Tag: predictive maintenance

AI & automation for telcos: Mapping the financial value

This is an update to STL Partners report A3 for telcos: Mapping the financial value, published in May 2020, which estimated the financial value of automation, AI and analytics (A3) through bottom up analysis of potential capex/opex savings or revenue uplift from integrating A3 into 150+ processes across a telco’s core operations.

The value is measured on an annual basis in dollar terms and as a proportion of total revenue for a “standard telecoms operator”. Access to the full methodology and definition of a standard telco is available in the report Appendix.

We categorise the value of automation, AI and analytics (A3) in telecoms across operational area, as well as type and purpose of A3 technology. Our graphic below summarises the value of A3 across the following six types of technology:

  1. Making sense of complex data: Analytics and machine learning used to understand large, mostly structured data sets, looking for patterns to diagnose problems and predict/prescribe resolutions.
  2. Automating processes: Intelligent automation and RPA to enable decision making, orchestration and task completion within telco processes.
  3. Personalising customer interactions: Analytics and machine learning used to understand customer data, create segmentation, identify triggers and prescribe actions to be taken.
  4. Support business planning: Analytics and machine learning used in forecasting and optimisation exercises.
  5. Augmenting human capabilities: AI solutions such as natural language processing and text analytics used to understand human intent or sentiment, to support interactions between customers or employees and telco systems.
  6. Frontier AI solutions: A number of individual AI solutions which have particular, specialist uses within a telco.

For further detail on this categorisation methodology, see STL Partners report The telco A3 application map

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What’s new in 2022

The colouring of the use case categories in the graphic below remains largely unchanged from May 2020. Some uses of A3 were reasonably mature in that timeframe and already rolled out in a typical telco, so their value was already well understood.

We estimate that the most valuable use case categories, primarily in networks and operations, deliver over $50 millions in annual benefits – and sometimes up to hundreds of millions. Throughout this report we express the value in dollar terms and as a percentage of savings within each domain. This is because while $50 million is clearly a significant sum, it accounts for just 0.33% of total revenues for our standard operator, so showing values for unique use case categories as a proportion of total revenues undermines the potential value A3 can add to individual teams, and in turn contribute to significant aggregate value across an operator.

Overview of the financial value of A3

financual-value-A3

Source: STL Partners, Charlotte Patrick Consult

In our May 2020 research, many of the more sophisticated uses of A3 were understood in theory but yet to be implemented. Researching these various newer uses cases throughout 2021 has revealed that many are now, at least partly, rolled out (although some are still waiting for cleaner data or more orchestration capabilities).

However, there were a few new case studies with financial benefits that necessitated more than small changes to the 2020 financial value calculations. Summarising the changes illustrated in the graphic above:

  • ​The most noticeable change in uptake for A3 was in the BSS domain. Vendors and telcos were not discussing much beyond RPA and basic analytics in 2020, but there are now a whole range of potential uses for ML (typically in the box labelled “Revenue management” in the graphic above). The question of how much additional financial value to assign to this is interesting – some of the A3 will ensure that the rating and charging systems can cope with the additional volume and complexity around 5G and IoT billing, so an allocation of revenue uplift has been assigned. However, this revenue benefit only accounts for around 6% of the additional $83 million in value from A3 in networks and operations estimated in this update.
  • We have added partner management as a new use case category, within operations. This is to allow A3 value to be added as telcos work with more partners and in new ecosystems, and accounts for 6% of additional value in networks and operations in this update.
  • An increase in the assumed value of A3 within marketing programs, owing to the addition of ML to improve the design of new offers.
  • The value of a previous use case category labelled “Troubleshooting” has been subsumed into “Unassisted channels”, as telcos find it difficult to implement troubleshooting tools for customers.
  • Some increase in financial benefit around customer chatbots and field services, due to new case studies showing financial value.

Our report includes a section for each of the first three columns of the graphic above (Networks and operations, customer channels, marketing and sales). The final column (other functions) doesn’t currently have financial calculations underpinning it as values are thought to be insubstantial in comparison to the first three columns.

Table of contents

  • Executive summary
  • Overview of the financial value of automation, AI and analytics (A3)
  • Financial value by business unit
    • BSS, OSS and networks
    • Customer channels
    • Sales and marketing
  • Appendix
    • Methodology for Calculating Financial Value
    • Augmented Analytics Capabilities

Related Research

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A3 for telcos: Mapping the financial value

What is analytics, AI and automation worth to telecoms operators?

This report is the second in a two-part series mapping the process and assessing the financial value of automation, analytics and artificial intelligence (AI) in telecoms. In the first report, The value of analytics, automation and AI for telcos – Part 1: The telco A3 application map, we outlined which type of technology was best suited to which processes across a telco’s operations.

In this report, we assess the financial value of each of the operational areas, in dollar terms, for an average telco. Based on our assessment of operator financials and operational KPIs, the figure below outlines our assumptions on the characteristics of an “average” telco used as the basis for our financial modelling. The characteristics of this telco are as shown below, with a slight skew towards developed market operator characteristics since this is currently where most industry proof points used in our modelling have been implemented.

The characteristics of an average telco

characteristics of an average telco

Source: STL Partners, Charlotte Patrick Consult

The first report in the series analysed how each A3 technology could be applied similarly across different functional units of a telecoms operator, e.g. machine learning or automation each have similar processes in network management, channel management and sales and marketing.

Evaluating AI and automation use cases in four domains

To measure financial impact, this report returns to a traditional breakdown of value by functional unit within the telco, breaking down into four key areas:

  1. Network operations: Network deployment, management and maintenance, and revenue management
  2. Fraud: Including services, online, and internal fraud risks
  3. Customer care: Including all assisted and unassisted channels
  4. Marketing and sales: Understanding customers, managing products, marketing programs, lead management and sales processes.

Through an assessment of nearly 150 individual process areas across a telecoms operator’s core operations, we estimate that A3 can deliver the average telco more than $1 billion dollars in value per year, through a combination of revenue uplift and opex and capex savings, equivalent to 7% of total annual revenues.

As illustrated below, core network operations management accounts for by far the greatest proportion of the value.

The relative value of automation, AI and analytics across telco operations

The relative value of AI, automation and analytics across telco operations

Source: STL Partners, Charlotte Patrick Consult

This likely still underrepresents the total, long term potential value of A3 to telcos, since this first iteration does not model the value of A3 processes in areas less unique to telecoms, including supply chain, finance, IT and HR. No doubt that even within the core area of operations, there are potential process areas that have yet to be discovered or proven, and which we have overlooked in our initial attempt to model the value of A3 to telcos. Meanwhile, this is focused purely on telco’s internal operations so also excludes any potential revenue uplift from new A3-enabled services, such as data monetisation or development of AI-as-a-service type solutions.

That said, operators cannot implement all of these processes at once. The enormous challenge of restructuring processes to be more automated and data-centric, putting in place the data management and analytics capabilities, training employees and acquiring new skills, among many others, means that while many leading telcos are well on their way to capturing this value in some areas, very few – if any – have implemented A3 across all process areas. As a benchmark, Telefónica is an industry leader in leveraging automation and AI to improve operational efficiency, and in 2019 it reported total operational savings of €429mn across the entire group. While this is primarily focused on customer facing channels, so likely excludes the value of A3 in many network operations processes, for instance energy efficiency which is delivering significant value to Telefónica and others, it suggests there remains lots of value still to capture.

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Methodology

The financial modelling for the value of A3 was done through an individual assessment of each of the 150+ process areas to understand the main activities within that area of operations, and how automation, analytics and/or machine learning and other AI technologies could be used within those activities. From there, we assess the value of integrating these technologies to existing operational functions to make them more efficient and effective. This means that we do not attribute any additional value to telcos from implementing new technologies that include A3 as a core element of their functionality, e.g. a multi-domain service orchestrator, implemented as part of software-defined networking.

Our bottom up assessment of each process is also validated through real-world proof points from operators or vendors. This means that more speculative areas of A3 application in operators are calculated to offer relatively limited value. As more proof points emerge, we will incorporate them into future iterations.

Table of contents

  • Executive Summary
    • Where is the largest financial benefit from A3?
    • What should telcos prioritise in the short term?
    • How long will it take for telcos to realise this value?
    • What next?
  • Introduction
    • Methodology
  • Breaking down the value of A3 by operational area
    • Network, OSS and BSS
    • Fraud management
    • Care and commercial channels
    • Marketing and sales
  • Conclusions and recommendations

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$1.4tn of benefits in 2030: 5G’s impact on industry verticals

Understanding the 5G opportunity in other industries

The aim of this report is to highlight the impact that 5G will have on global GDP between 2020 and 2030. To do this, we have focused on eight industries where we feel 5G will have the largest impact. Often when 5G is discussed, the focus is on the impact it will have on the consumer market. Here, we argue that 5G will unlock significant new revenue opportunities in the enterprise space, enabling innovative use cases that are currently impossible to scale commercially (with existing technologies).

Insight from this report is explored further in the following publications:

The document was researched and written independently by STL Partners, supported by Huawei. STL’s conclusions are entirely independent and built on ongoing research into the future of telecoms. STL Partners has written widely on the topic of 5G, including a recent two-part series into the short- and long-term opportunities unlocked by 5G, and lessons that can be learnt from early movers.

Comparing apples with apples: How to compare nascent 5G with established 4G

If you compare the technological specifications for 3GPP release 14 and 3GPP release 15 (the first 5G release), you might be underwhelmed. Despite the hype that 5G will be transformative, it does not appear to be delivering much more than incremental increases in speed and reliability. But, of course, 4G is now a mature form of connectivity (having been in-life for 6+ years) whereas 5G is still nascent.

To compare apples with apples, it makes sense to compare 5G release 16, where capabilities such as ultra-reliable low-latency and network slicing are being added, with LTE today.

Mature 5G benchmarked against the capabilities of mature 4G

Mature 5G benchmarked against mature 4G

Source: ITU, Nokia, ublox, gps world

Of course, these figures represent a best-case scenario occurring in a laboratory environment. This is true for both the 4G and 5G numbers. It’s also true that, in reality, it will take time before we see commercialised rollout of enhanced mobile broadband (“pure 5G”) rather than enhanced mobile broadband with 4G fall-back alongside fixed wireless access. Despite this, these figures make clear that when 5G reaches maturity, it will far outstrip the capabilities of 4G, and unlock new use cases.

Our assumption is that by 2025 5G technology will be mature, enabling massive M2M / IoT use cases as well as those that require ultra-reliable low-latency communications. Several of the 5G use cases we’ll go on to explore in more detail are reliant on this technology, so it is important to acknowledge that their commercialisation is only likely to start from around 2023 and in many markets they still won’t be fully deployed in 2030.

It’s not all about LTE: 5G must be compared to all available technology

Mobile is not the only form of connectivity used by enterprises. Plenty of industries are also making use of Wi-Fi, LPWAN, Zigbee, Bluetooth and fixed connectivity as part of their overall connectivity solution. When 5G is rolled out, in some cases, it will need to integrate with these existing technologies rather than replace them. The table below summarises some of the key benefits and shortcomings of current technologies, including highlighting the sorts of situations in which industries are making use of them.

Current technologies will not be entirely replaced by 5G, but it can address some of they key shortcomings

current technologies will not be entirely replaced by 5G, but it can address some of their key shortcomings

There are clear scenarios where 5G will be superior to existing technologies and bring significant benefits to industrial users. Ultimately, in particular, 5G will enable:

  1. Low latency and high bandwidth requirements for wireless connectivity
  2. Massive IoT through ability to handle high cell density
  3. Ultra-reliable and secure connectivity.

Table of Contents

  • Preface
  • Executive Summary
    • 5G enabled solutions are estimated to add c.$1.4 trillion to global GDP in 2030
    • Operators must embrace new business models to unlock significant revenues with 5G
    • Recommendations for operators: how to capitalise on the 5G opportunity
  • Introduction
    • Background
    • Comparing apples with apples: how to compare nascent 5G with established 4G
    • It’s not all about LTE: 5G must be compared to all available technology
    • 5G deployment: 5G will mature over the next ten years
  • 5G will add more than $1.4 trillion to the global economy by 2030
  • Mobile network operator strategic options with 5G
    • 5G alone will not change the game for operators
    • Strategic options for operators to add more value with 5G
  • 5G-enabled digital transformation in healthcare
    • Example 5G use case: Remote patient monitoring
    • Implications for telcos
  • 5G-enabled digital transformation in manufacturing
    • 5G can create $740bn in additional GDP by 2030
    • Example 5G use case: Advanced predictive maintenance
    • Implications for telcos
  • Conclusions for operators: how to capitalise on the 5G opportunity

Table of Figures

  • Figure 1: Mature 5G benchmarked against the capabilities of mature 4G
  • Figure 2: Current technologies will not be entirely replaced by 5G, but it can address some of their key shortcomings
  • Figure 3: Forecast of 5G deployment in major regions
  • Figure 4: Responses from industry surveys
  • Figure 5: 5G will contribute ~$1.4 trillion to global GDP by 2030
  • Figure 6: Manufacturing, energy & extractives and media, sports & entertainment industries will see the largest upticks to their industry thanks to 5G use cases
  • Figure 7: In 2030, manufacturing and construction will be the largest industry sectors (in 2030)
  • Figure 8: High income countries will see almost 75% of the benefit of 5G in 2025, but the share is more even across all geographies by 2030
  • Figure 9: 4G rollout did not produce sustainable revenue increase
  • Figure 10: What should telcos’ role be in 5G B2B?
  • Figure 11: As telcos move beyond just connectivity, they can increase their share of the wallet
  • Figure 12: Telcos must focus efforts in specific verticals – some are already doing this
  • Figure 13: Global impact of 5G on healthcare across four key contact points
  • Figure 14: Remote patient monitoring enables wearables to send data about the patient to the hospital for monitoring
  • Figure 15: Estimated impact of 5G-enabled remote patient monitoring
  • Figure 16: The potential roles for telcos can within healthcare
  • Figure 17: The TELUS Health Exchange as a point of coordination
  • Figure 18: There is opportunity for telcos’ to play multiple roles higher up the value chain in healthcare
  • Figure 19: Estimated impact of 5G on manufacturing GDP (USD Billions) by use case
  • Figure 20: Advanced predictive maintenance enables many sensors to send data about machinery for monitoring and optimisation