Telco AI: How to organise and partner for maximum success

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Our latest research shows that only one in five telco AI projects has made the leap from proof of concept (PoC) to live deployment. How can telcos improve the hit rate and achieve real performance improvements?

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