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.

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.

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

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

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

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.

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.
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]
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.

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]
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: