Artificial Intelligence in Telecommunication Version-1-by-Dr Diaelhag Khalifa

Artificial Intelligence in Telecommunication Version-1-Edited by Dr Diaelhag Khalifa.pdf

1. Date: 13-15 July 2025
Location: VocoHotel -Jeddah
Presentedby:
Dr-Eng.DiaelhagKhalifa
ARTIFICIALINTELLIGENCE
IN TELECOMMUNICATION

3. 08:00 am
DAY START
Session 1
09:00 am
09:30 am
Breakfast
11:30 am
12:30 pm
Tea Break
12:30 am
02:00 pm
Session 3
09:30 am
11:30 pm
Session 2
Course Time Table

4. Respect others'
opinions
Phones on
silent mode
Commitment
to time
Participation
Trainee Guidelines

5. CONTENTS
Introduction to Artificial Intelligence
Definition of AI and It’s Core technologies
History of Artificial Intelligence
Needs of Artificial Intelligence
Application of AI
Applications of AI in Telecom
AI roles in Telecom
Growing roles of AI in transforming Telecom
Benefits of AI in Telecom

6. Challenges of AI in Telecoms
Specific Application of AI in Telecoms
Real World Case Studies
Advantages and Disadvantages of AI
Futures of AI in Telecoms
Innovations of AI Solutions
CONTENTS

9. WHATISHUMANINTELLIGENCE?
It’sacompositionofabilitieslike
Feeling
RaviKumarBN,Asst.Prof,CSE,BMSIT
3
Learning
Understandingof
Language
PerceivingReasoning

10. WHATISINTELLIGENCE?
Theabilitytolearnorunderstandfrom
experience
Theabilitytoacquireandretainknowledge
Theabilitytorespondquicklyandsuccessfully
toa newsituation
Theabilitytousereasontosolveproblems
Ifintelligenceislearning,understanding,retaining,
responding,andusingreason thenwhatisAI?
RAVIKUMARBN,ASST.PROF,CSE,BMSIT 10

11. RAVIKUMARBN,ASST.PROF,CSE,BMSIT 11
QUICKANSWERFROMACADEMIA
Modelinghumancognitionusingcomputers.
Studyofmakingcomputersdothingswhichatthemoment
people arebetterat..
Makingcomputersdothingswhichrequireintelligence.

12. RAVIKUMARBN,ASST.PROF,CSE,BMSIT 12
MOREFORMALDEFINITIONOFAI
AIisabranchofcomputersciencewhichisconcernedwiththe
study andcreationofcomputersystemsthatexhibit
form of intelligenceor
Thosecharacteristicswhichweassociatewithintelligencein
humanbehavior.
Itisthescienceandengineeringofmakingintelligent
machines, especiallyintelligentcomputerprograms.

13. RAVIKUMARBN,ASST.PROF,CSE,BMSIT 13
WHAT’SINVOLVEDININTELLIGENCE?
Abilitytointeractwiththerealworld
 Toperceive,understand,andact
•e.g.,speechrecognitionandunderstanding
Searchingthebestsolution
ReasoningandPlanning
Modelingtheexternalworld–deliveryrobot
Solvingnewproblems,planning,andmakingdecisions
Abilitytodealwithunexpectedproblems,uncertainties
LearningandAdaptation
 Wearecontinuouslylearningandadapting
 ourinternalmodelsarealwaysbeing“updated”
•e.g.,ababylearningtocategorizeandrecognizeanimals

14. JOHNMCCARTHY
RAVIKUMARBN,ASST.PROF,CSE,BMSIT 14
(September4,1927–October24,2011)wasanAmerican
ComputerScientistAndCognitiveScientist.
McCarthywasoneofthefoundersofthedisciplineof
ArtificialIntelligence.
Hecoinedtheterm"ArtificialIntelligence"(AI)

15. RAVIKUMARBN,ASST.PROF,CSE,BMSIT 15
•Thinking/Reasoningvs.Behavior/Action.
•Successaccordingtohumanstandardsvs.successaccordingtoanideal
conceptofintelligence(rationality):
FourCategories.
Systemsthatthinklikehumans(focusonreasoningandhuman
framework).
Systemsthatthinkrationally(focusonreasoningandageneralconcept
ofintelligence).
Systemsthatactlikehumans(focusonbehaviorandhuman
framework).
Systemsthatactrationally(focusonbehaviorandageneralconceptof
intelligence).
VIEWSOFAIFALLINTOFOURCATEGORIESIN
TWO DIMENSIONS:

16. AI in telecommunications refers to the application of advanced
machine learning, natural language processing, and automation
capabilities to telecom networks and services. AI can interpret
vast amounts of network data, automate decision-making, and
deliver predictive insights in real-time.
By embedding AI technologies into telecom operations, service
providers can automate complex tasks, detect and prevent
issues before they escalate, and personalize customer
experiences. This creates not only more resilient networks but
also more agile business models.
DEFINITION OF AI AND ITS CORE
TECHNOLOGIES

17. RAVIKUMARBN,ASST.PROF,CSE,BMSIT 17
DEFINITIONOFAI
SystemsthatThinklikeHumans
“Theexcitingnewefforttomake
computersthink….Machinewith
minds,….”(Haugeland, 1985)
“[Theautomationof]activities
thatweassociatedhumanthinking,
activitiessuchasdecision–making,
problem solving,
learning…”(Bellman,1978)
SystemsthatThinkRationally
“Thestudyofmentalfacultiesthrough
theuseofcomputationalmodels”
(CharnaikandMcDermott,1985)
“Thestudyofthecomputationsthat
makeit possibletoperceive,reasonand
act”(Wintson, 1992)
SystemsthatActlikeHumans
“Theartofcreatingmachinesthat
perform
SystemsthatActRationally
“Afieldofstudythatseekstoexplainand
Functionsthatrequireintelligence
when performedbypeople”(Kurzwell,
1990)
Emulateintelligentbehaviorintermsof
computationalprocesses”
(Schalkoff,1990)
“The study of howto make computers do
things
“Thebranchofcomputerscience that
is
atwhich,atthemoment,peopleare Concernedwiththeautomationof
intelligent
better”(RichandKnight,1991) behavior”(LugerandStubblefield)

18. ACTINGHUMANLY :TURINGTEST
AlanTuring
Born:23JUN1912,London
Died:17JUN1954
computerscientist,mathematician,
logician,cryptanalystandtheoretical
biologist.
RAVIKUMARBN,ASST.PROF,CSE,BMSIT 18
“CanMachinethink?”->“CanMachinesbehave intelligently”
Operationaltestforintelligentbehavior:theImitation Game.
Thecomputerwouldneedtopossessthefollowing
capabilities:
NaturalLanguageProcessing
KnowledgeRepresentation
AutomatedReasoning
MachineLearning

19. •Intelligence: “ The Capacity to learn and solve problems”.
•Artificial Intelligence : Artificial Intelligence (AI) is the Simulation of
Human Intelligence by Machines.
1)The Ability to solve Problem.
2)The Ability to act rationally.
3)The Ability to act like Humans.
•The Central Principles of AI include:
1.Reasoning, knowledge, Planning, Learning and Communication.
2.Perception and the ability to move and manipulate objects.
3.It is the Science and Engineering of making intelligent machines,
especially intelligence computer programs.
DefinitionofAI

20. ARTIFICIAL INTELLIGENCE
Artificial Intelligence (AI) refers to the simulation of Human
Intelligence in machines that are programmed to think like humans
and mimic their actions. The Term may also be applied to any
machine that exhibits traits associated with a human mind such as
learning and problem-solving.

21. EXAMPLE OF A.I
Alexa and Siri, Amazon and Apple’s digital voice assistants, are much
more than a convenient tool –they are very real Applications of
Artificial Intelligence that is increasingly integral to our daily life. They
both rely on natural language generation and processing and machine
learning, forms of Artificial Intelligence, in order to effectively operate
and perform better over time.

23. WHY AI ?
Computers are fundamentally well suited to perform mechanical
computations, using fixed programmed rules. This allows
artificial machines to perform simple monotonous tasks
efficiently and reliably, which humans are ill-suited to.

24. WHAT IS AI?
Artificialintelligence,abbreviatedasAI,isaterm,whichgathersa
lotofhypetoday.
Inessence,AIisabranchofcomputersciencethatcreatesasystem
abletoperformhuman-liketasks,suchasspeechandtext
recognition,learning,problemsolving.UsingAI-drivensolutions,
computerscanaccomplishspecifictasksbyanalyzinghugeamounts
ofdataandrecognizinginthesedatarecurrentpatterns.

25. AI CONCEPT

26. NEED OF ARTIFICIAL INTELLIGENCE
There’s no doubt in the fact that technology has made our life
better. From music recommendations, map directions, mobile
banking to fraud prevention, AI and other technologies have
taken over. There’s a fine line between advancement and
destruction. There’s Always two sides to a coin, and that is the
case with AI as well.

28. APPLICATION OF AI
A Medical clinic can use AI systems to organize bed schedules,
make a staff rotation and provide medical information.
AI has Also application in fields of cardiology, neurology,
embryology, complex operations of internal organs etc.
It Also has an Application in image guided surgery and image
analysis and enhancement.

30. APPLICATION OF AI IN TELECOMMUNICATION
Application of Artificial Intelligence in Telecom industry include
handling large volumes of data using machine learning and analytics,
automating detection and correction of failures in transmission,
automating customer care services and database storage services.
Many Telecom Companies have started utilizing AI solutions to handle
increasing network complexities, expanding network, ever-changing
communication technologies and the humongous amount of data
generated as such.

31. TELECOMMUNICATION

32. COMMUNICATION
Exchangingofinformationbyspeaking,writing,orusing some
othermedium.

33. WHATISTELECOMMUNICATION ?
•Exchangeofinformationbyelectronicandelectricalmeans
overasignificantdistance.

34. BASICSOFTELECOMMUNICATION
•AbasicTelecommunicationconsistsofthreeprimaryunits
thatarealwayspresentinsomeform.
a.Transmitter
b.Medium
c.Reciever

35. TYPESOFCOMMUNICATION
Therearethreetypesofcommunication.
•Simplex
•Half-Duplex
•Full-Duplex

36. SIMPLEX
•Simplexisaonewaycommunication.
•ItMeansthatyoucanonlyeitherSENDorRECEIVE amessage
orinput.

37. HALF-DUPLEX
•ItsaTwowayCommunication.
•YoucanSENDandalsoRECEIVEmessages(Butoneatatime).

38. FULL-DUPLEX
Itisalsoa Twowaycommunication.
YoucanalsoSENDorRECEIVEmessages(bothatatime).

39. TELECOMMUNICATION
ISFULLYARTIFICIALINTELLIGENCE
Because…

40. AIIN TELECOMMUNICATION
•Humanintelligenceis,toHEARalimiteddistancesoundandto
SPEAKinlimiteddistancewithpersons.

41. AI INTELECOMMUNICATION
•Andtelecommunicationisafieldthatminimizethisdistanceby
usingArtificialIntelligence.

42. In 2024, nearly 90% of telecommunications companies reported
using AI, signaling a new era for the industry. As networks face
surging complexity and rising customer expectations.
AI steps in as the game-changer –transforming how telecoms
optimize performance, elevate user experiences and stay ahead
in a fiercely competitive landscape.
This blog will explore the most impactful AI use cases in
telecommunications and highlight how they drive efficiency,
innovation, and customer satisfaction across the industry.
AIIN TELECOMMUNICATION

43. AI IN TELECOMMUNICATION
Formulafortelecommunicationisthat:
Communication+Device=Telecommunication
AI is not an optional in the telecom landscape –it is
mission-critical. From managing rising data traffic to
delivering seamless user experiences, AI powers the
industry’s most transformative innovations.

44. COMMUNICATION

45. DEVICE

47. AI Role in the Telecommunications
According toIDC, 31.5% of the telecommunication organizations are primarily
working on utilization of current infrastructure and 63.5% are investing in AI-
driven systems.
Market Research Futurepredicts that, by 2023, global AI in telecommunication
market will reach $1 billion, with 32% CARG during 2018-2023.

48. THE GROWING ROLE OF AI IN
TRANSFORMING TELECOMMUNICATIONS
AI is no longer a future consideration –it is central to modern
telecom strategies. As 5G, IoT, and edge computing expand,
network complexity surges. AI provides the intelligence needed to
manage this complexity dynamically.
From intelligent traffic routing to predictive network maintenance,
AI enables telecom companies to operate smarter and more
efficiently.
As demand for bandwidth and low-latency connectivity rises, AI’s
ability to optimize resources and automate operations becomes
indispensable.

49. The momentum behind AI adoption is clear.
RecentelyIBM Institute for Business Value survey of 300 global
telecommunications leaders revealed that most communications service
providers are actively assessing and deploying generative AI across business
functions.
Nvidia’s2024 study reported that nearly 90% of telecom companies are
leveraging AI, with 48% piloting and 41% actively deploying AI-powered
solutions. More than half (53%) believe AI provides a competitive advantage.
These figures underscore AI’s growing importance as a competitive
differentiator in telecommunications.
KEY STATISTICS AND TRENDS HIGHLIGHTING
AI ADOPTION IN TELECOMMUNICATIONS

51. BENEFITS OF AI IN TELECOMMUNICATIONS
AI-powered automation, optimization, and predictive analytics are
transforming telecom operations for greater efficiency, reliability, and
customer satisfaction.
1.Network Automation
AI plays a pivotal role in automating network management tasks.
leveraging AI algorithms, telecom providers can automatically
detect, diagnose, and resolve network issues without human
intervention.
This automation reduces downtime, enhances service reliability, and
minimizes operational costs.
AI can dynamically allocate bandwidth and reroute traffic in
response to real-time network conditions, ensuring optimal
performance.

52. BENEFITS OF AI IN TELECOMMUNICATIONS
Efficient use of network resources is critical in telecommunications.
AI enables predictive analytics that helps providers forecast demand
and optimize capacity planning.
Accurately predicting usage patterns, AI allows your company to avoid
both underutilization and congestion.
This ensures cost-effective operations while maintaining high-quality
service for end users.
2. Resource Optimization

53. BENEFITS OF AI IN TELECOMMUNICATIONS
AI empowers you to gain deeper insights into network behavior.
Through anomaly detection and predictive maintenance, AI can
identify potential network failures before they occur.
The capabilities not only improve uptime and reduce repair costs
but also enhance the overall user experience by preventing service
disruptions.
AI-driven network optimization tools also continuously fine-tune
network configurations to maximize performance
3. Smarter Network Management

54. AI significantly enhances the customer experience through
automation and personalization.
AI-powered chatbotsand virtual assistants provide instant,
24/7 support, resolving common issues without human
intervention.
AI analyzes customer data to deliver tailored
recommendations, predict churn, and proactively offer
solutions.
This leads to increased customer satisfaction, loyalty, and
reduced operational expenses for your telecom company.
4. Better Customer Service
BENEFITS OF AI IN TELECOMMUNICATIONS

55. AI automates routine tasks, it also creates opportunities for
employee upskilling.
Telecommunications providers can redirect human resources toward
more strategic and creative roles.
AI-generated insights help teams make data-driven decisions and
foster innovation.
AI-driven training platforms enable continuous learning and skill
development, ensuring workforce readiness for future challenges.
5. Employee Growth and Development
BENEFITS OF AI IN TELECOMMUNICATIONS

56. •Growing number of telecom products & Subscribers
•5G/IOTBig data
•Economic pressure
•Optimization issues in Massive networks
•Fraud Issues
•Limitation in Resources
•Real -time solution in different part of Network
•Limitation for predicting the volume of traffic
Challenges

57. Challenges

58. KEY CHALLENGES TO UNLOCKING AI VALUE IN
TELECOMMUNICATIONS
The network handles enormous volumes of sensitive customer data,
and with AI in the mix, the stakes are even higher. Every algorithm
and automation introduce new opportunities for breaches, misuse, or
accidental exposure.
Without airtight governance and cybersecurity protocols, your
business risks losing customer trust and facing regulatory penalties.
To stay protected, telecom providers must rethink their data
strategies. From implementing AI-friendly encryption standards to
enforcing stricter access controls, proactive security measures are
essential to safeguarding both your operations and your customers.
1. Data Privacy and Security in Telecommunications Networks

59. KEY CHALLENGES TO UNLOCKING AI VALUE IN
TELECOMMUNICATIONS
AI thrives on modern, agile environments –but your legacy systems
weren’t built with AI in mind. Outdated architecture and siloeddata
make it difficult to deploy AI solutions seamlessly.
This disconnect can slow your digital transformation and increase
operational costs.
The need of a strategic approach to modernization. Whether through
APIs, cloud migration, or modular upgrades, integrating AI without
disrupting service requires balancing innovation with operational
continuity.
2. Bridging AI and Legacy Telecommunications Infrastructure

60. KEY CHALLENGES TO UNLOCKING AI VALUE IN
TELECOMMUNICATIONS
3. AI Implementation Costs for Telecommunications Providers
AI promises efficiency and scalability, but the upfront price tag can
be daunting. Between infrastructure upgrades, data preparation, and
hiring specialized talent, costs add up fast –especially when ROI
isn’t instant.
This means making tough decisions. Prioritizing use cases that
deliver measurable value and phasing implementation can help
reduce risks and make AI adoption financially viable.

61. KEY CHALLENGES TO UNLOCKING AI VALUE IN
TELECOMMUNICATIONS
4. Telecommunications’sAI Skills Shortage
AI is only as powerful as the people behind it –and right now, skilled
AI professionals are in short supply. From data scientists to AI
engineers, the race for talent is fierce, leaving many telecom
providers struggling to build capable teams.
Investing in training programs and creating partnerships with AI
experts will be critical to developing the in-house capabilities needed
to scale AI successfully.

62. 5. Ethics and Compliance in Telecommunications AI
KEY CHALLENGES TO UNLOCKING AI VALUE IN
TELECOMMUNICATIONS
AI decisions aren’t invisible –they directly affect your customers
and shape their trust in your brand.
Issues like algorithmic bias, opaque decision-making, and
regulatory non-compliance can turn AI from a competitive
advantage into a liability overnight.
To protect your business, transparency and accountability must
be built into every AI initiative. Regular audits, explainable AI
models, and adherence to evolving regulations will help you stay
ahead of scrutiny and preserve user confidence.

63. AI BIG PICTURE IN TELECOM

64. Data Sources in Telecom Industry
Telecom Data
Sources
Network
Element Status
Performance
Values
SW Logs &
Traces
Alarms
Configuration
Data
CDRs

66. SOLUTIONS
AI Solutions in
Telecom
Predictive
Maintenance
Network
Optimization
FraudDetection
Subscribers
Management

67. SUBSCRIBERS MANAGEMENT
Customer Churn Prediction (CCP)
Definition
churnisthephenomenon whereacustomerswitches
fromoneservicetoacompetitor’sservice
Thechurnratemeasuresthenumberofcustomer
transitioningawayfromaserviceoveraspecificperiod.
Types
voluntarychurn
Voluntarychurniswhenthecustomerinitiatedthe
servicetermination.
Involuntarychurn
meansthecompanysuspendedthecustomer’sservice
andthisisusually becauseofnon-paymentorservice
abuse.

68. IMPORTANCE OF THE ISSUE
Three main strategies have been proposed to generate more
revenues:
1.acquire new customers
2.upsell the existing customers
3.increase the retention period of customers (the most
profitable strategy)
Customer Churn Prediction (CCP)

69. Retaining the existing clients is the best marketing strategy.
Subscribers set is the most valuable asset in Telecom
Company.
it is more profitable to keep your existing clientssatisfied
than to constantly attract new clients
The technical progress and the increasing number of
operatorsraised the level of competition
IMPORTANCE OF THE ISSUE
Customer Churn Prediction (CCP)

70. Cost of attracting new customers is five to six times more
than simply retaining the customer.
WHY CUSTOMER CHURN PREDICTION
Customer Churn Prediction (CCP)

71. Case Study : SyriaTel
Case Study
Customer Churn Prediction (CCP)

72. 1.Thefirststageistoidentifysuitabledataforthemodelling
process.
2.Thesecondstageconsistsofthedatasemanticsproblem.
3.Stagethreehandlesfeatureselection.definefeatureselectionas“a
processoffindingasubsetoffeatures,fromtheoriginalsetof
featuresformingpatternsinagivendataset
4.Stagefouristhepredictivemodeldevelopmentstage.
5.Thefinalstageisthemodelvalidationprocess.Thegoalofthis
stageistoensurethatthemodeldeliversaccuratepredictions.
CHURN MANAGEMENT FRAMEWORK
Customer Churn Prediction (CCP)

73. FEATURE EXTRACTION
Customer Profile Data
History of Transactions
Customers Network Data
CDRs Data
IMEI Information
Customer Churn Prediction (CCP)

74. ProfileData&HistoryofTransactions
customer’sservices
offers
packages
informationgeneratedfromCRMsystem
customerGSMs
Typeofsubscription/PrepaidorPostpaid
Birthday/Age
Gender
UserCategory
thelocationoflivingandmore…
Feature Extraction :Profile Data & History of
Transactions
Customer Churn Prediction (CCP)

75. FEATURE EXTRACTION: IMEI INFORMATION &
CDRS DATA
Call details records “CDRs”
•Average of calls/SMS made by the customer
•The average of upload/download internet access
•The number of subscribed packages
•The percentage of radio access type per site
•The ratio of calls count on SMS count
•Internet transaction made by customers
•Charging information about calls, SMS, MMS
Mobile IMEI information:
•Brand
•Model
•Type of the mobile phone
•Dual or mono SIM device
Customer Churn Prediction (CCP)

76. FEATURE EXTRACTION: USER
NETWORK DATA
User Network Data
•Internet
•Calls
•SMS
User Social Network features
BuildasocialnetworkgraphbasedonCDRdatatakenforthelast4
months.Graphframelibraryonsparkisusedtoaccomplishthiswork.
Thesocialnetworkgraphconsistsofnodesandedges.
Nodes:representGSMnumberofsubscribers.
Edges:representinteractionsbetweensubscribers(Calls,SMS,and
MMS).
Customer Churn Prediction (CCP)

77. Classification Algorithms
Decision Tree algorithm
Random Forest algorithm
GBM algorithm
Logistic regression models (LRM)
Artificial neural networks (ANN)
Support Vector Machine (SVM)
PREDICTIVE MODEL: SUPERVISED
Customer Churn Prediction (CCP)

78. Predictive Model: Supervised
Customer Churn Prediction (CCP)

80. SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
Artificial Intelligence is revolutionizing the telecommunications
industry by enhancing network efficiency, customer experience, and
security.
AI transforms telecom with smarter network planning, predictive
maintenance, automation, and security, as demonstrated by leading
global operators.

81. SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
AI in network planning and optimization helps telecom providers
analyze vast datasets to enhance network performance and plan
expansions efficiently. By leveraging machine learning, you can
predict traffic patterns and optimize resource allocation, ensuring
seamless connectivity.
Telecoms use AI to create digital twins –virtual models of
networks –to simulate real-world conditions and test upgrades.
This approach saves time, cuts errors, and ensures your business
stays ahead of growing demand. It’s a game-changer for building
resilient, high-speed networks.
1. Network Planning & Optimization

82. REAL-WORLD CASE STUDY: NOKIA ’S AVA 5G
COGNITIVE OPERATIONS PLATFORM
5G networks surged in 2020, telecom operators faced a tidal wave of data
traffic and rising customer demands for flawless connectivity.
Nokia’s AVA 5G Cognitive Operations platform emerged as a beacon, wielding
AI to predict network failures seven days in advance with pinpoint accuracy.
By harnessing machine learning on Microsoft Azure, it cut customer
complaints by 20% and reduced on-site maintenance visits by 10% in real-
world trials, keeping millions seamlessly connected.
The platform strength lies in its ability to resolve issues 50% faster through
automated, data-driven actions, analyzing live network patterns to prevent
disruptions. Its cloud-based analytics ensure precise resource allocation,
consistently meeting SLAs while slashing operational costs.
This case reveals how AVA’s AI transforms chaotic 5G networks into models of
reliability and efficiency.

83. AI-powered network slicing enables telecommunications to create
virtual networks customized for specific use cases, such as IoTor
ultra-low-latency 5G applications.
Machine learning dynamically manages bandwidth allocation,
ensuring each slice meets performance requirements without
impacting others.
Analyzing traffic patterns, AI ensures optimal resource distribution,
supporting diverse applications like autonomous vehicles or smart
cities.
This means delivering specialized connectivity solutions that meet
unique customer demands, enhancing competitiveness in the 5G era.
SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
2. AI-Powered Network Slicing

84. REAL-WORLD CASE STUDY: FUTURISM TECHNOLOGIES
AI-DRIVEN NETWORK SLICING
Futurism Technologies turned to AI to enable dynamic network slicing
across shared infrastructure. Their system creates virtual slices
customized for applications like mobile broadband and low-latency
services.
The AI algorithms employed by Futurism Technologies analyze real -time
network data to predict traffic patterns and adjust resources
accordingly. This ensures that each network slice maintains optimal
performance, even under varying load conditions.
The system adaptability allows for seamless scaling and management of
network slices, meeting the specific requirements of different
applications and services.
The result is greater network efficiency and flexibility.
Futurism AI-powered slicing enables operators to deliver differentiated
services while reducing waste, unlocking new revenue opportunities in
enterprise and consumer markets.

85. AI predictive maintenance uses machine learning to analyze
equipment data, identifying wear patterns to forecast potential
failures. By scheduling maintenance before issues arise, it minimizes
unplanned outages, ensuring continuous network availability.
Real-time monitoring with AI allows telecoms to prioritize critical
maintenance tasks, optimizing technician schedules and resources.
This translates to fewer service disruptions and higher customer
satisfaction, as reliable connectivity becomes a competitive
advantage.
It’s a cost-efficient way to maintain robust infrastructure.
SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
3. Predictive Maintenance

86. REAL-WORLD CASE STUDY: VERIZON –PROACTIVE
NETWORK MAINTENANCE WITH AI PREDICTIVE
ANALYTICS
Verizon faced rising costs and disruptions from reactive network
maintenance. To stay ahead, they implemented an AI-powered predictive
maintenance system to detect potential failures before they impacted
customers.
AI models analyze continuous streams of equipment data to identify
anomalies and forecast issues. This allows Verizon to schedule targeted
maintenance, minimizing service interruptions and reducing operational
costs.
Verizon has improved network uptime and service quality. Predictive insights
not only help avoid outages but also support smarter, more cost-effective
maintenance planning across their vast infrastructure.

87. 4. Call Centre Automation
SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
AI in call centreautomation employs NLP-powered chatbotsto handle
customer inquiries instantly, reducing response times. These systems
analyze queries to provide accurate solutions or escalate complex issues
to human agents, improving operational efficiency. This allows
telecommunications to scale support without proportional cost increases.
By personalizing interactions based on customer data, AI enhances
engagement and satisfaction, fostering loyalty.
Automating routine tasks frees agents to focus on high-value interactions,
ensuring a seamless customer experience. It’s a practical solution for
modernizing support operations.

88. REAL-WORLD CASE STUDY: TELEFÓNICA’S
INTELLIGENT CALL ROUTING
In 2017-2018, TelefónicaGermany struggling with overloaded contact
centers. Customers faced long wait times and limited self-service options,
leading to frustration and damage to the brand reputation. Improving
accessibility and response efficiency became critical priorities.
To tackle this, Telefónicapartnered with Teneo. AI and implemented the
Open Question Conversational IVR solution. This AI-powered system now
handles nearly 1 million voice interactions and 200,000 text-based
inquiries monthly across channels like SMS and WhatsApp. It authenticates
customers, accesses account data for personalized responses, and offers
seamless omnichannelsupport without losing conversation context.
The impact has been significant. Telefónicaboosted IVR resolution rates by
6% and introduced over 400 general and 20 personalized use cases to meet
diverse customer needs.
This AI-driven upgrade improved operational efficiency, reduced pressure
on live agents, and restored customer satisfaction, marking a major
turnaround in its service operations.

89. Call Centre Automation

90. 5. AI-Driven Network Security
SPECIFIC APPLICATIONS OF AI IN
TELECOMMUNICATIONS
AI-driven network security uses machine learning to monitor traffic and
detect anomalies like fraud or cyberattacks in real time. By analyzing vast
datasets, it identifies threats faster than traditional methods, protecting
sensitive customer and operational data.
This ensures a secure network environment critical for trust and
compliance.
AI security adapts to evolving threats, reducing fraud-related losses and
maintaining service integrity. It provides detailed threat insights, enabling
rapid response and mitigation, which strengthens customer confidence.

91. REAL-WORLD CASE STUDY: BT ’S AI-POWERED
CYBERSECURITY MEASURES
BT has integrated AI into its cybersecurity framework to combat the
increasing sophistication of cyber threats. The company detects
approximately 2,000 potential cyber-attack signals every second, highlighting
the scale and complexity of modern cyber threats.
The AI system analyzes vast amounts of network data in real-time to identify
unusual patterns and potential security breaches. This enables BT to respond
swiftly to threats, mitigating risks before they impact services or compromise
customer data.
By leveraging AI for network security, BT enhances its ability to protect
critical infrastructure and maintain customer trust. The proactive defense
mechanism ensures robust protection against evolving cyber threats in the
telecommunications sector.

93. EXAMPLES OF AI IN TELECOMMUNICATIONS REAL -
WORLD CASE STUDIES
AI Use Cases in Telecom: Real-World Impact
AI is driving measurable impact across telecom operations, from customer
service and network reliability to fraud detection and 5G network slicing
1-Vodafone: Streamlining CustomerService with TOBi
Vodafone deployed TOBi, an AI-powered chatbotusing natural language
processing to handle inquiries across 11 markets. TOBi’smachine learning
resolves issues like billing or plan upgrades with precision, reducing the
strain on human agents.
TOBicut checkout times by over 47% and doubled transaction conversion
rates, managing 45 million conversations monthly.
Customers enjoy faster, reliable service, while agents tackle complex
tasks, strengthening Vodafone’s customer-centric reputation

94. 2. AT&T: Predictive Maintenance for Network Reliability
EXAMPLES OF AI IN TELECOMMUNICATIONS REAL -
WORLD CASE STUDIES
AT&T one of the world’s largest telecom providers, faced mounting challenges
maintaining its vast network. Unexpected equipment failures triggered
outages, frustrated customers, and inflated repair costs, exposing the limits of
manual maintenance routines.
To shift from reactive to proactive, AT&T deployed AI-powered predictive
maintenance. By analyzing real-time sensor data and historical performance
records, AI models identified early failure signals and triggered timely
repairs. Integrated self-healing features rerouted traffic instantly, minimizing
service disruptions.
This AI-driven approach has cut downtime and maintenance expenses
significantly. 3
AT&T now delivers more consistent service with fewer interruptions,
reinforcing its leadership and earning greater customer trust.

95. 3. China Mobile: Combating Fraud with AI Detection
EXAMPLES OF AI IN TELECOMMUNICATIONS REAL -WORLD
CASE STUDIES
Telecommunications fraud poses a significant threat, with fraudulent SMS and
rich media messages causing substantial financial losses and eroding customer
trust. Traditional rule-based systems struggled to keep pace with the evolving
tactics of fraudsters, often resulting in low detection accuracy and high reliance
on manual reviews.
China Mobile Shanghai collaborated with ZTE to develop an advanced AI -driven
anti-fraud system. This solution leverages a multimodal large language model
capable of analyzing and interpreting various content types, including text, audio,
video, graphics, and images. By integrating this system with network functions,
the AI can identify fraudulent intent in real-time and alert recipients accordingly.
The deployment of this AI-enhanced solution yielded impressive results: a 60%
reduction in reported fraud cases, a 99% accuracy rate in fraud detection, and an
80% decrease in the workload associated with manual reviews.
This initiative not only bolstered China Mobile’s defense against telecom fraud but
also set a new benchmark for AI applications in network security.

96. 4. Verizon: AI-Powered Network Slicing for 5G Public Safety
EXAMPLES OF AI IN TELECOMMUNICATIONS REAL -WORLD
CASE STUDIES
During emergencies, reliable and prioritized connectivity is not a luxury –it’s
a necessity. Public safety agencies often face network congestion at the worst
possible moments, which can delay critical communications and jeopardize
response efforts.
Verizon introduced the Frontline Network Slice, a dedicated 5G Ultra
Wideband virtual slice designed specifically for first responders. AI and
machine learning play a central role, dynamically managing network
resources to guarantee low-latency, high-priority access even during peak
network usage. This ensures seamless operation of vital tools such as body
camera live feeds, real-time vehicle data and coordination apps.
AI-driven network slice supports more than 40,000 public safety
organizations across the U.S., Verizon’s AI-powered network slicing delivers
consistent, fast, and secure communications when it matters most. First
responders enjoy enhanced reliability and performance, empowering them to
act decisively in life-or-death situations –and cementing Verizon’s reputation
as a leader in public safety connectivity.

101. FUTURE SCOPE OF AI
Looking at the features and its wide application we may definitely stick to
artificial intelligence. Seeing at the development of AI is that is it that the
future world is becoming artificial.
Biological intelligence is fixed, because it is an old, mature paradigm but
the new paradigm of non –biological computation and intelligence is
growing exponentially.
The Memory capacity of the human brain is probably of the order often
thousand million binary digits. But most of this is probably used in
remembering visual impressions, and other comparatively wasteful ways.
Hence we can say that as natural intelligence is limited and volatile too
world may now depend upon computers for smooth working.

102. AI in telecommunications is advancing from automating tasks to enabling
smarter, more strategic operations across the network. Leading operators are
deploying AI to drive predictive insights, automate complex decision-making,
and orchestrate network functions in real time.
Generative AI is starting to reshape customer interaction models, creating
natural, human-like conversations across digital channels and automating
support flows. At the same time, AI-powered analytics platforms are
delivering instant insights from vast network datasets –identifying
performance issues, optimizing energy consumption, and detecting anomalies
without human intervention.
These innovations reflect a major shift: AI is no longer just a tool to improve
efficiency –it is becoming a strategic enabler of new services, greater agility,
and next-generation customer experiences.
For telecom companies, embracing AI at this level is key to staying
competitive in a rapidly evolving digital landscape.
INNOVATIVE AI SOLUTIONS

103. AI is reshaping the telecommunications landscape faster than ever.
From powering smarter infrastructure to driving greener operations,
AI is now central to staying competitive and future ready.
Emerging Technologies in AI for Telecommunications
AI-DRIVEN INNOVATIONS TRANSFORMING
TELECOMMUNICATIONS

105. CONCLUSION
Till Now we have discussed in brief about Artificial Intelligence.
We have discussed some of its principles, its applications, its achievements etc.
The Ultimate goal of institutions and scientists working of AI is to solve majority of
the problems or to achieve the tasks which we humans directly can’t accomplish.
It is for sure that development in this field of Computer Science will change the
complete scenario of the world. Now it is the responsibility of creamy layer of
engineers to develop this field.
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Mmmmmmmmmmmmmmmmmm not yet finished