How telcos can make the world a safer place

Telecoms networks can support public safety

In the wake of the pandemic and multiple natural disasters, such as fire and flooding, both policymakers and people in general are placing a greater focus on preserving health and ensuring public safety. This report begins by explaining the concept of a digital nervous system – large numbers of connected sensors that can monitor events in real-time and thereby alert organizations and individuals to imminent threats to their health and safety.

With the advent of 5G, STL Partners believes telcos have a broad opportunity to help coordinate better use of the world’s resources and assets, as outlined in the report: The Coordination Age: A third age of telecoms. The application of reliable and ubiquitous connectivity to enable governments, companies and individuals to live in a safer world is one way in which operators can contribute to the Coordination Age.

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The chapters in this report consider the potential to use the data collected by telecoms networks to help counter the health and safety threats posed by:

  • Environmental factors, such as air pollution and high-levels of pollen,
  • Natural disasters, such as wildfires, flooding and earthquakes,
  • Infectious diseases
  • Violence, such as riots and shooting incidents
  • Accidents on roads, rivers and coastlines

In each case, the report considers how to harness new data collected by connected sensors, cameras and other monitors, in addition to data already captured by mobile networks (showing where people are and where they are moving to).  It also identifies who telcos will need to work with to develop and deploy such solutions, while discussing potential revenue streams.  In most cases, the report includes short case studies describing how telcos are trialling or deploying actual solutions, generally in partnership with other stakeholders.

The final chapter focuses on the role of telcos – the assets and the capabilities they have to improve health and safety.

It builds on previous STL Partners research including:

Managing an unstable world

Prior to the damage wrought by the pandemic, the world was gradually becoming a safer place for human beings. Global life expectancy has been rising steadily for many decades and the UN expects that trend to continue, albeit at a slower pace. That implies the world is safer than it was in the twentieth century and people are healthier than they used to be.

Global gains in life expectancy are slowing down

health and safety

Source: United Nations – World Population Prospects

But a succession of pandemics, more extreme weather events and rising pollution may yet reverse these positive trends. Indeed, many people now feel that they live in an increasingly unstable and dangerous world. Air pollution and over-crowding are worsening the health impact of respiratory conditions and infections, such as SARS-CoV-2. As climate change accelerates, experts expect an increase in flash flooding, wildfires, drought and intense heat. As extreme weather impacts the food and water supplies, civil unrest and even armed conflict could follow. In the modern world, the four horsemen of the apocalypse might symbolize infectious disease, extreme weather, pollution and violence.

As the human race grapples with these challenges, there is growing interest in services and technologies that could make the world a safer and healthier place. That demand is apparent among both individuals (hence the strong sales of wearable fitness monitors) and among public sector bodies’ rising interest in environment and crowd monitoring solutions.

As prevention is better than cure, both citizens and organisations are looking for early warning systems that can help them prepare for threats and take mitigating actions. For example, an individual with an underlying health condition could benefit from a service that warns them when they are approaching an area with poor air quality or large numbers of densely-packed people. Similarly, a municipality would welcome a solution that alerts them when large numbers of people are gathering in a public space or drains are close to being blocked or are overflowing.  The development of these kinds of early warning systems would involve tracking both events and people in real-time to detect patterns that signal a potential hazard or disruption, such as a riot or flooding.

Advances in artificial intelligence (AI), as well as the falling cost of cameras and other sensors, together with the rollout of increasingly dense telecoms networks, could make such systems viable. For example, a camera mounted on a lamppost could use image and audio recognition technologies to detect when a crowd is gathering in the locality, a gun has been fired, a drain has been flooded or an accident has occurred.

Many connected sensors and cameras, of course, won’t be in a fixed location – they will be attached to drones, vehicles and even bicycles, to support use cases where mobility will enhance the service. Such uses cases could include air quality monitoring, wildfire and flooding surveillance, and search and rescue.

Marty Sprinzen, CEO of Vantiq (a provider of event-driven, real-time collaborative applications) believes telecoms companies are best positioned to create a “global digital nervous system” as they have the networks and managed service capabilities to scale these applications for broad deployment. “Secure and reliable connectivity and networking (increasingly on ultrafast 5G networks) are just the beginning in terms of the value telcos can bring,” he wrote in an article for Forbes, published in November 2020. “They can lead on the provisioning and management of the literally billions of IoT devices — cameras, wearables and sensors of all types — that are integral to real-time systems. They can aggregate and analyze the massive amount of data that these systems generate and share insights with their customers. And they can bring together the software providers and integrators and various other parties that will be necessary to build, maintain and run such sophisticated systems.”

Sprinzen regards multi-access edge computing, or MEC, as the key to unlocking this market. He describes MEC as a new, distributed architecture that pushes compute and cloud-like capabilities out of data centres and the cloud to the edge of the network — closer to end-users and billions of IoT devices. This enables the filtering and processing of data at the edge in near real-time, to enable a rapid response to critical events.

This kind of digital nervous system could help curb the adverse impact of future pandemics. “I believe smart building applications will help companies monitor for and manage symptom detection, physical distancing, contact tracing, access management, safety compliance and asset tracking in the workplace,” Sprinzen wrote. “Real-time traffic monitoring will ease urban congestion and reduce the number and severity of accidents. Monitoring and management of water supplies, electrical grids and public transportation will safeguard us against equipment failures or attacks by bad actors. Environmental applications will provide early warnings of floods or wildfires. Food distribution and waste management applications will help us make more of our precious resources.”

Vantiq says one if its telco customers is implementing AI-enabled cameras, IoT sensors, location data and other technologies to monitor various aspects of its new headquarters building. He didn’t identify the telco, but added that it is the lead technology partner for a city that’s implementing a spectrum of smart city solutions to improve mobility, reduce congestion and strengthen disaster prevention.

Table of contents

  • Executive Summary
  • Introduction
  • Managing an unstable world
  • Monitoring air quality
    • Exploiting existing cellular infrastructure
    • Is mobile network data enough?
    • Smart lampposts to play a broad role
    • The economics of connecting environmental sensors
    • Sensors in the sky
  • Natural disasters
    • Spotting wildfires early
    • Earthquake alert systems
    • Crowdsourcing data
    • Infectious diseases
  • On street security
  • Conclusions – the opportunities for telcos
    • Ecosystem coordination – kickstarting the market
    • Devices – finding the right locations
    • Network – reliable, low cost connectivity
    • Data platform
    • Applications
  • Index

 

 

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Are telcos smart enough to make money work?

Telco consumer financial services propositions

Telcos face a perplexing challenge in consumer markets. On the one hand, telcos’ standing with consumers has improved through the COVID-19 pandemic, and demand for connectivity is strong and continues to grow. On the other hand, most consumers are not spending more money with telcos because operators have yet to create compelling new propositions that they can charge more for. In the broadest sense, telcos need to (and can in our view) create more value for consumers and society more generally.

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As discussed in our previous research, we believe the world is now entering a “Coordination Age” in which multiple stakeholders will work together to maximize the potential of the planet’s natural and human resources. New technologies – 5G, analytics, AI, automation, cloud – are making it feasible to coordinate and optimise the allocation of resources in real-time. As providers of connectivity that generates vast amounts of relevant data, telcos can play an important role in enabling this coordination. Although some operators have found it difficult to expand beyond connectivity, the opportunity still exists and may actually be expanding.

In this report, we consider how telcos can support more efficient allocation of capital by playing in the financial services sector.  Financial services (banking) sits in a “sweet spot” for operators: economies of scale are available at a national level, connected technology can change the industry.

Financial Services in the Telecoms sweet spot

financial services

Source STL Partners

The financial services industry is undergoing major disruption brought about by a combination of digitisation and liberalisation – new legislation, such as the EU’s Payment Services Directive, is making it easier for new players to enter the banking market. And there is more disruption to come with the advent of digital currencies – China and the EU have both indicated that they will launch digital currencies, while the U.S. is mulling going down the same route.

A digital currency is intended to be a digital version of cash that is underpinned directly by the country’s central bank. Rather than owning notes or coins, you would own a deposit directly with the central bank. The idea is that a digital currency, in an increasingly cash-free society, would help ensure financial stability by enabling people to store at least some of their money with a trusted official platform, rather than a company or bank that might go bust. A digital currency could also make it easier to bring unbanked citizens (the majority of the world’s population) into the financial system, as central banks could issue digital currencies directly to individuals without them needing to have a commercial bank account. Telcos (and other online service providers) could help consumers to hold digital currency directly with a central bank.

Although the financial services industry has already experienced major upheaval, there is much more to come. “There’s no question that digital currencies and the underlying technology have the potential to drive the next wave in financial services,” Dan Schulman, the CEO of PayPal told investors in February 2021. “I think those technologies can help solve some of the fundamental problems of the system. The fact that there’s this huge prevalence and cost of cash, that there’s lack of access for so many parts of the population into the system, that there’s limited liquidity, there’s high friction in commerce and payments.”

In light of this ongoing disruption, this report reviews the efforts of various operators, such as Orange, Telefónica and Turkcell, to expand into consumer financial services, notably the provision of loans and insurance. A close analysis of their various initiatives offers pointers to the success criteria in this market, while also highlighting some of the potential pitfalls to avoid.

Table of contents

  • Executive Summary
  • Introduction
  • Potential business models
    • Who are you serving?
    • What are you doing for the people you serve?
    • M-Pesa – a springboard into an array of services
    • Docomo demonstrates what can be done
    • But the competition is fierce
  • Applying AI to lending and insurance
    • Analysing hundreds of data points
    • Upstart – one of the frontrunners in automated lending
    • Takeaways
  • From payments to financial portal
    • Takeaways
  • Turkcell goes broad and deep
    • Paycell has a foothold
    • Consumer finance takes a hit
    • Regulation moving in the right direction
    • Turkcell’s broader expansion plans
    • Takeaways
  • Telefónica targets quick loans
    • Growing competition
    • Elsewhere in Latin America
    • Takeaways
  • Momentum builds for Orange
    • The cost of Orange Bank
    • Takeaways
  • Conclusions and recommendations
  • Index

This report builds on earlier STL Partners research, including:

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SK Telecom: Lessons in 5G, AI, and adjacent market growth

SK Telecom’s strategy

SK Telecom is the largest mobile operator in South Korea with a 42% share of the mobile market and is also a major fixed broadband operator. It’s growth strategy is focused on 5G, AI and a small number of related business areas where it sees the potential for revenue to replace that lost from its core mobile business.

By developing applications based on 5G and AI it hopes to create additional revenue streams both for its mobile business and for new areas, as it has done in smart home and is starting to do for a variety of smart business applications. In 5G it is placing an emphasis on indoor coverage and edge computing as basis for vertical industry applications. Its AI business is centred around NUGU, a smart speaker and a platform for business applications.

Its other main areas of business focus are media, security, ecommerce and mobility, but it is also active in other fields including healthcare and gaming.

The company takes an active role internationally in standards organisations and commercially, both in its own right and through many partnerships with other industry players.

It is a subsidiary of SK Group, one of the largest chaebols in Korea, which has interests in energy and oil. Chaebols are large family-controlled conglomerates which display a high level and concentration of management power and control. The ownership structures of chaebols are often complex owing to the many crossholdings between companies owned by chaebols and by family members. SK Telecom uses its connections within SK Group to set up ‘friendly user’ trials of new services, such as edge and AI

While the largest part of the business remains in mobile telecoms, SK Telecom also owns a number of subsidiaries, mostly active in its main business areas, for example:

  • SK Broadband which provides fixed broadband (ADSL and wireless), IPTV and mobile OTT services
  • ADT Caps, a securitybusiness
  • IDQ, which specialises in quantum cryptography (security)
  • 11st, an open market platform for ecommerce
  • SK Hynixwhich manufactures memory semiconductors

Few of the subsidiaries are owned outright by SKT; it believes the presence of other shareholders can provide a useful source of further investment and, in some cases, expertise.

SKT was originally the mobile arm of KT, the national operator. It was privatised soon after establishing a cellular mobile network and subsequently acquired by SK Group, a major chaebol with interests in energy and oil, which now has a 27% shareholding. The government pension service owns a 11% share in SKT, Citibank 10%, and 9% is held by SKT itself. The chairman of SK Group has a personal holding in SK Telecom.

Following this introduction, the report comprises three main sections:

  • SK Telecom’s business strategy: range of activities, services, promotions, alliances, joint ventures, investments, which covers:
    • Mobile 5G, Edge and vertical industry applications, 6G
    • AIand applications, including NUGU and Smart Homes
    • New strategic business areas, comprising Media, Security, eCommerce, and other areas such as mobility
  • Business performance
  • Industrial and national context.

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Overview of SKT’s activities

Network coverage

SK Telecom has been one of the earliest and most active telcos to deploy a 5G network. It initially created 70 5G clusters in key commercial districts and densely populated areas to ensure a level of coverage suitable for augmented reality (AR) and virtual reality (VR) and plans to increase the number to 240 in 2020. It has paid particular attention to mobile (or multi-access) edge computing (MEC) applications for different vertical industry sectors and plans to build 5G MEC centres in 12 different locations across Korea. For its nationwide 5G Edge cloud service it is working with AWS and Microsoft.

In recognition of the constraints imposed by the spectrum used by 5G, it is also working on ensuring good indoor 5G coverage in some 2,000 buildings, including airports, department stores and large shopping malls as well as small-to-medium-sized buildings using distributed antenna systems (DAS) or its in-house developed indoor 5G repeaters. It also is working with Deutsche Telekom on trials of the repeaters in Germany. In addition, it has already initiated activities in 6G, an indication of the seriousness with which it is addressing the mobile market.

NUGU, the AI platform

It launched its own AI driven smart speaker, NUGU in 2016/7, which SKT is using to support consumer applications such as Smart Home and IPTV. There are now eight versions of NUGU for consumers and it also serves as a platform for other applications. More recently it has developed several NUGU/AI applications for businesses and civil authorities in conjunction with 5G deployments. It also has an AI based network management system named Tango.

Although NUGU initially performed well in the market, it seems likely that the subsequent launch of smart speakers by major global players such as Amazon and Google has had a strong negative impact on the product’s recent growth. The absence of published data supports this view, since the company often only reports good news, unless required by law. SK Telecom has responded by developing variants of NUGU for children and other specialist markets and making use of the NUGU AI platform for a variety of smart applications. In the absence of published information, it is not possible to form a view on the success of the NUGU variants, although the intent appears to be to attract young users and build on their brand loyalty.

It has offered smart home products and services since 2015/6. Its smart home portfolio has continually developed in conjunction with an increasing range of partners and is widely recognised as one of the two most comprehensive offerings globally. The other being Deutsche Telekom’s Qivicon. The service appears to be most successful in penetrating the new build market through the property developers.

NUGU is also an AI platform, which is used to support business applications. SK Telecom has also supported the SK Group by providing new AI/5G solutions and opening APIs to other subsidiaries including SK Hynix. Within the SK Group, SK Planet, a subsidiary of SK Telecom, is active in internet platform development and offers development of applications based on NUGU as a service.

Smart solutions for enterprises

SKT continues to experiment with and trial new applications which build on its 5G and AI applications for individuals (B2C), businesses and the public sector. During 2019 it established B2B applications, making use of 5G, on-prem edge computing, and AI, including:

  • Smart factory(real time process control and quality control)
  • Smart distribution and robot control
  • Smart office (security/access control, virtual docking, AR/VRconferencing)
  • Smart hospital (NUGUfor voice command for patients, AR-based indoor navigation, facial recognition technology for medical workers to improve security, and investigating possible use of quantum cryptography in hospital network)
  • Smart cities; e.g. an intelligent transportation system in Seoul, with links to vehicles via 5Gor SK Telecom’s T-Map navigation service for non-5G users.

It is too early to judge whether these B2B smart applications are a success, and we will continue to monitor progress.

Acquisition strategy

SK Telecom has been growing these new business areas over the past few years, both organically and by acquisition. Its entry into the security business has been entirely by acquisition, where it has bought new revenue to compensate for that lost in the core mobile business. It is too early to assess what the ongoing impact and success of these businesses will be as part of SK Telecom.

Acquisitions in general have a mixed record of success. SK Telecom’s usual approach of acquiring a controlling interest and investing in its acquisitions, but keeping them as separate businesses, is one which often, together with the right management approach from the parent, causes the least disruption to the acquired business and therefore increases the likelihood of longer-term success. It also allows for investment from other sources, reducing the cost and risk to SK Telecom as the acquiring company. Yet as a counterpoint to this, M&A in this style doesn’t help change practices in the rest of the business.

However, it has also shown willingness to change its position as and when appropriate, either by sale, or by a change in investment strategy. For example, through its subsidiary SK Planet, it acquired Shopkick, a shopping loyalty rewards business in 2014, but sold it in 2019, for the price it paid for it. It took a different approach to its activity in quantum technologies, originally set up in-house in 2011, which it rolled into IDQ following its acquisition in 2018.

SKT has also recently entered into partnerships and agreements concerning the following areas of business:

 

Table of Contents

  • Executive Summary
  • Introduction and overview
    • Overview of SKT’s activities
  • Business strategy and structure
    • Strategy and lessons
    • 5G deployment
    • Vertical industry applications
    • AI
    • SK Telecom ‘New Business’ and other areas
  • Business performance
    • Financial results
    • Competitive environment
  • Industry and national context
    • International context

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End-to-end network automation: Why and how to do it?

Automation, analytics and AI: A3 unlocks value for operators

STL Partners has been writing about automation, artificial intelligence (AI) and data analytics for several years. While the three have overlapping capabilities and often a single use case will rely upon a combination, they are also distinct in their technical outcomes.

Distinctions between the three As

Source: STL Partners

Operators have been heavily investing in A3 use cases for several years and are making significant progress. Efforts can be broadly broken down into five different domains: sales and marketing, customer experience, network planning and operations, service innovation and other operations. Some of these domains, such as sales and marketing and customer experience, are more mature, with significant numbers of use cases moving beyond R&D and PoCs into live, scaled deployments. In comparison, other domains, like service innovation, are typically less mature, despite the potential new revenue opportunities attached to them.

Five A3 use case domains

Source: STL Partners

Use cases often overlap across domains. For example, a Western European operator has implemented an advanced analytics platform that monitors network performance, and outputs a unique KPI that, at a per subscriber level, indicates the customer experience of the network. This can be used to trigger an automated marketing campaign to customers who are experiencing issues with their network performance (e.g. an offer for free mobile hotspot until issues are sorted). In this way, it spans both customer experience and network operations. For the purpose of this paper, however, we will primarily focus on automation use cases in the network domain.  We have modelled the financial value of A3 for telcos: Mapping the financial value.

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The time is ripe for network automation now

Network automation is not new. In fact, it’s been a core part of operator’s network capabilities since Almon Strowger invented the Strowger switch (in 1889), automating the process of the telephone exchange. Anecdotally, Strowger (an undertaker by profession) came up with this invention because the wife of a rival funeral parlour owner, working at the local community switchboard, was redirecting customers calling for Strowger to her own husband’s business.

Early advertising called the Strowger switch the “girl-less, cuss-less, out-of-order-less, wait-less telephone” or, in other words, free from human error and faster than the manual switchboard system. While this example is more than 100 years old, many of the benefits of automation that it achieved are still true today; automation can provide operators with the ability to deliver services on-demand, without the wait, and free from human error (or worse still, malevolent intent).

Despite automation not being a new phenomenon, STL Partners has identified six key reasons why network automation is something operators should prioritise now:

  • Only with automation can operators deliver the degree of agility that customers will demand. Customers today expect the kind of speed, accuracy and flexibility of service that can only be achieved in a cost-effective manner with high degrees of network automation. This can be both consumer customers (e.g. for next generation network services like VR/AR applications, gaming, high-definition video streaming etc.) or enterprise customers (e.g. for creating a network slice that is spun up for a weekend for a specific big event). With networks becoming increasingly customised, operators must automate their systems (across both OSS and BSS) to ensure that they can deliver these services without a drastic increase in their operating costs.
    One  wholesale operator exemplified this shift in expectations when describing their customers, which included several of the big technology companies including Amazon and Google: “They have a pace in their business that is really high and for us to keep up with their requirements and at the same time beat all our competitors we just need to be more automated”. They stated that while other customers may be more flexible and understand that instantiating a new service takes time, the “Big 5” expect services in hours rather than days and weeks.
  • Automation can enable operators to do more, such as play higher up the value chain. External partners have an expectation that telcos are highly skilled at handling data and are highly automated, particularly within the network domain. It is only through investing in internal automation efforts that operators will be able to position themselves as respected partners for services above and beyond pure connectivity. An example of success here would be the Finnish operator Elisa. They invested in automation capabilities for their own network – but subsequently have been able to monetise this externally in the form of Elisa Automate.
    A further example would be STL Partners’ vision of the Coordination Age. There is a role for telcos to play further up the value chain in coordinating across ecosystems – which will ultimately enable them to unlock new verticals and new revenue growth. The telecoms industry already connects some organisations and ecosystems together, so it’s in a strong position to play this coordinating role. But, if they wish to be trusted as ecosystem coordinators, they must first prove their pedigree in these core skills. Or, in other words, if you can’t automate your own systems, customers won’t trust you to be key partners in trying to automate theirs.
  • Automation can free up resource for service innovation. If operators are going to do more, and play a role beyond connectivity, they need to invest more in service innovation. Equally, they must also learn to innovate at a much lower cost, embracing automation alongside principles like agile development and fast fail mentalities. To invest more in service innovation, operators need to reallocate resources from other areas of their business – as most telcos are no longer rapidly growing, resource must be freed up from elsewhere.
    Reducing operating costs is a key way that operators can enable increased investment in innovation – and automation is a key way to achieve this.

A3 can drive savings to redistribute to service innovation

Source: Telecoms operator accounts, STL Partners estimates and analysis

  • 5G won’t fulfil its potential without automation. 5G standards mean that automation is built into the design from the bottom up. Most operators believe that 5G will essentially not be possible without being highly automated, particularly when considering next generation network services such as dynamic network slicing. On top of this, there will be a ranging need for automation outside of the standards – like for efficient cell-site deployment, or more sophisticated optimisation efforts for energy efficiency. Therefore, the capex investment in 5G is a major trigger to invest in automation solutions.
  • Intent-based network automation is a maturing domain. Newer technologies, like artificial intelligence and machine learning, are increasing the capabilities of automation. Traditional automation (such as robotic process automation or RPA) can be used to perform the same tasks as previously were done manually (such as inputting information for VPN provisioning) but in an automated fashion. To achieve this, rules-based scripts are used – where a human inputs exactly what it is they want the machine to do. In comparison, intent-based automation enables engineers to define a particular task (e.g. connectivity between two end-points with particular latency, bandwidth and security requirements) and software converts this request into lower level instructions for the service bearing infrastructure. You can then monitor the success of achieving the original intent.
    Use of AI and ML in conjunction with intent-based automation, can enable operators to move from automation ‘to do what humans can do but faster and more accurately’, to automation to achieve outcomes that could not be achieved in a manual way. ML and AI has a particular role to play in anomaly detection, event clustering and predictive analytics for network operations teams.
    While you can automate without AI and ML, and in fact for many telcos this is still the focus, this new technology is increasing the possibilities of what automation can achieve. 40% of our interviewees had network automation use cases that made some use of AI or ML.
  • Network virtualisation is increasing automation possibilities. As networks are increasingly virtualised, and network functions become software, operators will be afforded a greater ability than ever before to automate management, maintenance and orchestration of network services. Once networks are running on common computing hardware, making changes to the network is, in theory, purely a software change. It is easy to see how, for example, SDN will allow automation of previously human-intensive maintenance tasks. A number of operators have shared that their teams and/or organisations as a whole are thinking of virtualisation, orchestration and automation as coming hand-in-hand.

This report focuses on the opportunities and challenges in network automation. In the future, STL Partners will also look to more deeply evaluate the implications of network automation for governments and regulators, a key stakeholder within this ecosystem.

Table of Contents

  • Executive Summary
    • End-to-end network automation
    • A key opportunity: 6 reasons to focus on network automation now
    • Key recommendations for operators to drive their network automation journey
    • There are challenges operators need to overcome
    • This paper explores a range of network automation use cases
    • STL Partners: Next steps
  • Automation, analytics and AI: A3 unlocks value for operators
    • The time is ripe for network automation now
  • Looking to the future: Operators’ strategy and ambitions
    • Defining end-to-end automation
    • Defining ambitions
  • State of the industry: Network automation today
    • Which networks and what use cases: the breadth of network automation today
    • Removing the human? There is a continuum within automation use cases
    • Strategic challenges: How to effectively prioritise (network) automation efforts
    • Challenges to network automation– people and culture are key to success
  • Conclusions
    • Recommendations for vendors (and others in the ecosystem)
    • Recommendations for operators

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Telco Cloud: Why it hasn’t delivered, and what must change for 5G

Related Webinar – 5G Telco Clouds: Where we are and where we are headed

This research report will be expanded upon on our upcoming webinar 5G Telco Clouds: Where we are and where we are headed. In this webinar we will argue that 5G will only pay if telcos find a way to make telco clouds work. We will look to address the following key questions:

  • Why have telcos struggled to realise the telco cloud promise?
  • What do telcos need to do to unlock the key benefits?
  • Why is now the time for telcos to try again?

Join us on April 8th 16:00 – 17:00 GMT by using this registration link.

Telco cloud: big promises, undelivered

A network running in the cloud

Back in the early 2010s, the idea that a telecoms operator could run its network in the cloud was earth-shattering. Telecoms networks were complicated and highly-bespoke, and therefore expensive to build, and operate. What if we could find a way to run networks on common, shared resources – like the cloud computing companies do with IT applications? This would be beneficial in a whole host of ways, mostly related to flexibility and efficiency. The industry was sold.

In 2012, ETSI started the ball rolling when it unveiled the Network Functions Virtualisation (NFV) whitepaper, which borrowed the IT world’s concept of server-virtualisation and gave it a networking spin. Network functions would cease to be tied to dedicated pieces of equipment, and instead would run inside “virtual machines” (VMs) hosted on generic computing equipment. In essence, network functions would become software apps, known as virtual network functions (VNFs).

Because the software (the VNF) is not tied to hardware, operators would have much more flexibility over how their network is deployed. As long as we figure out a suitable way to control and configure the apps, we should be able to scale deployments up and down to meet requirements at a given time. And as long as we have enough high-volume servers, switches and storage devices connected together, it’s as simple as spinning up a new instance of the VNF – much simpler than before, when we needed to procure and deploy dedicated pieces of equipment with hefty price tags attached.

An additional benefit of moving to a software model is that operators have a far greater degree of control than before over where network functions physically reside. NFV infrastructure can directly replace old-school networking equipment in the operator’s central offices and points of presence, but the software can in theory run anywhere – in the operator’s private centralised data centre, in a datacentre managed by someone else, or even in a public hyperscale cloud. With a bit of re-engineering, it would be possible to distribute resources throughout a network, perhaps placing traffic-intensive user functions in a hub closer to the user, so that less traffic needs to go back and forth to the central control point. The key is that operators are free to choose, and shift workloads around, dependent on what they need to achieve.

The telco cloud promise

Somewhere along the way, we began talking about the telco cloud. This is a term that means many things to many people. At its most basic level, it refers specifically to the data centre resources supporting a carrier-grade telecoms network: hardware and software infrastructure, with NFV as the underlying technology. But over time, the term has started to also be associated with cloud business practices – that is to say, the innovation-focussed business model of successful cloud computing companies

Figure 2: Telco cloud defined: New technology and new ways of working

Telco cloud: Virtualised & programmable infrastructure together with cloud business practices

Source: STL Partners

In this model, telco infrastructure becomes a flexible technology platform which can be leveraged to enable new ways of working across an operator’s business. Operations become easier to automate. Product development and testing becomes more straightforward – and can happen more quickly than before. With less need for high capital spend on equipment, there is more potential for shorter, success-based funding cycles which promote innovation.

Much has been written about the vast potential of such a telco cloud, by analysts and marketers alike. Indeed, STL Partners has been partial to the same. For this reason, we will avoid a thorough investigation here. Instead, we will use a simplified framework which covers the four major buckets of value which telco cloud is supposed to help us unlock:

Figure 3: The telco cloud promise: Major buckets of value to be unlocked

Four buckets of value from telco cloud: Openness; Flexibility, visibility & control; Performance at scale; Agile service introduction

Source: STL Partners

These four buckets cover the most commonly-cited expectations of telcos moving to the cloud. Swallowed within them all, to some extent, is a fifth expectation: cost savings, which have been promised as a side-effect. These expectations have their origin in what the analyst and vendor community has promised – and so, in theory, they should be realistic and achievable.

The less-exciting reality

At STL Partners, we track the progress of telco cloud primarily through our NFV Deployment Tracker, a comprehensive database of live deployments of telco cloud technologies (NFV, SDN and beyond) in telecoms networks across the planet. The emphasis is on live rather than those running in testbeds or as proofs of concept, since we believe this is a fairer reflection of how mature the industry really is in this regard.

What we find is that, after a slow start, telcos have really taken to telco cloud since 2017, where we have seen a surge in deployments:

Figure 4: Total live deployments of telco cloud technology, 2015-2019
Includes NFVi, VNF, SDN deployments running in live production networks, globally

Telco cloud deployments have risen substantially over the past few years

Source: STL Partners NFV Deployment Tracker

All of the major operator groups around the world are now running telco clouds, as well as a significant long tail of smaller players. As we have explained previously, the primary driving force in that surge has been the move to virtualise mobile core networks in response to data traffic growth, and in preparation for roll-out of 5G networks. To date, most of it is based on NFV: taking existing physical core network functions (components of the Evolved Packet Core or the IP Multimedia Subsystem, in most cases) and running them in virtual machines. No operator has completely decommissioned legacy network infrastructure, but in many cases these deployments are already very ambitious, supporting 50% or more of a mobile operator’s total network traffic.

Yet, despite a surge in deployments, operators we work with are increasingly frustrated in the results. The technology works, but we are a long way from unlocking the value promised in Figure 2. Solutions to date are far from open and vendor-neutral. The ability to monitor, optimise and modify systems is far from ubiquitous. Performance is acceptable, but nothing to write home about, and not yet proven at mass scale. Examples of truly innovative services built on telco cloud platforms are few and far between.

We are continually asked: will telco cloud really deliver? And what needs to change for that to happen?

The problem: flawed approaches to deployment

Learning from those on the front line

The STL Partners hypothesis is that telco cloud, in and of itself, is not the problem. From a theoretical standpoint, there is no reason that virtualised and programmable network and IT infrastructure cannot be a platform for delivering the telco cloud promise. Instead, we believe that the reason it has not yet delivered is linked to how the technology has been deployed, both in terms of the technical architecture, and how the telco has organised itself to operate it.

To test this hypothesis, we conducted primary research with fifteen telecoms operators at different stages in their telco cloud journey. We asked them about their deployments to date, how they have been delivered, the challenges encountered, how successful they have been, and how they see things unfolding in the future.

Our sample includes individuals leading telco cloud deployment at a range of mobile, fixed and converged network operators of all shapes and sizes, and in all regions of the world. Titles vary widely, but include Chief Technology Officers, Heads of Technology Exploration and Chief Network Architects. Our criteria were that individuals needed to be knee-deep in their organisation’s NFV deployments, not just from a strategic standpoint, but also close to the operational complexities of making it happen.

What we found is that most telco cloud deployments to date fall into two categories, driven by the operator’s starting point in making the decision to proceed:

Figure 5: Two starting points for deploying telco cloud

Function-first "we need to virtualise XYZ" vs platform-first "we want to build a cloud platform"

Source: STL Partners

The operators we spoke to were split between these two camps. What we found is that the starting points greatly affect how the technology is deployed. In the coming pages, we will explain both in more detail.

Table of contents

  • Executive Summary
  • Telco cloud: big promises, undelivered
    • A network running in the cloud
    • The telco cloud promise
    • The less-exciting reality
  • The problem: flawed approaches to deployment
    • Learning from those on the front line
    • A function-first approach to telco cloud
    • A platform-first approach to telco cloud
  • The solution: change, collaboration and integration
    • Multi-vendor telco cloud is preferred
    • The internal transformation problem
    • The need to foster collaboration and integration
    • Standards versus blueprints
    • Insufficient management and orchestration solutions
    • Vendor partnerships and pre-integration
  • Conclusions: A better telco cloud is possible, and 5G makes it an urgent priority

Predicting the future: Where next for SD-WAN?

Introduction

This document is the third in a mini-series of three reports which seek to explore SD-WAN technology from an enterprise perspective, covering the challenges that SD-WAN is designed to address, the differing types of SD-WAN product on the market today, and how we envisage SD-WAN-type services evolving in future.

The first two reports in the series are:

Future evolution of SD-WAN

Any decision made about SD-WAN aspects or management must be taken not just in context of enterprises’ current networking challenges, but also in context of how those challenges, as well as networking technology, are likely to evolve. This report assesses where we expect the industry to go next.

At STL Partners, we believe that SD-WAN under its current definition is not an end in itself. All indications are that enterprises are becoming increasingly cloud-centric, and we see no sign of this trend reversing. SD-WAN will no doubt be a key component of the multicloud ecosystem – but it will require an evolution beyond the confines of what is currently being packaged and sold.

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In short, existing SD-WAN services are just the first step on a longer journey towards integrated, software-driven WAN operations and networking on a broader scale. Enterprises and vendors planning SD-WAN rollout would do well to consider how that evolution could unfold.

As with any new technology, there are multiple pathways that this evolution could follow – none of which are yet well-understood. STL Partners has identified three emerging evolution pathways, which we explain in detail below. The options are:

  1. SD-WAN used as the first step towards SD-Branch: SD-WAN is deployed as a stepping stone technology towards more advanced, integrated management of enterprises’ LANs and branches alongside the WAN.
  2. SD-WAN sold “as a Service”: SD-WAN starts to be offered as a more fully cloud-based software service, free from vendor or hardware-based constraints.
  3. SD-WAN used as an enabling component of edge/IoT platforms: SD-WAN features and infrastructure are integrated with service providers’ edge computing and Internet of Things (IoT) platforms, with sales focus on enterprise automation and process optimisation, rather than the SD-WAN component itself.

These options are of course not mutually exclusive and are likely in practice to be adopted in some combination of the different elements. It is quite feasible, for example, that some service providers will start to “upsell” their existing SD-WAN customers onto a more integrated “SD-Branch” offering (#1) – and to sell a flavour of this same offering as a cloud-based software option (#2). Indeed, we have already seen this happening in the marketplace.

In addition, all three options share two things in common:

  • A move towards cloud-centricity: Their focus is on the LAN and branch, WAN (delivered in an even more flexible, cloud-native way), the edge (and edge computing and IoT), respectively.
  • Increasing use of AI technology: Artificial intelligence (AI) and machine learning (ML) are pouring into all areas of technology and network infrastructure is no exception. The dynamic nature of traffic patterns over SD-WAN make it a prime candidate for this kind of tech to enable, say, security threat detection or traffic routing optimisation. Whichever direction SD-WAN takes, it is sure to make use of AI/ML.

In this report, we detail each of the three options, with particular reference to how they might benefit both enterprise customers, and those who will provide such SD-WAN services.

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Telco AI: How to organise and partner for maximum success

Not a passing fad: AI is becoming a core capability for telcos

Artificial intelligence (AI) has become a key enabler of the digital transformation journey for service providers in the telecoms industry, providing them with the insights and capabilities they need to be more agile and take a more software-centric approach to their role.

The document was researched and written independently by STL Partners, supported by Nokia. STL’s conclusions are entirely independent and build on ongoing research into the future of telecoms. STL Partners has been writing about telcos’ AI opportunities since 2016, looking first at how AI might improve the customer experience and then at the critical role AI might play in the future of network operations.

In this report, we provide a comprehensive overview of the state of AI in the telecoms industry. Supported by nearly a dozen in-depth interviews plus an online survey of more than 50 leading telcos around the world, we explore where the industry is looking to progress and how it is planning to do so — and identify the strategic and business opportunities that are being enabled by AI.

This report will be followed by a sequel that quantifies some of the business outcomes telcos can expect from specific AI application areas. In the coming months, we will also publish a report discussing how AI technology is evolving and presenting our vision of the telco AI roadmap.

What is artificial intelligence?

Before going any further, it is important to clarify what we mean by “artificial intelligence”. To us, AI is about using computing capabilities to perform tasks traditionally associated with humans (such as inference, planning, anticipation, prediction and learning) in human-like ways (e.g., autonomous, adaptive). Our definition incorporates machine learning (ML), which we define as a subset of AI that focuses on the ability of machines to receive datasets and adapt responses in pursuit of a goal.

These definitions attempt to encapsulate the distinction between AI and other forms of rules-based automation — although we acknowledge that in practice these lines are easily blurred.

Practically speaking, AI sits on a continuum of other related technologies and concepts, which we have covered at length in our previous reports. Figure 1 illustrates this continuum and depicts the stages we expect telcos will have to go through as they to move from manual to automated and then to AI-augmented processes.

Figure 1: Moving toward AI

The progression of AI maturity in four steps

Source: STL Partners

A long-term ambition for many telcos is to reach the orange zone in Figure 1: a state in which their systems and processes run and learn from themselves with human input limited to the setting of desired business goals. Beyond the targeted use of ML in certain applications, however, the industry and society as a whole are far from realising that ambition. It is still unclear what fully autonomous systems in a telco might look like in practice, let alone whether they will ever be achievable.

Today, most telcos are still figuring out how to play in the blue zone. They’re using targeted data analysis to inform largely human-led decision-making processes, or they’ve implemented some fixed-policy automation where machines follow a script written and inputted by a human. This is valuable work, but it is not the focus of this report. Instead, we focus on the middle section of Figure 1: on those fledging opportunities that move beyond rules-based automation and into the realm of ML-supported automation

Cutting through the hype

AI has generated considerable industry noise and media attention — so much so, in fact, that a recent survey of leading telcos awarded AI the title of “most overhyped emerging technology”. We believe this hype originates in a general lack of understanding of what AI is (and is not), as well as unrealistic expectations about what it can do for a business, how quickly it can be deployed, and how much ongoing work will be needed to manage it. While there is consensus that the technology has great potential, many telcos doubt it will deliver everything that has been promised up to now.

For those disillusioned by the hype, it is worth noting the impact of AI is much likelier to be evolutionary than revolutionary. The line between automation and AI is blurred; so, too, is the progression between the two. While AI has the potential to unlock new business opportunities, realising that potential will require patience and long-term investment.

And yet, the truth is that telcos are uniquely positioned to take full advantage of AI technology — largely because they’re already used to dealing with the huge volumes of data AI relies on. When telcos automate systems, networks and processes — particularly with the injection of AI — they benefit from feedback loops that further improve those automated processes. This drives simplicity in an industry rife with complexity.

The digital transformation we all talk about depends on driving out complexity and becoming more agile, and the only way to do that is by automating intelligently. Looking ahead to the launch of 5G, it will become impossible for telcos to manage billions of connected devices without AI assistance.

Telcos’ current AI focus: Improving speed and efficiency

Key learnings on telco AI initiatives

Through our research, we have identified five primary domains of activity for telcos looking to make use of AI. The first three broadly relate to business process improvement, with the end goal of reducing costs and improving efficiency.

  1. Optimising existing networks and operations. Telcos are using AI not only for network planning and optimisation, but also to improve their human resources, accounting and fraud-management functions. For example, Telefónica has built an ML model capable of monitoring the status of the network, predicting possible failures and an optimising maintenance routes.[1] This has been particularly important in its rollout and maintenance of networks across rural Latin America, where it can take an engineer up to a day to travel to the site of a network fault.
  2. Improving sales and marketing activity. This includes upselling, cross-selling and agent augmentation. Globe Telecom, for example, has created a data-management platform that collates network signal information alongside information from billing and payment systems to provide personalised offers to its mobile customers.[2]
  3. Improving the customer experience. This includes use cases such as fault resolution, churn management, chatbots and virtual assistants. Vodafone has developed the chatbot TOBi, for example, which can handle 70 percent of customer requests and employs ML technology to further improve the support it offers to customers.[3]

The remaining two domains focus on using AI to enable new ways of working that go beyond a telco’s core connectivity offering, with a focus on growing revenues.

  1. Driving (and monetising) customer data. AI can help telcos aggregate massive volumes of anonymised customer data that can then be sold to third parties. Singtel’s DataSpark has taken a step down this data-as-a-service route, providing access to GPS and mobile network data that other organisations can incorporate into their applications and services.[4]
  2. Enabling or supporting new services. This includes cybersecurity and predictive analytics. As an example, AT&T is using ML to quickly identify normal and abnormal activity in it networks.[5] This sort of solution could be sold as a managed service to other enterprises in the future, unlocking a new revenue stream.

Contents of the full report include:

  • Executive Summary
  • Not a passing fad: AI is becoming a core capability for telcos
  • What is artificial intelligence?
  • Cutting through the hype 8
  • Telcos’ current AI focus: Speed and efficiency
  • How are telcos using AI today?
  • Sharing is caring: How telco AI initiatives are organised
  • Centralised AI initiatives
  • Cross-functional R&D units
  • Individual AI initiatives
  • The stumbling blocks for AI implementation — and how to get around them
  • AI initiatives need to be powered by high-quality data
  • Data governance is an essential requirement
  • Exploring the link between data maturity and AI success
  • The tricky transition from the lab to in-field deployment
  • Accept failure and embrace innovation
  • Revamp partnership strategies
  • New challenges, new expectations
  • Finding the impact: How telcos assess the benefits of AI
  • Different types of telcos, different levels of AI maturity
  • Conclusion

Figures:

  1. Moving toward AI
  2. Telco AI initiatives by domain
  3. Centrally coordinated AI initiatives are more likely to scale
  4. Poor data and a lack of internal skills are key challenges
  5. Telcos struggle with data management at every step of the AI journey
  6. Issues with data governance do not preclude AI implementation
  7. Only 1 in 5 AI projects has advanced to live deployment
  8. Collaborative partnering is key to AI success
  9. Nearly half of telcos have not gone live with AI
  10. Fixed-line and wholesale operators lag behind


[1] Source: Telefónica

[2] Source: Cloudera

[3] Source: Vodafone

[4] Source: DataSpark

[5] Source: AT&T

AR/VR: Won’t move the 5G needle

Introduction

This report explores the potential impact of virtual reality (VR) and augmented reality (AR) on the lives of consumers. It considers how quickly these technologies will go mass market and the implications for telcos, including those with their own entertainment proposition and those operators whose networks act as a conduit for other companies’ content.

Widespread use of VR and/or AR could fuel another major step-change in the traffic travelling over telecoms networks. All VR apps and many AR apps will require vast amounts of data to be processed to render the necessary digital images. In short, telecoms operators could and should benefit from mass-market adoption of VR and AR.

In the consumer market – the primary focus of the research stream for which this report was written – the promise of VR and AR is that they will transform digital entertainment and communications. In the 2015 report Amazon, Apple, Facebook, Google, Netflix: Whose Digital Content is King?, STL Partners identified the rise of increasingly immersive games and interactive videos enabled by VR and/or AR as one of the six key trends that could disrupt the entertainment industry.

If it lives up to its hype, VR could blur the line between live entertainment and the living room. The ultimate promise of VR is that people will be able to enjoy a movie or sports event from the inside, choosing from multiple viewpoints within a 360-degree video stream, potentially placing themselves in the midst of the action. For example, a consumer could use VR to “sit” next to the conductor at a classical music concert or alongside a manager at a football match, and hear every word he or she utters. They may even be able to experience a sports event from the perspective of an athlete by streaming live footage from mini-cameras mounted on helmets or other attire. Although still very expensive, VR production technology is already being used to create immersive games and interactive movies, as well as interactive documentaries and educational programmes.

Developing in parallel with VR, AR calls for digital graphics to be superimposed on live images of the real world. This can be used to create innovative new games, such as the 2016 phenomenon Pokemon Go, and educational and informational tools, such as travel guides that give you information about the monument you are looking at. At live sports events, spectators could use AR software to identify players, see how fast they are running, check their heart rates and call up their career statistics.

This report draws the following distinction between VR and AR

  • Virtual reality: use of an enclosed headset for total immersion in a digital 3D world.
  • Augmented reality: superimposition of digital graphics into the real world via a camera viewfinder, a pair of glasses or onto a screen fixed in the real world.

Note, an advanced form of AR is sometimes referred to as mixed reality. In this case, fully interactive digital 3D objects are superimposed on the real world, effectively mixing virtual objects and people with physical objects and people into a seamless interactive scene. For example, an advanced telepresence service could project a live hologram of the person you are talking to into the same room as you.

The net effect is that both live and living room entertainment could become much more personalised and interactive, particularly as bandwidth, latency, graphics processing and rendering technology all improve.

In time, mixed-reality services are likely to become almost universally adopted in the developed world. They will become a valuable aid to everyday living, providing the user with information about whatever they are looking at, either on a transparent screen on a pair of glasses or through a wireless earpiece. Engineers, for example, will use the technology to identify individual parts and detect faults, while consumers will rely on AR to retrieve information about whatever they are looking at, whether that be the route of an approaching bus, the menu of a nearby restaurant or the fat and salt content of a ready meal.

Contents:

  • Executive Summary
  • Takeaways for telcos
  • Introduction 
  • Progress and immediate prospects
  • VR: Virtually there?
  • Augmented reality springs back to life
  • 4K HD: Simple, but effective
  • Technical requirements
  • Image processing
  • Sensors and cameras
  • Artificial intelligence
  • Developer tools
  • Bandwidth and latency
  • Costs: Energy, weight and financial
  • Timeline for VR
  • Timeline for AR
  • Societal Challenges
  • AR: Is it acceptable in a public place?
  • VR: Health issues
  • VR and AR: Moral and ethical challenges
  • AR and VR: What do consumers really want?
  • Timelines and Forecasts
  • Conclusions for telcos
  • Opportunities for telcos

Figures:

  • Figure 1: Fantasy roleplaying title Skyrim VR has won praise from gaming critics
  • Figure 2: The definition of six degrees of freedom for VR
  • Figure 3: On paper, the Oculus Go looks impressive
  • Figure 4: Users of Ikea’s catalogue can see what furniture will look like in their room
  • Figure 5: A 3D holographic image of a sports event can appear in a living room
  • Figure 6: Google Lens can retrieve information about a shop or building you are looking at
  • Figure 7: How 3D sensors can map a room or an outdoor area in real time
  • Figure 8: Edge computing and telco cloud can get latency low enough for VR apps
  • Figure 9: The likely timeline for immersive VR with a wireless headset
  • Figure 10: The bulky Magic Leap One will be wired to a belt-mounted computer
  • Figure 11: Smart Sunglasses need to be chunky to fit in all the necessary tech
  • Figure 12: The timeline for live 3D holographic projections using wireless AR headsets
  • Figure 13: How AR and VR will develop over the next five years

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

Apple’s pivot to services: What it means for telcos

Introduction

The latest report in STL’s Dealing with Disruption stream, this executive briefing considers Apple’s strategic dilemmas in its ongoing struggle for supremacy with the other major Internet ecosystems – Amazon, Facebook and Google. It explores how the likely shift from a mobile-first world to an artificial-intelligence first world will impact Apple, which owes much of its current status and financial success to the iPhone.

After outlining Apple’s strategic considerations, the report considers how much Apple earns from services today, before identifying Apple’s key services and how they may evolve. Finally, the report features a SWOT (strengths, weaknesses, opportunities and threats) analysis of Apple’s position in services, followed by a TOWS analysis that identifies possible next steps for Apple. It concludes by considering the potential implications for Apple’s main rivals, as well as two different kinds of telcos – those who are very active in the service layer and those focused on providing connectivity and enablers.

Several recent STL Partners’ research reports make detailed recommendations as to how telcos can compete effectively with the major Internet ecosystems in the consumer market for digital services. These include:

  • Telco-Driven Disruption: Will AT&T, Axiata, Reliance Jio and Turkcell succeed? To find new revenues, some telcos are competing head-on with the major internet players in the digital communications, content and commerce markets. Although telcos’ track record in digital services is poor, some are gaining traction. AT&T, Axiata, Reliance Jio and Turkcell are each pursuing very different digital services strategies, and we believe these potentially disruptive moves offer valuable lessons for other telcos and their partners.
  • Consumer communications: Can telcos mount a comeback? The rapid growth of Facebook, WhatsApp, WeChat and other Internet-based services has prompted some commentators to write off telcos in the consumer communications market. But many mobile operators retain surprisingly large voice and messaging businesses and still have several strategic options. Indeed, there is much telcos can learn from the leading Internet players’ evolving communications propositions and their attempts to integrate them into broad commerce and content platforms.
  • Autonomous cars: Where’s the money for telcos? The connected car market is being seen as one of the most promising segments of the Internet of Things. Everyone from telcos to internet giants Google, and specialist service providers Uber are eyeing opportunities in the sector. This report analyses 10 potential connected car use-cases to assess which ones could offer the biggest revenue opportunities for operators and outline the business case for investment.
  • AI: How telcos can profit from deep learning Artificial intelligence (AI) is improving rapidly thanks to the growing use of deep neural networks to teach computers how to interpret the real world (deep learning). These networks use vast amounts of detailed data to enable machines to learn. What are the potential benefits for telcos, and what do they need to do to make this happen?
  • Amazon: Telcos’ Chameleon-King Ally? New digital platforms are emerging – the growing popularity of smart speakers, which rely on cloud-based artificial intelligence, could help Amazon, the original online chameleon, to bolster its fast-evolving ecosystem at the expense of Google and Facebook. As the digital food chain evolves, opportunities will open up for telcos, but only if the smart home market remains heterogeneous and very competitive.

Apple’s evolving strategy

Apple is first and foremost a hardware company: It sells physical products. But unlike most other hardware makers, it also has world-class expertise in software and services. These human resources and its formidable intellectual property, together with its cash pile of more than US$250 billion and one of the world’s must coveted brands, gives Apple’s strategic options that virtually no other company has. Apple has the resources and the know-how to disrupt entire industries. Apple’s decision to double the size of it’s already-impressive services business by 2021 has ramifications for companies in a wide range of industries – from financial services to entertainment to communications.

Throughout its existence, Apple’s strategy has been to use distinctive software and services to help sell its high-margin hardware, rather than compete head-on with Google, Facebook, Microsoft and Amazon in the wider digital services and content markets. As Apple’s primary goal is to create a compelling end-to-end solution, its software and services are tightly integrated into its hardware. Although there are some exceptions, notably iTunes and Apple Music, most of Apple’s services and software can only be accessed via Apple’s devices. But there are four inter-related reasons why Apple may rethink that strategy and extend Apple’s services beyond its hardware ecosystem:

      • Services are now Apple’s primary growth engine, as iPhone revenue appears to have peaked and new products, such as the Apple Watch, have failed to take up the slack. Moreover, services, particularly content-based services, need economies of scale to be cost-effective and profitable.
      • Upstream players, such as merchants, brands and content providers, want to be able to reach as many people as possible, as cost-effectively as possible. They would like Apple’s stores and marketplaces to be accessible from non-Apple devices, as that would enable them to reach a larger customer base through a single channel. Figure 1 shows that Apple’s iPhone ecosystem (which use the iOS operating system) is approximately one quarter of the size of rival Android in terms of volumes.
      • Artificial intelligence is becoming increasingly central to the propositions of the major Internet ecosystems, including that of Apple. The development of artificial intelligence requires vast amounts of real-world data that can be used to hone the algorithms computers use to make decisions. To collect the data necessary to detect patterns and subtle, but significant, differences in real-world conditions, the Internet players need services that are used by as many people as possible.
      • As computing power and connectivity proliferates, the smartphone won’t be as central to people’s lives as it is today. For Apple, that means having the best smartphone won’t be enough: Computing will eventually be everywhere and will probably be accessed by voice commands or gestures. As the hardware fades into the background and Apple’s design skills become less important, the Cupertino company may decide to unleash its services and allow them to run on other platforms, as it did with iTunes.

Content:

  • Executive Summary
  • Introduction
  • Apple’s evolving strategy
  • Playing catch-up in artificial intelligence
  • What does Apple earn from services?
  • What are Apple’s key services?
  • Communications – Apple iMessage and FaceTime
  • Commerce – Apple Pay and Apple Wallet
  • Content – iTunes, Apple Music, Apple TV
  • Software – the App Store, Apple Maps
  • Artificial intelligence and the role of Siri
  • Tools for developers
  • Conclusions and implications for rivals
  • Implications for rivals

Figures:

  • Figure 1: Installed base of smartphones by operating system
  • Figure 2: Apple’s artificial intelligence, as manifest in Siri, isn’t that smart
  • Figure 3: Apple’s services business is comparable in size to Facebook
  • Figure 4: The services business is Apple’s main growth engine
  • Figure 5: The strength of Apple’s online commerce ecosystem
  • Figure 6: iMessage is becoming a direct competitor to Instagram and WhatsApp
  • Figure 7: Various apps allow consumers to make payments via Apple Pay
  • Figure 8: Apple Pay is available in a limited number of markets
  • Figure 9: Unlike most Apple services, Apple Music is “available everywhere”
  • Figure 10: Apple’s App Store generates far more revenue than Google Play
  • Figure 11: Apple Maps’ navigation trailed well behind Google Maps in June 2016
  • Figure 12: SWOT analysis of Apple in the services sector
  • Figure 13: TOWS analysis for Apple in the service market

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