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

The ‘Agile Operator’: 5 Key Ways to Meet the Agility Challenge

Understanding Agility

What does ‘Agility’ mean? 

A number of business strategies and industries spring to mind when considering the term ‘agility’ but the telecoms industry is not front and centre… 

Agility describes the ability to change direction and move at speed, whilst maintaining control and balance. This innate flexibility and adaptability aptly describes an athlete, a boxer or a cheetah, yet this description can be (and is) readily applied in a business context. Whilst the telecoms industry is not usually referenced as a model of agility (and is often described as the opposite), a number of business strategies and industries have adopted more ‘agile’ approaches, attempting to simultaneously reduce inefficiencies, maximise the deployment of resources, learn though testing and stimulate innovation. It is worthwhile recapping some of the key ‘agile’ approaches as they inform our and the interviewees’ vision of agility for the telecoms operator.

When introduced, these approaches have helped redefine their respective industries. One of the first business strategies that popularised a more ‘agile’ approach was the infamous ‘lean-production’ and related ‘just-in-time’ methodologies, principally developed by Toyota in the mid-1900s. Toyota placed their focus on reducing waste and streamlining the production process with the mindset of “only what is needed, when it is needed, and in the amount needed,” reshaping the manufacturing industry.

The methodology that perhaps springs to many people’s minds when they hear the word agility is ‘agile software development’. This methodology relies on iterative cycles of rapid prototyping followed by customer validation with increasing cross-functional involvement to develop software products that are tested, evolved and improved repeatedly throughout the development process. This iterative and continuous improvement directly contrasts the waterfall development model where a scripted user acceptance testing phase typically occurs towards the end of the process. The agile approach to development speeds up the process and results in software that meets the end users’ needs more effectively due to continual testing throughout the process.

Figure 5: Agile Software Development

Source: Marinertek.com

More recently the ‘lean startup’ methodology has become increasingly popular as an innovation strategy. Similarly to agile development, this methodology also focuses on iterative testing (replacing the testing of software with business-hypotheses and new products). Through iterative testing and learning a startup is able to better understand and meet the needs of its users or customers, reducing the inherent risk of failure whilst keeping the required investment to a minimum. The success of high-tech startups has popularised this approach; however the key principles and lessons are not solely applicable to startups but also to established companies.

Despite the fact that (most of) these methodologies or philosophies have existed for a long time, they have not been adopted consistently across all industries. The digital or internet industry was built on these ‘agile’ principles, whereas the telecoms industry has sought to emulate this by adopting agile models and methods. Of course these two industries differ in nature and there will inevitably be constraints that affect the ability to be agile across different industries (e.g. the long planning and investment cycles required to build network infrastructure) yet these principles can broadly be applied more universally, underwriting a more effective way of working.

This report highlights the benefits and challenges of becoming more ‘agile’ and sets out the operator’s perspective of ‘agility’ across a number of key domains. This vision of the ‘Agile Operator’ was captured through 29 interviews with senior telecoms executives and is supplemented by STL analysis and research.

Barriers to (telco) agility 

…The telecoms industry is hindered by legacy systems, rigid organisational structures and cultural issues…

It is well known that the telecoms industry is hampered by legacy systems; systems that may have been originally deployed between 5-20 years ago are functionally limited. Coordinating across these legacy systems impedes a telco’s ability to innovate and customise product offerings or to obtain a complete view of customers. In addition to legacy system challenges, interview participants outlined a number of other key barriers to becoming more agile. Three principle barriers emerged:

  1. Legacy systems
  2. Mindset & Culture
  3. Organisational Structure & Internal Processes

Legacy Systems 

One of the main (and often voiced by interviewees) barriers to achieving greater agility are legacy systems. Dealing with legacy IT systems and technology can be very cumbersome and time-consuming as typically they are not built to be further developed in an agile way. Even seemingly simple change requests end in development queues that stretch out many months (often years). Therefore operators remain locked-in to the same, limited core capabilities and options, which in turn stymies innovation and agility. 

The inability to modify a process, a pricing plan or to easily on/off-board a 3rd-party product has significant ramifications for how agile a company can be. It can directly limit innovation within the product development process and indirectly diminish employees’ appetite for innovation.

It is often the case that operators are forced to find ‘workarounds’ to launch new products and services. These workarounds can be practical and innovative, yet they are often crude manipulations of the existing capabilities. They are therefore limited in terms what they can do and in terms of the information that can be captured for reporting and learning for new product development. They may also create additional technical challenges when trying to migrate the ‘workaround’ product or service to a new system. 

Figure 6: What’s Stopping Telco Agility?

Source: STL Partners

Mindset & Culture

The historic (incumbent) telco culture, born out of public sector ownership, is the opposite of an ‘agile’ mindset. It is one that put in place rigid controls and structure, repealed accountability and stymied enthusiasm for innovation – the model was built to maintain and scale the status quo. For a long time the industry invested in the technology and capabilities aligned to this approach, with notable success. As technology advanced (e.g. ever-improving feature phones and mobile data) this approach served telcos well, enhancing their offerings which in turn further entrenched this mindset and culture. However as technology has advanced even further (e.g. the internet, smartphones), this focus on proven development models has resulted in telcos becoming slow to address key opportunities in the digital and mobile internet ecosystems. They now face a marketplace of thriving competition, constant disruption and rapid technological advancement. 

This classic telco mindset is also one that emphasized “technical” product development and specifications rather than the user experience. It was (and still is) commonplace for telcos to invest heavily upfront in the creation of relatively untested products and services and then to let the product run its course, rather than alter and improve the product throughout its life.

Whilst this mindset has changed or is changing across the industry, interviewees felt that the mindset and culture has still not moved far enough. Indeed many respondents indicated that this was still the main barrier to agility. Generally they felt that telcos did not operate with a mindset that was conducive to agile practices and this contributed to their inability to compete effectively against the internet players and to provide the levels of service that customers are beginning to expect. 

Organisational Structure & Internal Processes

Organisational structure and internal processes are closely linked to the overall culture and mindset of an organisation and hence it is no surprise that interviewees also noted this aspect as a key barrier to agility. Interviewees felt that the typical (functionally-orientated) organisational structure hinders their companies’ ability to be agile: there is a team for sales, a team for marketing, a team for product development, a network team, a billing team, a provisioning team, an IT team, a customer care team, a legal team, a security team, a privacy team, several compliance teams etc.. This functional set-up, whilst useful for ramping-up and managing an established product, clearly hinders a more agile approach to developing new products and services through understanding customer needs and testing adoption/behaviour. With this set-up, no-one in particular has a full overview of the whole process and they are therefore not able to understand the different dimensions, constraints, usage and experience of the product/service. 

Furthermore, having these discrete teams makes it hard to collaborate efficiently – each team’s focus is to complete their own tasks, not to work collaboratively. Indeed some of the interviewees blamed the organisational structure for creating a layer of ‘middle management’ that does not have a clear understanding of the commercial pressures facing the organisation, a route to address potential opportunities nor an incentive to work outside their teams. This leads to teams working in silos and to a lack of information sharing across the organisation.

A rigid mindset begets a rigid organisational structure which in turn leads to the entrenchment of inflexible internal processes. Interviewees saw internal processes as a key barrier, indicating that within their organisation and across the industry in general internal decision-making is too slow and bureaucratic.

 

Interviewees noted that there were too many checks and processes to go through when making decisions and often new ideas or opportunities fell outside the scope of priority activities. Interviewees highlighted project management planning as an example of the lack of agility; most telcos operate against 1-2 year project plans (with associated budgeting). Typically the budget is locked in for the year (or longer), preventing the re-allocation of financing towards an opportunity that arises during this period. This inflexibility prevents telcos from quickly capitalising on potential opportunities and from (re-)allocating resources more efficiently.

  • Executive Summary
  • Understanding Agility
  • What does ‘Agility’ mean?
  • Barriers to (telco) agility
  • “Agility” is an aspiration that resonates with operators
  • Where is it important to be agile?
  • The Telco Agility Framework
  • Organisational Agility
  • The Agile Organisation
  • Recommended Actions: Becoming the ‘Agile’ Organisation
  • Network Agility
  • A Flexible & Scalable Virtualised Network
  • Recommended Actions: The Journey to the ‘Agile Network’
  • Service Agility
  • Fast & Reactive New Service Creation & Modification
  • Recommended Actions: Developing More-relevant Services at Faster Timescales
  • Customer Agility
  • Understand and Make it Easy for your Customers
  • Recommended Actions: Understand your Customers and Empower them to Manage & Customise their Own Service
  • Partnering Agility
  • Open and Ready for Partnering
  • Recommended Actions: Become an Effective Partner
  • Conclusion

 

  • Figure 1: Regional & Functional Breakdown of Interviewees
  • Figure 2: The Barriers to Telco Agility
  • Figure 3: The Telco Agility Framework
  • Figure 4: The Agile Organisation
  • Figure 5: Agile Software Development
  • Figure 6: What’s Stopping Telco Agility?
  • Figure 7: The Importance of Agility
  • Figure 8: The Drivers & Barriers of Agility
  • Figure 9: The Telco Agility Framework
  • Figure 10: The Agile Organisation
  • Figure 11: Organisational Structure: Functional vs. Customer-Segmented
  • Figure 12: How Google Works – Small, Open Teams
  • Figure 13: How Google Works – Failing Well
  • Figure 14: NFV managed by SDN
  • Figure 15: Using Big Data Analytics to Predictively Cache Content
  • Figure 16: Three Steps to Network Agility
  • Figure 17: Launch with the Minimum Viable Proposition – Gmail
  • Figure 18: The Key Components of Customer Agility
  • Figure 19: Using Network Analytics to Prioritise High Value Applications
  • Figure 20: Knowing When to Partner
  • Figure 21: The Telco Agility Framework