Global Artificial Intelligence in Telecommunications Growth Opportunities

Al, is yet to become a standard telecoms industry service. Instead of providing analytics as discrete Al-based tools, platforms, and services, telcos are embedding them in specialised loT solutions. Given the vast amount of data available from IoT setups, Early AVML deployments can help telecoms improve the capabilities of enterprise solutions. Telcos’ data accessibility will improve as 5G deployments expand. By utilising Al, they will be able to monetize emerging opportunities in corporate solutions and support new use cases.

Definition / Scope
Market Overview
Market Risks
Top Market Opportunities
Market Drivers
Market Restraints
Industry Challenges
Technology Trends
Regulatory Trends
Post COVID-19 Recovery
Market Size and Forecast
Market Outlook
Technology Roadmap
Distribution Chain Analysis
Competitive Landscape
Competitive Factors
Key Market Players
Strategic Conclusion
References
Further Reading
Appendix

Definition / Scope

Artificial intelligence allows the telecom industry to draw insights from large data sets, making it easier to manage daily operations, handle issues more quickly, and increase customer service and happiness. Artificial intelligence is a cutting-edge technology that can make decisions in the same way as humans do.

This technology focuses on developing an intelligent machine with advancements in features such as speech recognition, visual recognition, picture identification, and language translation, all of which are driving market growth. Technology has the ability to revolutionize the way numerous industrial sectors, particularly telecommunications, function and operate. It has a wide range of applications in telecommunications, including customer service and network performance.

The market for artificial intelligence in telecommunications can be divided into three categories: components, applications, and geography. The AI in telecommunications market can be divided into solutions/platform and services based on components. Professional and managed services are two types of services that can be found among them.

Cloud-based and on-premise solutions/platforms are sub-segments of the solutions/platforms segment. Predictive maintenance, fraud mitigation, cybersecurity, and intelligent CRM systems are some of the applications of AI in the telecommunications business. Customer analytics, network operations management, and marketing virtual digital representative are some of the other applications available.

During the projection period, customer analytics is predicted to have the greatest market share. This is mostly due to the telecom industry’s increased demand for customer analytics to evaluate customer data, which aids in sales planning and strategies.

The AI in telecommunications market may be divided into five regions: the Middle East and Africa, Asia Pacific, North America, South America, and Europe. In the global AI in telecommunications industry, North America is predicted to have the greatest market share. In North America, artificial intelligence in telecommunications is successfully employed for a variety of applications, including network security, network optimization, and virtual support.

Market Overview

The AI in telecommunication market is poised to grow at a CAGR of 45.1% during the forecast period to reach a Market Value of USD 9267.1 Million by 2027, from a Market Size of USD 694.8 Million in 2020.

The use of AI-enabled smartphones and the increasing acceptance of AI for various applications in the telecommunications industry are likely to drive the growth of the AI in telecommunications market. Incompatibility problems between AI technology and telecommunication systems are predicted to function as market barriers, resulting in integration complications in AI in telecommunication solutions.

Cloud deployment mode is expected to have a larger market share

The cloud deployment mode in the AI in telecommunications sector offers a number of advantages, including lower operational and maintenance costs, less complications, and greater scalability. As more organizations have begun to adopt the cloud-based deployment option, AI in telecommunication solution providers are working on the creation of robust cloud-based solutions for their clients. The cloud deployment technique is also user-friendly and simple to utilize.

Machine learning and deep learning technology is projected to have a larger market size

Machine learning and deep learning, as well as Natural Language Processing, are sub-segments of the AI in telecommunications market technology segment (NLP). During the projection period, the machine learning and deep learning sector is predicted to have the largest market size.

Machine learning and deep learning are the most reliable methodologies for tapping into the context of human-computer interactions and providing accurate predictions based on historical data. This technology can be used to automate telecommunication processes in the telecommunications business.

North America is expected to have the largest market size in the AI in telecommunication market

During the projected period, North America is expected to have the greatest market size in the AI in telecommunications industry. The market has seen considerable investment in the North American region, and several vendors have emerged to cater to the quickly growing demand. During the projection period, the region is expected to increase significantly. This region is home to many of the major programmes aimed at advancing AI in telecommunications technology. Enterprises and governments in this region have embraced AI in telecommunications technology to improve telecommunications solutions and services for a better customer experience.

Market Risks

Some of the Major Risks in the Global AI in Telecom Market include

Inability to combat the growing capex burden

Operators around the world are confronting a new wave of network investment, including 5G and low-power wide area networks, as well as gigabit fibre. Yet, with many IoT-centric 5G use cases still in their infancy and the view of internet as a utility challenging the premium pricing of fibre connectivity, the returns on this expenditure remain dubious.

Making the proper decisions on infrastructure switch-off, spin-off, and sharing will become increasingly important as operators deal with a more diverse portfolio of network assets.

Underestimating changing imperatives in privacy, security

Consumer concerns regarding the use of their online data are continuing to rise, fueled by rules like the General Data Protection Regulation (GDPR), as proven by our consumer research. Telcos face a significant challenge in ensuring that their customers’ data and experiences are safe and secure, with digital trust now a prominent topic for consumers and businesses alike, and authorities prioritizing data protection.

While operators realize the importance of security and trust in consumer interactions, many could do more, such as making “security by design” a cornerstone of their digital transformation initiatives.

Top Market Opportunities

Personalized Upselling

AI and Data Analytics can be coupled to assist you obtain a better understanding of your subscribers’ tastes and offer them better-tailored service packages at the times when they are most inclined to order.

Machine Learning algorithms (in particular, Natural Language Understanding, Image Recognition, and Video Processing) can discover the different types of information that your subscribers consume over time, range their interests, and find related content that you can give. Furthermore, Machine Learning and Data Analytics will allow you to give this personalized offer to your subscribers at a time when they are more likely to have more money and so be more likely to buy. The latter will be determined by the subscriber’s previous purchases.

For each of your subscribers, AI-enabled predictive modelling allows you to evaluate thousands of different indicators (Web, Device, Service, Value, Billing, Technical Stream, Social data) in order to anticipate their future behavior. As a result, it may be possible to create more effective, personalized offers for entire client segments as well as individual customers. In the same way that AI software may help you sell more effectively, it can also help you upsell more effectively. As new services become available, you’ll be able to determine which of your members should be notified first.

Predictive Maintenance

Maintaining a network becomes more challenging as it develops and becomes more sophisticated. Fixing problems can be a time-consuming and expensive procedure. Furthermore, it can result in downtime and service interruptions, which customers despise.

When it comes to predictive maintenance, AI can make a major difference. AI and ML (Machine Learning) algorithms can reliably predict and warn about potential hardware problems by recognizing trends in historical data. This enables telcos to be highly proactive in terms of maintaining their equipment and resolving issues before they affect end-users.

Machine Learning algorithms can assist you estimate how your network’s usage will change over time in the many regions it covers. To improve optimization outcomes, a variety of criteria can be considered, including time zone, hour, weather, national or regional holidays, and more.

Robotic process automation (RPA) for Telecoms

CSPs serve a large number of clients who conduct millions of daily transactions, all of which are vulnerable to human mistake. RPA (Robotic Process Automation) is a type of AI-based business process automation technology. RPA can improve telecommunications efficiency by helping telcos to manage their back-office operations and enormous quantities of repetitive and rules-based actions more easily. RPA frees up CSP personnel for higher-value-add work by automating complicated, labor-intensive, and time-consuming operations like billing, data entry, workforce management, and order fulfilment.

According to a Deloitte survey, 40% of Telecom, Media, and Technology executives said cognitive technologies have provided “significant” benefits, with 25% investing $10 million or more. Within the next three years, more than three-quarters of respondents expect cognitive computing to “significantly transform” their businesses.

Celaton assists telecommunications companies in streamlining inbound data such as emails, web forms, and postal mail by extracting and validating important facts from each conversation and offering proposed responses to service representatives, who then change messages before responding to clients. Meanwhile, Kryon supports operators in identifying essential operations to automate in order to maximize the efficiency of both digital and human workforces.

Market Drivers

Surging number of AI-enabled smartphones

The global AI in Telecommunications market is predicted to grow as the number of AI-enabled devices grows. When compared to ordinary phones, these phones include several functions such as picture recognition, voice recognition, strong security, and more. This is why it is gaining popularity among users.

During the projected period, the AI in telecommunication market is expected to rise due to the increasing use of AI-embedded smartphones and the growing usage of AI solutions in various applications. In addition, the adoption of 5G technology in mobile networks is likely to promote AI in the telecommunications industry. The Chinese government, for example, is working to modernize the telecommunications sector and provide better network service options. To develop the 5G network, China Telecom Corporation is constructing a new 5G base station in Lanzhou, the largest and capital city of Gansu Province in northwest China.

Increasing demand for effective and efficient network management solutions

The growing demand for effective and efficient network management solutions, as well as the increasing adoption of AI technology in the telecommunications industry, are providing AI solution providers with new revenue prospects. However, incompatibility between telecommunication systems and AI technology, which leads to integration complexity in these solutions, is predicted to constrain AI’s expansion in the telecommunications sector. The telecom industry is projected to leverage AI to improve customer service communication and create a more personalized user experience, resulting in increased consumer engagement. In the telecommunications industry, AI acts as a customer service agent which makes this process cost effective for the telecom industry.

Growing Over-The-Top (OTT) services

Over-the-top (OTT) services, such as video streaming, have changed how audio and video content is distributed and consumed. Consumer demand for bandwidth has increased significantly as more people use OTT services. Carrying such a high volume of OTT traffic results in a significant level of operating expenditure (OpEx) for the telecommunications industry. By reducing the amount of human interaction required for network configuration and maintenance, AI aids the telecom industry in lowering operational expenses. Additionally, automation allows telecom businesses to onboard clients faster and introduce new services in less time.

Advancements in technology and improving customer experience

During the projection period, AI in the telecom market would grow at a significant rate. The focus on technological developments and increasing client experience is credited with the company’s growth. Telecommunications is one of the fastest-growing businesses, with AI being used in a variety of ways, including to improve customer experience and network dependability. AI is primarily used in customer service applications by telecom firms. Chatbots and virtual assistants, for example, are used to reply to a large number of support requests for installation, maintenance, and troubleshooting.

Virtual assistants can also deploy and automate responses to support requests, making it easier for customers and lowering expenses. For example, after adopting a Chatbot called TOBi to answer customer enquiries, Vodafone Ltd. enhanced their customer experience by 68 percent.

Market Restraints

Investigating AI technology to perform predictive analytics

Investigating AI technology for predictive analytics is another difficulty. They’re leveraging cloud services to create and train machine learning models that will help them get useful, actionable information. However, these models are only useful if they are given large volumes of data to work with. Before launching a cloud-based AI service, industries must ensure that they have enough security in place to secure their sensitive data and that they are in compliance with all applicable regulations.

High infrastructure and maintenance costs

The high infrastructure and maintenance expenses of AI-based solutions are a major stumbling block to the worldwide AI in telecommunications market’s growth. In addition, the market is confronted with constraints linked to the complex integration process and security, both of which are limiting the worldwide industry’s growth.

Industry Challenges

Need to spend substantial amount of money on staffing

Algorithms monitor the network for unusual build-ups of activity that could be a precursor to harmful activities like Distributed Denial-of-Service (DDoS) attacks and attempted hacks. AI and CC are more reliable and faster techniques of anticipating network attacks. Even while cloud AI adoption is unavoidable, there are problems associated with it, the most major of which is equipping Manpower with AI and cloud computing skillsets. The telecom industries that use that system will have to invest a significant amount of money on their employees to provide them with the necessary knowledge and abilities to ensure their success.

Limitations in Protecting Privacy

Data privacy and varied algorithms are anticipated to provide hurdles to the business. The system employs machine learning and deep learning techniques to provide users with relevant results. Recommendation engines, search algorithms, and adtech networks are examples of technologies that can abuse the user’s private data. Furthermore, Al algorithms generate new information based on historical data without the user’s permission. As a result, the European Union enacted the General Data Protection Regulation to prevent the misuse of personal data. The demand for the technology is expected to be hampered as a result of this.

Technology Trends

Virtual Assistants for Customer Support

Conversational AI systems are distinctive application of AI in telecommunications. According to Juniper Research, virtual assistants have learnt to automate and scale one-on-one conversations so effectively that they are expected to save businesses up to $8 billion annually by 2022. The large volume of support requests for installation, set up, troubleshooting, and maintenance, which often overwhelm customer service centres, has led telcos to turn to virtual assistants for assistance. Self-service features that show clients how to install and manage their own devices can be implemented using AI.

Vodafone has launched TOBi, a chatbot that can answer a variety of customer care questions, after implementing TechSee’s technology and seeing a 68 percent increase in customer satisfaction. The chatbot scales responses to simple customer enquiries to meet subscriber demand for speed. MIKA, Nokia’s virtual assistant, recommends solutions to network issues, resulting in a 20% to 40% increase in first-time resolution rate.

AI for Predictive Maintenance

By combining data, complex algorithms, and machine learning approaches to anticipate future results based on prior data, AI-driven predictive analytics is assisting telcos in providing better services. This means that operators may utilize data-driven insights to monitor the status of equipment, predict failure based on patterns, and proactively resolve issues with communications hardware like cell towers, power lines, data centre servers, and even set-top boxes in customers’ homes.

In the short term, network automation and intelligence will allow for more accurate root cause investigation and issue prediction. In the long run, these technologies will support more strategic aims like creating new consumer experiences and efficiently dealing with evolving company needs. AT&T is testing a drone to improve its LTE network coverage and to use the analysis of video footage taken by drones for tech support and maintenance of its cell towers as part of an innovative approach that uses AI to support its maintenance procedures.

Regulatory Trends

Regulation in the USA

NIST guideline establishes a set of standards for suggested security controls for federal information systems. The NIST Cybersecurity Framework is an example of a widely used NIST standard. These standards are endorsed by the government, and corporations follow them because they cover security best practices controls across a wide range of industries. The NIST standards are based on best practices from a variety of security documents, organizations, and publications, and are intended to serve as a framework for federal agencies and projects that require stringent security.

In many circumstances, following NIST rules and recommendations will assist government agencies in meeting other regulations such as HIPAA, FISMA, and SOX. Frequently, NIST recommendations are created to assist agencies in meeting specific regulatory compliance needs.

Regulation in the Europe

The General Data Protection Regulation (GDPR) is the regulation that governs the Regulation of Artificial Intelligence (AI) in telecom.

The General Data Protection Regulation (GDPR) is the world’s most stringent privacy and security law. Despite the fact that it was designed and passed by the European Union (EU), it imposes duties on organizations anywhere that target or collect data about EU citizens. On May 25, 2018, the regulation went into effect. Those who break the GDPR’s privacy and security regulations will face severe fines, with penalties ranging in the tens of millions of euros.

Regulation in China

The Chinese government sees Al as a key part of its national policy and is working to put in place an Al regulatory structure in the near future. The State Council has included Al in the Report on the Work of the Government from 2017 to 2019, as well as intelligent manufacturing in 2020 and 2021, and has also promulgated a number of national strategic policies, including the New-generation I Development Plan and the Three-year Plan for New-generation Al Industry Development (2018-2020), which set out specific goals in technology achievement and the regulatory regime of Al in three eras from 2018 to 2030.

Regulation in India

The rapid breakthroughs and wide-ranging applications of Al and ML have sparked widespread interest, elevating them to national prominence. The government must urgently evaluate the development, funding, and wide-ranging ramifications of Al. There are currently no laws in India that apply to Al, BD, or ML.

At this point, it appears that the government’s aim is to promote Al and its application. Its approach in the Al sector is to maximize the late mover’s advantage’ by “consistently producing domestic pioneering technological solutions in Al as per its particular needs to assist leapfrogging and catching up with the rest of the globe.

Post COVID-19 Recovery

COVID-19 began in Wuhan, China, in December 2019 and has quickly spread around the world since then. In terms of confirmed cases and reported deaths, the United States, India, Brazil, Russia, France, the United Kingdom, Turkey, Italy, and Spain are among the worst impacted countries. Due to lockdowns, travel bans, and business shutdowns, the COVID-19 has had a negative impact on economies and businesses in a number of countries. The closure of various plants and factories has had a severe influence on global supply chains, negatively affecting manufacturing, delivery schedules, and product sales in the worldwide market.

A few companies have already stated that there may be delays in product deliveries and a drop in future sales. Furthermore, worldwide travel prohibitions implemented by countries in Europe, Asia, and North America are harming prospects for commercial cooperation and partnerships.

Market Size and Forecast

AI in Telecommunication Market was valued at USD 694.8 Million in 2020 and is projected to reach USD 9267.1 Million by 2027, growing at a CAGR of 45.1% from 2020 to 2027.

AI in Telecommunication Market, By Technology

Machine learning and deep learning

Based on Technology, Global AI in Telecommunication Market is segmented into Machine learning and deep learning, and Natural Language Processing (NLP).

Natural language Processing (NLP) technology is expected to grow at the fastest CAGR in the Global AI in Telecommunication Market over the forecasted period, owing to the use of NLP technology in the telecom industry to read information stored in digital format and understand human languages from various data sets.

During the projection period, the machine learning and deep learning sector is predicted to have the largest market size. Machine learning and deep learning are the most reliable methodologies for tapping into the context of human-computer interactions and providing accurate predictions based on historical data. This technology can be used to automate telecommunication processes in the telecommunications business.

Machine Learning and Deep Learning segment accounted for 64% of the Market Share and it is expected to generate revenues to the tune of USD 5.79 Billion in 2027 from a value of USD 444.7 Million in 2020 growing at a CAGR of 44.3% in the forecast period (2020 – 2027).

Natural Language Processing (NLP) technology holds a Market Share of 36% and is projected to grow at the highest CAGR of 47.2% to reach a value of USD 3.72 Billion in 2027 from a Market Size of USD 250.1 Million in 2020.

AI in Telecommunication Market, By Component

Solutions
Services

Based on Component, Global AI in Telecommunication Market is bifurcated into, Solutions and Services.

As telecom firms become more aware of the benefits and features of AI technology in the telecommunications industry, the service segment is predicted to grow at the fastest rate among the components. In addition, the growing use of AI in numerous applications in the telecommunications industry is propelling the service segment forward.

The Solutions segment dominates the Market with the highest market share and accounted for 73% of the Market Share and attained a value of USD 507.2 Million in 2020 and is poised to grow at a CAGR of 43.5% to reach a Market Size of USD 6.35 Billion in 2027.

The Services segment is the fastest growing and it attained a Market Share of 27% and is expected to reach a Market Size of USD 2.75 Billion in 2027 from a value of USD 187.8 Million in 2020.

AI in Telecommunication Market, By Application

Customer Analytics
Network Security
Network Optimization
Self-diagnostics
Virtual assistance
Others

Based on Application, Global AI in Telecommunication Market is segmented into Customer Analytics, Network Security, Network optimization, Self-diagnostics, Virtual assistance, and others.

Customer Analytics is expected to hold the largest share in the Global AI in Telecommunication Market by application, owing to the growing demand for customer analytics in the telecom industry for network operation management, customer data analysis, and as a marketing virtual digital representative.

Due to the fact that customer service automation saves telecom firms money, the virtual assistance segment is predicted to increase at the quickest rate over the forecast period. In the communication business, customer service chatbots can also be properly educated, since machine learning algorithms can automate enquiries and send clients to the most appropriate representative.

Network Security accounted for 20% of the Market Share and generated USD 139 Million of the total Market Revenue and is expected to grow at a CAGR of 42.6% to reach a Market Size of USD 1.66 Billion in 2027.

Network Optimization recorded a Market Share of 27% and is valued at USD 187.5 Million in 2020 and is projected to grow at a CAGR of 40.7% to attain a Market value of USD 2.04 Billion in 2027.

Customer Analytics held a Market Share of 37% and is poised to grow at a CAGR of 44.4% to reach a Market Size of USD 3.36 Billion in 2027 from a Market Size of USD 257 Million in 2020.

Virtual Assistance registered a Market Share of 10% and is poised to grow at a CAGR of 52.6% to reach a Market Size of USD 1.34 Billion in 2027 from a valuation of USD 69.5 Million in 2020.

Self-Diagnostics generated revenues to the tune of USD 27.8 Million in 2020 and is expected to grow at a CAGR of 45.3% to reach a Market Size of USD 380.1 Million in 2027.

The other segments generated revenues to the tune of USD 14 Million in 2020 and is expected to grow at a CAGR of 44.3% to reach a Market Size of USD 182.4 Million in 2027.

AI in Telecommunication Market, By Geography

North America
Europe
Asia Pacific
Rest of the World

The Global AI in Telecommunication Market is divided into four regions based on regional analysis: North America, Europe, Asia Pacific, and the Rest of the World. Because AI in telecommunications is effectively employed for network security, network optimization, and virtual help in North America, the region is likely to hold the highest share of the market.

North America accounted for USD 277.9 Million of the total Market Revenue and is expected to grow at a CAGR of 42.3% to reach a Market Size of USD 3.28 Billion in 2027.

Asia Pacific is valued at USD 173.7 Million in 2020 and is projected to grow at a CAGR of 48% to attain a Market value of USD 2.7 Billion in 2027.

Europe held a Market Share of 18% and is poised to grow at a CAGR of 46.4% to reach a Market Size of USD 1.8 Billion in 2027 from a Market Size of USD 125 Million in 2020.

The Rest of the World accounted for 18% of the Market Share and generated revenues to the tune of USD 118 Million in 2020 and is expected to grow at a CAGR 43.1% to reach a Market Size of USD 1.45 Billion in 2027.

Market Outlook

AI in Telecommunication Market was valued at USD 694.8 Million in 2020 and is projected to reach USD 9267.1 Million by 2027, growing at a CAGR of 45.1% from 2020 to 2027.

The integration of AI with future wireless networks is driving the global AI market in telecommunications. Artificial intelligence (AI) and machine learning (ML) technologies are projected to play a key role in the integration of 5G cellular networking technologies and network automation. To supply telecommunication services and tools, telecom companies are integrating AI technologies into their 5G cellular networks.

In 2020, North America dominated the global artificial intelligence market in the telecom industry, and it is likely to continue to do so during the forecast period. This is owing to an increase in the use of AI solutions, as well as the fact that customers in the region are early adopters of new technology. According to a Genpact study of working people in major economies like the United States, the United Kingdom, and Australia in November 2020, there was a strong desire to use AI in areas like telecommunications. Genpact is a multinational consulting organization that specializes in digital transformation.

Over the projected period, the Asia Pacific market is expected to grow at the fastest rate. This is due to the growing use of machine learning and natural language processing technology, as well as a number of 5G experimental projects. The AI market in the telecom industry is likely to benefit as a result of this. For example, in December 2020, China Telecom Corporation Ltd. opened a new 5G base station in Lanzhou in order to expand China’s 5G test projects.

Technology Roadmap

Reogma Market Report covers all the trends and technologies playing a major role in the growth of the Global AI in the Telecommunication market over the forecast period. Some of the trends are illustrated below

Artificial intelligence applications are changing the way telecoms optimize, operate, and provide service to their clients.

Customers are increasingly demanding higher quality services and positive customer experiences from communications service providers (CSPs) (CX). Key firms are capitalizing on these opportunities by harnessing massive volumes of data gathered over time from their massive client base. Networks, devices, mobile applications, complete customer profiles, service usage, and billing data are all banned.

Predictive Maintenance

One of the current developments in telecoms is AI-driven projecting analytics, which uses complex algorithms, data, and machine learning approaches to anticipate future results based on historical data. AT&T is utilizing machine learning to improve their end-to-end incident management process by detecting network faults in real time. The technology can handle 15 million alarms every day, restoring service before customers even discover there is a problem.

Distribution Chain Analysis

The Value chain of the Global 5G Network Infrastructure Market is as in the below image

Competitive Landscape

The key industry participants in the market include IBM Corp.; Microsoft Corp.; Intel Corp.; Google LLC; AT&T Intellectual Property; Cisco Systems, Inc.; Nuance Communications, Inc.; Evolv Technology Solutions, Inc.; H2O.ai; Infosys Ltd.; Salesforce.com, Inc.; and NVIDIA Corp.

Vendors in the industry are concentrating their efforts on expanding their client base through strategic initiatives such as partnerships, mergers and acquisitions, and collaborations. In May of this year, Vodafone Ltd. struck an outsourcing agreement with IBM Corp. This collaboration is expected to give the former company with a hybrid cloud-based digital platform that will help it improve customer engagement and corporate efficiency.

Key Market Developments

In July 2021 – Qualcomm Technologies, Inc. acquired an on-board Al research team and assets such as the Al research community’s high-quality video dataset from Twenty Billion Neurons GmbH.

In July 2021 – Qualcomm Technologies, Inc. collaborated with Foxconn Industrial Internet to launch Gloria Al Edge Box to boost the adoption of intelligent edge applications. The new system offers 70 trillion operations per second of computing power. BKAV Corporation is the first company to implement Gloria Al Edge Box.

Competitive Factors

Product launches, product approvals, and other organic growth tactics such as patents and events are being prioritized by a number of companies. Acquisitions, as well as partnerships and collaborations, were seen as inorganic growth tactics in the market.

These efforts have paved the road for market players to expand their business and client base. Artificial intelligence market participants in the telecommunication industry are expected to benefit from lucrative growth prospects in the future, thanks to increased demand in the worldwide market.

Key Market Players

The key industry participants in the market include

AT&T Inc. is a conglomerate. Telecommunications, media, and technology services are provided by the company around the world. Communication, WarnerMedia, and Latin America are the three segments in which the company operates. Consumers can get cellular and wireline telecom, video, and broadband services from the Communications section. Mobility, Entertainment Group, and Business Wireline are among the Communication segment’s business units.

Cisco Systems, Inc is a technology company that specializes in networking, security, collaboration, apps, and the cloud. Americas, Europe, the Middle East, and Africa, and Asia Pacific, Japan, and China are its three geographic segments. Infrastructure platforms, apps, security, and other products are among the company’s products and technology. Infrastructure Platforms are the company’s fundamental networking technologies, which include switching, routing, data center products, and wireless, all of which are designed to operate together to give networking capabilities as well as transport and store data.

Nuance Communications, Inc. is a provider of ambient clinical intelligence and conversational artificial intelligence (AI). Clinical documentation, solutions for physicians, radiologists, and care teams, as well as intelligent consumer interaction and security biometric solutions for numerous companies, are among the products and services offered by the company. Healthcare, Enterprise, and Other are the company’s segments. The Enterprise section is involved in providing automated customer solutions and services for voice, mobile, Web, and messaging channels employing speech, natural language comprehension, and AI. Voicemail transcription services make up the other portion.

Evolv Technologies Holdings Inc., formerly NewHold Investment Corp., is providing weapons detection security screening systems. The Company provides artificial intelligence (AI) touchless screening technologies for weapons detection, identity verification and health-related threats, by utilizing its Evolv Cortex AI software platform.

H2O.ai is a software development company. The company delivers marketing mix modelling, risk and fraud analysis, advertising technology, and customer intelligence solutions, as well as an open-source deep learning platform for applications and data products. Customers in California are served by H2O.ai.

Nvidia Corporation is an artificial intelligence computing company. Graphics and Compute & Networking are the two segments in which it operates. GeForce graphics processing units (GPUs), the GeForce NOW game streaming service and related infrastructure, and solutions for gaming platforms; Quadro/NVIDIA RTX GPUs for enterprise workstation graphics; virtual graphics processing unit (vGPU) software for cloud-based visual and virtual computing; and automotive platforms for infotainment systems are all part of the company’s Graphics segment. Data Center platforms and systems for artificial intelligence (AI), high-performance computing (HPC), and accelerated computing are part of its Compute & Networking sector.

Strategic Conclusion

In the telecommunications industry, artificial intelligence technologies are progressively aiding CSPs in managing, optimizing, and maintaining not only infrastructure but also customer support operations. AI has touched the telecom business in a variety of ways, including network optimization, predictive maintenance, virtual assistants, and robotic process automation (RPA).

AI is predicted to continue to flourish in this highly competitive field as Big Data tools and applications become more widely available and powerful.

References

Appendix

AI – Artificial Intelligence
ML – Machine Learning
BD – Big Data
NLP – Natural Language Processing
GDPR – General Data Protection Regulation
RPA – Robotic Process Automation
OTT – Over-The-Top
OpEx – Operating Expenditure
DDoS – Distributed Denial-of-Service
CC – Cloud Computing