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Publication Date: 17 10 2019

Authors: Eden Zoller, Adaora Okeleke, Mark Beccue

The AI Opportunity for Communications Service Providers

Leveraging AI for maximum impact


© 2019 Ovum. All rights reserved. Unauthorized reproduction prohibited.

Summary

In brief

Artificial intelligence (AI) is playing a central role in driving digital transformation and has the potential

to enhance value across every aspect of a communications service provider (CSP)'s business. AI can

take network optimization to new levels and make customer care more efficient and effective. It can

enable personalized marketing at scale and drive innovation in both enterprise and consumer

services. Many CSPs are already infusing AI across their operations, while others are at an earlier

stage in their AI journey. But whatever their position, CSPs need to leverage AI in a way that

produces optimum results and competitive advantage, particularly in the consumer domain where

they face increasing pressure from consumer-tech and commerce players with deep AI smarts. This

paper is designed to support CSPs with insights and actionable advice on how to maximize the AI

opportunity.

Ovum view

▪ Consumers have mixed feelings about AI, making it difficult territory to negotiate. Forty

percent of respondents in Ovum's Digital Consumer Insights 2019 survey see AI as a positive

development compared with 34% in 2018. This is encouraging, because consumers with this

view are the early adopters of AI services. But those still uncertain whether AI is a good or a

bad thing have increased as have those who think AI is a negative development. These

consumers need attention; removing misconceptions and instilling knowledge will create

confidence and foster an openness to AI services.

▪ Consumers see value in simple and advanced AI applications, which requires a careful

service mix. Although consumer take-up of AI assistants is growing, Ovum survey data

shows that consumers are interested in a range of other AI services, notably AI-powered

healthcare (40%) and AI-enhanced cybersecurity (36%). In the AI-assistant context,

consumers favor more familiar functions such as personalized recommendations (40%) but

also more advanced AI capabilities such as the ability to detect a user's mood (29%). CSPs

must maintain a careful balance between functionality that is within a consumer's comfort

zone and features that move them to the next wave of service innovation.

▪ Network resource planning and optimization will drive the AI adoption rate in the

network domain. Use cases for AI within the network domain span the entire lifecycle of a

network including design, planning, deployment, and management. However, according to

Ovum's recent ICT Enterprise Insights survey results, 55% of CSPs will prioritize the

implementation of AI for network planning and optimization over the next 18 months. Given

the complexity of CSPs' networks and their need to differentiate themselves from the

competition while at the same time driving cost efficiencies, it has become imperative that

CSPs augment existing capabilities with data-driven approaches such as AI to plan and

optimally allocate resources in line with the demands of the network.

Recommendations

▪ Make telco AI assistants an integral part of the telco data platform. AI services should be

aligned and closely integrated with telco data platforms. This inside-out, data-driven approach



means that telco data assets will be primed for AI training and implementations. A good

example of this is Telefónica's "fourth platform," a repository for data generated across the

business that is underpinned by advanced AI-powered analytics and technologies. This is

helping Telefónica drive a range of AI use cases including its AI voice assistant, Aura.

▪ Put data privacy first in the AI era and differentiate on trust equity. AI is raising the

stakes for data privacy, and telcos must get in front of consumer concerns by, for example,

championing transparency, which will be even more critical for privacy in the AI era. Ovum

survey data reveals that almost 30% of consumers expect AI to have a negative impact on

data privacy in general, while 43% think AI will lead to constant tracking and monitoring of

their activities. In the specific context of AI assistants, almost a quarter of consumers do not

trust them with personal data.

▪ Reskill and retrain staff. It is important that CTOs and CIOs at CSPs build their and their

employees' knowledge of AI. Start early by organizing workshops for both senior and mid- to

lower-tier staff on the benefits, potential use cases, and challenges. Address the issue of

access to AI talent by recruiting a few people with AI skills, and create a plan for these

specialists to retrain and upskill staff that already have transferable skills and have shown an

interest in picking up AI skills. To attract and retain these AI specialists, address your siloed

data, operations structures, and rigid processes. Partner with your outsourcing service

providers. These suppliers are also building out their AI capabilities and can support your

organization through training as you develop your AI capabilities. CSPs can also open AI

innovation labs to work with their partners to train employees.

▪ Vendors should work with your CSP customers to standardize processes. To accelerate

the adoption of AI, knowledge management and the standardization of existing processes are

critical. Meeting these requirements will demand that CSPs collaborate with their vendor

partners because some of the knowledge of the internal workings of a CSP's network lies with

the vendors (especially those providing managed network services). These CSP vendors

must therefore aim to work with their customers to simplify their AI journeys. Vendors with

consulting capabilities should provide their CSP customers with guidance on how to address

issues such as data format standardization.

AI in the CSP consumer service domain

Customer care and marketing are seen as low-hanging AI fruit

AI can impact every aspect of CSP operations across a wide range of use cases (summarized in

Figure 1). When it comes to consumer-facing AI use cases, there is a strong focus on AI solutions in

the customer care domain, which is consistent with a strong desire to leverage AI for efficiencies and

cost savings. This is even more critical at a time when CSPs are rolling out new technologies (e.g.,

5G) and increasingly sophisticated services (e.g., smart home). Although beneficial to consumers,

these developments can introduce more complexity, which in turn places additional pressure on

customer care resources and processes. There is no doubt that AI has the potential to transform

customer care. For example, AI tools can guide human agents in real time to provide best-fit solutions

to a particular issue. AI can power online virtual e-care assistants and chatbots on messaging apps to



automate more routine support tasks performed by human agents, giving them more time to focus on

revenue-generating activity.

Ovum's ICT Enterprise survey shows that creating personalized customer experiences is the second-

biggest challenge facing telcos: 71% say this is very important or important. It is no surprise then that

there is growing interest in leveraging AI to enhance customer relationship marketing, although efforts

are typically focused on more "simple" use cases, for example, leveraging machine learning (ML) to

create recommendations and offers based on high-level personalization. But this is only scratching

the surface. Ovum expects to see AI tools that can not only provide a recommendation but justify it by

calculating the tangible performance benefits. AI can also enable adaptive personalized marketing

across multiple channels in an integrated manner, ensuring the customer journey is automatically

tuned with integrated feedback collection.

Figure 1: AI can touch all parts of the CSP business

Source: Ovum

The rise of telco AI voice assistants

The use of AI to enhance existing consumer services or to create new ones is still in the early stages,

but one of the most interesting and bold developments on this front is the launch of telco AI voice

assistants. AI assistants can give consumers the ability to manage and interact with telco services in a

unified framework with the convenience and fun of a conversational or visual interface. These

attributes, coupled with telco-specific domain knowledge, have the potential to pull customers more

deeply into an operator's service ecosystem. This is particularly compelling in the smart home

environment, where a telco AI assistant can act as a central hub for orchestrating and adding value to

a range of telco services spanning communications, TV and video, commerce, home automation and

security, and assisted-living use cases.

AI assistants can also tap into and create detailed consumer data sets: service usage patterns,

transactions and purchase behavior, queries/search history, conversation topics, and more. These

insights can be used to better understand how and why people use services; this knowledge in turn

can be used to further improve telco service offerings and recommendations. Examples of telco AI

assistants include Djingo from Orange, Aura from Telefónica, NUGU from SK Telecom, GiGA Genie

from Korea Telecom (KT), and Magenta from Deutsche Telekom. These operators are competing in



an AI-assistant market dominated by the big consumer-tech players, the front-runners being Amazon

Alexa, Apple Siri, Google Assistant, Microsoft Cortana, and Baidu Duer. These AI assistants are

becoming popular and entrenched, and the companies behind them have AI expertise that is hard to

match. Ovum predicts the global installed base of leading AI-assistant devices will almost double from

4.8 billion in 2020 to 8.7 billion by the end of 2024.

In most markets, CSPs should not try and compete head-on with AI assistants from the big consumer-

tech and commerce players but instead look at other ways to make an impact. A powerful way of

doing this is to address consumer pain points with AI assistants, as shown in Figure 2. The factors to

address as a matter of priority are the perceived lack of utility, ongoing issues relating to variable

voice quality, and concerns over data privacy. But to be effective, improvements must be meaningful.

In the context of the voice interface, rather than applying tweaks (e.g., high-level personalization via

different AI-assistant voice options) look at ways to bring cutting-edge innovations from the lab to

products. A good example of the latter is the way that Google has implemented its groundbreaking

Duplex voice technology.

Figure 2: Low utility of AI assistants and their inability to understand users are top pain points

Source: Ovum Digital Consumer Insights Survey 2019: AI

AI will drive even more consumer use cases across multiple domains

The installed base of AI voice assistants is growing as is active usage, making them a very

conspicuous touchpoint for consumer interactions with AI. But there are many scenarios beyond AI

assistants where AI technologies are already in play or soon will be. Table 1 highlights just three

areas where AI is having a profound impact (media and entertainment, smart home, and commerce)

but others are opening up, such as smart cars, health, and assisted living.



A raft of overlapping developments is helping AI address new use cases, which in turn creates new

opportunities:

▪ Advances in computer vision are driving developments in facial recognition along with image,

object, and gesture detection.

▪ Cognitive computing is driving advances in emotion detection in increasingly nuanced ways,

enabling AI systems to recognize and interpret happiness, sadness, anger, and so on.

▪ AI-powered analytics can interrogate and extract meaning from unstructured data (e.g., text

messages, images, videos, audio, blogs, and IoT/sensor data), increasingly in real time or

close to it.

Table 1: A rich interplay of AI technologies across a growing number of scenarios

Media and entertainment Smart home Commerce

Voice e.g., voice recognition, natural language processing

Voice-controlled TV, music, games

Voice interactions with/control of home devices and appliances

Voice-driven shopping, identity verification, payments authentication

Immersive e.g., AR, VR, holograms

AR-enhanced TV and video, AR/VR games

Hologram-based AI assistants

AR/VR-enhanced in-store experiences

AR mobile shopping apps

Visual e.g., facial, image and object, and gesture

Facial recognition to verify access to restricted content or to gage audience reactions to visual media

Visual interactions with device screens

Control of devices and appliances via gesture

Visual product search and recommendations

Identity verification and payments authentication

Movement e.g., sensor networks, cameras, location technologies

Motion effects in games, gesture control

Security system activation, gesture control for lighting, etc.

Indoor mapping at retail venues

Footfall analysis

Predictive e.g., ML-based analytics and modelling

Personalized recommendations

Content development based on consumer preferences

Optimum times for automated lights to go on/off or to set heating

Offers on the fly, real-time pricing optimization

Contextual recommendations, automatic reordering

Emotion e.g., emotion/mood detection

Content recommendations according to mood

Assessment of consumer reaction to content based on emotional state

Inhabitant emotion detection to set optimum lighting, audio volumes, and temperature

Gage degree of consumer engagement with, and attitudes toward, a product/service based on emotional state

Source: Ovum

AI in CSP networks and operations domain

AI is critical to addressing the complexity in CSP networks and operations

The transition of CSPs to digital service providers (DSPs) will have a massive impact on the network

and supporting operations. The transformation of the network and services provided by CSPs is



resulting in changes that will lead to growing complexity in the network. The biggest challenge that

CSPs face is that network complexity is growing at a much faster rate than the skills or resources

required to manage the network. To put this into context, Ovum expects the number of connected

things recorded in 2017 to double within the next five years and the amount of traffic traversing CSPs'

networks to increase by a factor of five over the same period. This complexity will increase even

further with the deployment of 5G networks supported by technologies such as software-defined

networking and network functions virtualization.

Automation, therefore, will play a critical role in helping CSPs manage this growing complexity. With

automation, CSPs can mask all of the complexity within their networks and operations from customers

and augment the capabilities of their human workforce, utilizing machines to extend the productivity

levels of humans. However, the rules-based approach to automation cannot address the scale of the

challenge that CSPs face now and will face in the future. Therefore, an alternative approach that is

less reliant on human expertise and, at the same time, scales well to support evolving CSP

requirements will be critical.

Automated networks and operations can be achieved using data and machine intelligence obtained

using AI technologies. For example, machine learning and deep learning enable computer systems to

learn from massive amounts of data the typical trends associated with network infrastructure and the

supporting operations. Based on the analysis of these trends, they can detect anomalies or patterns

of interest and utilize these insights to trigger automated workflows. AI comes with the added

advantage of countering limitations such as speed and expertise to detect these trends and determine

optimal actions that need to be taken to achieve business objectives.

Multiple AI use cases for CSP networks exist; however, network planning and optimization tops the list

The AI-based approach is best suited to several CSP use cases, given how dynamically the CSP

network is evolving, the vast amounts of data, the speed at which CSP data assets are being

generated, and the increasing need to perform operations in real time. Ovum's report Using AI to

Improve Network Operations: Use Cases highlights several use cases including network traffic

prediction, fault prediction and remediation, and AI-assisted service assurance. While these use

cases are relevant, the priority CSPs place on them varies. According to Ovum's ICT Enterprise

Insights 2020 survey, of the possible AI use cases for a CSP's environment, most CSPs plan to utilize

AI to enhance their network resource planning and optimization operations within the next 18 months.

The use of AI in the planning and optimization of network resources is particularly critical given that

CSP services span multiple network domains with each supported by multitechnology and

multivendor capabilities. Utilizing manual functions alone is not enough to manage such a dynamic

network environment. Other use cases such as network fault prediction and detection and reduction of

power consumption at base stations are also considered critical, and there are plans to implement

these within the next 18 months.

CSPs are exploring and reaping the benefits of AI within their networks

Several top-tier CSPs (such as NTT DoCoMo, SK Telecom, and Vodafone) and vendors are already

either in trials or have commercially deployed some of these use cases. Table 2 provides examples of

these AI deployments and the use cases addressed.



Table 2: A snapshot of CSPs using AI to improve network operations

CSP Use case

BT Used AI in the closed-loop control of BT's programmable network to detect anomalies and take preemptive action rather than rely on reactive functions such as restoration/repair to ensure efficient, fair, and pragmatic resource allocation between users and between the services themselves.

China Unicom Used in the development of an AI-powered big data engine to support China Unicom's next-generation OSS. Use cases currently supported by APEX include self-healing and optimized allocation of capital expenditure.

MegaFon Ural Russia Used AI techniques in the planning and maintenance of transport network resources such as radio links to predict those that were most critical from a network construction standpoint. Results from trials indicate that network traffic was predicted successfully with an accuracy of over 97% against actual traffic that was achieved.

Telefónica Used AI/ML processes to create network planning models that optimized its capex, thereby achieving cost efficiencies. The CSP also leveraged AI to improve self-management of the network in Spain.

AT&T Used fully automated drones to repair cell towers. A deep learning algorithm was developed by AT&T's video analytics team to enable drones to inspect and analyze video footage to detect defects, anomalies, and repairs of its 65,000 cell towers.

SoftBank Implemented an ML-based radio access network design system. The system takes data from urban cell clusters and overlapping cells to determine the potential for the CSP to use carrier aggregation between cells. Compared to traditional network design methods, the service cut the lead time by 40%.

KDDI Performed a proof of concept using an AI-based monitoring system to detect fatal situations caused by, for example, software bugs with greater than 90% precision. Service was restored by using a migration technique that was approximately five times faster than the conventional technique.

KT Developed an open source–based deep learning planning platform called NeuroFlow that performs self-optimization. NeuroFlow diagnoses the network for faults caused by poor network design or failures in network components. The system also helps predict when a failure is eminent.

China Telecom Zhejiang Telecom (a wholly owned subsidiary of China Telecom) has implemented an AI engine to assist with route optimization, capacity planning, traffic prediction, and dynamic optimization of the network. This deployment has led to the identification of network vulnerabilities that were previously not detected.

Source: Ovum

Network monitoring and management will yield largest market opportunity for AI for CSPs

In a just-published report, Informa Tech's Tractica outlined the key telecoms AI use cases and

software spend on those use cases between 2018 and 2025. Figure 3 below shows the forecast

telecoms AI revenue by 2025 broken down by use cases.

Telecom AI software revenue is expected to grow from $419.0m in 2018 to more than $11.2bn in

2025 at a CAGR of 59.8%. Network monitoring and management use cases will represent the largest

market opportunity for telecoms AI over the forecast period. Cumulative revenue for the network

monitoring and management use cases over the forecast period is expected to reach $18.3bn, which

is about 45% of total telecoms AI software revenue. The transformation occurring in today's CSP

networks is the key driver for this growth and, as such, presents opportunities to vendors with network

https://www.tractica.com/research/artificial-intelligence-for-telecommunications-applications/



expertise combined with proven AI capabilities to bring AI-based solutions to support this

transformation.

From a regional perspective, Ovum expects Asia-Pacific to drive a higher proportion of revenue,

because CSPs in the region are early adopters of AI in the management of CSP networks.

Figure 3: Telecoms AI software revenue share by use case, world markets, 2025

Source: Tractica

CSPs must look before they leap into applying AI to their networks

As with other new technologies, there are key lessons and best practices that CSPs must bear in

mind when it comes to implementing AI within their network operations. These best practices include

▪ attract and retain employees with AI skills

▪ build a strong big data infrastructure and practice to support the infrastructure

▪ start small, learn, and grow your use cases

▪ work with vendors with combined network and AI expertise

▪ collaborate with industry bodies to standardize the implementation of AI within CSP networks

and associated operations



▪ explainable AI, which is critical to building trust in network AI.

Appendix

Authors

Eden Zoller, Distinguished Analyst and Principal Analyst, Smart Living

[email protected]

Adaora Okeleke, Senior Analyst, Telecoms Operations and IT

[email protected]

Mark Beccue, Principal Analyst, Tractica

[email protected]

Ovum Consulting

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providers, technology companies, and enterprise decision-makers thrive in the connected digital

economy.

Through our 150 analysts worldwide, we offer expert analysis and strategic insight across the IT,

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We create business advantage for our customers by providing actionable insight to support business

planning, product development, and go-to-market initiatives.

Our unique combination of authoritative data, market analysis, and vertical industry expertise is

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Ovum is part of Informa Tech, a B2B information services business serving the technology, media,

and telecoms sector. The Informa group is listed on the London Stock Exchange.

We hope that this analysis will help you make informed and imaginative business decisions. If you

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