Presentation at Light Reading's Automation Anywhere event in 2017.

1. Heavy Reading Presentation:
The Potential Impact of Robotic Process Automation &
Artificial Intelligence on Telecom Operations
James Crawshaw
Senior Analyst, Heavy Reading
November 2017
www.heavyreading.com

2. OTT threat drives need for automation – cost structure
• OTTs had luxury of greenfield operations which are easier to automate than
brownfield
• The more CSPs automate (call centers, network operations, etc.) the fewer
people are needed
• The more CSPs automate the fewer human errors occur, lowering the cost of
remediation
• Retained staff focus on higher order, exception-based activities that are harder
to automate. This is more interesting work and hence staff turnover (a cost to
the business) is lower.
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3. OTT threat drives need for automation – agility
• Telecoms has a reputation for being slow to introduce new
technologies or launch new services
• OSS/BSS is the fall guy - it can take a year to prep OSS/BSS, from
billing through to activation, to launch a new service
• This impedes operators’ ability to respond quickly to changing
consumer interests and competitive dynamics
• Greater automation means less manual “knitting together” of
disparate OSS functions and hence shorter TTM
• To compete with OTT, telcos need to be nimbler and the only way
to do this cost effectively is by increasing the level of automation,
not just in OSS/BSS but throughout the organization
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4. Automation across the service lifecycle
Stage Automation opportunity
Contact center Chat bots and interactive voice response systems. Aids for human contact
center operatives.
Order & fulfillment Self-service for consumers already prevalent. Needs more flexibility to cater to
enterprise customers.
Configuration and control Allow the customer to take control of network capacity and QoS, on-demand
Security Filter malicious traffic without requiring human intervention
Policy Adjust network resources (bandwidth, traffic priorities) to provide differentiated
services in the face of ever-changing network conditions
Assurance Error detection and fault reporting; reroute services to limit disruptions
Performance Quality monitoring and capacity analysis
Analytics Real-time picture of end-to-end services, network components, and
infrastructure
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Source: Heavy Reading

5. Intelligent process automation per McKinsey
1. RPA: software tool that automates routine tasks such as data
extraction and cleaning. Robot has user ID and performs rules-
based tasks such as accessing programs and systems, performing
calculations, creating reports, and checking files.
2. ML: algorithms that identify patterns in structured data, through
supervised or unsupervised learning, and provide insights or make
predictions.
3. NLP: a way for computers to analyze, understand, and derive
meaning from human language.
Cognitive agents combine ML and NLP to build a virtual agent that is capable of executing
tasks, communicating, learning from data sets, and even making decisions based on emotion
detection. Cognitive agents can be used to support customers over phone or via chat bots.
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6. The role for RPA in telecoms automation
• RPA can fill the gap between big bang transformation programs
and existing manual operations by automating disparate processes
at a lower cost.
• RPA is most effective for processes that require predictable and
high frequency interactions with multiple applications.
• Telecom operations include many mundane and repetitive but
essential processes that require multiple systems to be queried
and/or updated to complete the task. The tasks must be
completed reliably and accurately making the telecom industry a
textbook case for robotic process automation.
• However, to date RPA has seen greatest traction in the financial
services industry, not telecoms.
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7. O2 UK case study of RPA
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Source: London School of Economics

8. RPA conclusions
• RPA can be a cost-effective alternative to Business Process
Outsourcing of back office functions.
• RPA can be used for activities that don’t require much judgement
(though may involve following a set logic) such as logging in to
multiple network management systems in order to provision a
particular service.
• RPA can reduce some processes from days to minutes, reducing
customer “chase up” calls.
• RPA is highly scalable - a robotic workforce can be doubled almost
instantly when new products are about to be launched, and then
scaled back after the surge.
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9. Why the resurgence of interest in AI/ML?
• Breakthroughs in neural network theory around 2006
• Improvements in computing capacity: x86 CPUs, GPUs, FPGAs and
custom ASICs designed specifically for ML e.g. Google's Tensor
Processing Unit (TPU) and associated Tensor Flow software
libraries.
• The cloud makes computing capacity highly available and cheap.
• Massive data sets: online photos, email, video, gaming, search,
messaging, mapping and shopping are fertile ground for ML.
• Success stories: AlphaGo, Google Pixel Buds, image recognition, lip
reading, etc.
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10. AI in telecoms
• Route optimization – e.g. in IP transport or SDN switching
• Security – e.g., threat detection
• Traffic identification – e.g. for policy enforcement
• Anomaly detection – e.g. for failure warning
• SLA monitoring and enforcement
• Resource management – e.g. to auto-scale an NFV cluster
• Power management – e.g. in data centers
• Customer experience management – e.g. chatbots
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11. AI in network management
• The skills of highly trained network engineers are not easily turned into a set of
simple instructions that an RPA engine can follow.
• A cognitive engine, however, can learn from how people take decisions on
complex data sets in order to create its own rules rather than have them
explicitly programmed.
• The huge data-sets that network teams collect, from probes and management
systems, can be difficult for humans to analyze using standard tools.
• AI may reveal new insights enabling operators to run their networks more
efficiently and reliably.
• Example: Zhejiang Telecom implemented AI engine for route optimization,
capacity planning, traffic prediction and dynamic optimization of the network.
Led to an 8% increase in routing optimization and the identification of many
vulnerabilities which had previously been undetectable.
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12. AI in the contact center
• Telstra estimates that 30% of inbound calls to a contact center
could be resolved by AI chat bots.
• There is still a role for human agents at Telstra (they have 11,000
today) but with AI assistance Telstra estimates they can be 20-
35% more productive.
• Telstra is considering text sentiment analysis to enhance the
performance of its messaging and chat agents.
• Bell Canada championed a TMF catalyst that used sentiment
analysis of customer’s speech while navigating IVR systems. This
measures and tracks customer experience and helps decide an
automated next-best-action.
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13. SKtelecom - Applying AI Mobile Network Operation Solution
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SKtelecom’s AI Network Operation Solution, which
commercialized in October 2016, consists of:
• AI-driven automatic network optimization
• Real-time quality prediction through analysis of big
data from Ues, BBUs, core network, etc.
• Perceives symptoms and responds to them across
the network
It improves quality of service through optimization of
traffic transmission, automatic diagnosis of malfunctions
and recovery.

14. AI at AT&T
AI is evolving at AT&T through 3 generations of application types:
1. Speech recognition: voice biometrics, natural language processing
and video processing.
2. Network transformation: from hardware to software. The network
will be more self-healing and self-learning to better predict
network issues and solve them before they happen.
3. IoT, Security and Big Data: “Imagine your home appliances
communicating with each other and making shopping requests to
prepare a meal. Or your car driving itself for repairs and back
home without you lifting a finger”.
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15. AI at Swisscom
Swisscom uses enablers to make internal applications and processes smarter and
more efficient:
• Entity recognition
• Topic recognition
• Sentiment analysis
• Summarization
• Anonymization
• Trend Detection
• Keyword Analysis
• Model Training
• Search
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16. ETSI’s Experiential Networked Intelligence
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Source: ETSI