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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
© 2018 TM Forum | 6Information Security Level 2 – Sensitive© 2017 – Proprietary & Confidential Information of Amdocs6
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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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
© 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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Pe
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f A
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l In
telli
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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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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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