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:

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:

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

[1] Source: Telefónica

[2] Source: Cloudera

[3] Source: Vodafone

[4] Source: DataSpark

[5] Source: AT&T

Telco Strategy – New Revenue from New Business Models

There is almost universal agreement among telco executives that their industry needs to find new sources of revenue.

Despite the current gloomy economic climate, 93% of the delegates in Nice agreed that exploring new business models that generate new revenue is just as important in the near term as achieving operational efficiency and retaining customers

Three-quarters of the delegates characterised existing business and technical transformation efforts by their company or industry as either “not very effective” or “very poor”.

The delegates voted top 3 strategic actions for the industry as “Creating new levels of collaboration between service providers”, “Understanding the needs of upstream industries much better” and “Understanding the needs of end users much better”.

Open APIs – Where’s the joined-up commercial strategy?

There is a great deal of work being done on APIs by the operator and vendor community, but there is a real risk of this activity being derailed by the emergence of numerous independent “islands” of APIs and developer programmes.

It is still early days for the commercial model for APIs, but it is already becoming apparent that a one-size-fits-all solution will be difficult to achieve. It is important for operators to ensure that API platforms (and the associated revenue mechanisms) can service two distinct classes of customer:

Broad adoption by thousands, perhaps millions, of developers via automated web interfaces (similar to signing up for Google Adwords or Amazon’s cloud storage & computing services);

Large-scale one-off projects and collaborations, which may require custom or bespoke capabilities, such as being linked to subscriber data management systems or “semi-closed” or “private” APIs, for example with governments or major media companies.

Retail Services 2.0 – ‘Supermarket strategy’ not enough

The most attractive options around retail services involve turning the operator’s network (and possibly devices) into a platform of “enablers” for third party services and applications. These assets and capabilities may not be easy to deliver, but once in place, should provide a defensible source of value.

Whether a telco should also sell “enabled” services at retail depends upon their existing customer relationships, portfolio of existing in-house services and ease of developing retail partnerships.

Some applications simply cannot be “sold” through an operator’s retail store, as they will be integral parts of much larger services. Although Amazon can enable the sale of a huge variety of products, delivering fresh food or fuels, for example, would not fit with its logistics business. But suppliers of such goods might still exploit Amazon’s various online commerce enablers.

Devices 2.0 – Still no consistent industry strategy

Few fixed or mobile operators have successfully created new types of devices on their own. Few consumers, for example, would view their broadband “box” as a central hub of a home network – despite more than 10 years of discussion of interconnection with consumer electronics, utility meters and home automation.

In the mobile space, probably the most important customisation has been the configuration of the telco’s own portal as the default browser home page. If anything, the shift towards smartphones and PC-based mobile broadband has further weakened telcos’ role – the majority of 3G data traffic goes straight to and from the Internet from “vanilla” devices.

The future possibly holds some more hope. Delegates were strongly in favour of pushing for telco “control points” in otherwise open devices, which fits well with the heritage of SIM cards (which are expanding in capability) as well as standardisation in areas like the browser and widget frameworks (e.g. OMTP BONDI). Software pre-loaded with PC dongles or embedded 3G modems is another option.

In the converged triple/quadplay space, femtocells offer another point of control and service delivery, close to the customer, but delegates viewed the notion of a separate “gateway” product with less enthusiasm. New classes of devices such as mobile Internet devices (MIDs), operator-enabled consumer electronics (Internet TVs, 3G music players, in-car systems etc.) also hold promise, but are seen more as low-risk experiments at this point.

Online Video Distribution – Time to sort out the “Net Neutrality” Issue

Those pushing the ‘network neutrality’ issue are (deliberately or otherwise) causing confusion over differential pricing which creates public relations and regulatory risks for operators that need to be addressed.

Operators need to develop a suite of value-added products and services for third-parties sending digital goods over their networks so they can generate incremental revenues that will enable continued network investment.

Sending-party pays models may or may not work – this is an area where more experiments need to be tried. Distributors need to be working on disentangling bits that are able to be free from those that have to pay, not letting anyone get a free ride.

Enterprise Services 2.0 – A broader suite of platform services needed

Telcos need to learn how to develop, sell and support services which are customised, as well as mass-market “basic” applications and APIs. Ideally, the technical platform will be made up of underlying components (e.g. the API interface “machinery” and the associated back-office support systems) designed to cope with both ‘off the shelf’ and ‘bespoke’ go-to-market models for new services.

Especially in the two-sided model, there are very few opportunities to gain millions – or even tens of thousands – of B2B customers buying the same basic “product”. Google has managed it for advertising, while Amazon has large numbers of hosting and “cloud computing” customers – but these are the exceptions.

Perhaps the easiest and most universal horizontal markets will be enhancements to voice and messaging capabilities – after all, these are the ubiquitous cross-sector services today.

To really exploit unique assets and take friction out of business processes, there is a need to understand specific companies’ (or sectors’) processes in detail – and offer customised or integrated solutions. Despite the lower scale, the aggregated value may be even higher.

Technical Architecture 2.0 – Good Start, but Significant Gaps

Operators are in a unique position in that they have a fuller picture of customers than any single website or retailer or service provider. Several have already recognised this, and a number of vendors are offering scalable platforms which claim to be in line with the current EU legislation on data protection.

But as well as user profile data, the 2-sided business model requires on-demand response from the network infrastructure. Both the network and IT elements must work together to deliver this, implementing new control & monitoring systems such as Resource & Service Control Systems (RSC).

Most new applications are centred around apps stores, mash-up environments, XaaS environments, and smartphone Web browsers, etc. which do not demand a traditional service delivery platform (SDP). In addition, enabling services are becoming an essential element in operators’ core products.

These enabling services need a framework, which is highly flexible, agile and responsive, and integrated with the features defined by the Next Generation Mobile Networks (NGMN) alliance.

Telco 2.0 Pilots – How to trial Telco 2.0 business models

There is insufficient time to pursue the usual protracted telco timescales for research and deliberation. Moreover, projects with long lead times – such as those involving governments – are typically unsuitable. Some target industries are also experiencing lengthening sales/decision cycles in the recession, which are also not optimal conditions for pilots.

Web-based companies are often the most flexible, as are some academic institutions. There may also be a geographic dimension to this – countries with low regulatory burdens, or where it is unusual to have projects stuck for months with lawyers, are attractive for pilots.

Working alone may be fastest, but collaborating with other operators is likely to be more effective in demonstrating the validity of the Telco 2.0 concept.

© Copyright 2009. STL Partners. All rights reserved.
STL Partners published this content for the sole use of STL Partners’ customers and Telco 2.0™ subscribers. It may not be duplicated, reproduced or retransmitted in whole or in part without the express permission of STL Partners, Elmwood Road, London SE24 9NU (UK). Phone: +44 (0) 20 3239 7530. E-mail: contact@telco2.net. All rights reserved. All opinions and estimates herein constitute our judgment as of this date and are subject to change without notice.

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