectivity with value-added services, such as security, authentication, billing, systems integration and data analytics. However, in the connected vehicle market, specialist technology companies, systems integrators and Internet players are also looking to provide many of the services being targeted by telcos.

Moreover, it is not yet clear to what extent the vehicles of the future will rely on cellular connectivity, rather than short-range wireless systems. Therefore, this report spends some time discussing different connectivity technologies that will enable connected and autonomous vehicles, before estimating the incremental revenues telcos may be able to earn and making some high-level recommendations on how to maximise this opportunity.

 

  • Executive Summary
  • The role of cellular connectivity
  • High level recommendations
  • Contents
  • Introduction
  • The evolution of connected cars
  • How to connect cars to cellular networks
  • What are the opportunities for telcos?
  • How much cellular connectivity do vehicles need?
  • Takeaways
  • The size of the opportunity
  • How much can telcos charge for in-vehicle connectivity?
  • How will vehicles use cellular connectivity?
  • Telco connected car case studies
  • Vodafone – far-sighted strategy
  • AT&T – building an enabling ecosystem
  • Orange – exploring new possibilities with network slicing
  • SoftBank – developing self-driving buses
  • Conclusions and Recommendations
  • High level recommendations
  • STL Partners and Telco 2.0: Change the Game 

 

  • Figure 1: Incremental annual revenue estimates by service
  • Figure 2: Autonomous vehicles will change how we use cars
  • Figure 3: Vehicles can harness connectivity in many different ways
  • Figure 4: V2X may require large numbers of simultaneous connections
  • Figure 5: Annual sales of connected vehicles are rising rapidly
  • Figure 6: Mobile connectivity in cars will grow quickly
  • Figure 7: Estimates of what telcos can charge for connected car services
  • Figure 8: Potential use cases for in-vehicle cellular connectivity
  • Figure 9: Connectivity complexity profile criteria
  • Figure 10: Infotainment connectivity complexity profile
  • Figure 11: In-vehicle infotainment services estimates
  • Figure 12: Real-time information connectivity complexity profile
  • Figure 13: Real-time information services estimates
  • Figure 14: The connectivity complexity profile for deep learning data
  • Figure 15: Collecting deep learning data services estimates
  • Figure 16: Insurance and rental services’ connectivity complexity profile
  • Figure 17: Pay-as-you-drive insurance and rental services estimates
  • Figure 18: Automated emergency calls’ connectivity complexity profile
  • Figure 19: Automated emergency calls estimates
  • Figure 20: Remote monitoring and control connectivity complexity profile
  • Figure 21: Remote monitoring and control of vehicle services estimates
  • Figure 22: Fleet management connectivity complexity profile
  • Figure 23: Fleet management services estimates
  • Figure 24: Vehicle diagnostics connectivity complexity profile
  • Figure 25: Vehicle diagnostics and maintenance services estimates
  • Figure 26: Inter-vehicle coordination connectivity complexity profile
  • Figure 27: Inter-vehicle coordination revenue estimates
  • Figure 28: Traffic management connectivity complexity profile
  • Figure 29: Traffic management revenue estimates
  • Figure 30: Vodafone Automotive is aiming to be global
  • Figure 31: Forecasts for incremental annual revenue increase by service

AI: How telcos can profit from deep learning

The enduring value of connected assets

In the digital economy, the old adage knowledge is power applies as much as ever. The ongoing advances in computing science mean that knowledge (in the form of insights gleaned from large volumes of detailed data) can increasingly be used to perform predictive analytics, enabling new services and cutting costs. At the same time, the widespread deployment of connected devices, appliances, machines and vehicles (the Internet of Things) now means enterprises can get their hands on granular real-time data, giving them a comprehensive and detailed picture of what is happening now and what is likely to happen next.

A handful of companies already have a very detailed picture of their markets thanks to far-sighted decisions to add connectivity to the products they sell. Komatsu, for example, uses its Komtrax system to track the activities of almost 430,000 bulldozers, dump-trucks and forklifts belonging to its customers. The Japan-based company has integrated monitoring technologies and connectivity into its construction and mining equipment since the late 1990s. Komatsu says the Komtrax system is standard equipment on “most Komatsu Tier-3 Construction machines” and on most small utility machines and backhoes.

Komatsu’s machines ship with GPS chips that can pinpoint their position, together with a unit that gathers engine data. They can then transmit the resulting data to a communication satellite, which relays that information to the Komtrax data centre.

The data captured by Komtrax (and other Internet of Things solutions) has value on multiple different levels:

  • It provides Komatsu with market intelligence
  • It enables Komatsu to offer value added services for customers
  • It gives detailed data on the global economy that can be used for computer modelling and to support the development of artificial intelligence

Market intelligence for Komatsu

For Komatsu, Komtrax provides valuable information about how its customers use its equipment, which can then be used to refine its R&D activities. Usage data can also help sales teams figure out which customers may need to upgrade or replace their equipment and when.

Komatsu’s sales and finance departments use the findings, for example, to offer trade-ins and sales of lighter machines where heavy ones are underused. Its leasing firm can also use the information to help find customers for its rental fleet.

Furthermore, Komatsu is linking market information directly with its production plants through Komtrax (see Figure 1). It says its factories “aggressively monitor and analyse the conditions of machine operation and abrasion of components” to enable Komatsu and its distributors to improve operations by better predicting the lifetime of parts and the best time for overhauls.

Figure 1: How Komatsu uses data captured by its customers’ equipment

Source: Komatsu slide adapted by STL Partners

Value added services for customers

The Komtrax system can also flag up useful information for Komatsu’s customers. Komatsu enables its customers to access the information captured by their machines’ onboard units, via an Internet connection to the Komtrax data centre.

Customers can use this data to monitor how their machines are being used by their employees. For example, it can show how long individual machines are sitting idle and how much fuel they are using. Komatsu Australia, for example, says Komtrax enables its customers to track a wide range of performance indicators, including:

  • Location
  • Operation map (times of day the engine was on/off)
  • Actual fuel consumptionAverage hourly fuel consumption
  • Residual fuel level
  • High water temperature during the day’s operation
  • Dashboard cautions
  • Maintenance reminders/notifications
  • “Night Time” lock
  • Calendar lock
  • Out of Area alerts
  • Movement generated position reports
  • Actual working hours (engine on time less idle time)
  • Operation hours in each work mode (economy, power, breaker, lifting)
  • Digging hours
  • Hoisting hours
  • Travel hours
  • Hydraulic relief hours
  • Eco-mode usage hours
  • Load frequency (hours spent in four different load levels determined by pump pressures or engine torque)

 

Content:

  • Introduction
  • Executive Summary
  • The enduring value of connected assets
  • Tapping telecoms networks
  • Enabling Deep Neural Networks
  • Real world data: the raw material
  • Learning from Tesla
  • The role of telcos
  • Conclusions and Recommendations

Figures:

  • Figure 1: How Komatsu uses data captured by its customers’ equipment
  • Figure 2: Interest in deep learning has risen rapidly in the past two years
  • Figure 3: Deep learning buzz has helped drive up Nvidia’s share price
  • Figure 4: The key players in the development of deep learning technology
  • Figure 5: Mainstream enterprises are exploring deep learning
  • Figure 6: The automotive sector is embracing Nvidia’s artificial intelligence
  • Figure 7: Google Photos learns when users correct mistakes
  • Figure 8: Tesla’s Autopilot system uses models to make decisions
  • Figure 9: Tesla is collecting very detailed data on how to drive the world’s roads