Tag: Elisa

Building the learning telco

Organisational learning is key to telcos’ success in the Coordination Age

Developments in technology and organisational digital transformations increased the pressure on learning and development (L&D) departments in telcos. L&D departments, many of which were compliance-focused, were tasked with upgrading telcos’ entire skills inventories to ensure that workforces were fit for new ways of working (e.g. AT&T’s “Workforce Reskilling” effort announced in 2016).

What was perhaps under-appreciated initially was that the need for L&D would not go away:

  • Telcos continue to operate in dynamic environments that are inherently unstable (e.g. pandemics, climate crises, new and evolving technologies);
  • Traditional telco revenue streams have remained under pressure, requiring new and innovative thinking to identify opportunities for growth.

The VUCA acronym (first coined in 1987) – standing for volatility, uncertainty, complexity, ambiguity – provides a useful framework to describe the current telco environment.

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The telco’s highly VUCA environment

learning telco

Source: STL Partners

Telcos have made changes to organisation structures in order to accommodate this reality, e.g. “flattening” the organisation and decentralising decision-making to accelerate the pace at which organisations can take action (absorb change and innovate).

Additionally, they are recognising the importance of learning to this process. Workforce skills must remain relevant and collective corporate intelligence must evolve to decide and inform winning strategies.

This type of “organisational learning” requires conscious efforts on the part of both the organisation and individual employees. It is not enough to make L&D the sole responsibility of an L&D team, or an HR department and to task them with identifying appropriate content and courses to push out to employees.

Organisations need to foster an environment where learning is encouraged and enabled in pursuit of organisational improvement, customer satisfaction, innovation and growth. After all, it is impossible to improve/do something new without learning in the first instance. Learning tools, processes and practices are required – and barriers to learning should be removed.

Learning barriers can include:

  • L&D teams creating bottlenecks to learning (e.g. restricted course access)
  • The existence of knowledge silos
  • Beliefs that “knowledge is power”
  • A lack of clear goals around using knowledge/new capabilities for improvement (i.e. learningto create behaviour change)
  • No incentives for individuals or teams to engage in learning
  • Uncertainty about processes for capturing and sharing learning
  • Fear of failure inhibiting trials in order to learn something new.

This report considers the key practices associated with organisational learning and identifies lessons from telcos who are progressing towards becoming a learning organisation.

Table of contents

  • Executive Summary
  • Introduction
  • The value of organisational learning
  • Enabling organisational learning
    • Types of learning in organisations
  • Organisational learning in practice
    • Learning as an organisational priority
    • Identifying learning purpose
    • Content-based learning
    • Person-led learning (knowledge sharing)
    • Process-led learning
    • Trial, reflection and practice
    • Recognition and rewards for learning
  • Towards learning organisations
    • Findings
    • Evaluation
  • Conclusions
  • Index

Related Research

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Stakeholder model: Turn growth killers into growth makers

Introduction: The stakeholder model

Telecoms operators’ attempts to build new sources of revenue have been a core focus of STL Partners’ research activities over the years. We’ve looked at many telecoms case studies, adjacent market examples, new business models and technologies and other routes to explore how operators might succeed. We believe the STL stakeholder model usefully and holistically describes telcos’ main stakeholder groups and the ideal relationships that telcos need to establish with each group to achieve valuable growth. It should be used in conjunction with other elements of STL’s portfolio which examine strategies needed within specific markets and industries (e.g., healthcare) and telcos’ operational areas (e.g., telco cloud, edge, leadership and culture).

This report outlines the stakeholder model at a high level, identifying seven groups and three factors within each group that summarise the ideal relationship. These stakeholder and influencer groups include:

  1. Management
  2. People
  3. Customer propositions
  4. Partner and technology ecosystems
  5. Investors
  6. Government and regulators
  7. Society

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1. Management

Growth may not always start at the top of an organisation, but to be successful, top management will be championing growth, have the capabilities to lead it, and aligning and protecting the resources needed to foster it. This is true in any organisation but especially so in those where there is a strong established business already in place, such as telecoms. The critical balance to be maintained is that the existing business must continue to succeed, and the new growth businesses be given the space, time, skills and support they need to grow. It sounds straightforward, but there are many challenges and pitfalls to making it work in practice.

For example, a minor wobble in the performance of a multi-billion-dollar business can easily eclipse the total value of a new business, so it is often tempting to switch resources back to the existing business and starve the fledgling growth. Equally, perceptions of how current businesses need to be run can wrongly influence what should happen in the new ones. Unsuitable choices of existing channels to market, familiar but ill-fitting technologies, or other business model prejudices are classic bias-led errors (see Telco innovation: Why it’s broken and how to fix it).

To be successful, we believe that management needs to exhibit three broad behaviours and capabilities.

  1. Stable and committed long term vision for growth aligned with the Coordination Age.
  2. Suitable knowledge, experience and openness.
  3. Effective two-way engagement with stakeholders. (N.B. We cover the board and most senior management in this group. Other management is covered in the People stakeholder group.)

Management: Key management enablers of growth

management-leadership-vision-growth-indicators

Source: STL Partners

Stable and committed long-term vision for growth

The companies that STL has seen making more successful growth plays typically exhibit a long-term commitment to growth and importantly, learning too.

Two examples we have studied closely are TELUS and Elisa. In both cases, the CEO has held tenure in the long-term, and the company has demonstrated a clear and well managed commitment to growth.

In TELUS’s case, the primary area of growth targeted has been healthcare, and the company now generates somewhere close to 10% of its revenue from the new areas (it does not publish a number). It has been working in healthcare for over 10 years, and Darren Entwistle, its CEO, has championed this cause with all stakeholders throughout.

In Elisa’s case, the innovation has been developed in a number of areas. For example, how it couples all you can use data plans and a flat sales/capex ratio; a new network automation business selling to other telcos; and an industrial IoT automation business.

Again, CEO Veli-Matti Mattila has a long tenure, and has championed the principle of Elisa’s competitive advantage being in its ability to learn and leverage its existing IP.

…aligned with the Coordination Age

STL argues that the future growth for telcos will come by addressing the needs of the Coordination Age, and this in turn is being accelerated by both the COVID-19 pandemic and growing realisation of climate change.

Why COVID-19 and Climate change are accelerating the Coordination Age

COVID-19-and-Climate-change-Coordination-Age-STL

 

Source: STL Partners

The Coordination Age is based on the insight that most stakeholder needs are driven by a global need to make better use of resources, whether in distribution (delivery of resources when and where needed), efficiency (return on resources, e.g. productivity), and sustainability (conservation and protection of resources, e.g. climate change).

This need will be served through multi-party business models, which use new technologies (e.g. better connectivity, AI, and automation) to deliver outcomes to their customers and business ecosystems.

We argue that both TELUS and Elisa are early innovators and pathfinders within these trends.

Suitable knowledge, experience and openness

Having the right experience, character and composition in the leadership team is an area of constant development by companies and experts of many types.

The dynamics of the leadership team matter too. There needs to be leadership and direction setting, but the team must be able to properly challenge itself and particularly its leader’s strongest opinions in a healthy way. There will of course be times when a CEO of any business unit needs to take the helm, but if the CEO or one of the C-team is overly attached to an idea or course of action and will not hear or truly consider alternatives this can be extremely risky.

AT&T / Time Warner – a salutary tale?

AT&T’s much discussed venture into entertainment with its acquisitions of DirecTV and Time Warner is an interesting case in point here. One of the conclusions of our recent analysis of this multi-billion-dollar acquisition plan was that AT&T’s management appeared to take a very telco-centric view throughout. It saw the media businesses primarily as a way to add value to its telecoms business, rather than as valuable business assets that needed to be nurtured in their own right.

Regardless of media executives leaving and other expert commentary suggesting it should not neglect the development of its wider distribution strategy for the content powerhouse for example, AT&T ploughed on with an approach that limited the value of its new assets. Given the high stakes, and the personalised descriptions of how the deal arose through the CEOs of the companies at the time, it is hard to escape the conclusion that there was a significant bias in the management team. We were struck by the observation that it seemed like “AT&T knew best”.

To be clear, there can be little doubt that AT&T is a formidable telecoms operator. Many of its strategies and approaches are world leading, for example in change management and Telco Cloud, as we also highlight in this report.

However, at the time those deals were done AT&T’s board did not hold significant entertainment expertise, and whoever else they spoke with from that industry did not manage to carry them to a more balanced position. So it appears to us that a key contributing factor to the significant loss of momentum and market value that the media deals ultimately inflicted on AT&T was that they did not engineer the dynamics or character in their board to properly challenge and validate their strategy.

It is to the board’s credit that they have now recognised this and made plans for a change. Yet it is also notable that AT&T has not given any visible signal that it made a systemic error of judgement. Perhaps the huge amounts involved and highly litigious nature of the US market are behind this, and behind closed doors there is major change afoot. Yet the conveyed image is still that “AT&T knows best”. Hopefully, this external confidence is now balanced with more internal questioning and openness to external thoughts.

What capabilities should a management team possess?

In terms of telcos wishing to drive and nurture growth, STL believes there are criteria that are likely to signal that a company has a better chance of success. For example:

  • Insight into the realistic and differentiating capabilities of new and relevant markets, fields, applications and technologies is a valuable asset. The useful insight may exist in the form of experience (e.g. tenure in a relevant adjacent industry such as healthcare, or delivery of automation initiatives, working in relevant geographies, etc.), qualification (e.g. education in a relevant specialism such as AI), or longer term insight (which may be indicated by engagement with Research and Development or academic activities)

[The full range of management capabilities can be viewed in the report…..] 

 

2. People…

 

Table of Contents

  • Executive Summary
  • Introduction
  • Management
    • Stable and committed long-term vision for growth
    • …aligned with the Coordination Age
    • Suitable knowledge, experience and openness
    • Two-way engagement with stakeholders
  • People
    • Does the company have a suitable culture to enable growth?
    • Does the company have enough of the new skills and abilities needed?
    • Is the company’s general management collaborative, close to customers, and diverse?
  • Customer propositions
    • Nature of the current customer relationship
    • How far beyond telecoms the company has ventured
    • Investment in new sectors and needs
  • Partner and technology ecosystems
    • Successful adoption of disruptive technologies and business models
    • More resilient economics of scale in the core business
    • Technology and partners as an enabler of change
  • Investors
    • The stability of the investor base
    • Has the investor base been happy?
    • Current and forecast returns
  • Government and regulators
    • The tone of the government and regulatory environment
    • Current status of the regulatory situation
    • The company’s approach to government and regulatory relationships
  • Society
    • Brand presence, engagement and image
    • Company alignment with societal priorities
    • Media portrayal

Related research

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What can telcos learn from Silicon Valley?

Silicon Valley: The promise of “Open” Innovation and agile experimentation

Until the early 2000s, Closed Innovation, based on a model of internal, centralised research and development, was the de facto way for companies to protect intellectual property and gain competitive advantage. Latterly, assisted by the tailwinds of increasing connectivity, there has been a shift in mindset towards Open Innovation – sourcing and acquiring external expertise, scanning the environment, and tapping into ideas and input from beyond the four walls of the business. Today, the array of innovation models is varied and ever-expanding: scouting, crowdsourcing, idea competitions, collaborative design and development, spin-outs, corporate ventures, incubators, joint ventures, in- and out-licensing of intellectual property, consortia, innovation platforms and ecosystems to name but a few. Increasingly, this activity is taking place in clusters – auspicious geographic concentrations of interconnected companies and institutions – the most famous of which is Silicon Valley.

Thanks to a unique confluence of assets – the presence of tech giants and leading research universities, an abundance of venture capital and skilled labour, a disruptive culture, and a relatively benign regulatory environment – Silicon Valley is one of the world’s leading hotbeds of innovation.

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Hundreds of organisations of various sizes and industries – even those with plentiful local R&D talent in their home markets – have been drawn to the Valley in the hope of importing outside-in innovation, identifying new products and partners, and harnessing its ecosystem to solve strategic problems. Telcos are no exception: since the early 2000s, telcos’ core businesses have come under increasing pressure from OTT players as well as wider market forces to innovate and grow. Open Innovation is the antithesis of telcos’ traditional, vertically-integrated approach of translating their own R&D efforts into internally-developed products and services, typically tightly linked to their existing customer bases and offerings. Operators are hoping some of the Valley’s magic dust of disruptive thinking and speed of execution will rub off on them.

However, insiders sometimes quip that the Boeing 747s flying out of San Francisco International Airport have “amnesic” properties. The executive groups that typically descend upon the Valley, hoping to learn from its incumbents both large and small, take copious notes and leave fired up about re-energising innovation in their home base. But once back within the corporate environment, the seeds of innovation struggle to germinate and the majority of initiatives fail to generate any substantial return on objectives. There appears to be a degree of cognitive dissonance between the expectation of such engagements, and their impact.

Other approaches to the Valley, from CVCs (Corporate Venture Capital investments in start-ups) to environmental scanning and venture-building, are better established, with hundreds of corporate outposts currently in place. Four major routes to outside-in innovation, with illustrative examples are shown below.

Four major routes to outside-in innovation

Open Innovation

Unfortunately, truly transformational success stories are few and far between (gains tend to be small or incremental in nature) and there is a long tail of failures and missed opportunities.

For STL Partners, this raises a series of questions:

  • What are telcos hoping to learn from Silicon Valley and how are they going about it?
  • What are the challenges they face in implementing and operationalising what they learn?
  • What can they do differently to overcome some of the common pitfalls of Open Innovation to drive more significant successes?

In addition to its own primary and secondary research, STL Partners explored the challenges and opportunities in depth with Jean-Marc Frangos – Executive Fellow at INSEAD, Executive in Residence at the Plug and Play Tech Center, and Advisor to the Telecom Council of Silicon Valley and former Senior VP of BT’s Innovation function. Located in the Bay Area, Jean-Marc benefits from a 360° view of the disruptive technologies, revenue opportunities and shifts in the in the Valley landscape, and advises European and Asian players on how to integrate such innovations into the incumbent telecoms environment.

What are telcos hoping to do in Silicon Valley?

There are currently somewhere between 300 and 500 corporate outposts in Silicon Valley, as varied in their industries, size and depth of operations as they are in their motives, which are not exclusively tech-focused. The majority have a relatively small footprint, such as those acting as an innovation “antenna” or corporate venture capital (CVC) office, although some have established a more structured presence, for example an innovation lab or R&D centre.

Despite the diversity of these outposts, their common goal is to sense and respond to technology shifts, whether they be disruptive opportunities or disruptive threats. Many of these corporations may be struggling to keep pace with innovation in their own industry and are looking to infuse their organisation with a more entrepreneurial mindset and attract creative talent to gain competitive advantage. In the case of telcos, most are already facing disruption while the remainder can see it looming on the horizon.

The key drivers for innovation outposts include:

  • Keeping a finger on the pulse of trends originating in the Valley;
  • Scouting emerging technologies with a view to investment, incubation, acquisition or some form of collaborative partnership and identifying new channels to market, new business models or new people/processes;
  • Acquiring expertise or best practices from outside the organisation that can be internalised (e.g. to evolve the corporate culture) with a view to accelerating the innovation cycle from start-up through Minimum Viable Product (MVP) to initial production.

Table of contents

  • Executive summary
  • Introduction
  • What are telcos hoping to do in Silicon Valley?
    • The dominant innovation outpost models in Silicon Valley
    • What to learn in Silicon Valley: Four levels of learning
    • Increasing acceptance of evolving business models
  • What should telcos do differently?
    • Purpose: Match effort to expectation
    • Whom to learn innovation lessons from in Silicon Valley
    • People: Who goes to the Valley, and who stays home
    • Practices: Dos and don’ts
  • Telco dynamics and challenges
    • Ambidextrous transformation is a hard art to master
    • Two-speed IT puts the brakes on digital culture
    • Capital-intensive infrastructure companies have a bigger turning circle
    • Design thinking must infuse the transmission belt
    • Telcos may struggle to win the battle for tech talent
  • Conclusion
  • Index

Related research

 

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How to identify and meet new customer needs

Customer-led innovation at Telia and Elisa

In order to secure competitive advantage and long-term growth, telcos need to identify and meet new customer needs. The importance of this is confirmed by the STL Partner’s Telco investment priorities survey published in January 2021. Understanding customer needs and innovation, both essential for addressing new needs and driving growth, featured in the top ten priorities.

Telco top investment  priorities

top-telco-investment-priorities-stl

Source:  STL Partners, Telecoms priorities: Ready for the crunch?

This report seeks to identify best practice for telcos. Through in-depth interviews with senior managers in Elisa and Telia, and an expert in disruptive innovation, we identify the critical success factors and lessons learned in these organisations.

Telia created Division X in 2017, a separate business unit focused on commercialising and growing revenue from emerging businesses and technologies such as IoT (including 5G), data insights, and digital B2C services. Its focus is on customer needs and speed of execution, to spearhead and accelerate innovation, which it deems necessary in Telia’s drive to “reinvent better connected living”.

International Digital Services is Elisa’s third main business division, alongside Consumer and Corporate, which serve the domestic market. As International Digital Services has matured, it has focussed specifically on addressing new needs and developing new services, in both industrial and corporate domains.

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The report is based on interviews with:

  • Liisa Puurunen, Vice President, Brand, CX and start-ups, International Digital Services, Elisa — Liisa has a background in leading new businesses and start-ups in Elisa in the Consumer division and International Digital Services. Liisa’s role is to understand where there are new needs to be met, and to get best practise in place across the whole customer journey, within both industrial and corporate domains.
  • Annukka Matilainen, Development Director for Omnichannel and Smart Automation, Elisa —Annukka led the Consumer team’s response to COVID-19
  • Stephanie Huf, Head of Marketing, Division X, Telia — Stephanie’s role is to support the business lines in Division X to in engaging with customers to identify their needs. For example, her team identifies what customers want, defines the value propositions and works with product and business teams to test these in line with customer insight. (Since participating in this research Stephanie Huf has moved to a new role.)
  • Anette Bohman, Strategy Director, Division X, Telia  — Anette supports and guides Division X in defining Telia’s future.
  • John McDonald, FIRSTEP — John is a strategist in disruptive innovation in the health industry in Canada. He helps leaders create alignment around how the forces of disruption are unfolding and where to place the bets. FIRSTEP works with health organisations searching for fresh insights that spark new opportunities for growth.

Create a separate team to maximise new business opportunities

A separate team has many benefits

New business requires a separate, dedicated team. Its needs are different from day-to-day business and it needs its own focus.

One of the biggest learnings for Elisa in addressing new opportunities, is that there needs to be a ‘sandbox team’ with its own resources and budgets, rules, methods and mindset. It must have access to senior managers for decision making and funding, and strong leadership.

The sandbox team needs to be remote from the demands of day-to-day operations and implementation. If finding new needs is only part of someone’s job it is difficult to manage, as short-term demands will inevitably take precedence. Delivery and experimentation are different functions and they should be separate.

Liisa Puurunen’s team is a start-up in its own right. It is leaner than the usual Elisa approach and people are only brought into the team when there is a test to be done, keeping it flexible.

Rationale for a separate team

separate-team-rationale
Source: STL Partners

Contents

  • Executive Summary
    • Create a dedicated and separate team
    • Take a customer centric approach at all stages of innovation
    • Types of innovation will meet different new needs
  • Introduction
  • Create a separate team to maximise new business opportunities
    • A separate team has many benefits
    • Telia Smart Family: The case for a separate innovations team
    • Evaluate success in relevant ways that may be non-traditional
  • Take a customer centric approach to all stages of innovation
    • Ensure a customer centric culture
    • Start with a customer problem
  • Meeting needs and scaling bets
    • Co-create with customers, but choose them carefully
    • Elisa’s empowered teams enable a successful response to COVID-19
  • Types of innovation to meet different new needs
    • New needs in the core versus new businesses
    • Dedicate some resource to extreme innovation
    • Telia Data Insights: New Business innovation in response to COVID-19
    • The case for disruptive innovation
  • Plan exit strategies
    • Perseverance and pivoting can bring success
    • Be prepared to kill your darlings

Related research

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AI is starting to pay: Time to scale adoption

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AI adoption yields positive results

Over the last five years, telcos have made measurable progress in AI adoption and it is starting to pay off.  When compared to all industries, telcos have become adept at handling large data sets and implementing automation. Over the last several years the telecoms industry has gone from not knowing where or how to implement AI, to having developed and implemented hundreds of AI and automation applications for network operations, fraud prevention, customer channel management, and sales and marketing. We have discussed these use cases and operator strategies and opportunities in detail in previous reports.

For the more advanced telcos, the challenge is no longer setting up data management platforms and systems and identifying promising use cases for AI and automation, but overcoming the organisational and cultural barriers to becoming truly data-centric in mindset, processes and operations. A significant part of this challenge includes disseminating AI adoption and expertise of these technologies and associated skills to the wider organisation, beyond a centralised AI team.The benchmark for success here is not other telcos, or companies in other industries with large legacy and physical assets, but digital- and cloud-native companies that have been established with a data-centric mindset and practices from the start. This includes global technology companies like Microsoft, Google and Amazon, who increasingly see telecoms operators as customers, or perhaps even competitors one day, as well as greenfield players such as Rakuten, Jio and DISH, which as well as more modern networks have fewer ingrained legacy processes and cultural practices to overcome.

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Telecoms has a high AI adoption rate compared with other industries

AI pays off

Source: McKinsey

In this report, we assess several telcos’ approach to AI and the results they have achieved so far, and draw some lessons on what kind of strategy and ambition leads to better results. In the second section of the report, we explore in more detail the concrete steps telcos can take to help accelerate and scale the use of AI and automation across the organisation, in the hopes of becoming more data-driven businesses.

While not all telcos have an ambition to drive new revenue growth through development of their own IP in AI, to form the basis of new enterprise or consumer services, all operators will need AI to permeate their internal processes to compete effectively in the long term. Therefore, whatever the level ambition, disseminating fundamental AI and data skills across the organisation is crucial to long term success. STL Partners believes that the sooner telcos can master these skills, the higher their chances of successfully applying them to drive innovation both in core connectivity and new services higher up the value chain.

Contents

  • Executive Summary
  • Introduction
  • Developing an AI strategy: What is it for?
    • Telefónica: From AURA and LUCA to Telefónica Tech
    • Vodafone: An efficiency focused strategy
    • Elisa: A vertical application approach
    • Takeaways: Comparing three approaches
  • AI maturity progression
    • Adopt big data analytics: The basic building blocks
    • Creating a centralised AI unit
    • Creating a new business unit
    • Disseminating AI across the organisation
  • Using partnerships to accelerate and scale AI
    • O2 and Cardinality
    • AT&T Acumos
  • Conclusion and recommendations
  • Index

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How mobile operators can build winning 5G business models

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STL Partners has long believed that telecoms operators need to and can do more to add value to their consumer and enterprise customers and to society more generally. For the telecoms industry, the need to do more is illustrated by flat or declining revenues and rising capital expenditure and debt levels. The opportunity for telecoms to add more value is also clear. The demands of society now call for greater coordination between all players and new technology – 5G, analytics, AI, automation, cloud – is now spawning the Coordination Age.

Figure 1: The Coordination Age – new paradigm, new telco purposeThe coordination age overview

Source: STL Partners

Operators have the credibility, skills and relationships to contribute more in the Coordination Age. But the opportunity will not drop into their laps. Improved networks are not, of themselves, the driver of new value: it accrues to the provider of services that run on the network and it is up to operators to develop platforms and services that exploit ubiquitous, high-bandwidth connectivity.

So far, operators have found moving beyond connectivity challenging. There are a handful of success stories; most attempts to develop vertical solutions have failed to move the needle. In this report, we draw on successes and failures from within and outside telecoms to outline 8 core guiding principles for ambitious leaders within the telecoms industry who are determined to help their organisations to deliver more than connectivity.

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5G: A catalyst for change

In some ways, the challenge/opportunity for mobile operators has been present for the last 5-10 years: limited incremental revenue growth in voice, messaging, and data.

However, 5G is a catalyst for real change. There are internal pressures from the investments being made that mean operators must create new revenue streams. More positive reasons relate to increased demand for telco-driven services and the technological changes that telcos have implemented which will help the commercial side to adapt. Below are some of the main reasons why 5G has created a resurgent need to change business models.

  1. Making returns on network investments: It’s a given that 5G cannot be delivered without significant investment by the operators: be it in spectrum acquisition, upgrading the RAN and core network, managing a more distributed architecture of small cells, etc. Telcos can focus on ensuring that network runs efficiently to maintain margins, however many will need to look to new services. Data usage will surge, but the price customers will pay for each gigabyte will decline at a disproportionate rate.
  2. Building on telco cloud and edge computing platforms: Telcos have started to invest in developing their networks to become more like the cloud platforms that underpin the large cloud providers’ services. In fact, it’s a key part of the 5G core. Part of this has been the move towards SDN, network virtualisation and integrating edge computing. This flexible platform will allow telcos to innovate quickly and create new differentiated services on top if they have the desire to change their financial and operational models.
  3. Unlocking an enterprise business: Before 5G, mobile operators’ enterprise businesses have involved selling SIMs to enterprise customers with some forays into value-added services, such as cloud storage, mobile device management and M2M communications. Enterprises are genuinely interested in 5G and the capabilities it brings. For some, 5G has become an umbrella term for technological innovation. This is a good thing for the mobile industry, as it means enterprises will open doors to telcos and be keen to engage them for new solutions.
  4. Creating business value: 5G’s unique capabilities will enable use cases that solve real problems, particularly in industrial transformation. This last point is exemplified by research STL Partners previously conducted on the business value 5G brings to certain verticals by enhancing productivity, increasing output, creating efficiencies, etc. However, much of this value is extracted by the applications, solutions and services on top of the underlying network.

Figure 2: 5G enabled use cases could increase GDP by $1.5 trillion by 2030

Source: STL Partners

Table of Contents

  • Executive Summary
  • Introduction
  • 5G: A catalyst for chang
  • Guiding principles for mobile operators seeking to move beyond connectivity
    1. Select priority verticals and how you will compete in the them
    2. Adopt a new approach to resource allocation: less CapEx and more OpEx
    3. Material OpEx should focus on building new skills, assets, capabilities, relationships
    4. Establish senior management commitment and independence for the new venture
    5. Focus on commercial as well as technological differentiation in order to disrupt verticals
    6. De-emphasise network integration – at least to start with
    7. Recognise that M&A will be needed for market entry in most cases
    8. Realise that organic growth can work in exceptional operator or market circumstances
  • Conclusion

 

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Elisa Automate: Growing value with sisu

Elisa’s transformation journey

Almost every telco aspires to innovate and become a ‘digital services player’, selling more than just data, voice, messages, and entertainment services, but few have made significant inroads to this aim.

Yet Elisa, the market leader in Finland, which has a population of only just over 5.5 million people, can stake a claim to having achieved more than most.

The Finnish word ‘sisu’ has no direct English translation. It means a spirit of determination, independence and fortitude, and is considered by some Finns to be the heart of Finnish character.

Elisa and the other Finnish telcos certainly have plenty of sisu. They have resolutely charted their own course and prospered, with Elisa quadrupling its market valuation over the last ten years.

Elisa’s share price has quadrupled since 2009 

Source: Yahoo Finance

The genesis of Elisa Automate

Elisa’s overall strategy was based on a sound but uncommon piece of customer insight: nobody knows what a megabit of data actually is, so it is crazy to price data services based on the volume of data used. So Elisa and the other players in the Finnish market moved to unlimited data packages prices by speed (see report Sense check: Can data growth save telco revenues?).

The consequences of this decision have been that Finnish customers use a lot of data, and secondly, Finnish operators have built out coverage so that they can enjoy using it whenever and wherever.

This means that Elisa has to deliver a lot of data across its network.

Elisa’s data traffic has grown massively

Source: Elisa

Elisa has grown its revenues and EBIT too

Source: Elisa

Necessity can be the mother of invention

To manage profitability in a market where use and therefore data volume is effectively unlimited, Elisa had to tie its costs firmly to its revenues, and to do so elected to keep the ratios of capex and opex to revenue flat. This requires a very clear focus on cost management, and a determination to take every step possible to do so.

Elisa’s capex/revenue ratio is surprisingly low and stable

Elisa capex ratio

 

Source: Elisa

Out of this need came a powerful drive for automation: not to simply cut costs or reduce headcount, but to make the company as efficient as possible.

The result is Elisa Automate, a fully automated Network Operations Centre (NOC), one of three new business concepts that it is selling to others (in this case, telcos), along with Elisa SmartFactory and its video conferencing aggregation service.

Elisa is clearly succeeding, and not just in its financial results. For example, 18% of Finnish business customers say that it is the most innovative IT actor in its market, compared to 6% for CGI and 5% for Fujitsu.

STL Partners has long watched Elisa’s progress with a high degree of fascination. Elisa and its Finnish peers are a little like the Galapagos Islands of telecoms evolution but marked extraordinary by their distinctive approaches rather than extreme geographical isolation.

Contents:

  • Introduction
  • Elisa: creating an innovator
  • Building a stable foundation for innovation
  • Making the most of Finland’s advantages
  • The genesis of Elisa Automate
  • The early drivers of automation
  • The move towards ‘zero touch’
  • Augmenting human intelligence
  • Automation supports rapid mobile service revenue growth
  • Commercialising the opportunity
  • The value proposition
  • Customer spotlight: Orange Spain
  • Conclusions

Figures:

  1. Elisa’s share price has quadrupled since 2009
  2. Elisa’s data traffic has grown massively
  3. Elisa has grown its revenues and EBIT too
  4. Elisa’s Capex/Revenue ratio is surprisingly low and stable
  5. Elisa shares data showing network performance improvements through automation

Three new telco business models: Soft-net, Cloud-net, Compute-net

Introduction

This report outlines three new telecoms business models that builds on previous research where we have outlined our vision of an emerging third age of telecoms called the Coordination Age. This is based on a global need to improve the efficiency of resource utilisation is manifesting in industries and individuals as a desire to “make the world work better”. We discuss this concept in detail in the following reports:

We believe that three new business models for telcos are emerging as part of the Coordination Age.

  • The Soft-Net: the core business remains connectivity, but the softwarisation of the network through SDN / NFV enables the network to adapt and scale to support new, advanced connectivity services. This includes third-party digital and networked-compute services that depend on the physical network connectivity the Soft-Net provides.
  • The Cloud-Net: also connectivity-focused, but with the production, delivery and consumption of services increasingly effected via the cloud (i.e. cloud-native). SDN and virtualisation enable capacity and services to be spun up, managed and delivered on demand over any physical network and device.
  • The Compute-Net: the core business is to provide distributed, networked, compute- and software-based services, often for specific enterprise verticals. These depend on SDN and NFV to deliver the ultra-fast, low-latency compute, throughput and routing capabilities required.

The three new models represent distinct strategic options for telcos looking to either: optimise and evolve their existing connectivity business; create new value from cloud-based, ‘horizontal’ platforms; or expand into new vertical markets – or a combination of all three approaches. This is illustrated here:

Interdependence between the three future telco business models

Source: STL Partners

In other words:

  • The Soft-Net operates the physical and virtualised infrastructure that delivers flexible, advanced connectivity in support of Cloud-Net and Compute-Net services (as well as well as legacy communications and connectivity services, delivered in a more scalable and cost-effective way)
  • The Cloud-Net delivers flexible, on-demand connectivity over hybrid infrastructure (including that owned by multiple Soft-Nets) in support of the increasingly complex and variable networking requirements of globally distributed, digital enterprises
  • The Compute-Net delivers vertically focused, compute-enabled processes and outcomes across all areas of industry and society. In doing so, it relies on networking and cloud platform services supplied by the Soft-Net and Cloud-Net, which may or may not be vertically integrated as part of its own organisation.

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The three telecoms business models link to NFV / SDN strategies

One of the distinguishing features of these models is the different modes of telco engagement in NFV and SDN they are potentially driven by. In previous analyses, we have identified three pathways towards NFV and SDN deployment. This is how they link to the three business models:

Figure 1: The three future telco business models and corresponding NFV pathways

Source: STL Partners, NFV / SDN deployment pathways: Three telco futures

In the rest of this report, we define these telecoms business models in more detail and illustrate how they present a pragmatic framework for telcos to focus their technology investments and develop valuable new Coordination Age services.

Contents:

  • Executive Summary
  • Introduction
  • Three telco futures and Telco 2.0
  • Chapter 1: Three telecoms business models for the Coordination Age
  • Three new business models: but why ‘telco’?
  • Business model analysis: Telcos’ vs competitors’ strengths
  • Relationship between the Soft-Net, Cloud-Net and Compute-Net business models
  • Chapter 2: Roles of the Soft-Net, Cloud-Net and Compute-Net in a ‘driverless car-as-a-service’ ecosystem
  • A driverless car-as-a-service business involves coordination of data, processes and events across a broad supply chain
  • Soft-Nets provide the mainly wireless connectivity
  • Cloud-Nets provide the hybrid, on-demand wide-area networking
  • Compute-Nets design and coordinate the ecosystem
  • Conclusions
  • The Coordination Age: A new purpose for telecoms, and three models for realising it
  • Key takeaways for telcos

Figures:

  1. The three future telco business models and corresponding NFV pathways
  2. The Telco 2.0 infrastructure and service stack
  3. Interdependence between the three future telco business models
  4. Two examples of the three new business models
  5. The three new business models overview
  6. Telcos face some fierce competition as they move up the stack
  7. Telco expansion across the three business models
  8. Advantages and disadvantages of vertical integration
  9. Mapping the Soft-Net, Cloud-Net and Compute-Net roles in a driverless car environment
  10. Types of data and corresponding compute-based services in a driverless car-as-a-service ecosystem

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Elisa Smart Factory: How to win over industry leaders in two years

Elisa’s Smart Factory solution

As STL Partners has described in The Coordination Age: A third age of telecoms, moves are afoot in the global digital economy to improve the efficiency of resource utilisation by combining the digital and physical worlds in new and innovative ways. Elisa’s Smart Factory solution is a prime example of how telcos can address this need.

Coordinating manufacturing

In the case of manufacturing industries, understanding and managing the flow and progress of materials and goods through production processes has long been a critical component of business success.

Managing and continually improving complex processes is central to operational success on the supply-side of the manufacturing industry. This includes everything from a floor manager overseeing production, to time-and-motion studies, total quality management, just-in-time production, robotics and automation, and many other managerial and operational approaches.

A number of new concepts and practices are now emerging, driven by the same imperatives but arising to a degree independently and in different disciplines, for example:

  • Industry 4.0 ‘the fourth industrial revolution’ – the trend of automation and data exchange in manufacturing industries
  • Digital twins – a virtualised version of a real thing, a bit like an avatar but for a thing rather than a person. It can simulate the real item, interact with it, and exchange information and commands with other digital twins based on pre-defined rules
  • The Industrial Internet of Things (IIoT) – connecting industrial devices, sensors, equipment, etc., to gather and exchange information, and sometimes perform remote control

Numerous companies have embarked on the journey to incorporate and use such connected technologies. However the degree of progress made varies greatly.

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A growing industry

Connecting machinery is far from a new idea. Many industrial machines and processes are already highly connected and automated, and this goes back as far as sixty years in SCADA (Supervisory Control and Data Acquisition) systems in electricity power station control.

What is new is the ability and desire to link these systems together and allow data exchange and a degree of autonomy within managed bounds. This can optimise performance, improve productivity, and ultimately lead to new operational business models.

There are many different possible paths to achieving these ends. For instance, powerful industrial players and consortia are all trying to establish leadership in different ways. Heavyweight contenders on the industry side include GE, Bosch, Siemens, and PTC, with consortia including the somewhat mystically titled All Seeing Alliance.

STL Partners will explore the wider opportunity and main players competing in this field in an upcoming report titled ‘Why we need an Internet for Things’.

Enter Elisa, the innovative Finlander

Elisa is the leading Finnish mobile and fixed operator and No.2 player in Estonia. It has 6.2 million customers.

Yet despite its relatively small footprint compared to some of the industry giants, STL Partners regards Elisa as one of the most innovative operators in the world, and certainly in Europe. Indeed, 18% of Finnish business customers say that it is the most innovative IT actor in its market, compared to 6% for CGI and 5% for Fujitsu.

One of its notable recent innovations is a totally automated Network Operations Centre (NOC). To create this, Elisa had to go through its own journey of process engineering and automation.

Elisa now resells its Elisa Automate NOC solutions to other operators. Similarly, it has leveraged the IP and learning to create Elisa Smart Factory, a solution to help global enterprise customers achieve the levels of success Elisa has achieved itself.

Our thanks to Henri Korpi, EVP New Business Development, and Kari Terho, General Manager, Smart Factory at Elisa, who talked to us openly about the proposition, the business, and how it came into existence.

Contents:

  • Executive Summary 
  • Introduction
  • Understanding manufacturing customers’ problems
  • Unplanned downtime
  • Unstable production quality
  • Lack of visibility
  • Practical obstacles to smart manufacturing
  • How Elisa approached the solution
  • Creating a service operation centre
  • Smart Factory’s claims
  • How did Elisa get here?
  • “There’s loads of discussion of which platform is best. What you actually need is a solution”
  • Conclusions
  • Success factors and lessons for others
  • Challenges
  • Next steps

Figures:

  1. Downtime, data usage and visibility – the three dogs of manufacturing
  2. Elisa Smart Factory Schematic
  3. Elisa Smart Factory screenshot
  4. Typical business objectives of Smart Factory solutions
  5. What an Elisa 3D Digital Twin looks like
  6. A high level view from Elisa’s “End-to-End Cockpit”
  7. Results from Elisa’s automated NOC

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Network AI: The state of the art

Introduction

This report is part of a series exploring how telecoms operators can leverage artificial intelligence (AI) to improve their business operations, from customer experience to new services. Previous reports on AI in telecoms include:

This report explores the applications of AI for network operations, detailing the prerequisites and stages to implementing AI and automation in networks, real-world examples of what some telcos have done already, and their potential value across different application areas.

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We divide the applications for AI in telecoms networks into three main categories:

  • Fault detection, prediction and resolution: speeding up the process of identifying and resolving network faults, including predictive maintenance. This also includes identifying and mitigating network security risks, although security is a highly specialised field that merits its own report, so we do not cover it in detail here.
  • Network optimisation: optimising the use of network resources to mitigate the impact of network faults and adapt to or anticipate changes in demand. This is also the foundation for automated service provisioning in software defined networks, while insights on network usage and traffic could be valuable for new service creation.
  • Network planning and upgrades: optimising new infrastructure planning as well as the transition from legacy to next generation network solutions.

The first area is critical for all telcos, since service impairments are an inevitable element of running a network. The second is of immediate value for telcos that are still in the process of expanding existing network coverage and density, since it can enable operators to use their existing resources more efficiently. However, it is also increasingly tied into the first area of fault detection, since a large part of the fault resolution process is finding ways to re-route traffic from underperforming to underused assets, a process that is made easier with the adoption of SDN and NFV – processes can only be automated if they are software-based.

Compared with the first two categories, using AI for smarter network planning and upgrades is a nascent field. This is partially because many Tier 1 operators, who are leading the charge in adoption of AI elsewhere in network and business operations, completed the bulk of 4G deployments and have not yet fully embarked on 5G deployments. However, this report also looks at some innovative applications of image recognition models for network expansion in emerging markets.

While most of the data used for training and informing AI systems across network operations comes from operators’ own networks, telcos are also beginning to tap into new data sources to further refine their decision-making, such as using drones and image recognition to inspect towers, weather patterns and social media data.

Laying the foundations for AI in telecoms networks

Before jumping into how telcos are implementing AI for fault detection and resolution and in network operations, it is important to clarify what we mean by AI, and lay out the pre-requisites for any meaningful use of the technology.

What counts as AI? From automation to advanced AI

The term AI is nebulous – everyone has a different definition for it. Is it when a computer can make a faster, more accurate decision than a human?  Is it when a process is fully automated? Is it when the computer learns and continuously improves its decisions in real-time?

Wherever people draw the line between manual processes, (big) data analytics, automation and machine learning (ML) / AI, no company goes directly from manual to AI in one go. The transition is gradual. In this report we therefore use a broad definition of AI in this report, as outlined in Figure 1.

Figure 1: Not all AI is equal

Rules-based automation to machine learning

Source: STL Partners

Two transitions are happening in parallel as operators move from left to right on Figure 1. First, there is a shift towards increasingly intelligent analytics techniques, from rules-based automation, where policies outline if-then sequences of actions for the computer, to machine learning supported automation, where models are trained to fulfil an intent (a goal) based on guidelines from experts and historical data.

The second transition that occurs in the move towards more sophisticated AI systems relates to decision-making. In rules-based automation, computers don’t have any decision-making power, they can only take pre-defined actions in specific circumstances. Making the transition from telling computers how to do something to what you want them to do means giving computers decision-making power. Telcos can do this gradually, by requiring humans to verify and approve recommended decisions before they are implemented. But in the promised future 5G and ‘sliceable’ networks, human approval for routine decisions would require more network engineers than operators could profitably employ, or drastically slow down network operations. This is not just a technical issue for telcos but also a cultural one that demands clarity from management teams on the evolving role of network engineers.

Contents:

  • Executive Summary
  • Making the shift from manual operations to autonomous, intelligent networks
  • Recommendations
  • Introduction
  • Laying the foundations for AI in telecoms networks
  • What counts as AI? From automation to advanced AI
  • AI works at two levels for network operations
  • Data: The bridge between rules-based automation and ML
  • Fault detection, prediction and resolution
  • What is it worth?
  • How does it work?
  • Real-world example of a recommendation model: AT&T Tower Outage and Network Analyzer
  • Next step: From fixed to self-learning policies
  • Optimising network capacity
  • What are self-optimising networks worth?
  • Use case overview
  • How to do it
  • From self-optimising to knowledge-defined networks
  • AI for network planning
  • Telefónica case study
  • Driving automation internally versus partnering with vendors
  • Reasons for developing solutions internally
  • Reasons for partnering with a vendor
  • Vendor profiles
  • How AI fits with SDN/NFV
  • Conclusions and recommendations

Figures:

  • Figure 1: Not all AI is equal
  • Figure 2: Rules-based automation versus machine learning
  • Figure 3: A snapshot of rules-based automation versus machine learning
  • Figure 4: Overview of automation and AI in network operations
  • Figure 5: Telemetry is faster and uses less compute power than SNMP
  • Figure 6: Elisa growth of automated trouble ticket handling
  • Figure 7: Tupl results for automatic customer complaints resolution AI platform
  • Figure 8: Implementing fixed policies for fault detection and resolution
  • Figure 9: Visualisation of network alert clustering tool
  • Figure 10: A self-healing network
  • Figure 11: Elisa self-optimising network results
  • Figure 12: Elisa maintained flat capex intensity throughout 4G deployment
  • Figure 13: Finland 4G network performance, August 2018
  • Figure 14: Self-organising network example use cases
  • Figure 15: Numerous applications of machine learning and AI for 5G networks
  • Figure 16: Break self-optimising networks down into mini loops
  • Figure 17: The knowledge-defined network
  • Figure 18: Facebook TCO savings over traditional multilayer planning
  • Figure 19: Telefónica image recognition for network planning
  • Figure 20: Ciena Blue Planet overview
  • Figure 21: Google SDN layers
  • Figure 22: Overview of cross-industry initiatives relating to network AI and automation
  • Figure 23: Telefónica network automation roadmap
  • Figure 24: Overview of SK Telecom Advanced Next Generation OSS (TANGO)

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