© 2018 TM Forum | 1

AI Maturity Model

Doron Youngerwood, Director of AI Product Marketing, Amdocs

Professor Paul Morrissey, Global Ambassador; Head of the Data Analytics & CX Group, TM Forum

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• Introduction

• AI Maturity Model Objectives & Benefits

• AI Industry Use Cases

• AI Maturity Model Pillars

• How Will it Work

• Best Practice from the Telecom Industry & other Industries

• Next Steps

Agenda

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Act on Your Intelligence

Data-driven actionable insights to allow for better and faster decision making

Inform and automate decisions

Customer personalization at scale with predictive, proactive and contextual experiences

Redefine customer

engagements

Optimize operational processes, improve efficiencies and enhance agility

Transform operational processes

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Benefits

• Benchmark across the industry – identify and score your company's strengths and weaknesses

• Structure your AI strategy, goals & investment

• Sharing best practice will accelerate adoption and reduce repetitiveness for AI

• Help to bring regulatory concerns for compliance management

• Accelerate the transition to AI

• Adoption of a common use case library

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Poll #1

Which of the following best describes the state of your AI strategy?

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Use Cases

AI

Predictive Network Congestion

• Optimized Network Investment

• Reduced Customer Churn

Personalized Customer Engagement

• Improve NPS and CSAT

• Increase Revenue

• Reduce Customer Churn

Product Bundling Recommendation

• Maximized Product Performance

• Increase Revenue and ARPU

Empowered Care Agent

• Increase First Contact Resolution

• Reduce Repeat Calls

• Improve CX

Fraud Management

• Uncover anomalies

• Root cause analysis

Network Capacity Optimization

• Optimal capacity allocation

• Streamlined analysis-design-execution process

Chatbot

• Improve Customer Experience

• Increase Self-Service Rate

• Reduce Cost to Serve

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• Use Case Taxonomy

• Use Case Library

• Curate FX

Use Cases

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AI models adopted in Telco

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Poll #2

Do you have well defined use cases for AI?

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AI Maturity Model - Categories

Culture, People & Organization

How ready a CSP is to support AI impacts on, and transformation of the business

Customer

How well will the CSP’s approach to AI help customers from no impact to complete emersion

Operations

What, where and how CSPs are implementing AI across their business

Technology

AI technologies being leveraged, and how the CSP has gone about implementing AI solutions, in addition to data availability, storage and management

The state and nature of a CSP’s plan of action and roadmap to support AI

Strategy

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Core Pillars

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Core Pillars

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Core Pillars

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Core Pillars

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Core Pillars

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Poll #3

Data availability for AI solutions

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How will it work?

• Self-service

• Guided

• Partnership

© 2018 TM Forum | 18Information Security Level 2 – Sensitive© 2017 – Proprietary & Confidential Information of Amdocs18

Recommended photo tags using image recognition

Recommended products, using machine learning algorithms

AI is powering next-generation vending machines

Leveraging best practice from other industries

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Next steps & outcomes

Model Structure

Define the first level structure of the Maturity Model. Including:

• Define the top pillars• Identify all relevant sub-

pillars• Create levels of maturity

Questionnaire

Define maturity characteristics and assessment criteria statement

• Establish characteristics for every sub-dimension

• Define evaluation criteria per maturity level

• Develop objective assessment questions for each level of maturity

Integration with DMM

Provide a set of quick inputs for AI within DMM

Assessment & Scoring

It is highly unlikely that a CSP will have a uniform score across all of the core pillars, and the model needs to be designed to surface this complexity and nuance

Definitions

Agree on defining ontology, Taxonomy and terminology related to AI use

Practical recommendations – Identifying gaps and providing recommendations

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for Tomorrow…

• What should be the AI vision for the industry?

• What AI goals should the industry be gravitating towards?

• How do we establish the right AI maturity model context or series of contexts within a Service Providers business?

• Can we learn from outside the industry, with say what the automotive industry is doing?

• How can we leverage the best practices from, say the auto industry, as well as others into this piece of work?

• Can we, by taking on best practices from other industries, converge some core concepts into this model to address the application and implementation of AI across business, operations and technology sub-systems?

• How do we factor in the work being done by other standardization organizations e.g. ETSI, IEEE, Acumos etc.?

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Thank you

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Appendix

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Pe

rce

nta

ge o

f A

rtif

icia

l In

telli

gen

t U

se C

ases

100

90

80

70

60

50

40

30

20

10

0

0 1 2 3 4

AI Requirement

Billing

Retail Store

Marketing

Security

Sales

FieldServices

Contact Center

Network Planningand Engineering

© 2017 Gartner, Inc.

Key

Those areas toward the top-right corner have a stronger need for AI e.g. these use cases have problems using high-volume or high-variety data, and also have complex calculations.

Those areas toward the bottom-left corner often have fairly simplistic analytical requirements.

Size of Bubble = Total Number of Use Cases (simple count of the number of use cases in each operational area of the CSP).

Revenue Assurance

Fraud Management

Opportunities for AI

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AI Maturity Stages

AI Novice

AI Ready

AIProficient

AI Advanced

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This is a strategic milestone in our digital transformation journey. We wanted

to introduce a new way to engage with customers. We can target customers

with services that can offer them the most value when they are most likely to

need them

Ernesto R. Alberto, Chief Revenue Officer, Smart

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We are already seeing increased revenues of around 2% generated by

personalized and contextual customer engagements, and are reducing churn

by 20%

Matias Del Campo, Head of Consumer Marketing, Entel

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Service Providers’ AI Activities

TOBi, AI-powered virtual assistant

Launched in April, TOBi can handle a range of customer queries including device troubleshooting, usage and order tracking

Djingo, AI-powered virtual assistant

Launched in April, Djingo offers customers a way to navigate Orange TV, manage your connected home, make a call or access lots of other services

Pepper, A humanoid robot

Chat with customers, answer questions and give directions. It is also capable of detecting emotion and sentiment

Aura, AI-powered virtual assistant

Invested 48m euros and launched in Feb, Aura will carry out a wide range of functions including customer care, device management, security, product/service recommendations, etc

Adaptive Network Control

Automate network control and free up your IT staff. Use insightful data and ML to manage private and public networks. Immediately adapt network resources to changing demands

AT&T Threat IntellectIt uses data streams, analytics and intelligence to help detect threats. Threat Intellect is constantly learning to adapt to the latest global security issues.

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Thank you

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